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landscapes and their respective representation in the Kysuce region . ...... analysis in the regional to large-scale landscape research. ...... Kostanay, pp. 1-66.
Institute of Landscape Ecology, Slovak Academy of Sciences

LANDSCAPE AND LANDSCAPE ECOLOGY Proceedings of the 17th International Symposium on Landscape Ecology

Ľuboš HALADA, Andrej BAČA, Martin BOLTIŽIAR (editors)

Nitra 2016

Proceedings of the 17th International Symposium on Landscape Ecology: Landscape and Landscape Ecology, 27-29 May 2015, Nitra, Slovakia. Editors: Dr. Ľuboš Halada, Dr. Andrej Bača, Prof. Dr. Martin Boltižiar The papers were peer-reviewed. Published by the Institute of Landscape Ecology, Slovak Academy of Sciences, Bratislava Branch Nitra, Akademická 2, 94910 Nitra, Slovakia. Available online at www.uke.sav.sk. ISBN 978-80-89325-28-3 © Institute of Landscape Ecology, Slovak Academy of Sciences, Bratislava, Branch Nitra, 2016 Citation: Halada, Ľ., Bača, A., Boltižiar, M. (eds.) 2016: Landscape and Landscape Ecology. Proceedings of the 17th International Symposium on Landscape Ecology, 27-29 May 2015, Nitra, Slovakia, Institute of Landscape Ecology, Slovak Academy of Sciences, Bratislava, Branch Nitra, 365 pp. ISBN 978-80-89325-28-3

Institute of Landscape Ecology, Slovak Academy of Sciences

LANDSCAPE AND LANDSCAPE ECOLOGY Proceedings of the 17th International Symposium on Landscape Ecology 27-29 May 2015, Nitra, Slovakia

Ľuboš HALADA, Andrej BAČA, Martin BOLTIŽIAR (editors)

Nitra 2016

Halada, Ľ., Bača, A., Boltižiar, M. (eds.): Landscape and Landscape Ecology. Proceedings of the 17th International Symposium on Landscape Ecology

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Halada, Ľ., Bača, A., Boltižiar, M. (eds.): Landscape and Landscape Ecology. Proceedings of the 17th International Symposium on Landscape Ecology

CONTENTS Section 1 Theoretical issues of landscape ecology: current concepts and trends László MIKLÓS: The landscape, the European Landscape Convention and the law......... ....... 7 Václav ŽDÍMAL: Landscape, memory and historical sources............ .................................... 18 Florin ŽIGRAI: Meta-landscape ecology as a new ecological science (selected metascientific aspects) ................................................................................................................. 25 Section 2 Methods in landscape research Assen ASSENOV, Bilyana BORISOVA, Petar DIMITROV: Habitat diversity: a key category in landscape analysis for spatial planning in mountain conditions (a case study of the Banite municipality, Bulgaria) ......................................................................... 36 Peter BARANČOK, Mária BARANČOKOVÁ: Types of traditional agricultural landscapes and their respective representation in the Kysuce region .................................. 45 Martin BOLTIŽIAR, Branislav OLAH, Igor GALLAY, Zuzana GALLAYOVÁ: Transformation of the Slovak cultural landscape and its recent trends ............................... 57 Piotr KOCIUBA, Ihor KOZAK, Kajetan PERZANOWSKI, Daniel KLICH, Hanna KOZAK, Adam STĘPIEŃ: Forecasting of landscape dynamics: a case study at Roztocze Wschodnie (Eastern Poland) ............................................................................ 68 Jaromir KOLEJKA: Post-industrial landscape: its identification, typology and value ............ 75 Are KONT, Urve RATAS, Reimo RIVIS, Kadri VILUMAA, Agnes ANDERSON, Hannes TÕNISSON: Application of complex profile method in insular landscape studies in Estonia ................................................................................................................. 83 Ihor KOZAK, Kajetan PERZANOWSKI, Taras PARPAN, Piotr KOCIUBA, Daniel KLICH: Forecasting of the dynamics of beech and fir forests of the Polish Bieszczady and the Ukrainian Beskydy under the influence of climatic changes ............... 94 Anna KOZLOVA, Tamara DUDAR, Mykhailo SVIDENYUK: Ukrainian part of the Danube Delta: Landscape changes issues ............................................................... 103 Piotr KRAJEWSKI: Landscape changes in selected suburban area of Bratislava (Slovakia) ........................................................................................................................... 110 Mykola LUBSKIY, Alexander HUSIEV, Kateryna BOLOT, Kateryna ZHURBAS: Remote land degradation assessment in the vicinity of the Boryspil Airport ................... 118 Ivo MACHAR, Antonín BUČEK, Veronika VLČKOVÁ, Vilém PECHANEC, Jan BRUS: The application of landscape ecology to the prediction of changes in climatic conditions for growing agricultural crops. A case study from the Czech Republic .......... 124 Ksenia MEREKALOVA, Alexander KHOROSHEV: Trends in inter-component relationships during the recovery of disturbed landscapes ................................................ 132

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Halada, Ľ., Bača, A., Boltižiar, M. (eds.): Landscape and Landscape Ecology. Proceedings of the 17th International Symposium on Landscape Ecology

Alexander MKRTCHIAN, Daria SVIDZINSKA: Quantifying landscape changes through land cover transition potential analysis and modelling (on the example of the Black Tisza river basin) ...........................................................................................141 Andreia PEREIRA: Landforms and the shaping of cultural landscapes in mountain areas: A methodology for the analysis of permanent pastures landscape of Alto Barroso region (Northern Portugal) ...................................................................................150 Monika PŁUCIENNIK, Maria HEŁDAK, Ewelina WERNER, Jakub SZCZEPAŃSKI, Ciechosław PATRZAŁEK: Using the laser technology in designing land use .................162 Oimahmad RAHMONOV, Tadeusz NIEDŹWIEDŹ, Magdalena OPAŁA, Łukasz MAŁARZEWSKI, Piotr OWCZAREK: Landscape degradation and its effect on the soil-vegetation relations within juniper forest in the Fann Mountains (Western Pamir-Alay) ........................................................................................................................168 Martina SLÁMOVÁ, František CHUDÝ, Noemi BELJAK PAŽINOVÁ, Ján BELJAK: Multidisciplinary approach research on historical landscape structures ...........................176 Dagmar ŠTEFUNKOVÁ, Ján HANUŠIN: Analysis of the spatial and temporal distribution of selected landscape diversity indexes in detailed scale (Example of the viticultural landscape Svätý Jur) ..............................................................................185 Marlene TILLIAN, Wolfgang SULZER: Remote Sensing (UAV) for torrent inspection /survey in the alpine municipality of Weng im Gesäuse (Austria) ....................................192 Rositsa YANEVA, Georgi ZHELEZOV: Landscape diversity of the Danube plain – exploring the peculiarities in the Lom municipality, North-Western Bulgaria ..................204 Section 3: Landscape and ecosystem services: concepts and applications Olaf BASTIAN: Ecosystem and landscape services: development and challenges of disputed concepts ...........................................................................................................215 Peter FLEISCHER, Peter FLEISCHER jr., Erika GÖMÖRYOVÁ, Katarína STŘELCOVÁ, Slavomír CELER: Upscaling carbon fluxes from chamber measurement to the landscape scale in spruce forest disturbed by windthrow in the Tatra Mts. .................................................................................................................227 Ján KLEIN, Zdenka RÓZOVÁ: Evaluation of microclimatic factors in different layouts of built-up areas and vegetation cover of urban areas of Nitra municipality .....................236 Katarina Ana LESTAN, Nadja PENKO SEIDL, Mojca GOLOBIČ: Landscape heterogeneity as a tool for enhancing biodiversity.............................................................246 Jose MUÑOZ-ROJAS, Iain BROWN, Marc GONZALEZ-PUENTE, Felipe CORTINES-GARCIA, Julius PROELL, Rachel KEAY, Freideriki KARVOUNI: Using landscape services to address trade-offs and synergies in land-use policy and planning. A case study in Scotland ....................................................................................254 Marina PETRUSHINA: Landscape diversity and current state of landscapes of the nature reserve “Utrish” .......................................................................................................265

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Section 4: Landscape governance and management Aat BARENDREGT, Henk SIEPEL: Rigid nature policy and fixation in landscape ecological processes ........................................................................................................... 274 Ievhen BEREZHNYJ, Ruslan HAVRYLIUK, Iurij MASIKEVYCH, Iaroslav MOVCHAN, Grygorii PARCHUK, Oksana TARASOVA, Kateryna BOLOT: Small hydro power stations development in the Carpathians as a likely threat: EIA and SEA aspects ................................................................................................................................ 284 Beata FORNAL-PIENIAK, Maciej ŻONIERCZUK, Barbara ŻARSKA, Ewa ZARAŚJANUSZKIEWICZ, Czesław WYSOCKI: The shaping of rural landscape at municipal scale for nature conservation ........................................................................ 292 Viktor GAVRILENKO, Anastasia DRAPALIUK, Oleh KOKHAN, Iaroslav MOVCHAN, Dmytro GULEVETS, Kateryna ZHURBAS: National eco-network of Ukraine in the road transport and urban factors context ............................................... 297 Vasilije ISAJEV, Vera LAVADINOVIĆ, Vladan POPOVIĆ, Aleksandar LUČIĆ: Selection and breeding of Serbian spruce (Picea omorika) for urban areas in Serbia ...... 306 Oimahmad RAHMONOV: The role of Salix acutifolia as an ecological engineer during the primary forest succession ................................................................................. 312 Beata RASZKA, Malwina MIKOŁAJCZYK, Eliza KALBARCZYK, Robert KALBARCZYK, Magdalena FILIPIAK: Managing deteriorating landscapes: A proposal regarding the recreation of former landscape values. Case study of the village of Głusko, Western Pomerania, Poland.................................................................. 319 Hanna SKRYHAN: Studying the identity of urban landscape for participatory city planning ............................................................................................................................. 331 Tadeusz SZCZYPEK: Vegetation and the course of present aeolian morphogenesis in the area of Bledow “Desert” .......................................................................................... 341 Darijus VETEIKIS, Paulius KAVALIAUSKAS, Ričardas SKORUPSKAS: Assessing the optimality of landscape structure in a landscape plan (a Lithuanian example) ........... 348 Ewelina WERNER, Maria HEŁDAK, Monika PŁUCIENNIK: The influence of agricultural and forest land conservation in Poland on the protection of the countryside............................................................................................................... 359

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Halada, Ľ., Bača, A., Boltižiar, M. (eds.): Landscape and Landscape Ecology. Proceedings of the 17th International Symposium on Landscape Ecology

THE LANDSCAPE, THE EUROPEAN LANDSCAPE CONVENTION AND THE LAW László MIKLÓS Faculty of Ecology and Environmental Sciences, Technical University in Zvolen, Zvolen, Slovakia; [email protected] Abstract An integrated approach to the landscape management requires an exact formulation of the concept of landscape and its structure. Nowadays, basically at least two main streams of the landscape ecology and thus of the concept of landscape might be identified: we may say the “hard”, i.e. geosystem-based concepts, and the “soft”, i.e. cultural-heritage, value and perception-based concepts of landscape. This division is not a ranking, just a differentiation. Without any doubt a landscape type defined exactly, e.g. with relief dissection or soil depth, has a different normative effect than the characteristics of the beauty of a landscape part. It does not mean at all that the beauty and other similar values are not important indices of the landscape. The opposite is true: right because of their fuzzy character their implementation needs more diligence, in order not to lose them. Nevertheless, those different approaches to the landscape offer different possibilities for their implementation to legislation and to real planning processes. The geosystem approach became the basis for implementation of landscape ecological planning and eco-network planning in Slovakia. Recently, the cultural heritage concept is significantly supported by the European Landscape Convention. Key words: landscape; geosystem; law; European Landscape Convention. Introduction Theory and practice of landscape sciences decisively influence several basic concepts of sustainable development, as environmental protection, management of natural resources, nature conservation, landscape design and planning procedures, integrated watershed management, and other policies. This development requires an exact formulation - or reformulation - of the main object of our interest – the landscape, as well as its most exact formulation possible, but at the same time acceptable in the sphere of the policy, decision making, planning and projecting. The above mentioned problems, as well as the diversity of approaches, their recent changes (Wu, Hobbs, 2002, Nassauer, Santelmann, Scavia, Eds. 2007, Kienast, Wildi, Ghosh, Eds., 2007, Nassauer, Opdam, 2008, Mizgajski, Markuszewska Eds., 2010), even some kind of the “identity crisis” of the landscape ecology, which was according to Wu (2013) perceived at the turn of the new millennium - force us to reopen repeatedly theoretical questions on definition of the landscape and its implementation to real policies. Diverse understanding of the landscape concept The concept of the landscape occurs on broad scale of different sciences. Nowadays, basically at least two main streams should be identified: we may say the “hard”, geosystembased concepts of the landscape, and the “soft”, cultural-heritage, “values” and perceptionbased ones. This division is not a ranking, just a differentiation. It is important that different approaches offer different possibilities for their implementation to legislation and to real planning processes. Without any doubt a landscape type defined exactly, e.g. by relief dissection, soil depth or a biocoenoses, has a different normative effect than the characteristics of the beauty of a landscape, which is much more subjective and changing e.g. according to

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Halada, Ľ., Bača, A., Boltižiar, M. (eds.): Landscape and Landscape Ecology. Proceedings of the 17th International Symposium on Landscape Ecology

the persons, angle of view, etc. The first approach to the landscape is represented mainly by geographers and “geographically-biased” landscape ecologists, the second one by a very broad group of different specialists from other sciences, like social scientists, architects and artists. This group cannot be defined in a simple way but all its members might be considered as friends and lovers of the landscape. These differences between the approaches are obvious by mutual comparisons of definitions based on general system theory (Bertalanffy (1968), modified for geographical and landscape sciences by main representatives of the first group, such as Neef and others (Neef, 1967, Neef, E., Richter, H., Barsch, H., Haase, G., 1973), Chorley, Kennedy (1971), Preobrazhensky (Preobrazhensky, V.S., Minc, A.A., 1973), Sochava (1977), Krcho (1968, 1978), and the definition given e.g. by the European Landscape Convention (see below). Of course, the above stated does not mean at all, that the beauty and other similar characteristics or values of the landscape are not important indices of the landscape. The opposite is true: right because of their fuzzy character their implementation need more diligence, in order not to lose them in design and planning processes. Of course, this diversity of opinions on the landscape is nothing new. According to Naveh and Lieberman (1994) the landscape is historically perceived in two ways: as a tangible material reality and also as an intangible, mental and artistic experience, even as a way of life (genre de vive, Vidal de la Blache, 1922). Similar dichotomous understanding of the landscape, expressed by many other authors as e.g. Zonneveld (1981), Golley, Bellot (1991), Haber (2002, 2004), Hynek (2010). Hunziker, Buchecker and Hartig (2007), defined another dichotomy marked as space/place concept. However, for geographically educated landscape ecologists the „space-places” word-pair evokes first of all the research dimensions – the choric and topic dimension (e.g. Haase, 1973, 1980, Haase et al. 1991). These words evoke the same impression also in a common language (surely in Slavic languages) and for laymen. Also the first president of the IALE, I. Zonneveld, spoke about the huge diversity of landscape ecologists during the VIth International Symposium on Problems of Landscape Ecological Research (October 1982, Piešťany, Slovakia) where IALE was constituted. He considered for landscape ecologists simply all those who deal with landscapes (personal note of the author who attended to the Symposium). Many early stage landscape-ecological scientific conferences and symposia have been devoted to clarifying the basic concepts. For example, the 3rd, 4th and 5th International Symposia on the Problems of Landscape Ecological Research organised by the Institute of Landscape Ecology of the Slovak Academy of Sciences (Proceedings 1973, 1976, 1979), the Congresses of the Czechoslovak Geographers (e.g. the XVIth Congress, Zborník, 1978), the International Congress organized by the Netherlands Society of Landscape Ecology in Veldhoven (Tjalingii, de Veer, 1981), and the Allerton Park workshop (Risser, Karr, Forman, 1984) held after foundation of IALE in 1982 can be considered as constitutive ones. The complex, geosystem approach to the landscapes has been pronounced mainly in scientific circles around the German geographical/landscape ecological school, including scientific centres in Central Europe (Neef, E., Richter, H., Barsch, H., Haase, G., 1973, Proceedings 1976, Drdoš (ed.), 1983), and in the Soviet landscape sciences school - the “landshaftovedenyje” (Preobrazhensky, V.S., Minc, A.A., 1973, Sochava, V. B. , 1977, Preobrazhensky, Kupriyanova, Alexandrova, 1980). Another group of scientists, mostly from the Western European and American landscape ecological school focused on the structure of land cover and its spatial pattern (e.g. Tjallingii, de Veer, (Eds.). 1981, Forman, R.T.T., Godron, M., 1981, 1986, Risser, Karr, Forman, 1984, Turner, M., 1990, Nassauer, 1997). The third considerable stream of the landscapers prefers the understanding of the landscape as a “scape/shape” of the land, mainly based on the perception. It is to say that this stream is nowadays a very popular one. The shift of the “popularity” of different streams of

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Halada, Ľ., Bača, A., Boltižiar, M. (eds.): Landscape and Landscape Ecology. Proceedings of the 17th International Symposium on Landscape Ecology

understanding of the landscape is apparent also in Central Europe, if looking at the content of the comparable, repeatedly held and traditional 17 International Symposia on Landscape Ecological Research organised by the Institute of Landscape Ecology of SAS (from Proceedings …, 1973, 1976, 1979, up to Landscape …, 2012, 2015), but also according to the content of other landscape ecological and geographical symposia (e.g. Kozová, Hrnčiarová, Drdoš et al. 2007, Breuste, Kozová, Finka, M., Eds., 2009, Mizgajski, Markuszewska, Eds., 2010, Kolejka et al., 2011, IX. Kárpát-medencei…, 2013) or according to other sources (Longatti, Dalang, 2007). Between these schools there were never sharp divides, neither a considerable antagonism. This fact was confirmed by the common effort of different groups of scientists to establish the International Association for Landscape Ecology (IALE), which happened in Piešťany (Slovakia) in 1982 as one of the most considerable result of the VIth International Symposium on Problems of Landscape Ecological Research organised by Institute of the Landscape ecology of SAS (that time under the name Institute of Experimental Biology and Ecology of SAS). Landscape as a geosystem The concept of landscape as a geosystem is broadly accepted among geographers and landscape ecologists. It is based on the general system theory (Bertalanffy, L. von, 1968), which was modified by many scientists keeping the materialistic approach, as e.g. Neef (1967): Landschaft ist durch einheitliche Structur und gleiches Character Wirkungsgefüge geprägten konkreten Teil der Erdoberfläche, or Naveh and Liebermann (1994) as a concrete time-space system of the total ecosphere. A congregated system definition of landscape according to the understanding of many authors, as e.g. Krcho (1968, 1978), Chorley, Kennedy (1971), Demek (1974), Schmithüsen (1976), Sochava (1977), Preobrazhensky, (1983), Snacken, Antrop (1983), Miklós, Izakovičová (1997) and others may be presented as: landscape is a geosystem, an integrated complex of elements of geographical sphere and their interactions with each other. The geosystem definition of the landscape decisively helped in successful implementation of landscape-ecological conceptions as the Landscape Ecological Planning LANDEP (Ružička, Miklós, 1982) as well as the concept of the Territorial System of Ecological Stability (TSES, Buček, Lacina, 1984, Löw.et al., 1984, Miklós, 1996) to legislation and planning practice in the Slovak Republic, but also in other countries (e.g. Drdoš, J., Ed., 1983, Haber, 1990, Haase, et al., 1991, Golley, Bellot, 1991, Barsch, Sauppe, et al., 1993). At present, implementation of the system approach is connected with the concept of ecosystem services (Millenium …, 2005, Jones et al, 2012, Wu, 2013, Iverson et al., 2014, Grunnewald, Bastian, 2015). Landscape as a natural-cultural phenomenon A huge asset to the acknowledgment of landscape in politics is played by the European Landscape Convention (Council of Europe, Florence, 20th October, 2000). Nevertheless – like each international convention – also this one manifests compromises between professionals, diplomats and politicians. The definition of the landscape in Article 1 of the Convention allows understanding of the landscape in a very broad manner: "Landscape" is an area, as perceived by people, whose character is the result of the action and interaction of natural and/or human factors;…“. We may say that there is not a single word that would be incorrect. Nevertheless, it is a non-materialistic definition; landscape is defined as an imaginary entity based on perception of its character (compare with Zonneveld, 1991, Haber, 2002, and others). Other articles of the Convention define the landscape as an assembly of „heritage“, „values“, „quality“ (Article 5, 6). The problem is not the loose definition itself, but the possible ways of its acceptance, more precisely its non-acceptance in both the science and

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Halada, Ľ., Bača, A., Boltižiar, M. (eds.): Landscape and Landscape Ecology. Proceedings of the 17th International Symposium on Landscape Ecology

the practice. Namely, its acceptance is practically barely controllable in “hard” policies, as in protection, management and planning, since whatever perception of the landscape of whomever might be considered as legal, the planning and the design practices may apply the theoretical provisions of the Convention almost in an arbitrary way. The Convention serves as the main pillar for the landscapers who consider the landscape to be a phenomenon, the “shape/scape” of the land, the cultural-heritage value. The specialists from this group do not always insist on the exactly defined indicators of the landscape as a geosystem, or on the knowledge of the elements of landscape, of their physical structure (see e.g. Breuste, J., Kozová, M., Finka, M., Eds., 2009). Thus, the scientific knowledge may not be sufficiently applied to practice, and the tension between credibility, saliency and legitimacy of acceptance of scientific information appears (Tress, Tress, 2001, Cash et al. 2003, Nassauer, Opdam, 2008). Nevertheless, the above mentioned never means that this approach has no implementation to practical landscape protection and design. On the contrary, there is a huge amount of good practices with application of landscape values to the design (Nassauer, Ed., 1997, Nassauer, Opdam, 2008, Breuste, Kozová, Finka, Eds., 2009, Mizgajski, Markuszewska, Eds., 2010, Nassauer, 2012, Foo et al., 2015). Landscape architects, designers and conservationists prefer many times fuzzy information on landscapes, more than exactly defined indicators of regulative character, because they offer them more freedom, i.e. less obligations. The problem is that the quality of implementation of landscape information is hardly controllable, less normative, as in case of numeric or other exact regulatives. The holistic approach to the landscape and the legal tools Each status of the landscape, whatever holistically perceived, the status of its structure, its quality, values, the "shape/scape", is the result of the land-use of each single material spot of the landscape (of the land unit - Zonneveld, 1989, Haber, 1990, 2008). Those single spots create the „shape“ of the land also in a choric dimension. We can evaluate this shape as more or less attractive, valuable, with lower or higher quality. Thus each policy starts with a simple question: Do we like the present structure of the landscape or not? If yes, we shall try to keep the quality, extent, position of each single spot as they are now. It is a conservative approach. If not, we try to promote changes. If we wish to protect or change this shape, the values, the quality, there is no way to do it „holistically“, we have to protect or change the use of each respective spot. In our countries, management of changes is regulated by legal procedures – by spatial planning. Our exercises showed that for the decision on the optimal use of the landscape according to the real materialistic conditions we need to define the landscape, as a system of material elements with precisely defined indicators (geosystem), which plays the role of a physical regulative for the decision making process. If the elements of the landscape are not tangible, if they are not related to a regulative, then the politicians, the planners, the designers may apply the information on the landscape in an arbitrary way, not as an obligatory regulative dependent on physical properties. Furthermore, it is to mention, that each part of the landscape has its owner or user, who can be forced to keep or change the use of his ownership only according to the legal, well defined normative tools. This circumstance also presses upon the materialistic definition of each spot of the landscape. Landscape in environmental politics and in the European Landscape Convention In the international environmental politics the real material object of the geographical sphere appears in different conceptual forms. It is quite usual to hear on the same forum about policies dealing separately with the items as the environment, the landscape, the mountains, the ecosystems, the biodiversity, the forests, the watersheds, the waters, the soils, as if they all were separate, independent “items”. In the international context, there are available separate

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Halada, Ľ., Bača, A., Boltižiar, M. (eds.): Landscape and Landscape Ecology. Proceedings of the 17th International Symposium on Landscape Ecology

programs, institutions and project sources for all those separate “items”. The question is if all the above mentioned “items” are dependent on each other. Are they all components of one integrated system? Of course, they are! Also speakers-specialists, but also politicians, decision makers and planners often underline the need for an integrated management for each of those items. Then there appears another question: what policy, what program, what project source is devoted to an integrated approach? Which program deals with a concrete territory e.g. with a complex problem of scarce water sources, poor soils in a xerophilous ecosystem, with afforested mountain landscape environment endangered by erosion? Of course we cannot expect that the politicians are experts in natural sciences. Neither we cannot or do not want to restrict the classic approaches to the management of single components of the geosystems as to the soil, water etc. On the other hand, it is quite inadequate to consider the landscape, the environment, the geosystem, the ecosystem or other complex concepts as separate “items”. Unfortunately, that separative approach is accepted – often unconsciously - also by environmentalists or landscapers. Even a rigid juridical interpretation of the European Landscape Convention “supports” this separation. Let us open a few basic questions concerning the text of the Convention: a) The Preamble of the Convention read as follows: “Believing that the landscape is a key element of individual and social well-being ... Acknowledging that the landscape is an important part of the quality of life for people…” Article 5 reads: „Each Party undertakes: to recognise landscapes in law as an essential component of people’s surroundings ...” Juridical reading of these provisions clearly mirrors the common understanding unfortunately very wide-spread – that the landscape is something like an aspect, probably of a material reality(?), as a thing beside others, or - as it reads - only an element, part, component of the quality of the life. In reality, the general public, decision makers, planners and designers understand under the landscape mostly visual, aesthetic, conservational or cultural aspects of the landscape. The sharp question evoked by this reading is: can the people live (somewhere and somehow, even not in well-being) without/outside the landscape, or the landscape being “only” a part of their well being, but not an inevitable condition for their life? So, the critical difference between the present reading of the Convention and the materialistic understanding of the concept of landscape is if the landscape is an omnipresent material reality, or only a chosen aspect of this reality. Of course, we may expect that the scientists will promote the complex materialistic approach. In any case, the correct text should express that the landscape is an objective omnipresent frame and condition for life and well-being of the people, and that all/chosen aspects, attributes or properties of the landscape are important, some of them even inevitable parts of their life. b) Article 15 of the Convention reads as follows: 1 Any State … may ... specify the territories to which the Convention shall apply. 2 Any Party may, … by declaration ... extends the application ... to any other territory 3 Any declaration … may ... be withdrawn by notification ...“ The rigid juridical reading of these provisions clearly states that the Parties are not obliged to apply the Convention on the whole territory, they can extend or narrow its application. In reality, it mirrors again the wide-spread understanding, that the landscape exists there, where its certain aspects/values appear, meaning of course mostly the visual, aesthetic, conservational or cultural values. From the point of view of the law, but unfortunately also of the general public, it may evoke an understanding that the landscape(s) “exist” only somewhere, in nice places, the rest of the country is simply a territory without “landscape”. We would like to believe that each Party signed the Convention with a good will. Anyway, during the application of the Convention one cannot exclude political/economic problems. Since the Parties are quite free to apply the Convention according to their (good?) will, they

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may treat the landscape actually as they did prior to the Convention, without strict legal sanctioning! Moreover, if the Convention will not be applied on the whole territory, the landscapes outside of the application might worsen in their quality. Implementation of the landscape concept to the law and practice Generally we may recognize three basic lines of the legal implementation of the concepts of the landscape into the practice: a) Preservation of landscapes by classic nature conservation The nowadays most-favoured stream of the preservation of the landscape “values” - which mostly reflexes the “shape/scape” of the land - has the oldest practice and tradition. The oldest national parks and landscape parks were declared as protected basically because of their beauty, rareness, wilderness, etc. In this way it is applicable also today all over the world through the practice and rules of nature conservation. For example, in Slovakia according to the provisions of the Act No. 543/2002 Z.z. on nature and landscape conservation the authorities can declare the territories with characteristic features, specific historic structures (historic landscapes), with unique natural structures or even significant single small-area landscape elements, for protected in different categories and degrees. The Act provides possibilities for declaration of protected landscape areas (§ 18), national parks (§ 19), protected sites (§21), natural reservations (§22), natural monuments (§ 23), community protected areas (§25a) or private protected areas (§31), moreover also NATURA 2000 sites (§26, 27). So, theoretically there is no legal conceptual limitation to declare whatever territory with values as protected. Nevertheless, there are other important constraints, namely that the nature conservation authority is obliged to: - negotiate with the owners or users of the land, what is a hard problem; - (in the case of declaration of protected areas) provide compensation for the limitation of the common modus of the use (according to § 61 section 1a to 1d of the Act No. 543/2002). At present a large portion of Slovakia is under protection (23.3 % are nature conservation areas, plus 26,2 % the bird areas, plus a large extent of the water protection and forest protection areas), which means limitations and needs for financial compensations. Therefore, there appeared a lack of political will to cover more territory with whatever protection. So, each eventual attempt would need an extraordinary argumentation, hard negotiation, conviction of the owners and the finances for compensation. b) Landscape elements as elements of ecological networks This approach means the possibility to maintain the most important landscape elements as biocentres, biocorridors, buffer zones/interactive elements. In Slovakia the ecological networks are defined as the Territorial System of Ecological Stability (TSES) at its various levels. The TSES and its elements are legally defined as „significant landscape elements” in the Act No. 543/2002 Z.z. (§ 2, section 2a and 2c). Practical implementation of the TSES is ensured through several other acts, such as:  Act on Territorial Planning and Building Order, amendments 262/1992 Zb. and 237/2000 Z.z.: the elements of TSES are obligatory regulative of territorial plans.  Act on Land Arrangement and Land Ownership 331/1991 Zb. and its amendment 549/2004 Z.z.: the TSES is an obligatory part of each Land Arrangement Project, The revision of the TSES is a legal cause for enactment the land arrangement procedure.  Act on Environmental Impact Assessment 127/1994 Z.z. and 24/2006 Z.z.: defines that TSES is an obligatory object of impact assessment.  the Water Act 364/2004 Z.z.: forces the utilisation of the water protecting function of TSES with the coordination of water management tasks.

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 Act 7/2010 Z.z. on Flood Protection: enacts that the long-term management plan should project also the TSES, important landscape elements and the eco-stabilising measures. The TSES elements play functional roles in all of those acts. For example, in Act No. 7/2010 on flood protection the TSES elements are obligatory as protective elements against flood, in land arrangement projects as erosion protective elements, etc. The ecological network serves also for nature conservation, as a qualitatively new concept. No doubt, that an ecologically stable landscape bears also the “values” desired by nature conservationists and landscapers. TSES projects are broadly implemented to different executive projects and became the most successful landscape ecological conception involved in environmental policy in Slovakia after 1989. It presents also a practically proven procedure for ideas of the integrated landscape management. c) Planning of the optimal use of the territory This approach considers implementation of the properties of landscape elements as a regulative for decision on the optimal use of the territory through spatial planning process. In Slovakia for that reason, the methodology of the landscape-ecological planning LANDEP (Ružička, Miklós, 1982, 1990, Miklós, Špinerová, 2011) was developed and implemented. The LANDEP was defined as an obligatory part of physical planning in the Act on Territorial Planning and Building Order, amendment no. 237/2000 Z.z. This Act defines the landscape as the geosystem in § 139 as: “(5) Landscape is a complex system of space, location, georelief and other mutually, functionally inter-connected material natural elements and elements modified and created by a man, in particular geological basement and soil creating substratum, water bodies, soil, flora and fauna, artificial objects and elements of utilisation of territory, as well as their connections determined by socio-economic phenomena in the society. Landscape is the environment of the man and other living organisms.” “(1) Regulative of spatial arrangement ... and functional utilisation of territory is a binding guideline which guides the localisation and arrangement of a certain object or realisation of a certain activity in territory. It is expressed through values of properties of elements of landscape structure by words, figures and graphically, if possible. Regulator has a character of bans, limitations or supporting factors in relation to spatial arrangement and functional utilisation of territory. In this way regulator determines banned, limited and acceptable activity or function in territory.” The optimum use of the territory includes, of course, also the localisation of the elements of the TSES, as well as the nature conservation areas, all as the functional areas with ecostabilising function. So, this way is theoretically the most complex one for the implementation of the landscape to the legislation. Conclusion The present development of landscape sciences is on much higher level than several decades ago. Also the acceptance of our science in practice has improved, several landscapeecological concepts have been successfully applied to policies and planning processes. Objectively, the theory and the practice of landscape ecology decisively influenced the basic pillars of sustainable development, as environmental protection, management of natural resources, nature conservation including the implementation of the NATURA 2000, landscape planning procedures, integrated watershed management, and of course also the “birth” of the European Landscape Convention. In spite of that, the acceptance of those issues is still not on the desired level, there is still a gap between politics and science. Therefore,

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further development of both the theory and application of landscape ecological concepts is constantly a priority for all specialists in landscape sciences. This publication is a result of the project of Hungary-Slovakia Cross-border Co-operation Programme 2007-2013 HUSK 1101/121/0287 „Adaptív szőlészeti növényvédelmi előrejelző rendszer kifejlesztése a határmenti borvidékek összefogásában a versenyképesség növelése érdekében/Vývoj adaptívneho predpovedného systému ochrany rastlín v spolupráci prihraničných vinárskych oblastí v záujme zvyšovania ich konkurencieschopnosti“. References Barsch, H., Sauppe, G. et al, 1993. Zur Integration landschaftsoekologischer und sociooekologischer Daten in gebietliche Planungen. Potsdamer Geographische Forschungen, Band 4. Universität Potsdam, 226 p. Bertalanffy, L. von, 1968. General System Theory. Foundations, Development and Applications. George Brazileer, New York. Penguin Books. Breuste, J., Kozová, M., Finka, M. (Eds.), 2009. European Landscapes in Transformation: Challenges for Landscape Ecology and Management. European IALE Conference 2009, Salzburg (Austria), Bratislava (Slovakia). Buček, A., Lacina, J.,1984. Biogeografický přístup k vytváření územních systému ekologické stability krajiny. Zprávy Geografického ústavu ČSAV Brno, 21, 4, pp. 27-35. Cash, D.W. et al (2003) Knowledge systems for sustainable development. Proc Nat Acad Sci 100(14):8086–8091. Chorley, R. J., Kennedy, B. A. , 1971. Physical Geography - A System Approach. Prentice Hall International Inc, 370 p. London. Demek, J. 1974. Systémová teorie a studium krajiny. Studia geographica, 40. Drdoš, J. (Ed.), 1983. Landscape Synthesis: Geoecological Foundations of the Complex Landscape Management. VEDA, Bratislava. Foo, K., Gallagher, E., Bishop, I., Kim, A.M. 2015. Critical Approaches to Landscape Visualization. Landscape and Urban Planning, Volume 142, Special Issue, 244 pp. Forman, R.T.T., Godron, M. 1981. Patches and structural components for landscape ecology. BioScience, 31, pp. 733-740. Golley, F. B., Bellot, J. 1991. Interactions of landscape ecology, planning and design. – Landscape and Urban Planning 21, p. 3 – 11. Grunewald,K., Bastian, O. (Eds.). 2015. Ecosystem Services – Concept, Methods and Case Studies. Springer-Verlag Berlin, Heidelberg, 312 pp. Haase, G. 1973. Inhalt und Terminologie der topischen und chorischen Landschaftserkundung. Beitrag zu III.rd International Symposium, ÚBK SAV, 19 pp. Haase, G. 1980. Izuchenie topicheskih i choricheskyh struktur, ich dinamiki i razvitiya v landshaftnyh sistemah (in Russian) In: Struktura, dinamika i razvitiye landshaftov. AN SSSR, Institut geografii, Moskva, p. 57-81. Haase, G. at al. 1991. Naturraumerkundung und Landnutzung. Geochorologische Verfahren zur Analyse, Kartierung und Bewertung von Naturräumen. Beiträge zur Geographie. Berlin, 34, I und II. Haber, W. 1990. Using landscape ecology in planning and management. In: Zonneveld, S., Forman R. T. T.: Changing Landscapes on Ecological Perspective. Springer-Verlag, New York, p. 217-232. 14

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Haber, W. 2002. Kulturlandschaft zwischen Bild und Wirklichkeit. In: Schweizerisches Akademie der Geistes und Sozialwissenschaften. Bern, Akadenmievorträge, Heft IX, 19 pp. Haber, W. 2004. Landscape ecology as a bridge from ecosystems to human ecology. Ecological Research, 19, p. 99–106 Haber, W. 2008. Naturschutz in der Kulturlandschaft – ein Widerspruch in sich? NatSch_KuLa. Laufener Spezialbeiträge 1/08, p. 15- 25. Hunziker, M., Buchecker, M., Hartig, T. 2007. Space and Place – Two Aspects of the Humanlandscape relationship. In: Kienast, F., Wildi, O., Ghosh, S., (Eds.): A changing world. Challenges for landscape research, Vol. 8., Landscape Series. Springer, Dordrecht. p. 47 62 Hynek, A. 2010. Krajina: objekt, nebo konstrukt? In: Herber, V. (Ed.). Fyzickogeografický sborník 8, Fyzická geografie a kulturní krajina, Masarykova univerzita, Brno, p. 138-142. Iverson, L., Echeverria, C., Nahuelhual, L., Luque, S. 2014. Ecosystem services in changing landscapes: An introduction. Landsc Ecol. 29, p.181–186 Jones, K.B., Zurlini, G., Kienast, F., Petrosillo, I., Edwards, T.,Wade, T.G., Li, B., Zaccarelli, N. 2012. Informing landscape planning and design for sustaining ecosystem services from existing spatial patterns and knowledge. Landscape Ecol. 28, p.1175–1192 Kolejka, M. et al. 2011. Krajina Česka a Slovenska v současném výzkumu. Masarykova univerzita, Pedagogicka fakulta, Brno. Kozová, M., Hrnčiarová, T., Drdoš, et al. 2007. Landscape Ecology in Slovakia. Development, Current State, and Perspectives. Monograph. Contribution of the Slovak Landscape Ecologists to the IALE World Congress 2007 and to the 25th Anniversary of IALE. Bratislava: Ministry of the Environment of the Slovak Republic, Slovak Association for Landscape Ecology – IALE-SK, 2007, CD ROM, 541 pp. Krcho, J., 1968. Prírodná časť geosféry ako kybernetický systém a jeho vyjadrenia v mape. Bratislava, Geografický časopis 20, 2, p. 115-130. Krcho, J., 1978. The spatial organisation of the physical-geographical sphere as a cybernetic system expressed by means of measure as entropy. Acta Fa. Rer. Nat. Univ. Comenianae, Geographica, 16, p. 57-147. Landscape ecology: from theory to Practice. 2012. Proceedings of the 16th International Symposium on Problems of Landscape Ecological Research. ILE SAS, Bratislava Landscape and landscape ecology. 2015. Symposium abstracts. 17th International Symposium. ILE SAS, Bratislava. Longatti, P., Dalang, T. 2007. The Meaning of “Landscape” – An Exegesis of Swiss Government Texts. In: Kienast, F., Wildi, O., Ghosh, S., (Eds.). A changing world. Challenges for landscape research, Vol. 8., Landscape Series. Springer, Dordrecht. p. 47 62. Löw, J. et al. 1984: Zásady pro vymezování a navrhování územních systémů ekologické stability v územně-plánovací praxi. Agroprojekt, Brno, 55 pp. Millennium Ecosystem Assessment. 2005. Ecosystems and Human Well-being. Island Press, Washington, DC. 948 pp. Miklós, L., 1996. The concept of the territorial system of ecological stability in Slovakia. In: Jongmann, R.H.G. (Ed.): Ecological and Landscape Consequences of land-use change in Europe. ECNC publication series on Man and Nature 2., Tilburg, p.385-406. Miklós, L., Izakovičová, Z. 1997. Krajina ako geosystém (The landscape as a geosystem. In Slovak).VEDA, Bratislava, 150 pp. 15

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Miklós, L., Špinerová, A. 2011. Krajinno-ekologické plánovanie LANDEP (Landscape Ecological Planning LANDEP. In Slovak). VKÚ, a.s., Harmanec, 158 pp. Mizgajski, A., Markuszewska, I. (Eds). 2010. Implementation of landscape ecological knowledge in practice. The Problems of Landscape Ecology. Volume XXVIII. Polish Association for Landscape Ecology, Wydawnictvo Naukowe Adam Miczkiewicz University, Poznań. Nassauer, J.I. (Ed.). 1997. Placing nature: culture and landscape ecology. Island Press. Washington, D.C. Nassauer, J.I., Opdam, P. 2008. Design in science: extending the landscape ecology paradigm. Landscape Ecol (2008) 23:633–644 Nassauer,J.I., Santelmann, M.V., Scavia, D. 2007. From the Corn Belt to the Gulf: Societal and Environmental Implications of Alternative Agricultural Futures. Resources for the Future Press. Washington,D.C.260 pp. Nassauer, J. I. 2012. Landscape as medium and method for synthesis in urban ecological design. Landscape and Urban Planning, Vol. 106, p. 221-229. Naveh, Z., Liebermannn, A. 1994. Landscape ecology - theory and application. Second edition. Springer-Verlag. New York, Berlin, Heidelberg. 360 pp. + 75 pp. of supplement. Neef, E. 1967. Die theoretischen Grundlagen der Landschaftslehre. H. Haack, Gotha/Leipzig. 152 pp. Neef, E., Richter, H., Barsch, H., Haase, G. 1973. Beitrage zur Klärung der Terminologie in der Landschaftsforschung. Beitrag d. Institut d. Geographie and Geoökologie d. Akad. d Wiss. d. DDR. Beilage zu Práce a materiály z biológie krajiny, 20. UBK SAV, Bratislava. Preobrazhensky, V. S. 1983. A system orientation of landscape research in geography and its present-day realization. In: Drdoš, J. (ed.). Landscape Synthesis. Geoecological Foundations of the Complex Landscape Management. VEDA, Bratislava, p. 31-36. Preobrazhensky, V.S., Kupriyanova, T.P., Alexandrova, T.D. 1980. Issledovanie landšaftnych sistem dľa celej ochrany prirody. In: Struktura, dinamika i rozvitije landšaftov, AN SSSR, Institut geografii, Moskva, p. 11-25. Preobrazhensky, V.S., Minc, A.A. 1973. Sootnoshenye ponyaty geosystema a ekosystema. In: Práce a materiály z biológie krajiny, 20. Proceedings of IIIrd International Symposium on the Landscape Ecological Research. ÚBK SAV, Bratislava. Proceedings of IIIrd International Symposium on the Problems of Landscape Ecological Research, 1973. Práce a materiály z biológie krajiny, 20, UBK SAV, Bratislava. Proceedings of IVth International Symposium on the Problems of Ecological Landscape Research, 1976. ÚEBE SAV Bratislava. Proceedings of Vth International Symposium on Problem of Ecological Landscape Research, 1979. ÚEBE SAV, Bratislava. Risser, P.G., Karr, J.R., Forman, R.T.T. 1984. Landscape ecology: directions and approaches. Illinois Natural History Survey. Special Publ. 2, Champaign. Schmithüsen, J. 1976. Grundlagen der Landschaftskunde. Allgemeine Geosynergetik. Berlin. Snacken, F., Antrop, M. 1983. Structure and dynamics of landscape systems. In: Drdoš, J. (ed.). 1983. Landscape Synthesis: Geoecological Foundations of the Complex Landscape Management. Bratislava, VEDA, p. 10 -30. Sochava, V. B. 1977. Vvedenie v uchenie o geosystemach (in Russian). Nauka. Novosibirsk.

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Tjallingii, S. P., de Veer, A. A. (Eds.). 1981. Perspectives in Landscape Ecology. Proceedings of the International Congress organized by the Netherlands Society for Landscape Ecology, Veldhoven, Wageningen. Tress B. , Tress G. 2001. Capitalising on Multiplicity: a Transdisciplinary Systems Approach to Landscape Research. Landscape and Urban Planning 57. P. 143–157. Turner, M. 1990. Spatial and temporal analysis of the landscape patterns. Landscape Ecology, 4, 1, p. 21-30. Vidal de la Blache, P. 1922. Principes de géographie humaine. Posthumus súbor článkov. Zdroj: Encyklopedia Britannica, http://www.britannica.com/EBchecked/topic/627886/ Paul-Vidal-de-la-Blache. Wu, J. 2013. Key concepts and research topics in landscape ecology revisited: 30 years after the Allerton Park workshop. Landscape Ecol. 28, p.1–11. Wu, J, Hobbs, R. 2002. Key issues and research priorities in landscape ecology: an idiosyncratic synthesis. Landscape Ecol. 17, p.355–365. Zborník XVI. zjazdu československých geografov. 1978. Slovenská geografická spoločnosť pri SAV Bratislava - Československá spoločnosť zeměpisná pri ČSAV. Zonneveld, I.S. 1981. Land(scape) ecology, a science or a state of mind. In: Tjallingii, S. P., de Veer, A. A. (Eds.). Perspectives in Landscape Ecology. Proceedings of the International Congress organized by the Netherlands Society for Landscape Ecology, Veldhoven 1981, 9 – 15; Wageningen. Zonneveld, I.S. 1989. The land unit - a fundamental concept in landscape ecology and its application. Landscape Ecology, 3, p. 67-86. IX. Kárpát-medencei környezettudományi konferencia. 2013. Symposium abstracts, Miskolci Egyetem, Sapientia Erdélyi Magyar Tudományegyetem. Miskolc.

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LANDSCAPE, MEMORY AND HISTORICAL SOURCES Václav ŽDÍMAL Mendel University in Brno, Brno, Czech Republic; [email protected] Abstract Landscape memory is a very common term today and its usage is various. The term is usually connected with social sciences as ability of organisms to save and use information about previous experience. One of the possible explanations is a memory as composition of biotic and abiotic components. Even in this situation the information is the main term. The saved information here is the result of landscape history and particular place. Land changes in the past determine the nowadays-local basic characteristics. Each change creates and saves this information. Afterwards they influence possibilities of particular place and limit its usability. Landscape memory is comparable to the mechanical system that reacts to stimuli term from external environment without changing inner organization. This information is written down as a palimpsest with the whole history of the place. The specific place „remembers“ the history and the present, tolerates only specific land use and returns landscape to the previous conditions or trends after the wrong usage. Suitable land use provides a good basis for ecosystem services. The example could be meandering and subsequent straightening of rivers, deforestation, relocation and change in soil layers. These changes in the past affected the present management and it is important to identify them. We can use these archival materials: the maps of the 2nd and 3rd (II and III) military mapping, basic maps and other maps and historical aerial photographs. Landscape memory is needed to be respected during the landscape planning and we have to learn the landscape history, the saved information and the memory of particular place thoroughly. Key words: landscape; memory; history. Introduction The modern landscape is inextricably linked to its past. If the landscape memory is closely linked to its history, the reaction to the landscape in the past would be similar to the reaction to any disruption in it. A specific example might be the way river courses were changed in the past and changing the conditions of dampness. The original riverbeds are still visible today in the landscape, waterlogged in their original locations and with different soil conditions. During flooding they sometimes return to their original locations, which they “remember”. Similar identifications can be made of overlaying ground when pipelines are laid and land improvement work undertaken, of areas following the extraction of raw materials, of former settlements and other interventions in the landscape. Exceptions are the total reshaping of the landscape with the loss of memory, e.g. in areas of surface mines. The term memory is often used today in various fields. Memory is most commonly associated with biological and social sciences. Even in the biological sciences memory can be viewed in various ways. Biologists most often define memory as the ability of an organism to store, sort and provide information. Memory here can be associated with the learning process, where changes are made in mental activity based on experience. This experience allows better adaptation to environmental conditions. In the social sciences, social memory can indicate the content of the memory of an individual if that content consists of social phenomena, processes and events, or that which some communities remember. Also linked to this is historical memory, which humanity develops through the study of historical documents.

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In physics and technology, memory is most commonly associated with media for storing data and with shaping the memory of material, where the material is deformed when heated up to a certain temperature and then automatically returns to its original shape. In geology (Kukal, Němec, Pošmourný, 2005) the term memory is based on the assumption that each geological period has left its traces in the landscape, which the landscape has more or less memorized. It has remembered the oldest geological history in the form of the current structure and makeup of the landscape, which has never been completely replaced. Associations between the terms landscape and memory have not escaped archaeology. Beneš and Brůna (1994) distinguished two levels of landscape memory. The first level includes archaeological layers consisting of abiotic and biotic components, including their context. The second level lies at the intersection of philosophy, ecology and cybernetics, and landscape memory here is a certain mechanism with built-in feedback, plus an additional dimension waiting for decryption. In landscape ecology, geography and related disciplines, the term memory also has a different meaning. Two directions most often appear here, natural and social. The first of these concerns the landscape itself and involves multiple approaches. According to Cílek (2004), memory depends on relief, climate, substrate, soil and water surfaces. Sádlo (1994) defines memory as the ability to regenerate a former state associated with cybernetics and manner of self-management. At the same time he admits the possibility of wiping memory clean and losing it. This situation is also described by Sklenička and Lhota (2002). We can call the second direction social, which is more diverse. There are a variety of approaches here as well, including cultural, sociological, psychological, educational, historical, and more. At the level of culture, Cílek (2004) connects memory with retaining all kinds of sights and their integration into the life of today’s community. Machar presents another form of landscape memory. In 2014 he tracked the place-names in the landscape as part of the spiritual component of landscape memory and as part of intangible and traditional folk culture. And of course this includes the memory of the inhabitants of the area, with their ability to store, sort and provide this information. In this context, memory can be lost when people move to a new location. The continuity of information about a particular location is lost and it is difficult to recover it. And people find identity in landscape and place (Taylor, 2008). This direction also includes well known book Landscape and Memory by Simon Schama (1996). It aims to reveal the richness, complexity and antiquity of landscape and show how much we lose by destroying and not using landscape. The book Landscape and Memory tries to be a means for finding and discovering what we know, but it escapes our knowledge and understanding. Some authors combine the above approaches. Kučera (2009) describes the memory of the environment as independent of human memory, which is composed of elements, structures, and a return to a specific condition. He likewise describes the importance people often attach to different parts of the landscape and how people remember them. Taylor (2008) does not even separate landscape and memory. Marcucci (2000) use landscape history as a planning tool to improve description, predicting and prescription in landscape planning. Palang (2011) describes two approaches. One of these is the landscape biographical approach, another way is dependency approach this could help in dealing with the uniqueness of each landscape. The term memory is not always used, even if the content is similar. Antrop (2005) treats the past as important for the future. Cílek (2004) connects the past with a sense of anticipation

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for the future and as a result of losing the past, he sees a foggy future. Marcucci (2000) similarly considers history as a planning tool. The concept of memory may have different meanings in the landscape and therefore when using it, it is suitable to further define it so that the reader understands the author’s meaning. Materials and methods In this work, the term landscape memory can be mostly compared to a mechanical system (Míchal, 2000), which responds to stimuli from the external environment without changing its internal organization. In cases with great influence from the biotic component, the term landscape memory can also be likened to an adaptive system, according to Míchal (2000). In both cases, it maintains its homeostasis. Cumming (2011) use similar concepts of resilience and spatial resilience. Therefore, landscape memory, or its history rather, may give clues to the landscape processes that take place here today and will take place in the future. This must be taken into account and we can likewise employ it in today’s management and in planning for the future. This can save costs, eliminate conflicts, and improve the workings of ecosystem services. And that is the story of a particular place and a particular landscape. The Czech Republic has a considerable amount of documentation, which makes it possible to reconstruct the former state of the landscape. The main documents are topographical maps (Skokanová et Havlíček, 2010), cadasters, archival aerial photographs and direct or indirect signs. Most of the documents are only listed here. We can use: the First Austrian Military Survey 1:28 800 (1763-1768), the Second Austrian Military Survey 1:28 800 (1836–1852), the Third Austrian Military Survey 1:25 000 (1876– 1880), the Maps of Provisional Military Survey from 1923 to 1933 at scales 1:10 000 and 1:20 000, the Definite Military Maps from 1934–1938 1:20 000, the Military Topographic Map (1953–1990), the Basic Map of Czech Republic (1980–2014), the Land Records (14th century), the Stable Cadaster (1817), the Cadaster of Land (1927), the Cadaster of Real Estates of the Czech Republic (1993), the aerial photographs (1938-2014), and more. Topographical maps and cadasters depict the landscape relatively well, but only at some point. A long period of time still needs to be depicted. And the consequences of the activity of these unknown periods need not have an effect on anything smaller. Identifying them is possible with field research and observations made from above on the basis of direct or indirect signs. The greatest experience in identifying extinct historical objects, i.e. human activities in the past, is enjoyed by aerial archaeologists. Gojda (2004) divides the signs identifying immovable objects into direct – soil and shadow signs – and indirect – vegetation, aridity, humidity and more. In landscape ecology, visible traces can usually be detected using aerial photographs from regular surveys of the Czech Republic. To uncover short-term visible traces, it is necessary to use certain conditions that make observing specific objects in the “right” situation possible. The “right” situation cannot be defined in advance with certainty, but generally there must be harmony between the crop and its vegetative phase, the moisture conditions of the soil, the height and direction of the sun, i.e. lighting in the right spectral band, and the presence of a perceptive observer in a suitable station. It is likewise advisable to use extreme or unusual circumstances such as drought or wet conditions. These mostly involve the long-term observation of the selected site before the right conditions occur. This way we can uncover many past activities in the landscape.

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Results and discussion Three locations (Fig. 1) were selected as a demonstration of landscape memory and the possible use of historical documents (Fig. 2). The first example is a floodplain of the Bečva River at Osek nad Bečvou. Up until the modern era, this floodplain had long been a landscape close to nature where the river meandered and damp meadows and groves decomposed. The river was straightened and changed into a trapezoidal channel between 1895 and 1933 (Lacina, 2013). These modifications made the intensive use of the floodplain possible, and today fields predominate here, as well as some development. Traces of the original riverbed can still be seen here.

Fig. 1 Location of analyzed areas. Heavy rainfall in the summer of 1997 led to a maximum flow rate greater than what the channel had been designed for. Depth and bank erosion created two levels of the channel, which is five times wider than the originally modified channel. The flow began to meander outside the modified channel. The river essentially began to behave the way it behaved long ago in the past (Fig. 2).

Fig. 2 The meanders of Bečva River.

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The second example is waterlogged and periodically flooded area of the original flow of the Šatava River. The river was straightened and agricultural cultivated area increased. Soils in the original riverbed and the waterlogged area have different physical and chemical properties. The present land use is limited by different soil properties (Fig. 3).

Fig. 3 Periodically flooded area of the original flow of the Šatava River. The third example is the area, where sand has been excavated. At first, sand was removed from the location where farming had taken place, and subsequently the remaining pit was filled with sludge from the nearby sugar refinery in Židlochovice, with the land then forested via volunteer seeding. When the area of arable land was increased in the 1970s, the land came under cultivation and was not reclaimed. Although it is used for agricultural purposes today, cultivation is not possible under adverse moisture conditions. Any other use is therefore practically impossible. The entire process has taken place without clear ownership relationships (Fig. 4).

Fig. 4 The former sand mine near the village of Nosislav (© GEODIS).

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Conclusion The study of historical documentation is particularly important where the continuity of knowledge about a certain place has been interrupted. Therefore, the landscape memory or rather its history, may give clues to the landscape processes that take place here today and will take place in the future. This must be taken into account and we can likewise employ it in today’s management and in planning for the future. This can save costs, eliminate conflicts, and improve management of ecosystem services. The Czech Republic has a considerable amount of documentation, which makes possible to reconstruct the former state of the landscape. Three examples show how the history of the landscape is reflected in contemporary development. The regulated and adjusted Bečva River widened its channel during major flooding and again created a meandering course. Heavy rainfall in the summer of 1997 led to a maximum flow rate greater than what the channel had been designed for. Depth and bank erosion created two levels of the channel, which is five times wider than the originally modified channel. The flow began to meander outside the modified channel. The river essentially began to behave the way it behaved long ago in the past. The area of the original flow of the Šatava River is waterlogged and periodically flooded by ground water. The river was straightened and agriculturally cultivated area increased. Soils in the original riverbed and the waterlogged area have different physical and chemical properties. The present land use is limited by different soil properties. The former sand mine near the village of Nosislav is agriculturally managed, but its cultivation is difficult. At first, sand was removed from the location where farming had taken place, and subsequently the remaining pit was filled with sludge from the nearby sugar refinery in Židlochovice, with the land then forested via volunteer seeding. When the area of arable land was increased in the 1970’s, the land came under cultivation and was not reclaimed. Although it is used for agricultural purposes today, cultivation is not possible under adverse moisture conditions. Any other use is therefore practically impossible. The entire process has taken place without clear ownership relationships. These development options were inferred through studying historical sources and obtaining a good knowledge of the territory, and this knowledge makes it possible to propose alternative ways of using its consistent history, i.e. the landscape memory. References Antrop M., 2005. Why landscapes of the past are important for the future. Landscape and Urban Planning. 70: 21–34. Beneš J., Brůna V., 1991. Has landscape the memory? In: Archaeology and landscape ecology. Nadace projekt sever, Most, pp. 37–46. (in Czech). Cílek V., 2004. Landscape and memory. Archinet.cz, Praha, pp. 1–2. (in Czech). Cílek V., 2004. Enter the Landscape. Středočeský kraj, Praha, pp.1–112. (in Czech). Cumming S.C., 2011. Spatial resilience: integrating landscape ecology, resilience, and sustainability. Landscape Ecology 26: 899–909. Gojda M., 2000. Landscape Archeology. Academia, Praha, pp. 1–238. (in Czech). Kučera Z., 2009. How we sense landscape and its memory. Geographic Perspectives 18: 6–7. (in Czech). Kukal Z., Němec J., Pošmourný K. 2005. Geological memory of the landscape, Czech Geological Survey, Praha, pp.1–224. (in Czech).

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Lacina J., 2013. Changes of vegetation in Bečva floodplain after flood in July 1997. In: Herber V. (Eds.), Physical geography and cultural landscape in the 21st century. Masaryk University, Brno, pp. 7–14. Machar I., 2015. Local place names as a part of landscape memory (Case study from Haná region, Czech Republic). AUC Geographica 49: 61–69. Marcucci D.J., 2000. Landscape history as a planning tool. Landscape and Urban Planning 49: 67–81. Míchal I., 1994. Ecological Stability. Veronica, Brno, pp. 1–276. (in Czech). Palang H., Spek T., Stenseke M., 2011. Digging in the past: New conceptual models in landscape history and their relevance in peri-urban landscapes. Landscape and Urban Planning 100: 344–346. Sádlo J., 1994. Landscape as interpreted text. In: Archaeology and Landscape Ecology. Nadace projekt sever, Most, pp. 47–54. (in Czech). Schama S. 1996. Landscape and memory. Fontana Press, London. pp. 1v652. Sklenička P., Lhota T., 2002. Landscape heterogenity - a quantitative criterion for landscape reconstruction. Landscape and Urban Planning 58: 147–156. Skokanová H., Havlíček M., 2010. Military Topographic Maps of the Czech Republic from the First Half of the 20th Century. Acta Geod. Geoph. Hung. 45: 120–126. Taylor K. 2008., Landscape and Memory: cultural landscapes, intangible values and some thoughts on Asia. In: 16th ICOMOS General Assembly and International Symposium: ‘Finding the spirit of place – between the tangible and the intangible’, Quebec, Canada.

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META-LANDSCAPE ECOLOGY AS A NEW ECOLOGICAL SCIENCE (SELECTED META-SCIENTIFIC ASPECTS) Florin ŽIGRAI External co-operator of Department of Geography and Applied Geoinformatics University of Prešov, Slovakia; [email protected] Abstract The ever more frequent and complicated socio-economic and ecological/environmental problems in the globalising world represent a great challenge for science. One of possible approaches to their solution is the meta-scientific one. Such approach is based on the cognition of science itself and its individual disciplines, their inner structure, organisation and communication with the surroundings. The aim is to find out to what extent they are able to generalise their time-spatial contextual, comprehensive as well as integrating entities and experience into universal regularities and laws, which may help solve these problems. The meta-scientific approach presented in this paper deals for the first time worldwide with metalandscape ecology as a new ecological science. Meta-landscape ecology dealing with landscape ecology as a science, represents the most generalized and most integrated form of meta-scientific superstructure of landscape ecology, sharing its scope with meta-geographical and meta-ecological entities, approaches, principles and general laws. The key topics and issues of meta-landscape ecology are: • preservation of authenticity and determination of identity for landscape ecology, • time-spatial contextuality, complexity and integrity of landscape ecology, • social-scientific relevance of landscape ecology, • transfer of landscape-ecological knowledge from theory to practice, • position and cooperation of landscape ecology among sciences involved with the study of the relationship between humans and landscape, • scientific-managerial marketing of landscape ecology (landscape eco-sciencing, landscape eco-branding, and landscape eco-labelling), • meta-scientific synthesis and principles of landscape ecology, • landscape ecology as a scientific system, • research efficiency of landscape ecology and • prognosis of development of landscape ecology. Fresh meta-scientifically oriented knowledge of landscape ecology is not only epistemologically important for the theoretical, methodological, educational, and applied development of landscape ecology itself but also for its generalising nature for broader universal validity of other scientific disciplines with idiographic regional entity involved with landscape research and its relationship to human society. Key words: meta-landscape ecology and its key topics; issues and results. Introduction Meta-landscape ecology is a new landscape-ecological discipline, the main research object of which is landscape ecology as a science. Establishment of meta-landscape ecology also means, beside other, easing of the theoretical landscape ecology of its meta-scientific superstructure what facilitates its concentration on the solution of theoretical problems of landscape as its principal research object. It will also facilitate formulation of the synthesised view of landscape in its simplified complexity.

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The most important meaning of meta-landscape ecology in the context of empirical landscape ecology lies in generalisation of research results into structure, genesis, production, and dynamics of the individual landscape systems and ecosystems. The principal contribution of meta-landscape ecology to the development of applied landscape ecology lies in elaboration of theory, methodology, and language of landscape-ecological planning, design and management. Apart from it, meta-landscape ecology can also contribute to the development of didactics of landscape ecology by deepening of theory, methodology and language in tuition of landscape ecology in universities, as well as to the development of the inherent personal capabilities and the obtained specialised knowledge. In addition, metalandscape ecology acts as a mediator or “speaker” of landscape ecology in establishing contacts with other scientific disciplines necessary for cooperation and participation in solution of crosscut issues such as sustainable socio-economic development of the society maintaining the ecological-environmental potential of the environment and landscape as its part. Materials and methods The basis to the article about meta-landscape ecology as a new ecological science was constituted with the author’s long-year empirical, methodical, theoretical, applied and didactical knowledge and cognition obtained using the inter-, intra- and trans-disciplinary deductive-inductive and idiographic-nomothetic approach together with the author’s published results in several meta-scientific oriented papers. Results Meta-landscape ecology as the meta-scientific superstructure of landscape ecology is situated at the intersection of meta-science, geography, ecology and landscape ecology. Position of landscape ecology is in the common area of geographical and ecological entities, research approaches, principles and general laws. The meta-science creates the meta-scientific and philosophical background of meta-landscape ecology (Fig. 1).

Fig. 1 Position of meta-landscape ecology among meta-science, landscape ecology and geography. The contemporary development of science is characterized by a parallel contradictory trend, i.e. on the one side differentiation of science into new disciplines and subdisciplines

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and their clustering on the other. This general development also concerns landscape ecology, the scientific discipline existing on the limits of ecology and geography. The above-mentioned general trend is also connected with the efforts of the particular scientific discipline and subdiscipline to establish their positions and importance within the framework of the whole system of sciences. The position of meta-landscape ecology as a branch of meta-science in the framework of science system Meta-science as the most integrated level of learning about science (with its generalizing nature) influences and generates special meta-sciences such as meta-landscape ecology. It is situated in the area shared by the meta-geographical and meta-ecological entities, approaches, principles and general laws. Meta-landscape ecology creates simultaneously scientific superstructure of landscape ecology as the highest degree of ecology at the choric level of landscape. At the same time the ecological aspect of landscape ecology expands the research spectrum of science as a compound, multistage poly-functional system. The position of metalandscape ecology as a branch of meta-science in the framework of science system is illustrated on the (Fig. 2).

Fig. 2 Scheme of information flow between science, meta-science, meta-landscape ecology and ecology of science. The scientific difference between landscape ecology and meta-landscape ecology results from the comparison of their main research objects, nature, research approaches, research aims and principles illustrated on the (Fig. 3).  The main research object of landscape ecology is landscape in relationship to society from ecological point of view in opposite to research object of meta-landscape ecology, which is landscape ecology as a science from meta-scientific point of view.  The nature of landscape ecology is mixed nomothetic / idiographic in opposite to nature of meta-landscape ecology, which is generalized and meta-scientific.

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 The main research approach of landscape ecology is geographical, bio-ecological and human-ecological, while the main research approach of meta-landscape ecology is metaanalysis, meta-synthesis, meta-theory, methodology and meta-language.  The main research aims and principles of landscape ecology and meta-landscape ecology, which are listed in this scheme, are derived from the main research object and nature of landscape ecology and meta-landscape ecology.

Fig. 3 Scheme of different main research objects, natures, approaches, aims and principles of landscape ecology and meta-landscape ecology. Meaning and goal of meta-landscape ecology is searching for common integrated and generalized features of landscape ecology with the purpose of determination of its regularities and general laws on empirical, methodical, theoretical and applied level. The deductive and inductive information flow of each of these levels is illustrated in Fig. 4. The principal implementing tools of meta-landscape ecology in terms of the needs and development of the theoretical-methodical landscape ecology are landscape-ecological metatheory, methodology, and meta-language as its main meta-scientific categories. In this way, the theoretical-methodical landscape ecology facilitates formulation of its supporting pillars i.e. landscape ecological theory, landscape-ecological methodical approaches, and landscapeecological language. → Landscape-ecological theory consists of the generalised empirical knowledge and methodical approaches to landscape research from ecological point of view. Meanwhile, the theoretical landscape ecology focuses on the theory of hierarchy, scale, dimension, and spatialecological processes in landscape. → Landscape-ecological meta-theory (theory about theories) deals with the system of concepts and statements of landscape ecological theory. Its task is to establish the frontier of landscape ecological theory and to determine its wholeness. Practical contribution of landscape-ecological meta-theory presents strengthening of social-

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scientific relevance and capacity of landscape ecology to solve ecological/environmental problems in landscape. → Landscape-ecological method represents certain specific methodical procedures applied to research of structure, genesis, processes, and functions in landscape. → Landscape-ecological methodology (method about methods) analyses landscapeecological methods, procedures, concepts and categories. Its task is to derive generally valid principles and rules of landscape ecological methods. Practical contribution of landscape ecological methodology presents faster, efficient, economical and cheaper basic and applied landscape ecological research to all studied territories. → Landscape-ecological language as a certain combination of natural and artificial languages, represents the set of specific terms linked to landscape-ecological research in its completeness. → Landscape-ecological meta-language (language about language) serves to the description of landscape- ecological language. Is task is to seek universally valid laws of structure, properties, and limits of landscape-ecological language. Practical contribution of landscape-ecological meta-language presents easier understanding and cooperation among landscape ecologist and other scientists by basic and applied landscape research.

Fig. 4 Meaning and goal of meta-landscape ecology. One of the most important tasks of meta-landscape ecology is the research of information flow of entities, processes, regularities and general laws between geography, ecology, landscape ecology, meta-science and philosophy. The scheme of the first approximation of this information flow on their empirical, methodical, didactical, applied, theoretical and metascientific levels is schematically illustrated on the (Fig. 5).

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Fig. 5 Information flow of entities among geography, ecology, landscape ecology, metascience and philosophy. → In the framework of the general basis of landscape ecology (A) they are the intradisciplinary vertical information flows separately in the landscape ecology, in the geography, and in ecology (A1), and the interdisciplinary horizontal information flows among geography, landscape ecology and ecology on the empirical, methodical, didactical, applied and theoretical level (A2) on the mutually support each other. → In the framework of meta-scientific superstructure of landscape ecology (B) it is the intradisciplinary vertical information flow between general basis of landscape ecology and its meta-scientific superstructure separately in geography, in ecology and in landscape ecology (B1). The interdisciplinary horizontal information flow is between metageography, meta-ecology and meta-landscape ecology with their meta-scientific and philosophical aspects (B2),which mutually support each other. → In the framework of the general basis of landscape ecology and its meta-scientific superstructure (C) they are the information flows between general basis of landscape ecology, geography and ecology and their meta-scientific superstructure as the intradisciplinary vertical information flow separately in the framework of landscape ecology, geography and ecology by the induction way (C1) and opposite by the deduction way (C2). In this way, each of the mentioned scientific disciplines is simultaneously a carrier of combined horizontal and vertical information in the context of landscape ecology, ecology and geography. The second approximation of the interdisciplinary horizontal information of metalandscape ecology flow between meta-geography and meta-ecology is illustrated on the (Fig. 6).

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Fig. 6 Meta-landscape ecology lying in the penetration area of meta-geographical and metaecological gravitation fields with their meta-scientific entities, research approaches, principles and general laws. Meta-landscape ecology as the meta-scientific superstructure of landscape ecology situated in the penetration area of meta-geographical and meta-ecological gravitation fields with their meta-scientific entities, research approaches, processes, principles and general laws, it is then possible in a similar way to landscape ecology, but on higher meta-scientific level, to distinguish three types of meta-landscape ecology: - The ‘ecological’ meta-landscape ecology with the distinct prevalence of entities, research approaches, processes, principles and general laws pertaining to the meta-ecological gravitation field (Ʃ e > Ʃ g); - The ‘geographical’ meta-landscape ecology with the distinct prevalence of entities, approaches, processes, principles and general laws pertaining to the meta-geographical gravitation field (Ʃ g > Ʃ e), and eventually - The ‘ecological/geographical’ meta-landscape ecology with the mixed interpretation of meta-landscape ecology with approximately quantitatively balanced representation of entities, approaches, processes, principles and general laws of meta-ecological and metageographical gravitation field (Ʃ e = Ʃ g). This mixed type of meta-landscape ecology is most important for its sustainable development and authenticity.In case of meta-landscape ecology does not possess any metaecological entity, approach or principle, but only meta-geographical entity, approach and principle, it becomes pure meta-geography and vice versa if meta-landscape ecology does not possess any meta-geographical entity, approach or principle, but only the meta-ecological one it becomes pure meta-ecology. This is how meta-landscape ecology in the two cases loses its authenticity.

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Each science, meta-landscape ecology not excluded, consists of basic research categories, which creates the system of that science. Research categories or elements of system of metalandscape ecology are: research object (what is researched), research approach (how is researched), research goal (why it is researched), subject of research (who researches) and research contribution (what was researched). These research categories are interconnected with particular information about metalandscape ecological entities, approaches, processes, principles and general laws. The system of meta-landscape ecology consists of its meta-scientific nature together with the interior and exterior structure of meta-landscape ecological research. This system represents the general frame of research, development, and changes of metalandscape ecology as a branch of meta-science, which is the most integrated level of learning about science. Its correspondents to the merit of the individual internal and external factors of a developing science, which represents a complex multi levelled, internally differentiated, socially determined and historically changing multifunctional system, the need and indispensability of generalization of single methodological analytical and synthesizing approaches and inner arrangement of the scientific disciplines to a comprehensive system, emerged (Viceník, 2000). The learning about the science itself or meta-science is such a system. This is the way we can understand meta-landscape ecology as landscape-ecological scientific discipline under formation, which should deal with the structure of the system of landscape-ecological science, its history, relationships between the parts of the system, as well as with the management of the whole system of this science. The correct definition of the meta-landscape ecology requires working out of the necessary criteria and conditions such as nature, principles, research object, research approach and research goal of meta- landscape ecology, as well as the subject of research of meta-landscape ecology. Criteria and conditions of definition of meta-landscape ecology → The nature of meta-landscape ecology can be denoted the generalized and integrated in penetration of meta-ecological and meta-geographical entities, approaches, principles and general laws. → The principles of meta-landscape ecology present the top degree of generalization, that is, the regularity of accumulated knowledge and results of meta-scientific orientated landscape-ecological research. These include for example the principle of inseparability of meta-geographical and meta-ecological entities of meta-landscape ecology, the principle of stability of meta-landscape ecology, and principle of integration of meta-landscape ecology. → The research object of meta-landscape ecology includes general meta-landscapeecological aspects (particularly meta-, intra-, inter- and trans-disciplinary ones which define and explain the notion and importance of meta-landscape ecology, as well as its principles, typification, position and cooperation with other sciences), applied meta-scientific aspects of landscape ecology (aimed at the solutions to ecological, environmental and socio-economic problems in landscape from ecological point of view, as a challenge for landscape ecology), and regional meta-scientific aspects of landscape ecology, (which analyse the development and present status of landscape ecology on example of a chosen territory). → The research approaches includes first of all above mentioned meta-theory, methodology, meta-language as well as meta-analysis and meta-synthesis. → The research goal of meta-landscape ecology is in particular to obtain new meta-data, applying new methodological approaches and to formulate new meta-scientific regularities and general laws.

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→ The subject of research, in our case, landscape ecologist with its ability of generalization, combination and coordination is responsible for the successful meta-landscape ecological research. On the basis of these criteria it is possible to formulate the definition of meta-landscape ecology mentioned in the Fig. 7.

Fig. 7 Definition of meta-landscape ecology. In my opinion, preservation of the authenticity of landscape ecology as the prerequisite of its unity, objective definition and future development, determination of its principles as well as time-spatial contextuality, complexity and integrity of landscape ecology together with its development and prognosis belong to most important research issues of meta-landscape ecology, what indirectly correspondents with six key research issues of landscape ecology mentioned in article (Wu, Hobbs, 2002). The following three criteria were selected for the determination of authenticity of landscape ecology: → inseparability of geographical entities, approaches and principles from ecological entities, approaches and principles, → landscape-ecological trinity (process-structure-scale) in research of ecosystems on the choric scale of landscape and their relationship to society and → balanced relationship between the landscape-ecological research object and approach. Key research issues of meta-landscape ecology are: - authenticity and identity of landscape ecology, - principles of landscape ecology, - regularities and general laws of landscape ecology, - system of landscape ecology, - classification and typification of landscape ecology, - time-spatial contextuality, complexity and integrity of landscape ecology, - social-scientific relevance of landscape ecology,

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- research efficiency of landscape ecology, - development of landscape ecology, - competitiveness of landscape ecology and - management of landscape ecology. The most important of the achieved results of meta-landscape ecological research are: → definition of authentic meta-landscape ecology and landscape ecology (Žigrai, 2015); → approximation of position, meaning and tasks of meta-landscape ecology; → analysis of the meaning of meta-landscape ecology for the development of the theory, methodology, application and education of the landscape ecology; → working up of meta-landscape-ecological principles and types; → determination of relationship between basic and applied landscape-ecological research; → analysis of the interdependence between theory and practice in landscape ecology; → description of transfer mechanism of landscape ecological knowledge from theory to practice as a multistage process; → determination of main parameters of social- scientific relevance of landscape ecology as the reflection of its theoretical-applied and educational development; → determination of main parameters of time-spatial contextuality, complexity, and integrity of the development and cognition of landscape ecology and → calling attention to paradigm as scientifically relevant notion for the prognosis of the development of landscape ecology. Discussion Meta-landscape ecology dealing consistently and systematically with landscape ecology as a science represents the most generalized and most integrated form of meta-scientific superstructure of landscape ecology. The majority of the landscape ecologists are in the present deeply concerned with the basic, methodical and applied research of landscape from ecological point of view. Up to now, only little attention has been paid to the partial theoretical and meta-scientific problems of landscape ecology as a science. For instance, identity and authenticity of landscape ecology (Wiens, 1999, Wu, Hobbs, 2002), future tasks of landscape ecology (Brandt, 1998, Hobbs, 1997, Metzger , 2008), toward a unified landscape ecology (Bastian, 2001, Wiens, 1999), inter-disciplinarity, crossdisciplinarity and trans-disciplinarity of landscape ecology ( Brandt, 1998, Naveh, 1998, Wu, 2006, Tress, Tress, Fry, 2005), concept and state of landscape ecology (Wiens, 1992, Leser, H., 1997, Brandt, 1998, Turner, 2005), principles and methods in landscape ecology (Farina, 1998), landscape ecology paradigm (Nassauer, Opdam, 2008), theory and application of landscape ecology (Naveh, Liebermann, 1994), key issues, topics and research priorities in landscape ecology Wu, Hobbs, 2002, 2006), and societal dimension of the landscape ecology (Oťaheľ, 1999). However, these concepts, tasks, issues, topics, research priorities and challenges of landscape ecology were not classified as those of meta-landscape ecology as a consistent and systematic science. They were included mostly in the theory and methodology of landscape ecology. But its research content is above all the landscape itself, in contrast to metalandscape ecology where the principal research object is landscape ecology as science. This is the reason why we have not met so far with the notion of meta-landscape ecology in specialized literature.

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Conclusion The above mentioned main achieved results of meta-landscape ecological research are in the first stage concentrated first of all on landscape ecology as a main research object of metalandscape ecology. In the second stage attention will be paid to the analysis of the metaanalysis, meta-synthesis, meta-theory, methodology and meta-language of meta-landscape ecology, as well as on the self-organisation and management of landscape ecology and its trans-disciplinary collaboration with other sciences when addressing the serious ecological and environmental problems in landscape. In my opinion, the focus of future development of landscape ecology should be specification, globalisation, inter-multi- and trans-disciplinarity, economisation, comprehensiveness and application of landscape-ecological research. It will be then possible to strengthen the authenticity of landscape ecology as a very nice and useful science, which deserves protection of its face, shape and sound. References Bastian, O., 2001. Landscape Ecology: Towards a unified discipline? Landscape Ecol. 16: 757–766. Brandt, J., 1998. Key concepts and interdisciplinarity in Landscape Ecology: A summing-up and outlook. In: Dover, J.W. & Buince, R.G..H. (Eds.): Key concepts in Landscape Ecology, IALE (UK), Garstgang, 421-434. Farina, A., 1998. Principles and methods in landscape ecology. In: Chapman & Hall London. Hobbs, R.J. 1997. Future landscapes and the future of landscape ecology. Landscape and Urban Planning 37, 1–9. Leser, H., 1997. Landschaftsökologie: Ansatz, Modelle, Methodik. Stuttgart (Ulmer). Nassauer, J. I., Opdam, P., 2008. Design in science: extending the landscape ecology paradigm. In: Landscape Ecology, 23, p. 633-644. Metzger , J. P.; 2008. Landscape ecology: perspectives based on the 2007 IALE world congress. Landscape ecology, Volume 23, Issuee 5, pp 501-504 Naveh, Z. and Liebermann, A.S. 1994. Landscape Ecology. Theory and application. New York, Springer-Verlag, 360 p. Naveh, Z., 1998. Transdisciplinary challenges for landscape ecology facing the post-industrial information society. In: Proceedings from CZ-IALE Conference, Prague, 22-28. Oťaheľ, J., 1999. Societal dimension of the landscape ecology. (in Slovak). In Hrnčiarová, T., Izakovičová, Z. (eds), Krajinnoekologické plánovanie na prahu 3. tisícročia. ÚKE SAV, Bratislava, p. 54-59. Tress, G., Tress, B. and Fry, G., 2005. Clarifying integrative research concepts in landscape ecology. Landscape Ecol. 20: 479–493. Turner, M.G., 2005. Landscape ecology: What is the state of the science? Ann. Rev. Ecol. Syst. 36: 319–344. Viceník, J., 2000. Introduction to problems of methodology of sciences I. ORGANON F7No. 1, FÚ SAV Bratislava, p.78-89. (in Slovak). Wu, J. & Hobbs R. (2002). Key issues and research priorities in landscape ecology: an idiosyncratic synthesis. Landscape Ecology 17, 355–365. Žigrai F., 2001. Position, meaning and tasks of meta-landscape ecology. (Some theoretical and methodological notes). Ekológia (Bratislava) 20: 3, 11−22. Žigrai F., 2015. Preservation of authenticity and determination of identity of landscape ecology as one prerequisite of its future development (selected theoretical and metacientific aspects). Ekológia (Bratislava) 34, (2):186–206.

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HABITAT DIVERSITY: A KEY CATEGORY IN LANDSCAPE ANALYSIS FOR SPATIAL PLANNING IN MOUNTAIN CONDITIONS (A CASE STUDY OF THE BANITE MUNICIPALITY, BULGARIA) Assen ASSENOV1, Bilyana BORISOVA1, Petar DIMITROV2 1

Department of Landscape Ecology and Environmental Protection, Faculty of Geology and Geography, Sofia University “St. Kliment Ohridski”, Sofia, Bulgaria; [email protected]; [email protected] 2 Space Research and Technology Institute, Bulgarian Academy of Sciences, Sofia, Bulgaria; [email protected] Abstract Habitat diversity in mountain areas is essential for a variety of landscape taxa which are the basis of landscape planning. Habitat diversity and landscape diversity are the most important categories that reflect the functional nature of the biodiversity category. In the context of the sustainable development paradigm, the existence of modern societies depends very much on spatial analysis and spatial planning, where the scientific categories of habitat and landscape diversity are of increasing significance. The main objective of this study is to increase the objectivity and relevance of spatial analysis in the regional to large-scale landscape research. This study attempts to integrate habitat diversity as a criterion in the landscape differentiation processes, including landscape mapping in GIS environment and subsequent analyses of the structure - function relations. Such an approach is prompted by the high natural heterogeneity of mountainous areas and the determining significance of habitats as an information indicator for major natural relationships in small areas. The approach is applied in the area of the Banite Municipality, Smolyan Region in the Rhodope Mountains of Southern Bulgaria. Concurrent analysis of habitat diversity in the area with the peculiarities of contemporary land cover is used; the latter being applied as a criterion for analysis of the landscape systemic character. The results provide argumentation that supports the introduction of habitat types information in complex landscape analysis, which allows for elaboration of landscape indicators for assessment of the degree of anthropogenic pressure, as well as for designing of adaptive forms of land use planning. Key words: habitats biodiversity; landscape biodiversity; landscape scale; mountain watershed; Banite Municipality. Introduction The idea that the world is in “green revolution that facilitated by technology will be driven by our need to reconcile with nature” (Brown, 2009) is widely perceived over the last decade. Landscape ecology has increasingly clear impact on contemporary models of systematic thinking in geographical science oriented in support of sustainable development. Standing out is the growing role of analyses on the spatial characteristics of complex systems, their hierarchy, temporal variability, and the landscape functions and ecological processes, which result from them (Bastian et al., 2012; Lausch et al., 2015). Concordance of spatial and functional approach creates analytical base supporting various aspects of landscape planning, evaluation of landscape change, integrated resource management and environmental management. Biodiversity is the basic connecting link in these complex studies - a strong

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indicator of the nature of trans-hierarchical interactions, spatial heterogeneity, ecological integrity, and the potential of ecosystem/landscape goods and services (Jorgensen and Nielsen, 2013; Walz and Syrbe, 2013; Harrison et al., 2014). The importance of biodiversity for balanced development of landscapes is clearly recognized in the new EU 2020 Biodiversity strategy. Biodiversity is one of the main indicators used by the EU for implementing the policy of sustainable development (Eurostat, 2015). The term "habitat diversity" (United Nations, 1997) is practically present among the EU policies and tools in the environment sector as a sub-category of the basic term “biodiversity”. It fits in the tradition of classical ecology as being informative for practical purposes, including for sustainability assessments, and convenient for field studies and monitoring. The term "landscape" shows a very strong position in the trans-disciplinary spatial research. But despite the support provided by the European Landscape Convention (ELC, 2000), its practical use is highly dependent on the scope of the study (mainly regional) and derivatives of this circumstance - the transfer of accurate data in the landscape scale, opportunities for identifying and mapping of complex natural and anthropogenic systems, adequate interpretation of their social aspects and so on. In the scientific literature, the relationship between the habitats of species (Council Directive 92 /43 /EEC, 1992), landscapes and fauna taxonomic diversity is considered in the studies of several authors: Tews et al. (2004), Vanbergen et al. (2007), Jačková and Romportl (2012), McGuire (2014) and others. The relationship between landscape structure and biodiversity is analysed in detail by Walz (2011), who states that biodiversity is always determined as a centre of reference, and the landscape structure is a key element to understanding species biodiversity. The term “natural habitat” is defined in the Habitats Directive (Council Directive, 1992) and is distinguished from the term “habitat of species”. Under the Directive, natural habitats are terrestrial or aquatic spaces, distinguished by geographic, abiotic and biotic features, whether entirely natural or semi-natural. In the same document “habitat of species” is an environment defined by specific abiotic and biotic factors, in which the species lives at any stage of its biological cycle. If the abundance of purely scientific interpretations of the concept of a landscape is reviewed (Neef, 1967; Forman and Godron, 1986), a certain similarity with the above-mentioned definition of natural habitat is found. Such similarity can be observed in practical and comparative analysis of some features of habitats and landscape units at low levels of landscape hierarchy. One of the authors of this study (Assenov, 2006) has carried out a comparative analysis between habitat types of herbaceous vegetation in the Western Rhodopes, phyto-sociological taxa and natural landscapes (in accordance with the proposed Geo-ecological landscape classification by Popov, 2001). The conclusions of this study indicate that the landscape and habitat diversity can be interpreted as two different subcategories of the scientific concept of biodiversity. At the lower levels of the landscape scale (at topological level) it is possible to establish a certain resemblance between landscapes and habitat type up to the overlap of the two subcategories - Assenov (2006) proves that in the group of agricultural land and artificial landscapes complete identity between landscapes and habitats is possible. Within this context, the present study is an attempt for coherent use of the concepts of biodiversity, habitat diversity and landscape diversity in the system of integrated landscape research. It aims to facilitate the holistic landscape analysis in the transition from regional to large-scale landscape scale, to increase its information value and above all - the correctness of the practical decisions. We are seeking the answers of the following questions: Is it possible the natural habitat to be an indicator of the state of landscapes and under what conditions? Is it

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possible natural habitat to be used as a spatial unit, replacing the landscape? What is the role of habitats in the “structure-function” classical analysis of landscapes? We believe that such an approach will help to expand the applicability of landscape analysis, including for territorial planning in Bulgaria, especially in regard of territories which are naturally heterogeneous and highly sensitive to anthropogenization (in the context of this study - mountain landscapes). In Bulgaria landscape planning is applied primarily in the field of urban studies and architecture. To achieve the main goal several major tasks are fulfilled: 1. Integrating of habitat diversity data into the information base used for landscape differentiation, and mapping in GIS environment; 2. Coordinated use of landscape structure and habitat diversity analyses in order to define the indicators for the state of landscapes; 3. Evaluation of the informative role of habitat diversity data when analysing the relation “landscape structure - ecosystem services” in mountain watersheds. Materials and methods The object of study covers Banite Municipality in the Smolyan administrative region of Bulgaria (Fig. 1). Geographically the municipality falls in the Rhodope Mountains and specifically is located on the southern macro-slope of Prespanski Ridge. The area is drained by the cross-border Arda River. The altitude of the territory varies from 450 m in the Arda River valley to 2001 m at Prespa peak, and the average altitude of the municipality is 681 m. The name of the municipality is associated with thermal waters with a flow of 17 l/sec. and water temperature of 35-43ºС. The municipality has a high share of forest cover – 64.4% of its territory (MDP 2014-2020). On shady slopes with wet soils the Balkan endemic and tertiary relic species - Haberlea rhodopensis, is widespread. Banite Municipality has a population of 4923 people (NSI, 2011), representing 4 % of the total population of Smolyan region. The economic profile of the municipality is represented by forestry, milk processing, textile and clothing industry, and construction. Crop production is traditionally associated with tobacco and potato. Livestock production is fully in the family private sector. The main priority in the development of Banite Municipality is tourism and especially spa treatment. Half of active companies in the municipality operate in the field of tourism, trade and transport. For the purposes of the study were applied three basic steps: 1. Differentiation of landscapes and GIS mapping; 2. Comparative spatial analysis of the landscape map and the original map of habitats within the main catchment areas (in this case, Davidkovska River watershed, a tributary of Arda, within Banite Municipality); 3. Attempt to determine the indicative importance of habitats in terms of landscape characteristics and the degree of their anthropogenic transformation. In Bulgaria there is no officially accepted classification system of landscapes. For that reason this study is strongly influenced by the European experience (Wascher, 2005; Mücher et al., 2010) and regional studies of the authors in mountainous regions of the country (Borisova et al., 2015). For the purposes of landscape differentiation the following criteria are used: 1) relief - morphological division on Digital Elevation Model ASTER GDEM (NASA, 2011) is applied, thus the territory is divided in three basic forms of relief - flatted surfaces and gentle slopes, steep slopes and valley bottoms. The separation is based on quantitative criteria - the values of the slope inclination and the TPI index (Topographic Position Index) (Weiss, 2001; Jenness, 2006); 2) geological base – the relief-forming importance of the geological layers and their participation/properties in soil formation processes are used as

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main criteria for generalization of the input data (1:100 000); 3) climate – the classification of climate types based on the humidity index of Thornthwaite (Topliiski, 2006) is applied, which is highly informative for the systematization of landscapes in terms of their degree of humidity or aridity, for the nature of the potential vegetation and the activity of substance and energy exchange; 4) Vegetation - both data for CORINE land cover 2006 (http://eea.government.bg/bg/projects/korine-14/kzp-danni-clc-data) and for habitat types is used. The information has been confirmed by field studies (August 2013 and September 2014); 5) soils - national database of MOEW is used (2013).

Fig. 1 Map of Banite Municipality Developing and analysing of the digital landscape map is realized through methods of spatial analysis and mapping in GIS environment using the ESRI ArcGIS 10, ESRI ArcView 3.2. and FRAGSTATS (McGarigal et al., 2012). Map of the habitat types (using the classification of the Habitats Directive) was prepared for part of Davidkovska River basin within Banite municipality. The mapped territory is part of the NATURA 2000 site Rodopi – Sredni (BG 0001031). The map is prepared using publicly available information from the Bulgarian web site for NATURA 2000 (http://natura2000.moew.government.bg/). This information consists of separate maps of the distribution of each habitat type within the Rodopi – Sredni site. The maps, which are available in .pdf file format, were downloaded and geo-referenced in GIS. Then a polygon layer was created by manual vectorisation. The final map shows that the habitat diversity in the studied part of Davidkovska River basin includes 15 habitat types from Directive 92/43 (Fig. 3). For the correctness of the analysis, landscape metric tests were conducted over a larger area, covering landscapes with similar characteristics in the territories of neighbouring municipalities of Smolyan, Laki and Banite. Results and discussion Habitat diversity is held on the fourth level of the landscape differentiation. Analysed territory has a high natural landscape heterogeneity dominated by forest landscapes and complex configuration of landscape mosaic influenced by the diversity of landscape (Fig. 2). The differentiation performed is a prerequisite for the identification of landscape features in 39

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their complexity. An essential aspect of the analysis could be their interpretation as ecosystem/landscape services (De Groot et al., 2010), which can be identified (as available, potentially possible or threatened by degradation) through various levels of the landscape scale. Presented landscape map provides a basis for differentiation of functionally targeted areas for integrated nature use, which are appropriate for applying the principles of landscape planning in practice. Demarcated landscape levels, including those in the main catchment areas, are the precondition for coordinating the objectives and activities between the regional and local planning and management.

Fig. 2 Fragment of the landscape map of Banite Municipality – Davidkovska River watershed The area of study represents part of the NATURA site BG 0001031 Rodopi - Sredni and includes 15 habitat types under Directive 92/43 (Fig. 3). The comparative analysis of the

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landscape map and the map of habitat types shows that the available habitat types are involved in the formation of eight categories of landscapes at the fourth level of differentiation. There is a high degree of overlapping in the range of habitat types and landscapes, which stems from the methodological decisions for differentiation. In this sense, the focus is primarily on the discrepancies which may explain the changes in conditions of orderliness in landscapes, having impact on biodiversity.

Fig. 3 Map of habitat types – Davidkovska River watershed (produced according to NATURA 2000 data: http://natura2000.moew.government.bg/ - BG 0001031 Rodopi - Sredni) The maps show that the discrepancies are limited in size and result from differences in the micro-relief forms and derived hydro-climatic conditions. For example, in the western part of

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the municipality, in the highest part of the southern slope of Prespa peak (2001 m) Habitat type 9410 almost matches the landscape genus Coniferous forest on Cambisols. Discrepancies arise from the appearance of 9170 Galio-Carpinetum oak-hornbeam forests in fragments in the area. It can be expected that at the fifth level of landscapes differentiation much more clear overlapping of habitats and landscapes should be observed. Unfortunately, due to too generalized information about soil diversity in the area available, the landscapes differentiation at the fifth level is impeded and somewhat conditionally all landscapes are presented as developed on Cambisols. We believe that the insertion of more accurate data on soil diversity will highlight important structural features in the landscape organization and may significantly contribute to find practical solutions for overcoming the signs of degradation and fragmentation. Even more impressive is the analysis of "white spots" on the map of habitats which represent the heavily anthropogenic vegetation that could not be attributed to a specific habitat type. The comparative analysis on the characteristics of the landscapes, in which they fall, can be a certain basis for the establishment of process deviations and even destruction of important landscape features and functions. Deepening of the analysis on landscape configuration using FRAGSTATS for selected indicators (SHDI, PR, AREA_MN, ED, CONTAG, PROX, MESH) gives grounds to take account of the violation of the natural landscape contours and development of fragmentation processes. The relatively high values of Splitting index (SPLIT) and Landscape Shape Index (LSI) could be considered as confirmation of the thesis. On the landscape map this could be clearly traced in the spatial organization of the landscapes of oak-hornbeam forests, especially in the location of transitional woody shrub vegetation in the midst of the natural landscapes of pine forests. We believe that the main negative role in this regard is the nature of land use, which is traditionally dispersed in separate independent areas of mountain pastures, meadows and small areas of farmland. In modern land use it should be borne in mind that induced violations of landscape structure can occur rapidly and grow into destructive processes. Conclusion The results allow affirming that habitat diversity plays important role for landscape differentiation in mountain spaces. Based on the study the following conclusions could be drawn: 1) In heterogeneous environment habitat diversity reflects important structural features of the landscape. Habitat diversity can be highly informative connection in the trans-hierarchical landscape analysis. 2) For the purposes of large-scale territorial analyses in areas with high anthropogenic pressure, natural habitats can be used as spatial units, reflecting to a high degree the landscape features (without seeking identity between habitat types and landscapes, which may compromise the systemic nature of the landscape). We consider such a solution as acceptable approach in cases where landscapes cannot be identified due to limitations of data or other technical/procedural reason (for example in land-use planning or environmental assessment). We find particularly appropriate the combined use of habitat types and watersheds. 3) The analysis of habitat diversity within the landscape, aided by landscape metrics, can be an important indicator of anthropogenic transformation of landscapes and for detecting the processes of destruction. Such an approach can act as an important corrective to the objectivity of the conclusions of the “structure-function” analysis of landscapes, including in regards of the identification of ecosystem/landscape services.

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This research is sponsored by the "National, European, and Civilizational Dimensions of the Culture – Language – Media Dialogue" Program of the "Alma Mater" University Complex in the Humanities at Sofia University "Saint Kliment Ohridski", funded by the Bulgarian Ministry of Education and Science – Bulgarian Science Fund. References Assenov A., 2006. Corelation between Habitat and Landscape Diversity. National Scientific Conference with International Participation “20 Years Union of Scientists in Bulgaria – Branch Smolyan”, October, 20-21, 2006, Smolyan, Bulgaria. pp. 445-456. Bastian O., Haase D., Grunevald K., 2012. Ecosystem properties, potentials and services – The EPPS conceptual framework and an urban application example. Ecological Indicators 21, 7–16. Borisova B., Assenov A., Dimitrov P., 2015. The Natural Capital in Selected Mountain Areas in Bulgaria. In: M. Luc, U. Somorowska, J.B. Szmańda (Eds.) Landscape Analysis and Planning: Geographical Perspectives. Springer Geography, 91-108. http://link.springer.com/chapter/10.1007%2F978-3-319-13527-4_6 Brown L. R., 2009. PLAN B 4.0. Mobilizing to Save Civilization. Earth Policy Institute 1350 Connecticut Ave. NW Suite 403 Washington, DC 20036. www.earthpolicy.org CORINE Land Cover 2006. http://www.eea.europa.eu/data-and-maps/data/corine-land-cover2006-raster Council Directive 92 /43 /EEC of 21 May 1992 on the conservation of natural habitats and of wild fauna and flora. http://ec.europa.eu/environment/nature/legislation/habitatsdirective/index_en.htm De Groot R.S., Alkemade R., Braat L., Hein L., Willemen L., 2010. Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecological Complexity 7, 260–272. EU 2020 Biodiversity strategy. http://ec.europa.eu/environment/nature/biodiversity/comm2006/2020.htm ELC, 2000. European Landscape Convention. Florence, 20.X.2000. http://www.coe.int/t/dg4/cultureheritage/heritage/Landscape/default_en.asp Eurostat, 2015. Sustainable development indicators. http://ec.europa.eu/eurostat/web/sdi/indicators/natural-resources Forman R.T.T., Godron M., 1986. Landscape Ecology, Wiley & Sons, New York. 620 pp. Glossary of Environment Statistics, 1997. Studies in Methods, Series F, No. 67, United Nations, New York. Harrison P.A., Berry P.M., Simpson G., Haslett J.R., Blicharska M., Bucur M., Dunford R., Egoh B., Garcia-Llorente M., Geamănă N., Geertsema W., Lommelen E., Meiresonne L., Turkelboom F., 2014. Linkages between biodiversity attributes and ecosystem services: A systematic review. Ecosystem Services 9, 191–203. Jačková K., Romportl D., 2012. The Relationship Between Geodiversity and Habitat Richness in Šumava National Park and Křivoklátsko PLA (Czech Republic): A Quantitative Analysis Approach. Journal of Landscape Ecology 1 (1):23–38. Jenness J., 2006. Topographic Position Index (tpi_jen.avx) extension for ArcView 3.x, v. 1.3a. Jenness Enterprises. Available at: http://www.jennessent.com/arcview/tpi.htm

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Jоrgensen S.E., Nielsen S.N., 2013. The properties of the ecological hierarchy and their application as ecological indicators. Ecological Indicators 28:48–53 Lausch A., Blaschke Th., Haase D., Herzog F., Syrbe R.U., Tischendorf L., Walz U., 2015. Understanding and quantifying landscape structure – A review on relevant process characteristics, data models and landscape metrics. Ecological Modelling 295:31–41 McGarigal K., Cushman S.A., Ene E., 2012. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. http://www.umass.edu/landeco/research/fragstats/fragstats.html McGuire J., 2014. Landscape Diversity, Habitat Diversity, & Rodent Richness: Cascading Effects. 2014 GSA Annual Meeting in Vancouver, British Columbia (19–22 October 2014) Paper No. 289-14. Mücher C., Klijn J., Wascher D., Schaminee J., 2010. A new European Landscape Classification (LANMAP): A transparent, flexible and user-oriented methodology to distinguish landscapes. Ecological Indicators 10:87-103. MDP Banite 2014-2020. Municipal Development Plan for 2014-2020 and implementation of monitoring activities, control and subsequent evaluation of policies. MOEW http://www.moew.government.bg/?lang=en NASA Land Processes Distributed Active Archive Center (LP DAAC), 2011. ASTER GDEM Version 2. LP DAAC. NATURA 2000 - http://natura2000.moew.government.bg/ Neef E., 1967. Die Theoretischen Grundlagen der Landschafflehre. Haach, Gotha. Leipzig. Popov А., 2001. Geoecological landscape classification of Bulgaria. Principles and approaches. (in bulgarian). Sofia University Annual. Book 2 – Geography, vol.91: 27-38. Tews J., Brose U., Grimm V., Tielborger K., Wichmann M.C., Schwager M., Jeltsch F., 2004. Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures. J Biogeogr 3, 1:79-92. Topliiski D., 2006. Climate of Bulgaria. (in Bulgarian), Amstels, Sofia. 360 pp. Vanbergen A. J., Watt A. D., Mitchell R., Truscott Anne-Marie, Palmer S. C. F., Ivits E., Eggleton P., Jones T. H., Sousa J. P., 2007. Scale-specific Correlations between Habitat Heterogeneity and Soil Fauna Diversity along a Landscape Structure Gradient. Oecologia 153:713-725. Walz U., Syrbe R.U., 2013. Linking landscape structure and biodiversity. Ecological Indicators 31:1– 5. Walz U., 2011. Landscape Structure, Landscape Metrics and Biodiversity. Living Rev. Landscape Res., 5, (2011), 3. http://lrlr.landscapeonline.de/Articles/lrlr-2011-3/ Wascher D. (Ed.), 2005. European landscape character areas. Typologies, cartography and indicators for the assessment of sustainable landscapes. Final Project Report (ELCAI), Environment and Sustainable Development (4.2.2). Landscape Europe. http://www.paesaggiopocollina.it/paesaggio/dwd/lineeguida/elcai_projectreport.pdf Weiss A., 2001. Topographic Position and Landforms Analysis. Poster presentation, ESRI User Conference, San Diego, CA.

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TYPES OF TRADITIONAL AGRICULTURAL LANDSCAPES AND THEIR RESPECTIVE REPRESENTATION IN THE KYSUCE REGION Peter BARANČOK, Mária BARANČOKOVÁ Institute of Landscape Ecology of the Slovak Academy of Sciences Bratislava, Slovak Republic; [email protected]; [email protected] Abstract The territory of Kysuce region is characterized by a high representation of the traditional agricultural landscape (TAL), which occupies almost 12 % of the area. Two types and four subtypes of TAL were determined within this region. The first type (with two subtypes) is represented by TAL with Dispersed Settlements. First subtype referrers to localities with typical dispersed settlements and second subtype to localities with specific type of settlement characteristic for Kysuce region. The second type (with its two subtypes) is represented by TAL of Arable-Land, Grasslands and Pastures. The most represented is the subtype with typical forms of anthropogenic relief (FAR). Another subtype represents TAL with preserved forms of anthropogenic relief and dominant non-forest woody vegetation or forest vegetation. Key words: traditional agricultural landscape (TAL); types of TAL; Kysuce region; landscape structure; landscape changes. Introduction The Kysuce region is a significant historical region of Slovakia known for its specific natural characteristics and values, as well as for its characteristic type of settlements that resulted from its colonization. The region has a rich past which has been ultimately reflected in the character of its population, culture, architecture, folklore and tradition (Tóthová et al., 1996). The Kysuce region is located in the north part of Slovakia and from the orographic view it covers a part of the Slovak-Moravian Carpathians, Western Beskids and Middle Beskids. In the west, it is limited by the state borders with the Czech Republic that run along the ridge of Javorníky Mts. and Turzovská vrchovina Mts. In the northwest the region’s borders pass alongside the Moravsko-Sliezske Beskydy Mts. and Jablunkovské medzihorie Mts. In the northeast the Kysuce region neighbors with Poland, while the borders pass along the ridge of the Kysucké Beskydy Mts. In the east the borders of the region reach the eastern edge of Kysucká vrchovina Mts., which together with the Kysucká brána Gate, also form the southern border of its territory. Southwestern part of Kysuce region is represented by Javorníky Mts. The Kysuce region has a very important status within Slovakia as regards the traditional agricultural landscapes (TAL) due to its geographical location, natural conditions, historical development, type of settlements and human utilization of the land. The once dominantly woody region had been gradually inhabited by humans, who founded settlements in the valleys alongside the river streams, hewn the forests and transformed them into meadows, pastures or arable land. Because of the region’s specific natural conditions the dominant earning activity in the region was shepherding and similarly associated activities that provided ample food for animals even during the periods of prolonged and exceptionally cold winters. On the deforested areas, from the lowest located fields up to the mountain ridges, there arose mainly grasslands and pastures, and due to this development even the localities in higher altitudes had been permanently inhabited, predominantly by a specific type of settlements called “lazy”. However, so long as a man intended to continuously exercise farming activities 45

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and to grow a variety of crops on the surrounding slopes, he was forced to modify and adjust its surrounding and therefore an entire system of different types of terraces that ran from the foot of the slopes and often up to the mountain ridges was established. The aforementioned character of the country is, respectively was, typical for the whole Kysuce region and therefore the representation of the traditional agricultural landscape (TAL) in this region is significantly high. During the period of collectivization a substantial part of TAL located at positions of lower altitudes was destroyed by intentional land consolidation and balks were plowed. By way of this process large tracts of arable land were created, which were gradually converted into large-scale re-cultivated highly productive grasslands. Individual elements of TAL were preserved only in the vicinity of the settlements, on the steeper slopes with difficult access for agricultural machinery, or on the relatively small areas that were less suitable for large-scale land use. Today, the land use continues to be further changed and these activities negatively affect the existence of TAL. The dominant change in land use is the abandonment of the original forms of farming in the area. In the first stage, individual terraces cease to be used as arable land, as a result fallow farmlands are formed or the land is converted into mown grasslands or areas with vegetation for grazing. Later a total abandonment of the farmlands occurs and the abandoned lands are gradually covered with woody vegetation. Part of the terraced lands especially at higher elevations has been afforested and part of the lands at lower elevations in the vicinity to the local municipalities is now used by the newly built houses. Although the territory of Kysuce region has been affected by these negative changes mainly in the view of the existence of TAL, a relatively considerable number of important localities have been preserved and the main goal of this article is to capture the characteristics and nature of TAL in their structural variability through the process of mapping of these structures. TALs represent ecosystems that consist of a mosaic of small-scale arable fields and permanent agricultural cultivation such as grasslands, vineyards and high-trunk orchards. TALs are described as landscapes where traditional sustainable agricultural practices are currently carried out and biological diversity in conserved (Harrop, 2007). These areas represent regions with specific combinations of natural and cultural diversity with high visual quality and public preferences (Tempesta, 2010). They are significant as unique islands of species-rich plant and animal communities. History has recorded many successive and even devasting landscape changes, which have barely left any TAL relics today (Marini et al., 2011). The changes are seen as a menace, as a negative evolution because they cause a loss of diversity, coherence and identity, which were characteristic for the traditional cultural landscapes that are rapidly vanishing (Antrop, 2005).Traditional land-use systems in Europe have mainly persisted in upland and remote areas where physical constraints have prevented agricultural modernisation (Plieninger et al., 2006). Substantial parts of traditional landscapes create the agricultural landscape. This appears as a mosaic of small-scale arable fields and permanent agricultural cultivations depending on the specific regional agrarian culture. Based on land use element, the following classes of traditional agrarian landscape have been distinguished (Špulerová et al., 2011): TAL with Dispersed Settlements, TAL of Vineyards, TAL of Arable-Land, Grasslands and Orchards and TAL of Arable-Land and Grasslands.

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Materials and methods The mapping of the TAL within the territory of Kysuce region took place between the years of 2009 and 2010 according to the methodology developed in the framework of the project “Research and conservation of biodiversity in traditional agricultural landscape of Slovakia” (Špulerová et al., 2009a, 2009b, Dobrovodská et al., 2010). In these early years the mapping was primarily aimed at identification of areas with TAL, mapping of the specific types and forms of anthropogenic relief and overall assessment of the area in terms of preservation of TAL. Currently the mapping process continues at a more detailed level and the focus is also on the detailed mapping of the state and changes in biodiversity, which in the given territory is conditioned on the existence of TAL. The monitored territory of Kysuce region was determined on the basis of naturalresidential types of regions of Slovakia (Miklos, 2002). The Kysuce region comprises of 24 municipalities, 23 of which belong to the district of Čadca town – Čadca, Čierne, Dlhá nad Kysucou, Dunajov, Klokočov, Klubina, Korňa, Krásno nad Kysucou, Makov, Nová Bystrica, Olešná, Oščadnica, Podvysoká, Radôstka, Raková, Skalité, Stará Bystrica, Staškov, Svrčinovec, Turzovka, Vysoká nad Kysucou, Zákopčie, Zborov nad Bystricou and one municipality belongs to the district of Žilina city – Lutiše. Čadca town includes two cadastral areas – cadastral areas of Čadca and Horelica; Turzovka town also includes two cadastral areas – cadastral areas of Turzovka and Turkov; cadastral area of Olešná village comprises of two separated units – cadastral area of Olešná I and Olešná II; Nová Bystrica village from the date of the construction of water reservoir „Nová Bystrica“ includes three cadastral areas – cadastral areas of Nová Bystrica, Harvelka and Riečnica. The monitored territory of Kysuce region represents 29 cadastral areas. In the preparatory phase concerning the task solutions of TAL mapping the identification of mosaic landscape structures was determined for the monitored territory. It represents extensively exploited small-scale elements of arable lands, permanent agricultural crops, permanent grass vegetation, orchards and currently unused areas with a low degree of succession. The identification was carried out on the basis of aerial photographs in 1 km2 network. In the monitored area 51 squares of mapping were determined, which shall represent the most important localities standing in the centre of the interest in the project aimed at the nationwide mapping of TAL. During the field work conducted on the territory of Kysuce region in 2009 and 2010 it was proven that the applied procedure of mapping is not appropriate for the given territory and that the aforementioned localities assigned within 51 squares of mapping often did not include the most important, the most valuable or the best preserved elements of TAL within the territory. Therefore a nation-wide mapping process of elements of TAL was implemented and for this purpose the original methodology as cited earlier in this chapter has been also modified. During the process of reviewing the occurrence of potential localities with preserved TAL, over 502 squares of mapping were specifically earmarked within the territory of Kysuce region. Results and discussion The types of TAL and its representation within Kysuce region As part of a nationwide mapping of TAL four types of TAL were defined in terms of land use: 1) TAL with Dispersed Settlements, 2) TAL of Vineyards, 3) TAL of Arable-land, Grasslands and Orchards, and 4) TAL of Arable-land, Grasslands (Špulerová et al., 2011, 2012). In the territory of Kysuce region the vine is not grown, neither larger areas of orchards have been preserved, therefore, these two types of TAL are not found in this territory. On the

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other hand, some specific features are found in the character of dispersed settlement and in the mosaic of TAL of arable-land, grasslands and pastures with various representation of nonforest woody vegetation. Based on the findings from the mapping of terrain there were only 2 types of TAL earmarked within the monitored territory of Kysuce region and each of these types was further classified into two subtypes: - type 1 - Traditional Agricultural Landscape with Dispersed Settlements: - subtype 1a – localities with typical dispersed settlements (called “kopanice“, “lazy“), including local structures, smaller areas with forms of anthropogenic relief (FAR) in the vicinity of structures, and often larger plots without FAR, which are however an integral part of this complex utilized by inhabitants of the settlements; - subtype 1b – localities with a specific type of settlement that are characteristic for Kysuce region, which could be characterized as a set of several closely situated dispersed settlements, which do not form a closed village; individual groups of houses – “lazy”, “osady”, “dvory” – form sort of separate units and these types of settlements are usually located at higher elevations in valleys or mountains, and at a greater distance from the intra-urban areas of municipalities; - type 4 - Traditional Agricultural Landscape of Arable-Land, Grasslands and Pastures: - subtype 4a – localities with typical TAL, with typical forms of anthropogenic relief, which include merely the respective plots with terraces, stepped field margins, stacks, and the like, but do not include the traditional type of settlement, respectively only some isolated settlements are found here; - subtype 4b – localities with TAL, dominated by non-forest woody vegetation, or areas with vegetation similar to that of typical forest vegetation, but the forms of anthropogenic relief (FAR) are still evident. The relative representation of the individual types and subtypes of TAL in the territory of Kysuce region is stated in Table 1 and shown in Fig. 1 and 2 (the legend for each type stated in the table is the description mentioned above). Almost 12% of the land area out of the total area of Kysuce region can be now characterized as areas with TAL. The largest representation, almost 10% of the total area, is attributed to the typical types of TAL with typical FAR, including areas with dispersed type of settlement. The construction objects of dispersed settlement, or other construction and technical structures occupy a very small part of the area. The largest representation of TAL can be found in the cadastral areas of Dlhá nad Kysucou, Zákopčie and Lutiše villages, where these forms occupy about one quarter of their territories. Especially in the cadastral area of Lutiše village the largest number of complexes with elements of TAL has been preserved and even today they are often used in the traditional way. The forms of TAL belong to the dominant elements of the landscape significantly shaping the overall character of the given area. In 16 cadastral areas the representation of TAL ranges from 10 to 15 % (rarely up to 18 %) within their areas and only in 3 cases, the presence of these forms is less than 5%. More detailed data are stated in Table 1. Individual types of TAL are most often found in parts of the monitored territory lying outside the main chains of settlements that stretch along major rivers such as River Kysuca and lower watercourses of Čierňanka and Bystrica. Therefore, the overall representation of TAL is greater in cadastral areas that wholly expand to or at least interfere with the surrounding mountain ranges, such as e.g. Oščadnica, Klokočov, Zákopčie, Nová Bystrica, Skalité, Riečnica, Raková, Lutiše and Vysoká nad Kysucou villages, as well as Čadca town. On the contrary, the lowest representation of TAL is in the parts of the territory with

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continuous forest complexes or in the lower and middle parts of the main valleys, where these forms were destroyed during the period of collectivization by land consolidation. Dominant representation in Kysuce region belongs to areas with TAL of arable-land, grasslands and pastures (subtype 4a). They are evenly represented throughout the territory, but the highest representation of this type of TAL can be found in the cadastral areas of Oščadnica, Skalité, Nová Bystrica, Lutiše and Zákopčie villages, where the most important mosaics of FAR have been preserved. The second most important type of TAL within the territory is the TAL with dispersed settlements (subtype 1a). It is interesting that dispersed settlements have greater representation in the central and northwestern part of the territory, while in the eastern and southeastern part their occurrence is rare. Typical dispersed settlements are concentrated in the upper parts of the slopes or on the mountain ridges that surround the central valleys. The most typical and best preserved forms of dispersed settlements can be found in the cadastral areas of Zákopčie, Dlhá nad Kysucou and Turkov villages, which orographically fall within the Vysoké Javorniky Mts. and in the cadaster areas of Klokočov, Korňa and Raková villages, which orographically fall into Turzovská vrchovina Mts. There are some significant differences between these two areas with greater representation of dispersed settlements, which are contingent on the nature of the territory, especially on its geomorphological characteristics. Within the area of Javorníky Mts. that are less structured by large valleys the settlements are located mainly in central and upper parts of the slopes or in the mountain ridges. The individual isolated smaller settlements (courtyards) consist of several houses with farming buildings, inhabited by several families (mostly 2 to 6) thus determining their total size. The area of Turzovská vrchovina Mts. is divided by several larger valleys facing southeast to the main valley of the whole region, through which the river Kysuca flows. Typical dispersed settlements are, like in Javorníky Mts., concentrated in elevated positions. The main settlements, however, are concentrated on the bottoms of these valleys or on the adjacent lower parts of slopes. Due to the narrowness of valleys the creation of larger and more compact residential units was not possible; therefore very distinctive settlements were often created in these parts, which could be labelled as a set of several closely situated “types of dispersed settlements”. They do not form a closed community, each group of houses (settlements, courtyards) form a sort of separate units, which are separated by meadows, pastures, arable land or even by small forests and elements of non-forest woody vegetation (this type of settlement was mapped in the territory as subtype 1b). This type of settlement is characteristic mainly for Klokočov village, northern parts of cadastral areas of Olešná and Raková villages, and partly for Čierne and Skalité villages. In several places in these areas it is difficult to draw the borderline as to whether the given settlement should be considered as a particular type of “dispersed settlement” or as a compact closed settlements, respectively whether it should be considered as a typical “dispersed settlement” because of the increasing distance between the settlements. Apart from the typical forms of TAL, i.e. TAL of arable-land, grasslands and pastures and TAL with dispersed settlements, the areas of TAL with preserved FAR currently dominated by non-forest woody vegetation or by vegetation resembling forests (subtype 4b) were also mapped. In terms of methodology of mapping of TAL only the areas where the presence of non-forest woody vegetation does not exceed 50 % of coverage of the entire allocated polygon were supposed to be mapped. However, in the territory of Kysuce region the areas of subtype 4b are quite common and they document the overall condition and current development of the territory, respectively they document the historical changes that took place in the territory in terms of intensity, type and extent of land use.

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Explanations: 1) for names and designation of subtypes of TAL see text and Fig. 1; 2) for the description of level of land use see article and Fig. 3; 3) percentage of the area of the whole monitored area.

Tab. 1 Representation of traditional agricultural landscape (TAL) in Kysuce region according to cadastral areas and levels of land use of TAL

Halada, Ľ., Bača, A., Boltižiar, M. (eds.): Landscape and Landscape Ecology. Proceedings of the 17th International Symposium on Landscape Ecology

Legend: 1 – subtype 1a - localities with typical dispersed settlements; 2 – subtype 1b - localities with a specific type of settlements; 3 – subtype 4a - localities with typical structures of arable-land, grasslands and pastures; 4 – subtype 4b - localities of TAL

Legend: 1 – subtype 1a - localities with typical dispersed settlements; 2 – subtype 1b - localities with a specific type of settlements; 3 – subtype 4a - localities with typical structures of arable-land, grasslands and pastures; 4 – subtype 4b - localities of TAL dominated by non-forest woody vegetation; 5 – boundaries of region; 6 – municipalities/cadastral areas and boundaries of cadastral areas.

Fig. 1 The spatial situation of distribution of types of TAL in Kysuce region

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Legend: 1 – total TAL; 2 – subtype 1a, localities with typical dispersed settlements; 3 – subtype 1b, localities with a specific type of settlements; 4 – subtype 4a, localities with typical structures of arable-land, grasslands and pastures; 5 – subtype 4b, localities of TAL dominated by non-forest woody vegetation; 6 – boundaries of region; 7 – municipalities/cadastral areas and boundaries of cadastral areas.

Fig. 2 The representation of individual types of TAL according to the cadastral areas

Halada, Ľ., Bača, A., Boltižiar, M. (eds.): Landscape and Landscape Ecology. Proceedings of the 17th International Symposium on Landscape Ecology

Legend: 1 – regularly farmed mosaics of lands – more than 70 % of lands in the polygon are farmed (A): 1a – largely preserved original mosaics used mainly as arable land and meadows, less used for other purposes – their current use corresponds to the traditional structure of the landscape (Aa), 1b – former mosaics predominantly covered by grassy vegetation – mosaics of grassland areas now regularly used as meadows or pastures (Ab); 2 – occasionally used or partially abandoned mosaics covered by grassy vegetation – 30 to 70 % of lands in the polygon are occasionally or regularly farmed (B); 3 – largely abandoned mosaics covered by non-forest woody vegetation – occasional farming of lands in the polygon up to 30 % (C); 4 – unused and permanently abandoned mosaics with a substantial presence of non-forest woody vegetation and forest vegetation (D); 5 – boundaries of region; 6 – municipalities/cadastral areas and boundaries of cadastral areas.

Fig. 3 Levels of land use of traditional agricultural landscapes in Kysuce region

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The formation of areas with a high proportion of non-forest woody vegetation implies the gradual abandonment of conventional farming on areas with TAL. Now, mostly the areas in the vicinity of settlements are utilized. The more distant areas are both completely abandoned and eventually already covered by woody vegetation, or they are only sporadically grazed or mown and gradually covered by woody vegetation (although the process is slower here). A large part of the areas, which are now mostly covered by non-forest woody vegetation, began to overgrow with woody vegetation after a period of collectivization. These are mainly the areas with FAR in the upper parts of the slopes that are used as permanent grasslands, which became relatively isolated after the land consolidation in the lower and middle parts of the slopes and their farming has become difficult in terms of accessibility, respectively it was not necessary, since the number of domestic animals bred in the farms had decreased and the need of procuring food for them was reduced respectively. Parts of the original areas with TAL once abandoned by its original inhabitants were later systematically afforested and currently they are covered by contiguous forest vegetation, whose first generation already reaches a mature age for hewing. Evidences of the existence of TAL are well preserved elements of FAR found under the woody vegetation and the presence of several species of plants in the undergrowth, which are characteristic for meadows, pastures, or for the edges of arable land. Specific examples of localities with areas of TAL with a high presence of non-forest woody vegetation or forest vegetation are the cadastral areas of Riečnica, Harvelka and partly Nová Bystrica villages. These areas were during the years 1974 to 1985 completely displaced because of the construction of the water reservoir “Nová Bystrica”. Houses and all other buildings were destroyed, the areas of arable land have been grassed and the areas surrounding the reservoir were afforested or left to self-development. At present, only the larger complexes of meadows located at a greater distance from the water surface are occasionally used and regularly mowed. However, individual forms of FAR are preserved as unused grass vegetation, permanent grasslands in various stages of coverage by non-forest woody vegetation, or can be found under the woody vegetation forming a continuous forest. Land use Based on the mapping of occurrence and structural characteristics of the respective types of TAL within the monitored area, a picture of the current structure of the landscape, which was formed during the historical development of the territory, can be obtained. Already on the basis of this structure it is partly possible to deduct the method and intensity of land use. However, these data supplement the categories of levels of use of the polygon, respectively of the monitored territory of TAL, and they express the overall nature of mosaic of TAL in relation to the use and management of land. The methodology of mapping of TAL has established three levels (where level 1 was divided in two sublevels) and since within the territory of Kysuce region we have also mapped areas with TAL dominated by non-forest woody vegetation or by forest vegetation (subtype 4b), as well as areas that currently are not used at all, we have set the fourth level too: A. Regularly farmed mosaics of lands – more than 70 % of lands in the polygon are farmed: Aa. Largely preserved original mosaics used mainly as arable land and meadows, less used for other purposes – their current use corresponds to the traditional structure of the landscape; Ab. Former mosaics predominantly covered by grassy vegetation – mosaics of grassland areas now regularly used as meadows or pastures; B. Occasionally used or partially abandoned mosaics covered by grassy vegetation – 30 to 70 % of lands in the polygon are occasionally or regularly farmed;

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C. Largely abandoned mosaics covered by non-forest woody vegetation – occasional farming of lands in the polygon up to 30 %; D. Unused and permanently abandoned mosaics with a substantial presence of non-forest woody vegetation and forest vegetation. The representation of respective categories of land use within the monitored territory is shown in Table 1 and illustrated in Fig. 3. Legend for each category stated in the table is the description mentioned above. Based on the data stated in Table 1 we can conclude that the originally used mosaics of lands, in which the plots with arable land, permanent grasslands and those used mainly in three-field or alternate farming systems were evenly represented, are now gradually extinguished. Traditionally, only lands in the vicinity of dwellings, either within dispersed settlements or on the boundaries of the local municipalities, are cultivated. A large part of lands that were used as arable land are now covered by grassy vegetation and mainly cultivated by small breeders, therefore the representation of TAL of subtype 1b (as to the land use classification) is here significantly higher. Generally, the most represented within the territory are the areas of TAL with 2nd level of land use (B), that are predominantly covered by permanent grasslands, and the cultivation of which is mainly concentrated in some part of the given polygon (here the land use is more or less regular), or in rarer cases the greater part of the lands is cultivated in the polygon (but never all lands), but these are cultivated only sporadically at irregular intervals and according to the situation and needs of their owners. Occasionally a parcel of arable land is found in these mosaics, but this kind of land use is very rare. The most common occurrence is the existence of fallow farmlands that are sporadically used for the cultivation of grains, root crops, or different species of plants used for animal feed. An increasing part of the lands, as well as of whole areas, is unused as evidenced by data concerning the areas belonging to the categories of 3rd (C) or 4th (D) level of land use. Given the overall development of the territory, this trend will continue into the future. In terms of representation of TAL within the individual cadastral areas and the level of their land use it can be concluded that the most cultivated areas are those in the northwestern part of Kysuce region, in Klokočov, Korňa, Olešná, Raková, Staškov, Vysoká nad Kysucou villages and Turzovka town, in the northeastern part, i.e. in Skalité and Čierne villages, and in the southeastern part, i.e. in Lutiše, Radôstka and Nová Bystrica villages. The portion of polygons with a higher level of land use (Aa, Ab and B together) represents here 80 % or more. Conversely the least cultivated TAL are in Riečnica and Harvelka villages, whose territories have been displaced due to the construction of water reservoir “Nová Bystrica”, where the portion of polygons with a low level of land use (C and D together) represents almost 100 % and further in Zborov nad Bystricou, Dunajov and Krásno nad Kysucou villages, where this share is above 50 %. Special status belongs to municipalities that have currently a very small share of TAL. These residues of TAL are located in the vicinity of dwellings, whereas they are quite intensively cultivated, as is the case of Podvysoká village, or they are located in a less accessible or less agriculturally significant locations and therefore are undercultivated or totally abandoned, as is the case of Klubina and Stará Bystrica villages. Conclusion In our publication we represent some of the results reached in the process of solving the project tasks related to the mapping of TAL in the Kysuce region. This region has a specific position within Slovakia in the terms of its historical development and human utilization.

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Here, a man made great changes in the landscape in the process of colonization and utilization of land for agricultural purposes. A rich mosaic of TAL has been formed here, which has been and is affected by relatively large and dynamic changes during the next period. Capturing and understanding of these structures and processes that take place here, is an important part of our knowledge of the history and changes in the country, where we live and where our descendants are supposed to live too. Acknowledgement: This article was supported by grant project VEGA 2/0078/15 Ecological optimization of the utilization of landslide areas in selected parts of the flysch zone in regard to the traditional farming. References Antrop M., 2005. Why landscapes of the past are important for the future. Landscape and Urban Planning 70: 21–34. Dobrovodská M., Špulerová J., Štefunková D., 2010. Survey of historical structures of agricultural landscape in Slovakia. In: Living Landscape. The European Landscape Convention in research perspective. 18-19 October, Florence. Conference Materials. Volume II. Short Communications: pp. 88–92. Harrop S. R., 2007. Traditional agricultural landscapes as protected areas in international law and policy. Agriculture, Ecosystems & Environment 121: 296–307. Marini L., Klimek S., Battisti A., 2011. Mitigating the impacts of the decline of traditional farming on mountain landscapes and biodiversity: a case study in the European Alps. Environmental science & policy 14: 258–267. Miklós L., 2002. Natural–settlement nodal regions. Map 24, scale 1 : 500 000. In: Atlas of landscape of the Slovak Republic. First edition. Ministry of the Environment SR, Bratislava; Slovak Environmental Agency, Banská Bystrica, pp. 206–207 (in Slovak). Plieninger T., Höchtl F., Spek T., 2006. Traditional land-use and nature conservation in European rural landscapes. Environmental science & policy 9: 317–321. Špulerová J., Dobrovodská M., Lieskovský J., Bača A., 2012. Typification of historical structures of agricultural landscape in Slovakia. Životné prostredie 46: 3–10 (in Slovak). Špulerová J., Dobrovodská M., Lieskovský J., Bača A., Halabuk A., Kohút F., Mojses M., Kenderessy P., Piscová V., Barančok P., Gerhátová K., Krajčí J., Boltižiar M., 2011. Inventory and Classification of Historical Structures of the Agricultural Landscape in Slovakia. Ekológia (Bratislava) 30: 157–170. Špulerová J., Dobrovodská M., Štefunková D., Halabuk A., 2009a. Methodology for mapping of historical structures of the agricultural landscape. In: Monitoring and evaluation of the environment VIII. FEE TU Zvolen a ÚEL SAV Zvolen, pp. 209–215 (in Slovak). Špulerová J., Štefunková D., Dobrovodská M. et al. (Babicová D., Bača A., Barančok P., David S., Halabuk A., Halada Ľ., Hrnčiarová T., Izakovičová Z., Kanka R., Kollár J., Lieskovský J., Petrovič F., Ružičková H., Válkovcová Z.), 2009b. Manual for mapping of historical structures of the agricultural landscape. ÚKE SAV, Bratislava, 16 pp. Tempesta T., 2010. The perception of agrarian historical landscapes: A study of the Veneto plain in Italy. Landscape and Urban Planning 97: 258–272. Tóthová Z., Sabo P., Čárska H. (eds.), (Bevilaqua D., Čárska H., Darnady A., Derka T., Galvánek J., Gerát R., Hudek V., Korňan J., Kuderavá Z., Matejová M., Pietorová E., Sabo P., Šulgan M., Tóthová Z., Urbanová V., Vrábel J.), 1996. Kysuce region nature protection and cooperation on the sustainable development. Published by the IUCN Foundation, World Union of Nature Protection, Slovakia, Bratislava, Cambridge, United Kingdom and Gland, Switzerland, 253 p (in Slovak). 56

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TRANSFORMATION OF THE SLOVAK CULTURAL LANDSCAPE AND ITS RECENT TRENDS Martin BOLTIŽIAR1,2, Branislav OLAH3, Igor GALLAY3, Zuzana GALLAYOVÁ3 1

Department of Geography and Regional Development, Faculty of Natural Sciences, Constantine the Philosopher University in Nitra, Nitra, Slovakia; [email protected] 2 Institute of Landscape Ecology Bratislava, branch Nitra, Slovak Academy of Sciences, Nitra, Slovakia; [email protected] 3 Department of Applied Ecology, Faculty of Ecology and Environmental Sciences, Technical University in Zvolen, Zvolen, Slovakia; [email protected]; [email protected]; [email protected] Abstract The paper is focused on a long-term development of the selected Slovak cultural landscape types (plains, basins, uplands, highlands, and mountains) in approximately 200 years. This period shaped the main land use features as well as on the recent transformation trends. The land use development analysis showed that several distinct periods can be distinguished. Each of these periods was characteristic for certain types of land use changes depending both on the landscape character or the socioeconomic situation. The recent trends as land use intensification (intense agriculture, sub-urbanisation, industrial construction) or extensification (agricultural land overgrowing) are considered to be common for the whole Slovak territory. Special transformation trends reflect more local conditions, human needs and preferences (construction of water reservoirs, wind calamities or tourism resorts) and though they are spatially isolated and small they influence the majority of Slovak inhabitants. Key words: cultural landscape; land use; transformation trends; Slovak Republic. Introduction Modern approaches to land cover and land use changes focus on very recent time horizons using automated, semi-automated or manual interpretation of satellite images (EEA, 2007; Bontemps et al. 2009; Schneider et al. 2009) on various geographical coverage (from regional or continental to global). The main scope for such surveys is to provide the most updated information on spatial distribution of land related phenomena mainly for operational reasons and for future landscape forecasting. However cultural landscape development is a continuous long lasting process reflecting both natural conditions and human society needs. If we want to anticipate the future, we have to know and understand the past. In the assessment of the cultural landscape development the complex mechanism that ruled formation of the natural environment in remote geological eras can be skipped. The stabilisation of cultural, social and production relationships in our cultural landscape has been settled in the periods of several recent centuries. It was the transformation of feudal to capitalist system in the 18–19th centuries and from capitalist to socialist system and back to free market democratic system in the 20th century that strongly changed the cultural landscape in the Central Europe. Land use has undergone significant changes during that time. These changes were caused mostly by technological development, changed political and property situation. The economic situation and social preferences are the main driving forces of land use change in the recent decades, especially in post-socialist countries. These drivers are often combined with unexpected natural disturbances that have occurred more frequently and strongly during the last years. Preserved historical geographical data sources (e.g. historical maps) combined with modern methods and tools (GIS, remote sensing) enable to identify relatively precisely the

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cultural landscape development of a certain territory. This method has been widely applied in recent landscape ecological and geographical research (e.g. Füleky and Major 1993; Skanes and Bunce 1997; Bürgi 1999; Cousin 2001; Petit and Lambin 2002; Bender et al. 2005, Munteanu et al. 2014). In the Central European landscape ecological school the research is based on the common scientific background and availability of historical maps (e.g. Kolejka 1987; Oťahel et al. 1993; Žigrai and Drgoňa 1995; Lipský et al. 1999; Olah et al., 2006). The paper presents the main transformation trends of the Slovak cultural landscape as identified within study areas representing various landscape types (Fig. 1). These study areas represent both the main natural landscape types in Slovakia (from plains to high mountains) and the selected specific land use types (in connection with mining activities, dispersed rural settlements etc.) or land use transformation examples (urbanisation, amelioration, land abandonment, wind calamity etc.).

Fig. 1 Study areas in the Slovak Republic: 1 – Podunajská Rovina plain, 2 – Podunajská (Nitrianska) Pahorkatina hill land, 3 – Podunajská (Žitavská) Pahorkatina hill land, 4 – Turnianska Kotlina basin, 5 – Žiarska Kotlina basin, 6 – Zvolenská Kotlina basin, 7 – Ilavská Kotlina basin, 8 – Rajecká Kotlina basin, 9 – Turčianska Kotlina basin, 10 – Hornádska Kotlina basin, 11 – Košická Kotlina basin, 12 – The Štiavnické Vrchy Mts., 13 – The Kremnické Vrchy Mts., 14 – Mt. Poľana, 15 – The Bukovské Vrchy Mts., 16 – The High Tatra Mts. and Popradská Kotlina basin Materials and methods In the research, we used the following historical maps and aerial photographs from the Slovak territory: mining maps showing the mining towns and their surroundings (1720-1750, with various large to medium scales, which belong to the oldest preserved maps in Slovakia), 1st Austrian Ordnance Maps (1772-1784, 1:28 800), stable cadastre maps from the 19th century (1866-1888, 1:2 880), 2nd Austrian Ordnance Maps (1822-1853, 1:28 800), 3rd Austrian Ordnance Maps (1888-1900, 1:25 000), Military Topographic Maps (1952-1956, 1:25 000 – 1:50 000), aerial photographs (1949) and orthophoto maps (2003, 2007, 2009, 2010, 2012). All historical maps used in this study were georeferenced in GIS (ArcView 3.2 or ArcGIS 10) using affine transformation into S-42 (Pulkovo) or S-JTSK (Křovák) coordinate systems. Although the historical maps’ cartographic accuracy varied (e. g. the 1 st

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Ordnance Survey maps’ RMS error was 100-300 m in the mountain regions) they still serve as an important and unique data source. Since the identified land use forms from different sources varied the final land use were unified into the wider comparable categories: forests, shrubs (area covered with a mix of grasslands, trees and shrubs), non-forest woody vegetation (solitary trees, lines of trees and/or bushes in open landscape), grasslands, permanent crops (vineyards, orchards), fields, built-up areas (residential, industrial, recreational), water, wetlands, rocks and in high-mountain landscapes also subalpine and alpine vegetation. In order to measure (calculate) the rate of land use change the intensity coefficient was assigned to each land use category. The land use intensity coefficients enable to classify land use categories accordingly to their prevailing natural to artificial character. They express natural value of land use categories regarding to ecosystem dominant (plant) species and their structure (Olah et al. 2006): 1 – native species and structure (forest, natural water body, rocks and debris, alpine and subalpine vegetation or other natural ecosystems) 2 – native species but altered structure or size (transitional woodland/shrub, non forest woody vegetation), 3 – nonnative species but natural structure (grasslands), 4 – non-native species nor structure (arable land, permanent crops), 5 – no vegetation or no native species, introduced species (built-up areas, open quarries). The intensity of land use change occurred between the studied time horizons was calculated as follows: R = i 2-1 + i 3-2 + ...i m-n, where: R – the rate of land use change, i 2-1 – the 2nd subtracts the 1st time horizon land use intensity coefficient. The land use change rate can be distinguished as relative and absolute. The relative land use change rate refers to overall direction of land use changes. Positive numbers express land use intensification and negative numbers refer to land use extensification. Absolute rate of land use change expresses the total amount of changes in land use regardless to their direction. This expression is useful to emphasise the least stable land use spots in the landscape. Results and discussion The transformation of the Slovak cultural landscape between the 18th and 20th century The Slavonic colonisation since the 6th century is one of the milestones of the Slovak cultural landscape formation, laying down the basic distributions of settlements and agricultural land use mainly in the lowlands and basins. Since the 11-13th centuries Christian monasteries, feudal castles with their villein villages and merchant towns (especially free mining and royal towns) played a crucial role in the landscape development. Later the Slovak territory experienced also two important non-Slavonic colonisations. The German colonisation has begun in the 13th century in order to replace the inhabitants loss after the Tartar’s invasion. The Wallachian colonisation in the period of 14-17th centuries saturated the mountain and sub-mountain areas and introduced specific types of land use, which led to the various kinds of dispersed rural settlements as Podpolianske lazy, Myjavské kopanice, Novobanské štále or Oravské rale). In the 18th century the cultural landscape of plains and basin bottoms has already been stabilised for centuries. The relatively small towns and peasant villages, agricultural land and roads lied on the hilly parts of the landscape due to periodical floods occurring in the floodplain areas. The river and streams alluviums were left untouched (with the exception of the towns where the river banks were strengthened due to safety reasons) or used as occasional pastures (Fig. 1 – study areas 1, 2, 3, 4). For the landscape of the Central Slovakia mining towns, this period already meant the decline of the medieval mining golden era (Fig. 1 – study areas 12 and 13, Fig. 2). The landscape was heavy deforested, its relief changed due to

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the surface mining, ore spoil dumps and the construction of mine and surface water management system. Although the 19th century was the period of significant social, national and economic changes in the Austro-Hungarian Empire, the historical maps from the middle of the century showed that the land use was very similar compared to the previous century. The urbanised areas slightly increased mainly in the large towns and a new form of transport type emerged – railways (e.g. Košicko-bohumínska railway). The land use of mining towns changed in two directions. The higher situated areas returned to forests and the lower parts were urbanised mainly as new industrial or residential areas (Fig. 2). The rural areas’ land use seemed to stay almost unchanged in the all studied areas. The first half of the 20th century represented a relative stagnation of the land use development due to WW I, the global economic crisis in 1930s and WW II. However the urbanised areas have increased and even new towns, interconnected with a new industrial development, have been founded, like Baťovany (now Partizánske), Svit and Poprad, which is a result of several villages merged together (Fig. 3, year 1988).

Fig. 2 Land use changes of the Banská Štiavnica’s mining landscape in the Štiavnické Vrchy Mts. (study area 12)

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Fig. 3 Examples of deforestation in 1822 and urbanisation until 1988 and land use changes during the whole studied period 1772-2012 and the 2004 wind calamity area in the High Tatras National Park area (study area 16) The changes of cultural landscape since the 1950s is characterised by a giant industrial and residential urbanisation (newly founded towns as Žiar nad Hronom – Fig. 4 or Nová Dubnica – study area 7 in Fig. 1) and an agricultural land intensification especially in the plains and basins bottoms. The exponential population growth was followed by massive urbanisation, where smaller to medium towns increased their areas by 2-3 times (Fig. 1, in the study areas 61

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3- Zlaté Moravce, 7- Dubnica nad Váhom, 8- Rajec, 9- Martin, 10- Spišská Nová Ves), large cities even by 4 times in the period 1900-2000 (Fig. 1, in the study areas 2- Nitra, 6- Zvolen and Banská Bystrica, 16- Poprad, 11- Prešov). The formerly naturally limited (e.g. wetlands) or barren areas have been ameliorated (rivers banks strengthening, drainage and irrigation mainly in the Podunajská Nížina lowland, the Východoslovenská Nížina plains and the basins bottoms) to provide enough space for either the construction of new urban areas or transportation corridors or for collectivised intensive agriculture. An example can be seen in the Turnianska Kotlina basin (Fig. 5) with an important international corridor (gas and oil pipeline) and national corridor (road, railway and electric lines). Up to the 19th century, the basin bottom was occupied by large wetlands that were later in the 20 th century either turned into fish ponds or arable land (after ameliorations). The stable areas (without any land use change during the studied period over 200 years) remained only on locations that provided either the most preferable conditions for use (settlements or fields and permanent crops) or strict limits (bare rocks or forests on steep slopes). The collectivisation of agricultural lands led also to a significant homogenisation of the landscape structure, e.g. in the Podunajská Nížina plain (study area 1) the total number of patches was reduced by 31% and the mean patch size increased by 45% (Fig. 6) The landscape hydrological conditions were adjusted to the society water supply by construction of large water dams or drinking water reservoirs (e.g. VN Starina, Fig. 7), and to transportation and energy needs by building river channels and hydroelectric (power) plants (e.g. Gabčíkovo – Fig. 6, or the Vážska Cascade). The end of the 20th century was a time of the society and economy transformation, which stopped construction of block of flats (almost in every larger town), industrial recession (mainly heavy industry) and land abandonment (especially mountain and marginal areas).

Fig. 4 Socialist urbanisation and industrialisation of the Žiar nad Hronom cadastre in the Žiarska Kotlina basin (study area 5)

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Fig. 5 Examples of the Turnianska Kotlina basin before wetlands amelioration (1853) and after it (2012) with increased urbanisation, and the areas stable in terms of land use throughout the whole studied period 1784 – 2012 (study area 4)

Fig. 6 Land use changes in the Danube river system and surrounding landscape due to socialist collectivisation in 1950s, the Gabčíkovo hydroelectric plant and transport channel construction in 1990s (study area 1 – Podunajská Rovina plain)

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Recent transformation trends The last decade landscape transformation represents 2 rather polarised trends. On one hand it is an intensification of land use – massive building of sub-urbs, construction of new industrial parks, highways and motorways. The massive sub-urbanisation takes place in the vicinity of almost every Slovak city or town. Increasing income of the upper-middle class allowed the former residents of the blocks of flats to be able to afford their own house and garden within city proximity. The highway and motorway construction is a response to continuously growing numbers of cars and heavy local and transition traffic. It is considered to be an important precondition for future economic development of the whole Slovak Republic and especially remote and marginal regions, therefore it is strongly supported by the national and regional authorities. New industrial parks, logistic trade centres and multifunction shopping centres spread on fertile arable land around cities. Everyday life of people is more and more dependent on cars. The new residential areas are usually remote from the city centre, where people commute to their work and large shopping centres, where people use to shop are remote form the city centres and the residential areas. These remote locations and consequent transport needs then shape the land use patterns around the cities.

Fig. 7 Vanishing open agricultural landscape in the Bukovské vrchy Mts. due to the Starina water reservoir (VN Starina) construction and land abandonment (study area 15) The opposite significant land use trend is extensification (or abandonment) of the agricultural landscape as a result of unprofitable farming and changing social preferences. To be preserved, the fields and permanent grasslands as secondary vegetation formations demand a certain amount of subsidiary energy in form of human utilisation. Approx. 80% of the Slovak permanent grasslands is localised in the agriculturally less profitable sub-mountain and mountain areas with prevailing small to medium villages urbanisation. The economic regression of the 1990s combined with negative demographic trends (emigration of young inhabitants to larger towns and broken peasants’ links to their land as a result of the 40 years of collectivised property) rapidly led to a large land abandonment and extending secondary succession (Fig. 7). This overgrowing of grasslands by shrubs and trees not only impacts the grasslands biodiversity and causes conflicts with the NATURA 2000 main goals but also results in a significant loss of the cultural landscape, its scenery and traditional character as a secondary effect. The overgrowing is affected by natural conditions but the social and economic factors are even more significant (unwillingness to use the grasslands). Although it 64

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is rather easy to identify overgrowing patches in the landscape, an exact quantification of its rate is quite difficult due to a continuous character of the process. The results from the middle Slovakia Poľana Mt. showed that in the period of 1949-2012 almost 2/3 of the grasslands had been overgrown (Olah et al. 2006). Natural disasters are a relatively new phenomenon affecting recent land use in Slovakia. Although these effects do not represent land use trends, they occur more frequently. In the mountain and high mountain areas mainly wind calamities in forests cause significant economic loss and consequent nature conservation problems. These wind calamities are considered to be a serious problem of the last few years but the historical maps showed that they have already occurred several times in the same territory (Fig. 3, large deforested areas in NE part in 1822 and in 1915). For centuries floods have meant a serious trouble and they limited a potential use of the alluvium plains. In the past people unable to avoid periodical floods built their settlements on the higher situated locations above the inundation zones. After the catastrophical plains floods in the 1960s the largest rivers were regulated and the nearby land ameliorated. It opened the alluvial zones formerly excluded from urban areas to urbanisation. At present, floods increasingly endanger the sub-mountain and upland areas. The smaller streams’ floods are very intense and destruct mainly objects localised in alluviums. The predisposition for floods is affected by the site natural conditions (relief, soil and hydrology) and land use but it is often triggered by the local extreme climate events (Gallay, 2009). Conclusion The Slovak cultural landscape transformation trends could be summarised into the following main points: • The most stable land use in the long-term horizon is linked either to low situated parts with low inclination (fields and settlements on the plains, basin and valleys bottoms) or to higher parts with steeper slopes (high mountain vegetation, forests, shrubs, local secondary grasslands). The majority of changes occurred between these two locations. • Intensification of land use prevailed on lower situated areas and extensification on higher and remote locations. The exceptions were recorded only in the territories where the land use was affected by new socio-economic phenomena – such as tourism centres development in the High Tatras Mts. or the water reservoir construction in the Bukovské Vrchy Mts. • The second half of the 20th century represents the most dynamic period of the cultural landscape transformation with significant changes of natural conditions (mainly hydrological and climate) and large block agriculture intensification and industrialisation interconnected with urbanisation. • The contemporary trends are the intensification of land use in the plains and the basin bottoms, especially urbanisation in the cities’ vicinity and the land abandonment and overgrowing in higher and remote areas. The identified transformation trends are supported by the results of other authors studying long-term landscape changes in Slovakia – amelioration and collectivisation in the Záhorská Nížina plain (Cebecauerová, 2007), rapid urbanisation of the larger cities since 1950s (Pucherová, 2004; Chrastina, 2005), land abandonment in remote dispersed settlement areas (Petrovič, 2006), relative stable high mountain areas (Boltižiar, 2007) or forest wind calamities in the Tatra Mts. (Falťan et al., 2008). The landscape changes studies from the neighbouring post-socialist countries as the Czech Republic or Hungary revealed almost the same long-term but also recent transformation trends, e.g. continuous land use intensification in lowlands (Lipský, 2000; Demek et al., 2008), conversion of agricultural landscape since

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1950s (Kubeš, 1994; Lipský et al., 1999), land abandonment in marginal areas after 1990s (Bartoš et al., 1999; Kolejka, Marek, 2006), unstable land use in the Tokaj mountains foothill (Csorba, 1996) and agricultural land abandonment in the Hungarian agricultural plains (Chrastina, Boltižiar, 2006). The most recent transformation trends of the Slovak landscape correspond also to panEuropean land cover changes identified by the CORINE Land Cover methodology (EEA, 2007). Land use intensification (urban residential sprawl, construction of new industrial sites and transport infrastructure, intense agriculture) and land abandonment followed by afforestation is occurring mainly on former permanent grasslands (EEA, 2006). The land use polarisation causes a loss of highly valuable landscape types (e.g. low intensity farmlands) and on the other hand it increases a pressure on the environment, human health and wellbeing. The study results and their comparison with neighbouring post-socialist countries and panEuropean trends supports the hypothesis that thought natural conditions determine the basic land use distribution, the land use change driving forces are mainly of socio-economical nature. The paper was supported by project of grant agency VEGA 2/0117/13 Assessment of status and dynamics of habitats using combination of modelling and remote sensing. References Bartoš, M., Těšitel, J., Kušová, D., 1999. Marginal areas – historical development, people and land use. In: Kovář, P. (Ed.): Nature and Culture in Landscape Ecology. Prague, 109 – 113. Bender, O., Boehmenr, H.J., Jens, D., Schumacher, K.P., 2005. Analysis of land-use change in a sector of Upper Franconia (Bavaria, Germany) since 1850 using land register records. Landscape Ecology, 20: 149 – 163. Bontemps, S., Defourny, P., Van Bogaert, E., Weber, J.-L., Arino, O., 2009. GlobCorine – A joint EEA-ESA project for operational land dynamics monitoring at pan-European scale. Sustaining the Millenium Development Goals. Paper presented at the 33rd International Symposium on Remote Sensing of Environment, Stressa, Italy, 4–8 May, 2009. 15 pp. Boltižiar, M., 2007. High-mountain landscape structure of Tatra Mts. UKF, ÚKE SAV a SNK MAB, Nitra. 248 pp. (in Slovak). Bürgi, M., 1999. A case study of forest change in the Swiss lowlands. Landscape Ecology, 14: 567 – 575. Cebecauerová, M., 2007. Analysis and assessment of changes of landscape structure (case study of selected part lowland Borská nížina and the mountains Malé Karpaty). Geographia Slovaca 24, 2007. 136 pp. (in Slovak) Cousin, S.A.O., 2001. Analysis of land-cover transitions based on 17th and 18th century cadastral maps and aerial photographs. Landscape Ecology, 16: 41 – 54. Csorba, P., 1996. Landscape-ecological change of the land use pattern on the east foothill area of the Tokaj Mountains (Hungary). Ekológia (Bratislava), 15: 115-127. Demek, J., Havlíček, M., Chrudina, Z., Mackovčin, P., 2008. Changes in land-use and the river network of the Dyjsko-Svratecký úval (Czech Republic) in the last 242 years. Journal of Landscape Ecology, 1 (2): 22 – 51. EEA, 2006: Land accounts for Europe 1990-2000. Towards integrated land and ecosystem accounting. EEA technical report 11/2006.

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EEA, 2007: CLC2006 technical guidelines. EEA technical report 17/2007. Falťan, V., Bánovský, M., Jančuška, D., Saksa, M., 2008. Land cover changes of Vysoke Tatry Mts. Foreland afrter wind-storm. Geo-grafika, Bratislava. 96 pp. (in Slovak) Füleky, G., Major, I., 1993. Changes in the landscape during the last 200 years in the region of the Zala Valley and effect on the economic activity of the area. Landscape and Urban Planning, 27: 265 – 267. Gallay, I., 2009. Landscape-ecological evaluation of abiotic complex of CHKO – BR Poľana. TU vo Zvolene, Zvolen. (in Slovak) Chrastina, P., 2005. Landscape development as a phenomena of environmental history (on example of Trenčianska basin). Historická geografie, 33: 9 – 19. (in Slovak) Chrastina P., Boltižiar M., 2006. Cultural landscape of NE part of Bakonyi forest in Hungary (present in past context). Historická geografie, 34, suppl. 1: 175 – 188. (in Slovak) Kolejka, J., 1987: Landscape-historical synthesis materials, methods and results. Ekológia (ČSSR), 6 (1): 51 – 62. Kolejka, J., Marek, D., 2006. Sustainable land use convergence in border area in Central Europe. IN: Vogtmann, H., Dobretsov, N. (Eds.): Environmental security and sustainable land use - with special reference to Central Asia. Springer, pp 183 – 198. Kubeš, J., 1994. Bohemian agricultural landscape and villages, 1950 and 1990 land use, land cover and other characteristics. Ekológia (Bratislava), 13 (2): 187 – 198. Lipský, Z., 2000. Monitoring of cultural landscape changes. ČZU LF, Praha. 71 pp. Lipský, Z., Kopecký, M., Kvapil, D., 1999. Present land use changes in the Czech cultural landscape. Ekológia (Bratislava), 18 (1): 31 – 38. Munteanu, C., Kuemmerle, T., Boltižiar, M., Butsic, V., Gimmi, U., Halada, Ľ., Kaim, D., Király, G., Konkoly-Gyuró, E., Kozak, J., Lieskovský, J., Mojses, M., Müller, D., Ostafin, K., Ostapowicz, K., Shandra, O., Štych, P., Walker, S., Radeloff, V. C. 2014. Forest and agricultural land change in the Carpathian region – A meta-analysis of long-term patterns and drivers of change. Land Use Policy. 39, 5: 685-697. Olah, B., Boltižiar, M., Petrovič, F. Gallay. I., 2006. Land-use development of Slovak biosphere reserves of UNESCO. TU a SNK MaB, Zvolen. 140 pp. (in Slovak) Oťahel, J., Žigrai, F. Drgoňa, V., 1993. Landscape use as a basis for environmental planning (case studies of Bratislava and Nitra hinterlands). Geographical studies, 2: 7 – 84. Petit, C.C., Lambin, E.F., 2002. Impact on data integration technique on historical landuse/land-cover change: Comparing historical maps with remote sensing data in the Belgian Ardennes. Landscape Ecology, 17: 117 – 132. Petrovič, F., 2006. The changes of the landscape with dispersed settlement. Ekológia (Bratislava), 25, suppl. 1: 65 – 89. Pucherová, Z., 2004. Land use development on the border of Zobor Mts. and Žitavská pahorkatina (on the example of selected villages). FPV UKF, Nitra. 142 pp. (in Slovak) Schneider, A., Friedl, M. A., Potere, D., 2009. A new map of global urban extent from MODIS satellite data. Environmental Research. Letter 4: 11 pp. Skanes, H.M., Bunce, R.G.H., 1997. Directions of landscape change (1741 - 1993) in Virestad, Sweden - characterised by multivariate analysis. Landscape and Urb. Planning, 38: 61 – 75. Žigrai, F., Drgoňa, V., 1995. Landscape-ecological analysis of the land use development for environmental planning (case study Nitra). Ekológia (Bratislava), 14, suppl. 1: 97 – 112.

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FORECASTING OF LANDSCAPE DYNAMICS: A CASE STUDY AT ROZTOCZE WSCHODNIE (EASTERN POLAND) Piotr KOCIUBA1, Ihor KOZAK1, Kajetan PERZANOWSKI2, Daniel KLICH2, Hanna KOZAK3, Adam STĘPIEŃ1 John Paul II Catholic University of Lublin, Lublin, Poland, Department of Landscape Ecology: [email protected] [email protected]; [email protected] 2 Department of Applied Ecology: [email protected]; [email protected]; 3 Department of Nature Preservation; [email protected] 1

Abstract The study was performed within the border zone between Poland and Ukraine, at the region of Roztocze Wschodnie. The main aim of the study was an assessment of changes in the landscape structure of the area, that was influenced by the deportation of Ukrainians from Poland in 1944-1947 years. The second aim was an assessment of possibility for forecasting landscape changes using CELLAUT 2.0 model. Maps were analysed from the period before World War II (1936), some 30 (1965) and 60 years later (1996). Landscape changes were determined with ArcGIS 10.3 and Fragstats 4.1. Forests and settlements as the land cover types were analyzed. Components of landscape mosaic were elaborated with ArcGIS 10.3. Then they were transformed to ASCII files to be used in the Fragstats programme and subsequently in the CELLAUT 2.0 model that was created for an improvement of the analysis of landscape dynamics. The model, based on the theory of cellular automata, was verified in selected landscapes within the border zone. The main assumption for creation of this type of the model, was the development of the potential provided by cellular automata, to predict changes in the landscape (He et al., 2011; Mitsova et al., 2011). Structural changes in this area were reflected in a decrease of settlements' density, distribution changes (deviational ellipse), and shift towards the west. Other elements of landscape structure underwent transformations, significantly changing the productive and cultural function of a landscape. Forecasts of possible changes in landscape dynamic obtained in the CELLAUT 2.0 model, were compatible with trends found from the comparison of historic and current maps, and indicated most probable directions of future changes. Perspectives for the development of the CELLAUT 2.0 model, in its application for forecasting of the dynamics of landscape mosaic within Polish-Ukrainian border zone, are presented. Key words: landscape; changes; mosaic; models; predictions. Introduction Development of technology provoked changes in the analysis and presentation of the dynamics in the landscape (Urbanski, 2012). The study of the landscape and analysis of its changes in the past, as well as prediction of possible dynamics in the future (Kozak et al., 2013) using cellular automata (He et al., 2011; Mitsova et al., 2011) turned out to be of extremely good prospects. At the present state, Roztocze Wschodnie landscape located in Eastern Poland influences not only the geological processes (Buraczyński, 1999) and climate (Izdebski, 1971) by shaping the terrain and vegetation (Wysocki, 2002), but also historical events. Particularly those associated with the mass deportation of Ukrainians (Gil, 2004) in the years 1944-1947,

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which resulted in a reduction of population and settlement area. This however provoked the weakening of landscape transformation and intensified spontaneous forest succession processes in the landscape. The main aim of the study was an assessment of changes in the landscape structure of the area that was influenced by the deportation of Ukrainians from Poland. The second aim was an assessment of possibility for forecasting landscape changes using CELLAUT 2.0 model (Fig. 1). Materials and methods The study area is the Roztocze Wschodnie mesoregion (Kondracki, 1981) located within Polish borders in the provinces of Lubelskie and Podkarpackie. The maps were analysed from the period before World War II (1936), some 30 (1965) and 60 years later (1996). Changes in the landscape of Roztocze Wschodnie (within years: 1936, 1965 and 1996) were determined with ArcGIS 10.3 and Fragstats 4.1 (McGarigal, Marks, 1995). Data from 2014 year were used for verification of model. Layer with inclines for the study area was established based on the map SRTM 1-ARC. Components of landscape mosaic were elaborated with ArcGIS. Then they were transformed to ASCII files to be used in the Fragstats programme and subsequently in the CELLAUT 2.0 model. The main land cover types were analysed: forests and settlements. We also assessed changes in settlements distribution in Roztocze Wschodnie between years 1936 and 1965 with Standard Deviational Ellipse (Scott, Janikas, 2010). We forecasted potential changes in the landscape of Roztocze Wschodnie (for next 30 years) using CELLAUT 2.0 model.

Fig. 1 CELLAUT 2.0 model interface The model was developed in the Java software using Geotools library 13 and coordinates PUWG 1992. The CELLAUT 2.0 model was written modularly which allows an easy way to its expansion by another rule of forecasting. In the model, we can distinguish four main blocks: block loading and defining elements in the landscape, natural succession block of

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landscape elements, block the interaction between the defined elements and block simulation. In the model the simulations conduct for the entire map area that is directly related to the block defining elements of the landscape. Assumptions of the model are: for the forecast of landscape changes assumed was an application of 8 neighbours rule; and an appearance of build up area in the cell (i, j) depends from the accessibility to the road (there is higher probability for the appearance of built up area in direct proximity of the road, that decreases together with growing distance from the road); built up area cannot occur within the cell at forested area; built up area cannot appear at the slope exceeding 6˚; appearance of forests may occur at non-forested areas only; roads and rivers remain unchanged during simulation. We define the function of transition from state into (where ( finite aggregation or countable)) as the number of non-negative satisfying the condition:

In our case, the term "transfer function" replaced by the term "transition matrix" because with us is a finite number of states ( ). A collection of in the CELLAUT 2.0 model is a set of elements present or potential in the study area. Transition matrix is a matrix whose elements are the transition probabilities . It is a probability matrix, wherein, for each condition occurs:

Results and discussion The analysis of maps for the whole of the Roztocze Wschodnie mesoregion (Fig. 2) showed significant changes in land cover (years 1936-1965-1996) mainly in settlements and forests. The total number of settlements (NP) decreased from 97 in 1936 to 64 in 1965. The area of settlements (CA) decreased from 1,523.47 ha (4.95% of total area) in 1936 to 662.65 ha (2.15%) in 1965. In 1996, the noticeable increase of settlements to 1,324.46 ha (4.31%), not reach the surface from before 1936. The changes of forests had the character completely opposite to settlements changes (Fig. 2). Forested area increased from 11,529 ha (37.47 % of total area) in 1936, to 16,723 ha (54.36%) in 1965 and 17,936 ha (58.30%) in 1996. There was a change in distribution of built up area. The Standard Deviational Ellipse (Scott, Janikas, 2010) – that considers the direction of dispersion of built up area has been moved within 5.6 kilometres towards northwest in 1965 compared to 1936 (Fig. 3). It was as result of deportations of the Ukrainians, which dominated in the Roztocze Wschodnie (Kubijovyč, 1983). The effects of displacement are clearly visible on the example of one of the nonexistent villages, for example the village Brusno Stare. The village in 1939 had 1,150 inhabitants (1,095 Ukrainians, 50 Jews, 5 Poles). After the deportation of Ukrainians village ceased to exist, leaving only the cemetery with tombstones sculpture (Kozak et al., 2014b).

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Fig. 2 Landscape elements in mesoregion: A- in 1936, B - in 1965, C - in 1996.

Fig. 3 Standard Deviational Ellipse settlement data analysis for study mesoregion Roztocze Wschodnie.

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A significant impact on the landscape had the number of farms, which also has been significantly reduced. For example, in 1936 on the study area there was 5,631 farms. Their number decreased after the deportations. In 1965 there were 2,898 farms (they made up 51.57% of the number of households in 1936). The analysis of changes in built up areas and forests in 1996 till the current state allowed us to notice a tendency leading to the disappearance of small settlements and increase in surface of greater ones. The character of the landscape elements dynamics (Fig. 4) for the analyzed mesoregion presented in the paper was confirmed in data from the literature (Kosiniak-Kamysz, 2010). For example, the number of householders in Werhrata village decreased from 1,130 in 1930 to 127 in 1960 (Kosiniak-Kamysz, 2010). A noticeable trend of the development for the whole of the Roztocze Wschodnie mesoregion was shown by the CELLAUT model (Kozak et al., 2014), while in this article it was provided with the use of CELLAUT 2.0 model. In prognosis using CELLAUT 2.0 model we can notice an increase in built up areas without deportation conditions (Fig. 5). The built up area can reach the level of 7.3% of total area in 2045. Unfortunately, this did not happen (due to deportations) and the loss of people, cultural and economical possibilities had negative effects for Polish State. The model has confirmed the character of historical changes and predicted much smaller built up area (4.2%) at the end of prognosis for 2045 due to deportation influences (Fig. 5), while without deportations the built up area should be greater (7.4%). The projections showed that even after 100 years (year 2045 as compared to year 1936) the built up area will not be renewed. Accordingly, the number of inhabitants who lived in the region before deportations will not be renewed as well. This may result in the loss of traditional village system, cultural identity and Ukrainian cultural heritage (tserkwy, cemeteries, and sacred landscapes) in this region.

Fig. 4 Landscape elements dynamics of Roztocze Wschodnie mesoregion from 1936-2014

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The change of the landscape presented the decrease in built up areas (the analysis does not include the decrease in the fields and meadows; changes in the area of grasslands, crop fields or scrublands, what eventually can provide much more valuable data about landscape changes that will be present in our future models and publications). The increase in the forest area in future may lead to the weakening of agriculture and increase of the forestry in this mesoregion. It will be also possible to increase the development of tourism, especially concerning historical traditions of the region.

Fig. 5 Prediction of settlements area in Roztocze Wschodnie: upper line – without deportation; lower line – influences of deportation; control points – data from maps.

Conclusions The changes in the landscape structure of the Roztocze Wschodnie and assessment of possibility for forecasting landscape changes using CELLAUT 2.0 model was analyzed in this article. These changes were mainly caused by the deportation of Ukrainians from Poland in the years 1944-1947. The main direction of the changes is an increase in the area occupied by forests and decrease in the number and areas of settlements. The model has confirmed the character of historical changes and predicted much smaller built up area in the end of prognosis for 2045 due to deportation, comparing with built up area not influenced by deportation. The projections showed that even in 2045 the percentage of built up area that was in 1936 will not be renewed. It similarly happens with the number of inhabitants who lived in the region before deportation. This results in the loss of traditional village system, cultural identity and Ukrainian cultural heritage for the Roztocze. The CELLAUT 2.0 model may be considered a perspective tool for forecasting the dynamics of landscape mosaic within Polish-Ukrainian borderland. We would like to appreciate the Polish Ministry of Science and Higher Education for financing the research project (Project No. N N 309 014638).

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References Buraczyński J., 1999. Roztocze: Construction, relief, landscape, Lublin: Maria CurieSklodowska University. Department of Regional Geography, pp.1-188. (in Polish). Gil A. 2004. The deportation of Ukrainians from Poland in the years1944-1946 as a problem in contemporary Polish-Ukrainian relations. Institute of East-Central Europe Press, Lublin, pp.1-30. (in Polish). Izdebski K., 1971. Roztocze. Warsaw, Wiedza powszechna. (in Polish). He H. S., Yang J., Shifley S. R., Thompson F. R., 2011. Challenges of forest landscape modeling – Simulating large landscapes and validating results. Landscape and Urban Planning 100: 400–402. Kozak I., Parpan V., Parpan T. Kozak H. 2013. Landscape Ecology (modern approach). Precarpathian Uniwersity Press. Ivano-Frankivsk, pp. 1–216. (in Ukrainian). Kozak I., Kozak H., Kociuba P. 2014. Analysis of settlements dynamic in landscape of Tarnogród Plateau and East Roztocze with the use of computer models. In: Rak J. R. (Ed.), An influence of the natural resources as well as cultural, culinary and industrial heritage on the tourism attractiveness of the region the Carpathian Mountains – the Podkarpackie region – the Roztocze. Muzeum Regionalne im. Adama Fastnachta, Brzozów, pp. 283–298. (in Polish). Kozak H., Kozak I.,Stepień A. 2014b. Greek Catholic cemetery in Stare Brusno as examples of sacred art in the cultural landscapes of the borderlands. In: Rak J. R. (Ed.), An influence of the natural resources as well as cultural, culinary and industrial heritage on the tourism attractiveness of the region the Carpathian Mountains – the Podkarpackie region – the Roztocze. Muzeum Regionaln im. Adama Fastnachta, Brzozów, pp. 299–319. (in Ukrainian). Kondracki J. 1981. Physical Geography of Poland, Warsaw. 5th edition, PWN. (in Polish). Kosiniak-Kamysz K., Woźny K. 2010. Touristic characteristics of Southern Roztocze. Katowice, GWSH. (in Polish). Kubijovyč V., 1983. Ethnic groups of the South-Western Ukraine (Galyčyna-Galicia) 1.1.1939. Logos. München. McGarigal K. and Marks B.J. 1995. FRAGSTATS: a spatial pattern analysis program for quantifying landscape structure. USDA Forest Service. GTR PNW-351. Mitsova D., Shuster W., Wang X., 2011. A cellular automata model of land cover change to integrate urban growth with open space conservation. Landscape and Urban Planning 99, 2: 141–153. Scott L. M. Janikas M. V. 2010. Spatial Statistics in ArcGIS. In Fischer M. M., Getis A. (eds) Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications. Springer-Verlag Berlin Heidelberg. pp. 39-53. Urbański J., 2012. GIS in natural research. Gdańsk: GIS Centre. (in Polish). Wysocki Cz., 2002. Applied Phytosociology. Warsaw. SGGW. (in Polish).

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POST-INDUSTRIAL LANDSCAPE: IDENTIFICATION, TYPOLOGY AND VALUE Jaromír KOLEJKA Masaryk University, Faculty of Education, Brno, Czech Republic; [email protected] Institute of Geonics, Ostrava, Czech Republic; [email protected] Abstract The industrial heritage in any landscape is represented by objects and other traces left by industry and associated human activities, which do not serve the present industry and inhabitants in the same way as they did before. Spatial concentrations of objects of such heritage form post-industrial landscapes (PILs).The paper deals with the procedures for PIL identification on a national level, classification of PILs and their initial assessment for management. Publicly accessible data sources on man-made land forms, land use, brownfields, mined sites, contaminated sites, industrial architectural heritage, etc. were used with GIS technology. The outline, area, content and topic description are essential for decision making about PIL futures. The generic classes of PILs were assessed from the viewpoint of their possible impact on human lives and activities. Preliminary PIL management proposals were developed. Key words: post-industrial landscape; mapping; GIS; classification; management. Introduction Western countries have for a long time made efforts to preserve important monuments of the industrial period from demolition and provide them with new roles in contemporary economies. Outstanding examples of former industrial activity – industrial buildings, areas and exceptionally landscapes have in some cases come under international protection as world heritage sites. The post-industrial landscape is a historical continuation of the industrial landscape (Kučerjavij, 1999, Kolejka, 2006). While the defining characteristics of the industrial landscape are active and recent, they become “dead” and fossil in the post-industrial landscape. The Post-industrial landscape is an area, whose structural, functional and physiognomic characteristics were to a considerable degree formed directly and indirectly by previous industrial activities and by an earlier industrial society. These activities resulted in specific changes in the natural, economic, human and spiritual landscape structures but these no longer serve their original purpose. A post-industrial landscape represents a territorial concentration of PIL indicators and covers a sufficiently large area of the Earth’s surface (Kolejka et al., 2012). The existence of post-industrial landscapes is a generally accepted fact of the contemporary world. However, its scientific research still fails to meet requirements for their mapping and classification. However, it is primarily individual monuments in the landscape related to past industrial activities which receive most interest from professionals. Many industrial cities (e.g. London, Manchester, Liverpool,, Hamburg, Düsseldorf, Vienna, Prague, Thessaloniki, Glasgow, Boston, etc.) have launched redevelopments of industrial sites and surrounding housing quarters, usually with the objective to transform them into modern residential and service facilities. Industrial heritage sites have also become tourist attractions. However, significantly less attention is paid to wider areas of industrial heritage (some exceptions are represented by the Emscher-Park in Ruhrland, Germany - Fragner, 2005; 75

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Blaenavon area as UNESCO WHS in Wales - Rogers, 2006; Dearne Valley in South Yorkshire, England - Ling, Handley, Rodwell, 2007; Lower Lusatia in Germany; Copenhagen, Denmark - Hansen, Winther, 2006). While industrial landscapes have been part of the research portfolio of academic institutions for a considerable length of time, postindustrial landscapes are less frequent in publications. The relations between industrial heritage and the surrounding landscape are not still subject to intensive studies but interest in this issue is growing promisingly (see references). Needless to say that even abroad it is initiated primarily by architects. Although the term “postindustrial landscape” became frequent in specialized literature at the turn of century and various planning and protection measures concerning its future are seriously considered, its geographical definition (delimitation and content) remains vague (see Loures, 2008). In the case of Slovenia (Hladnik, 2005), the industrial landscape as a special landscape type is defined according to the share of industrial areas (registered in the CORINE project) in the entire area of an administrative unit. According to Ling et al. (2007), any area significantly affected by mining (on example of Dearne Valley) and showing numerous abandoned buildings, brownfields, etc. subjected to rehabilitation programs and requiring other than conventional approach to decision making about its future can be considered as a post-industrial landscape). Stuczynski et al. (2009) developed an original concept of geographic identification of postindustrial regions in the EU based on structural and social factors. Since 2000, the expert community has dealt with a number of aspects of the post-industrial landscape. Traditionally, architectural (Cashen, 2006), economic (Shahid and Nabeshima, 2005, Dunham-Jones, 2007) and social aspects (Kirkwood, 2001, Kirk, 2003, Hansen and Winter, 2006) of this type of landscape have been the dominant focus of study (in the geographic context). Landscape science deals with its ecological aspects, particularly focusing on the occurrence of biotic communities and species (Kirkwood, 2001, Kirk, 2003) as well as environmental factors (soil and water remediation - Keil, 2005). Another aspect of the postindustrial landscape is vegetation succession in former industrial or other abandoned areas. Among other things, this spontaneous process has initiated the establishment of a new scientific discipline, restoration ecology, (Naveh, 1998). In the post-industrial landscape an “industrial nature” (Cílek, 2002) or a “new wilderness” (sensu Lipský, 2011) will, left to itself, develop irrespective of the original conditions. This paper is devoted to PIL identification and specific assessment in the Czech Republic. In theory, we can find sufficient evidence supporting the existence of post-industrial landscapes in given territorial concentrations. Yet, as can be expected, the reality of actual data availability is different. The range of PIL indicators (see chapter “Materials and methods)” can be covered by available good-quality data only partially. A number of geodatabases containing information regarding required PIL indicators (i.e. in all regions of the country) were created in the Czech Republic in the past. However, this information usually must be interpreted appropriately from the PIL identification viewpoint. The aims of the paper are to establish the methodology of identifying, mapping, classifying and value assessment of post-industrial landscape using the example of the Czech Republic. This is one of the most industrialized region of Europe with deep structural changes in the two last decades which for which there are data depicting the industrial heritage in four landscape structures (primary – natural, secondary – economic, third – human/social and quaternary – spiritual). The application of GIS processing technologies for the purposes of objective delineation of PILs allows decision makers, territorial planners as well as wide publics to gain classified scientific knowledge about the industrial heritage in the landscape. If the purpose of the 76

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presented method is to identify, map and classify PILs, the data must be available so that the procedure could be repeated for purposes of comparison in other countries as well. Materials and methods The geodatabases used in the research represent critical thematic requirements for PIL identification and mapping in the Czech Republic, although they fail to cover all the required subjects. The ZABAGED (basic set of geographic data) cartographic database produced by the Czech Office for Surveying, Mapping and Cadastre encompasses mining sites, industrial sites, waste deposits and mine dumps. The CORINE Land Cover 2006 database administered by the Ministry of the Environment of the Czech Republic records industrial areas (Class 121), mining sites (Class 131) and waste deposits (Class 132). CENIA, state organization, administers the National Inventory of Contaminated Sites, i.e. a localized list of chemical burdens. CzechInvest, state organization, runs the Czech brownfields catalogue, which includes data on their original use and present state in the form of a site catalogue with geographic position. The Czech Geological Survey created a list of mining subsidence areas distinguishing mining subsidence sites (over 4 km2) and points (less than 4 km2). The Czech Statistical Office administers, among other things, the delimitation of cadastral areas of individual district towns. Areas of these towns may represent “urban PIL”, whose industrial heritage is significantly spread among other types of sites and their functions. Such sites were excluded from mapping. Similarly, the processing excluded such areas in cities over 50,000 inhabitants identified from published aerial photographs provided by GEODIS BRNO where the situation with the industrial heritage is similar to district capitals. Over 16,000 different georeferenced records describing the points and areas were used in the processing procedure. These consisted of 8,000 cases of recorded old chemical burdens, approx. 850 registered brownfields, approx. 250 large waste deposits and mine dumps, approx. 900 industrial areas, approx. 50 large-scale subsidence areas over 4 km2 and about 1,300 small-scale subsidence sites. These were used to identify 105 PILs in the Czech Republic. The PIL descriptions took into account 5,000 objects of industrial architectural heritage registered by the Research Centre for Industrial Heritage of the Czech Technical University in Prague. The methodology of PIL mapping and classification on the national level of the Czech Republic consists of successive steps using GIS technology: 1. Construction of a polygon layer from a point layer related to old chemical contamination by constructing 500 m buffer zones around individual points to delimitate a conventional zone of the given locality’s environmental impact. 2. Construction of a polygon layer from brownfields point layer by setting 500 m buffer zones around the points to delimitate the conventional zone of their environmental impact (visual or perceptive ones) of the given point. 3. Construction of a polygon layer from a point layer related to small-scale subsidence sites. The objective of this step is identical with the preceding ones. A 500 m buffer zone was used as well. 4. Formation of similar buffer zones along the outer edges of sites – polygons of large-scale mining subsidence areas (over 4 km2), mine dumps, waste deposits or industrial areas with brownfields. Other industrial areas without brownfields or without any contact with these were excluded from the processing. The 500 m buffer width symbolizes physical and visual impact of these objects. 5. Integration of buffer polygon layers around points and areas of all types of sites identified from the geodatabases. This step allows the combination and connection of sites representing all the PIL indicators into heterogeneous areas. 6. Filtering urban areas of over 50,000 inhabitants as well as district town cadastres of all sizes. Although these places may contain major concentrations of abandoned buildings and areas, the “postindustrial” sites are less significant here and do not tend to form PILs. Moreover, in comparison to concentrations of current residential, commercial and production activities, the 77

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area of industrial heritage is insignificant. 7. Elimination of small-scale areas according to conventional determination of minimum area of individual post-industrial landscape units covering 5 km2. In this understanding, areas of less than 5 km2 may be seen as “cores of postindustrial landscape”, while areas of 5 km2 and more may be classified as “post-industrial landscapes” at the national level. 8. Graphic simplification of areas identified in the preceding steps (frequently of highly bizarre shapes with jagged edges) using a suitable GIS tool (Simplify Polygon in ArcGIS), without significantly changing the resulting area and its surface shape (Fig. 1). 9. Genetic classification of PILs and assigning them to an 1 to 4-word classification type according to a predefined scheme. Thus defined PILs are classified according to dominant activities (from one to four ones) in order of their significance (area or point share in PIL identification indicators). Results and discussion A total of 128 cases were found of PILs in the Czech Republic. They form several major regional concentrations (Fig. 1). The most important is the “North-Bohemian post-industrial arch” from the extreme west of the Czech Republic to the Upper-Elbe River course in Eastern Bohemia. The second is the “Central Bohemian post-industrial belt” from Pilsen to Prague. The third is the „East-Bohemian – Central-Moravian post-industrial cluster" from the MiddleElbe River course in the west to the areas nearby Ostrava in the east. The fourth accumulation of PILs is the "South-Moravian post-industrial circuit" in the broad vicinity of the City of Brno in the south-eastern part of the Czech Republic.

Fig. 1 Czech Republic. Territorial distribution of post-industrial landscapes The strong point of this procedure is that it provides a clear PIL outlines, it is based on the use of generally available digital geodata, it uses a mapping methodology that produces a genetic PILs’ classification based on simple numerical features of each identified PIL.

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The application of this method can be repeated by using similar data everywhere. The data has shown that different indicators of PILs always occur together. Where one is found, the other indicators are usually present too, regardless they were not recorded in the databases. However, the field verification survey has showed their presence. The weakness of the procedure is undoubtedly the fact that it is based only on a part of the theoretically possible indicators of PILs (the other indicators are not covered by existing data). However here again, it is noted that all kinds of indicators occur together in clusters. The disadvantage of the procedure is the reliance on a thematically incomplete data set of indicators (e.g. brownfields) when their owners or administrators only releasing a limited selection to public use. The computer data processing was twice personally intervened by the investigator: 1. when determining the width of buffer (500 m) around the point and polygon indicators of PILs, and 2. when defining the minimum size of an individual PIL (5 km2). Expert opinions may differ, but here the final selection was based on published examples and inspirations from the past used in other contexts (Hladnik, 2005).

Fig. 2 Eastern Bohemia. Types of post-industrial landscapes

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The classified PILs were further classified in a GIS in terms of their "environmental value". This is meant to indicate their importance for the quality of the environment, both locally and in the wider (national) sense. The sites were classified according to their environmental values derived from the presence of various objects (e.g. heap dumps, objects of industrial architecture, anthropogenic landforms, etc.), rather than origin (economic sectors in the past). A total of 14 types of PIL were distinguished by their "environmental values". Figure 2 shows the example of NE Bohemia, where a total of 10 genetic types of postindustrial landscapes can be differentiated.

Fig. 3 Eastern Bohemia. Values of post-industrial landscapes In this part of the Czech Republic, 6 types of PIL "environmental value" (Fig. 3) were distinguished. A similar reclassification procedure in GIS was then undertaken for the identification of an initial management measure for each PIL. This was based on each PIL´s genesis and on the nature and value of the indicators (Fig. 4). The method described here represents PILs with standardized types of measures. In order to be useful it will necessary to specify properties of PILs (according to land use forms, extent of damage or injury, etc.) and according to these develop a framework for proposing measures for action at the local level. These can range from the proposal for a complete destruction of a PIL, through its adaptation for new industrial or non-industrial purposes, to its preservation as an open air museum for educational purposes. 80

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Fig. 4 Eastern Bohemia. Initial management measures in post-industrial landscapes Conclusion The results are intended to serve decision-makers and the broader public at the national level. The GIS methods have produced a clear delineation of individual PILs, genetically classified them, indentified their relationship to the environment (positive or negative value) and addressed initial management measures that need to be deployed. In the next phase of the study, it is necessary to consider detailed information on each PIL and develop management proposals for their future. This paper was compiled on the basis of research undertaken during the project "The fate of Czech post-industrial landscape" No. IAA 300860903 supported by the Grant Agency of the Academy of Sciences of the Czech Republic. References Cashen D., 2007. Redeveloping a North Florida Post-Industrial Landscape. Journal of Undergraduate Research 8: 3, http://www.clas.ufl.edu/ jur/200701/papers /paper_cashen.html Cílek V., 2002. Industriální příroda – problémy péče a ochrany. Případový problém: buštěhradská halda. Ochrana přírody 57: 313-316.

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Dunham-Jones E., 2007. Economic Sustainability in the Post-Industrial Landscape. In: Tanzer K., Longoria R. (Eds.): The Green Braid. Towards an Architecture of Ecology, Economy, and Equity, An ACSA Reader, Routledge, London, pp. 44-59. Fragner B., 2005. Postindustriální krajina (Porúří-Emscher Park). Vesmír 84: 178-180. Hansen H., Winter L., 2006. The Heterogenous (Post-) Industrial Landscape of Copenhagen: Location Dynamics and Divisions of Labour. In: Proceedings of the Sixth European Urban & Regional Studies Conference, 21st - 24th September 2006, Roskilde, pp. 1-26. http://www.byforskning.dk/publikationer/Siden%20publikationer/artikler/ Hogni20 Hansen0LarsWinther.pdf. Hladnik D., 2005. Spatial structure of disturbed landscapes in Slovenia. Ecological Engineering 24: 17–27. Keil A., 2005. Use and Perception of Post-Industrial Urban Landscapes in the Ruhr. In: Kowarik I. & Körner S. (Eds.): Wild Urban Woodlands. Springer, Berlin-Heidelberg, pp. 117-130. Kirk J., (2003): Mapping the Remains of the Postindustrial Landscape. Space and Culture 6: 178-186. Kirkwood N. (2001): Manufactured Sites. Rethinking the Post-Industrial Landscape. Taylor and Francis, London, 272 p. Kolejka J., et al., 2012. Postindustriální krajina Česka. Soliton, Brno. Kolejka J., 2006. Rosicko-Oslavansko: Krajina ve spirále. Životné prostredie 40: 187-194. Kučerjavij V. P., 1999. Urboekologija. Vidavnictvo Svit, Lviv, 359 p. Ling Ch., Handley J., Rodwell J. (2007): Restructuring the Post-industrial Landscape: A Multifunctional Approach. Landscape Research 32: 285–309. Lipský Z., 2011. Protichůdné tendence současného vývoje české venkovské krajiny a jejich důsledky: opuštěná půda a vznik nové divočiny v kulturní krajině. In: Kolejka J., et al.: Krajina Česka a Slovenska v současném výzkumu, Masarykova univerzita/Soliton, Brno, pp. 196-222. Loures L., 2008. Industrial Heritage: the past in the future of the city. WSEAS Transactions on Environment and Development 4: 687-696. Naveh Z., 1998. Ecological and Cultural Landscape Restoration and the Cultural Evolution towards a Post-industrial Symbiosis between Human Society and Nature. Restoration Ecology 6: 135-143. Rogers S., 2006. Forgotten Landscapes. Forgotten Landscapes Partnership, [cit. 2010-02-06] URL: www.forgottenlandscapes.org.uk/FL_ProjectBrief Aug06.doc. Shahid Y., Nabeshima K., 2005. Japan's Changing Industrial Landscape, World Bank Policy Research Working Paper No. 3758. URL: http://ssrn.com/abstract=844847. Stuczynski T., et al., 2009. Geographical location and key sensitivity issues of post-industrial regions in Europe. Environmental Monitoring and Assessment 151: 77–91,

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APPLICATION OF COMPLEX PROFILE METHOD IN INSULAR LANDSCAPE STUDIES IN ESTONIA Are KONT, Urve RATAS, Reimo RIVIS, Kadri VILUMAA, Agnes ANDERSON, Hannes TÕNISSON Institute of Ecology, Tallinn University, 5 Uus-Sadama, Tallinn, Estonia; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected] Abstract The method of complex profile or landscape transect has been used for nearly 100 years in landscape studies in Estonia. This method is one of the ways to analyse landscape patterns, to express the whole spatial structure of landscapes and to bring out interrelationships between their components. The method is preferred in specific comprehensive studies of two or more landscape components, most frequently describing the relations between soils and vegetation. The more variable the topography and relative heights, the more distinct and persistent are the landscapes. The method has been used also in insular landscape studies for many decades. Insular landscapes have been formed in land-sea interface, and their development is usually strongly influenced by the surrounding water bodies. The insular landscapes of Estonia are characterized by vertical zonation patterns caused by the extent and duration of seawater impacts. Iterative topographic surveys, measurements and analysis of the properties of different landscape components have made it possible to estimate the trends and velocity of changes in the landscapes of small islands. The method of complex profile is an appropriate tool for examining also the structure and dynamics of the zonal patterns in detail. Key words: landscape transect; landscape components; small islands; Baltic Sea. Introduction Landscape is a mosaics of landforms, water bodies, vegetation types and land use units depending on the variation of local and regional environmental conditions (Urban et al., 1987). Every landscape represents a system of closely interrelated components  abiotic and biotic  in a definite structural complex that is constantly enriched with new features in course of its development. The ancient topography and lithological compositions of bedrock, as well as accumulation or erosion and distribution of glacial deposits and landforms, have played an important role in contemporary landscape development (Puurmann et al., 2004). This is well reflected on landscape transects, giving an explicit picture of the relationships between different landscape components. Landscape transect is a graphic representation of a vertical cross-cut of a territory where the spatial extent, properties and relationships of natural components of landscapes are indicated (Kont et al., 1994). This method is one of the ways to analyse landscape patterns, to express the whole spatial structure and to bring out mutual relationships between landscape components based on the measured quantitative characteristics. The results can be directly used in addressing and solving ecological problems based on the information about natural processes and the effects of anthropogenic impacts. The profile line or transect connects a number of small (14 m2) test sites where specific studies are carried out including permanent observations and measurements and repetitive analysis to explore the dynamics of a natural system. The spatial distribution and the limits of 83

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different landscape components’ (deposits, soil, vegetation) taxonomic units usually do not coincide. The importance of a component in the formation and development of a landscape may also be different. The boundaries between neighbouring landscape units are rarely very strict and well-defined. Most often we face transitional zones, which are known as ecotones in classical ecology. These broader zones are usually characterized by more intensive matter and energy cycles. We can also find the components in certain landscapes, which have decisive role in the formation of the properties of some other components. As a rule, topography combined with geological structure determines the physical and chemical properties of soils, moisture conditions, and the structure and species composition of plant communities. The variety of components selected in complex profile studies depends on the goals and character of the research. The method is preferred in specific comprehensive studies of two or more landscape components, most frequently describing the relations between soils and vegetation (Buchter et al., 1991; Ratas et al., 1997). The method of complex profile was first used in Estonia by Eduard Markus (18891971) whose studies focused on spatial and temporal shifts of forest-mire boundaries (Markus 1925a, 1925b). Since the middle of the 20th century in the conditions of the Soviet occupation, the application of the method was particularly favourable due to the restrictions in the use of large-scale accurate maps in the whole Soviet Union. Exact measurements in the field and the compilation of high-accuracy landscape transects were the substitution to the lack of maps. An urgent need for comparison of the landscape transects compiled in different parts of Estonia led to the elaboration of a common methodology in cooperation with many researchers (Kink et al., 1988). The treatment of the data obtained from the field measurements along the transects but also from laboratory analysis of sediment and soil samples, make it possible to examine the structure and mutual relationships between different landscape components. For example, abrupt changes in the quantitative characteristics of certain components along the transect gradient show well-defined limits between their taxonomic units. At the same time, discordance in the properties of mutually related components (soil and vegetation) indicate a certain instability in the system, which might be caused either by excessive human impact or passing a stage of rapid natural development. The latter is quite often the case in the development of insular landscapes in Estonia due to tectonic land uplift and the emergence of new pieces of land from the sea. So far, the most in-detail analysis on the mutual relationships of soil-vegetation quantitative characteristics and their spatial changeability is from kame field landscape in continental Estonia (Makarenko, 1997). The method of complex profile with various degrees of accuracy has been applied in insular and coastal landscape studies over the last decades in Estonia. More simplified profiles have been compiled for describing the general natural structure of some larger islands and bringing out the role of different components in the formation and development of the islands. A lot of complex profiles have been compiled for studying the small islands (1–300 hectares in area) of Estonia, which have emerged from the sea during the last 3,000 years. As these landscapes are relatively young, the formation and development of different natural components and gradual reduction in seawater impact along topographic gradients are clearly observed. One important component controlling vegetation cover is the soil, and both vegetation and soil are influenced by topography, amongst other things (Solon et al., 2007). As the landscape variety of small islands is strongly dependent on topography, the routs of the transects have been selected following the highest topographic variety. In earlier stages of development when the islands are subject to direct seawater influence, they look quite similar with each other. In later stages, the regional differences are becoming much more apparent. The character and properties of soil parent material (both bedrock and

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Quaternary deposits) start to play important role in the formation of the soil taxa and plant communities and lead to a diverse structure of the landscapes (Ratas et al., 1997). Two clearly different belts on topographic gradients can be distinguished: 1) rapidly changing contemporary shore with prevailing natural processes and 2) inner parts with older beach ridges where natural processes have slowed down and most changes are caused by human activities (Kont et al., 2015). The landscape diversity of contemporary seashores is closely related to geomorphic shore types. Based on geology, the slope of the primary relief, and the prevailing shore processes, eight major shore types can be distinguished in Estonia: cliff shore, rocky shore, scarp shore, till shore, gravel-pebble shore, sandy shore, silty shore, and artificial shore (Orviku, 1992). The exposures to wave activity as well as the topography of the coastal seabed are also very important factors determining the character and velocity of changes on the seashores. The shores exposed to waves and storm surges are highly vulnerable and changing quickly. The boundaries between different landscape units on topographic gradient there are distinct and well defined. Flat and low-lying shores are usually regularly flooded in high sea-level conditions, and the structure of landscape units is dependent on the extent and duration of floods. The influence of saline seawater is first of all reflected in the species composition (the presence of halophytes) of the plant communities. The vegetation on shore meadows show a gradient inland, due to factors such as salinity and water content in the soil, water fluctuations and duration of flooding (Jerling, 1999). The areas in the zones of direct seawater impact are known as transwatery marine elementary landscapes with halophilous plant species and soils rich in Na+ and Cl– (Ratas et al., 1988). On higher elevations and at a distance from today’s shoreline, the effect of seawater is decreasing, showing a much lower content of sodium and potassium in topsoil (Ratas et al., 2014). Microtopography has also an important role in the formation of species composition, vegetation structure and geochemical functions, thus affecting the balance of plant nutrients in natural wetlands including seashores. The shore ecosystems in the Baltic Sea are characterized by rapid physical changes in both time and space (Strandmark et al., 2015). The complex profile method is one of the tools for observing and predicting the trends and magnitude of changes in coastal and insular landscapes. This is particularly evident in comparison with the initial and repetitive measurement and analysis results of certain characteristics from the same sampling points (Ratas et al., 2014). The time interval of repetitive measurements and analysis depends on the main goal of the research. A couple of decades ago, in the frames of the Estonian State Coastal Monitoring Programme, the field experiments were repeated at five-year intervals. But this time span turned out to be too short to define changes in well-developed plant communities at higher elevations of the islands. On geologically active and rapidly changing seashores, the time intervals for recurrent experiments should be shorter. The main aim of this paper is to introduce the complex profile method in studying the structure, functioning and trends of development of unstable and quickly changing landscapes like small islands in Estonia. Study sites The studied islands – Koipse and Vilsandi – are selected from different parts of Estonia (Fig. 1). Both small islands are located in the area of tectonic land uplift, and are of similar age (2,000–3,000 years) but their geomorphic and landscape structures diverge essentially due to their different area, geological structure and geographical location. Koipse (0.34 km2, 7 m a.s.l.) is situated in Kolga Bay (part of the Gulf of Finland), close to mainland Estonia. Koipse is formed on Cambrian sandstone covered with 5 to 10 m thick layer of Quaternary deposits. The major plant communities are heath meadows on primitive 85

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sandy soils of acidic reaction. The 190 m long Koipse transect crosses a system of parallel ridges of coastal formations that are sporadically covered with aeolian sands (Fig. 2).

Fig. 1 Location of study sites. Vilsandi (8.8 km2; 7.6 m a.s.l.) is located at the western border of the West-Estonian archipelago. The landscapes of the island are strongly influenced by the bedrock consisting of Silurian limestone and dolomite but also by lithological and mineral composition of glacial and marine sediments and the character of shore processes. The 2.2 km long transect crosses four principal parts of the island: (1) bedrock plateau; (2) depression in the bedrock filled with glacial deposits; (3) a system of beach ridges consisting of limestone shingle; and (4) marine accumulation plain made up of sands (Fig. 3). The parent material for the soil formation process within various types of landforms is highly heterogeneous. The diverse complex of soils is closely related to the variegated pattern of plant communities. Alvar grassland and forest predominate in the plant cover. Methods The complex profiles compiled for insular landscape studies consist of four principal parts: (1) profile line; (2) topographic belt; (3) tables of components’ syntaxa and numeric data; and (4) graphs of ecological indicator values, light interception and biodiversity indices. Profile line or transect is usually selected with the aim of crossing the most characteristic parts of the investigated islands, and connects a number of test areas where specific studies are carried out and can be repeated after a period of time at the same sampling points. Transects, as a rule, are not absolutely straight lines. They turn on the tops of positive landforms, in the middle of depressions and on the water bodies. The drawings of profiles are usually compiled in 1:2,000 horizontal and 1:200 vertical scales. For better visualization, a schematic picture of the vegetation types are drawn above the profile line and lithogenetic types of rocks and deposits marked with stratigraphic indices are drawn out just below the profile line. So, the profile line gives a basic impression of the studied landscape with relief, vegetation and geological structure up to 2 m depth. 86

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Fig. 2 Koipse complex profile of 1995.

Soil taxon: Kh – Rendzic Leptosol, Kk – Calcic Regosol Plant community: Ran2 – seashore meadow with Glaux maritima community, Ran8 – seashore meadow with Arrhenaterum elatior community, Lok1 – dry alvar meadow with Thymus serpyllum-Sedum acre community, Lok6 – dry alvar meadow with Festuca ovina-thymus serpyllum community, Lon1 – moist alvar meadow with Sesleria caerulea-Carex flacca community

Fig. 3 Vilsandi complex profile of 1995. 87

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Topographic belt is a 50 m wide topographic plan of the transect on either side of the profile line in its total length. Formerly, the topographic belts were delimited by plane table topographic survey. Differential Global Positioning System (DGPS) is used for that purpose today. The elevations are bound to local bench-marks and transferred to altitudes. All topographic elements are indicated on the belt and are marked by conventional signs. The locations and numbering of test areas and sampling points are also shown on the topographic belt as well as on the profile line. Table is the most important focal part of the complex profile describing the measured and analysed results, comprising geomorphological, soil and vegetation syntaxa and salient numeric data about the characteristic features of the syntaxa. The number of rows in the table depends on the aim of the research. Surface slopes and azimuths of the profile are usually presented in the two first rows. The next rows contain the data about landforms, rocks and deposits, water regime, soils and vegetation. The syntaxa of different landscape components is based on local classifications. The selection of variables characterizing the properties of the landscape components is closely related to the tasks of the research. For regular landscape monitoring, the number of necessary characteristics is much smaller than for specific scientific landscape ecological studies, for instance. Examination of Quaternary deposits is the principal part of geomorphological research made on the profiles. Two-metre pits (sufficient in landscape studies) are dug for mineral material examination, and bore-holes of various depths are drilled in organogenic sediments. The type of bedding (texture), prevailing granulometric and mineral composition are provisionally determined in the field. Sediment samples are often taken for a more detailed laboratory analysis. On the basis of morphometric parameters and lithogenetic types of the deposits lithomorphogenetic taxons of relief forms are determined and presented in the table. Peat species and their approximate rate of decomposition, based on the share of preserved organic remains, are determined and estimated in the field. Some peat samples, most interesting to dating because of their specific bedding conditions or content of charcoal are taken for radiocarbon (14C) dating. Much attention is devoted to the study of soils. Soil profiles are usually analysed in both geomorphic as well as special soil pits, which are additionally dug in case of a complex soil pattern. The main morphological features – genetic horizons and their succession in the soil profile, thickness, colour, texture, etc. are described in the field. Soil samples are taken for laboratory analysis. Topsoil (O-, A-and T-horizons) and subsoil (E-, B- and C-horizons) samples are taken separately. Humus content, humus supply, pHKCl, cation exchange capacity, specific surface area, content of organic carbon and total nitrogen, C/N ratio, and mechanical composition are analyzed or calculated from the obtained results. Analysis of vegetation is conducted to assess the relations between organic and inorganic components of nature. The study of vegetation on complex profiles has three main purposes: (1) describing the plant communities (Paal, 1997), making it possible to compare observations through several years; (2) investigating the correlations between the parameters of plant communities and the environment; (3) comparing different plant communities in similar habitats but with different history of development. The vegetation is described with the help of sample plots (Braun-Blanquet releves), characterizing all vegetation types along the transect. The number of sample plots is dependent on the structure of vegetation site types. For the description of dwarf shrub and herb, moss and lichen layers 2 m x 2 m plots are used. For the tree layer, measurements are made within a circle of approximately 0.1 ha in area (radius ca. 18 m). Adherence of tree crowns (in tenths), species composition, mean height and approximate age of trees are determined within the circle. The layers of dwarf shrubs and herbs, mosses and lichens are

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described at the species level. Total coverage of both layers and the percentage of the cover of each species are estimated separately. Different systems of classification are used in the determination of vegetation syntaxa. Graphs are indicating some properties of the most changeable factors (e.g. light, moisture) and ecological indicator values of plant communities in relation to these factors. The quantitative characteristics of the most changeable factors are either measured in the field or calculated after the fieldwork on the basis of the measured data. The descriptive stage of vegetation analysis is usually followed by further treatment of the data. Species diversity, characterizing indirectly the distribution of biomass between plant species, is a good indicator showing the complexity of the functional structure of the plant community (Magurran, 2004). The species diversity is calculated using the Shannon-Weaver diversity index (Whittaker, 1965). The ecological indicator value, which is another necessary variable of vegetation, is calculated by converting cover data with indicator values (from Ellenberg et al., 1991) to weighted averages. The ecological indicator values are calculated in relation to light, soil pH, moisture, nitrogen and salinity. The information received from the repeated calculations of these values indicates cumulative changes in the environment that cannot be easily recorded by instruments. The correlations between ecological indicator values and the corresponding measured environmental variables are also important: low correlations show a rapidly changing state of the landscape. The collected data are subjected to statistical analysis. We have also used cluster and correlation analysis. The obtained data in tables and graphs give a good overview about the changeability of the properties of different landscape components and their mutual relationships along the profile. The more abrupt changes in the quantitative characteristics of the components spatially coincide, the more distinct boundaries between the landscape units exist, and vice versa, the more the boundaries diverge in space, the broader transitions are evident. Thus, complex profiles help to compile more accurate large-scale landscape maps. Repetitive measurements and analysis reveal both spatial and temporal changes in the properties of landscape components indicating the trends of development of the landscapes, in general. Results The regional variation of the Estonian landscapes is well expressed also in the comparison of Vilsandi and Koipse islands (Ratas et al., 1997). Emergence of the islets in the West-Estonian archipelago (Vilsandi) opened the limestone and dolomites of the bedrock, which later have been subjected to both marine abrasion and accumulation. Areas where weathering and marine abrasion predominate exist in the form of bedrock plains whose limestone and dolomites are either directly opening on the surface or are covered with a thin layer of eluvial debris rich in alkaline minerals. Accumulation plains and ridges of coastal formations are made up of sand and gravel, which in places are rich in acidic minerals. As a result of different geomorphic preconditions, the spectra of soil and vegetation units reveal significant variety. The relationships between soils, topography and parent material on Vilsandi Island are well expressed by the results of cluster analysis based on soil pH, specific surface area, carbon and sodium content and carbon/nitrogen ratio (Fig. 4). Two major clusters can be distinguished. One of them includes all sandy (Podzols, Cambisols) and saline seashore (Salic Fluvisols) soil taxa and the other assembles the soils formed on limestone plateau (Rendzic Leptosols) and ancient beach ridges made up of calcareous shingle (Calcic Regosols). The subclusters do not differentiate very well (Kont et al., 2015). For instance, the only difference between very thin (Kh´) and thin (Kh´´) Rendzic Leptosols on limestone is in the thickness of 89

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the A-horizon, while all the other parameters have quite similar values. At the same time, some measured parameters within a single soil unit may considerably vary. For example, the humus content of very thin Rendzic Leptosol on limestone (Kh´) varies from 5.5 to 31.4%, and the specific surface area from 70 to 424 m2/g. The great variety of physical and chemical characteristics within a single soil unit can be explained by different geological preconditions, first of all by different granulometric composition of soil, but also by different complexes and proportions of clay minerals in the parent material. The vegetation taxa are clustering similarly to the soils showing a close relationship between them, although the limits do not always coincide. For example, within the limits of a single soil taxon more than one grassland or forest taxas may occur.

Fig. 4 Cluster analysis (Ward’s method) results of soils from Vilsandi complex profile. Soil taxon: Kh’ – Rendzic Leptosol (very thin), Kh’’ – Rendzic Leptosol (thin), K – Eutric Regosol, Kk – Calcic Regosol, Khg – Gleyic-Rendzic Leptosol, LkI – Cambic Podzol, Kg – Gleyic Cambisol, Gh – Calcaric Gleysol, Go – Mollic Gleysol, Gr – Salic Fluvisol (rarely flooded), Arg – Salic Fluvisol (frequently flooded) Plant community: Nõk – dry boreal heath meadow, Lok – dry alvar meadow, Lon – moist alvar meadow, Ran – seashore meadow, Ras – paludifing seashore meadow, ll – Arctostaphylos alvar forest/shrubland, kl – Calamagrostis alvar forest/shrubland, ph – Vaccinium vitis-idaea boreal forest, sü - meso-eutrophic boreo-nemoral hillock forest

The landscape pattern in Koipse reflects much more uniform ecological conditions than are typical of Vilsandi (Figs 2 and 3). These are due to a comparatively simple geomorphic structure of the islet. The deep lying Cambrian sandstone has no direct influence on the soil forming processes. Both marine and aeolian deposits are rich in acidic minerals such as quartz and feldspars, and poor in mineral nutrients available for plants. The main external factor affecting the ecological conditions of the sites is temporary inundation of the beaches. The soil and vegetation units on the Koipse transect are much better defined by their numeric characteristics than those on Vilsandi. The two soil types – Podzols and Arenosols – are clearly different from each other in all measured parameters. The Cambic Arenosols (Lo) that

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occur in the zones of temporary inundation have much lower values of humus content, humus supply and cation exchange capacity than the Cambic Podzols (LkI) with a well developed Ahorizon have. The plant communities in the higher central part of the islet are richer in species and of higher total cover of the field layer but of lower demand in relation to pH and nitrogen than the seashore communities on lower absolute marks. Tab. 1 Sample correlations (correlation matrix) between plant communities and topsoil variables of the studied transects. Vilsandi Ts-Hs Ts-N -0.13 0.06 EIV-L 0.02 0.33* EIV-M 0.12 EIV-pH 0.05 EIV-N - -0.14 -0.50*** 0.47*** 0.20 SW -0.18 -0.04 RLI-F Ts-SSA 0.42** 0.87*** 0.13 0.31* Ts-pH 0.45** 1.00 Ts-N 1.00 Ts-HS Koipse Ts-Hs Ts-N -0.47 -0.57 EIV-L 0.37 0.33 EIV-M -0.64 EIV-pH -0.60 -0.58 -0.44 EIV-N 0.66 0.43 SW 0.53 0.61 RLI-F Ts-SSA 0.82** 0.97*** -0.77* -0.81** Ts-pH 0.66 1.00 Ts-N 1.00 Ts-HS *** P 0.99 ** P 0.95

Ts-pH Ts-SSA RLI-F SW EIV-N EIV-pH EIV-M EIV-L -0.05 -0.16 -0.18 -0.08 -0.02 0.15 0.34* 1.00 -0.01 -0.07 0.07 -0.52*** 0.03 0.01 1.00 0.51*** 0.35* -0.08 0.39** 0.32* 1.00 0.13 -0.37** 0.21 -0.03 1.00 0.37*** 0.57*** -0.37** 1.00 -0.03 -0.31 1.00 0.42** 1.00 1.00

Ts-pH Ts-SSA RLI-F SW EIV-N EIV-pH EIV-M EIV-L 0.58* -0.62 -0.87*** -0.63** 0.65** 0.95*** -0.24 1.00 0.14 0.41 0.30 -0.04 0.40 -0.21 1.00 0.75** -0.71* -0.91*** -0.58* 0.75** 1.00 0.86*** -0.40 -0.61* -0.37 1.00 -0.31 0.54 0.29 1.00 -0.63 0.65 1.00 -0.77* 1.00 1.00

*P

0.91

EIV-L, ecological indicator value in relation to light; EIV-M, ecological indicator value in relation to moisture; EIV-pH, ecological indicator value in relation to pH; EIV-N, ecological indicator value in relation to nitrogen; SW, ShannonWiener diversity index; RLI-F, relative light interception in the field layer; Ts-SSA, specific surface area of the soil A-horizon; Ts-pH, pH of the soil A-horizon; Ts-N, nitrogen content of the soil A-horizon; Ts-HS, humus supply of the soil A-horizon.

The results of the correlation analysis of the data of both transects show a number of significant relationships between some ecological indicator values of plant communities and the respective measured parameters of their biotopes (Tab. 1). Essential differences occur between the transects in the significance of the sample correlations of the same pairs of parameters. Only one pair of the parameters coincide in high positive correlation at over 95% probability level on both transects. This is the pH value of the soil A-horizon and the ecological indicator value in relation to pH of the plant communities. The other strong correlations on the compared profiles diverge. For instance, significant positive correlation between the Shannon-Weaver diversity indices and topsoil humus supplies, and negative correlations between the diversity indices and ecological indicator values in relation to moisture as well as between topsoil nitrogen content and ecological indicator values in relation to nitrogen of the plant communities on the Vilsandi transect are evident. At the same time in Koipse, the most notable positive correlation appears between the ecological indicator values in relation to nitrogen of the plant communities and topsoil pH, and negative correlations between relative light interception in the field layer and ecological indicator

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values in relation to light and pH. In Vilsandi is worth mentioning the negative correlation between the nitrogen content of the soil A-horizon and the ecological indicator value in relation to nitrogen. The highest nitrogen content in the soil A-horizon was fixed in the sample plots where lichens have an essential coverage and which are poor in herbs. Finally, it can be said that landscape ecological investigations by complex profiles are a good tool for ascertaining structural and functional relationships between landscape compartments and estimating the state of landscapes. The method is particularly relevant in insular landscape studies because of the highly dynamic character and changeability of these areas. We have recorded quite substantial changes on the studied islands over the last decades. The northern part of Vilsandi and western part of Koipse transects have suffered from strong erosion. The shoreline positions have changed and the contemporary seashores have receded by 10 m. The biggest changes have taken place in the central parts of the islands. Due to the cessation of traditional human activities (mowing, grazing, etc.), the formerly widespread semi-natural plant communities like seashore and alvar grasslands have overgrown with shrubbery and even forest. The pines planted on Koipse Island in the 1980s have destroyed the former crowberry heaths. The open landscapes are disappearing, the biodiversity and recreation value are decreasing on the studied islands (Kont et al., 2015). Conclusion Assessment and forecast of global climate change impacts on landscapes and ecosystems has been a hot topic in recent decades. For that reason, the knowledge of the functional relationships between natural components and the factors influencing these relationships is crucial. Knowing the roles of natural processes versus human impacts is also very important in predicting reliable development scenarios of our environment. The better we know the mechanisms and processes shaping the environment, the better we can adapt to global changes. We have recorded quite substantial changes on the studied islands over the last decades. The shoreline positions have changed and the contemporary seashores have receded by 10 m on the studied islands. The biggest changes have taken place in the central parts of the islands. Due to the cessation of traditional human activities (mowing, grazing, etc.), the formerly widespread semi-natural plant communities like seashore and alvar grasslands have overgrown with shrubbery and even forest. The method of complex profile in landscape studies is still an efficient tool for examining and observing the factors and processes. Repetitive gradient studies on complex profiles with appropriate time interval are useful also in the frames of long-term monitoring programmes. The current paper is supported by the Estonian Science Foundation grants No. 9191, 7563, the target-financed projects No. 0282121s02 and SF0282119s02 funded by the Estonian Ministry of Education and Research and by the institutional grant ENCHANTED. References Buchter B., Aina P. O., Azari A. S., Nielsen D. R. 1991. Soil spatial variability along transects. Soil Technology 4, 3: 297314. Ellenberg H., Weber H. E., Düll R., Wirth V., Werner W., Paulissen, D. 1991. Zeigerwerte von Pflanzen in Mitteleuropa. Scripta Geobotanica 18: 1248. Jerling L., 1999. Sea shores. Acta Phytogeographica Suecica 84: 169–185.

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Kink H., Kont A., Raik A., Ratas U., Zobel M. 1988. Instructions for compiling landscape complex profile. Tallinn. (in Estonian). Kont A., Kull O., Rooma I., Makarenko D., Zobel M. 1994. The kame field ecosystems studied by landscape transects. In: Punning J.-M (Ed.), The influence of natural and anthropogenic factors on the development of landscapes. The results of a comprehensive study in NE Estonia. Inst. of Ecol., Publ. 2, pp. 161189. Kont A., Ratas U., Rivis R. 2015. Estonian landscapes studied on complex profiles. In: Järvet A. (Ed.),Year-book of the Estonian geographical society 40, Tallinn, pp. 60–78. Magurran, A.E., 2004. Measuring biological diversity. Blackwell Publishing, Oxford. Makarenko D. 1997. Kame field ecosystems under different levels of alkaline deposition. Master’s Degree Dissertation, University of Tartu, Institute of Geography. (in Estonian). Markus E. 1925a. Die Transgressioon des Moores über den Sandwall bei Laiva Sitzungsberichte der Naturforscher-Gesellschaft bei der Universität Dorpat 32(1/2): 8–14. Markus E. 1925b. Das Komplexenprofil von Jätasoo. Sitzungsberichte der NaturforscherGesellschaft bei der Universität Dorpat 32(1/2): 15–35. Orviku K. 1992. Characterization and evolution of Estonian seashores. Doctor`s Degree Dissertation. University of Tartu. Paal, J. 1997. Classification of Estonian vegetation site types. Tallinn. (in Estonian). Puurmann E., Ratas U., Rivis R. 2004. Diversity of Estonian Coastal Landscapes: Past and Future. In: Palang H., Sooväli H., Antrop M., Setten G. (Eds.), European Rural Landscapes: Persistence and Change in a Globalising Environment. Kluwer Acad Publ, Dordrecht, Boston, London, pp. 411–424. Ratas U., Puurmann E., Kokovkin T. 1988. Genesis of islets geocomplexes in the Väinameri (the West-Estonian Inland Sea). Acad. Sci. Estonian S.S.R. Department of Chemistry, Geology and Biology, Tallinn. Ratas U., Nilson E., Kont A., Puurmann E., Kokovkin T., Truus L., Kannukene L., Rivis R. 1997. Insular landscapes. In: Ratas U., Nilson E. (Eds.), Small Islands of Estonia. Landscape ecological studies. Institute of Ecology. Publication 5: 66–130. Ratas U., Roosaluste E., Rivis R., Kont A. 2014. Changes in landscape diversity in small islands of SE Hiiumaa. In: Järvet A. (Ed.), Year-book of the Estonian geographical society 39, Tallinn, pp. 51–70 Solon J., Degórski M., Roo-Zielinska E. 2007. Vegetation response to a topographical-soil gradient. Catena. Soil Water Erosion in Rural Areas, 71, 2: 309-320 Strandmark A., Bring A, Cousins S.A.O, Georgia Destouni G., Kautsky, H., Gundula Kolb G., de la Torre-Castro M., Hambäck P.A. 2015. Climate change effects on the Baltic Sea borderland between land and sea. Ambio 2015 Jan; 44 (Suppl 1): 28–38. Urban D.L., O'Neill R.V., Shugart H.H. 1987. Landscape ecology. BioScience 37: 119127 Whittaker R.H. 1965. Dominance and diversity in land plant communities. Science 147: 250260.

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FORECASTING OF THE DYNAMICS OF BEECH AND FIR FORESTS OF THE POLISH BIESZCZADY AND THE UKRAINIAN BESKYDY UNDER THE INFLUENCE OF CLIMATIC CHANGES Ihor KOZAK1, Kajetan PERZANOWSKI2, Taras PARPAN3, Piotr KOCIUBA1, Daniel KLICH2 1

Department of Landscape Ecology, John Paul II Catholic University of Lublin, Lublin, Poland; [email protected]; [email protected]; 2 Department of Applied Ecology, John Paul II Catholic University of Lublin, Lublin, Poland; [email protected], [email protected]; 3 The Ukrainian Research Institute of Mountain Forestry, Ivano-Frankivsk, Ukraine; [email protected] Abstract The study concerned forecasts for the dynamics of beech (Fagus sylvatica L.) and fir (Abies alba L.) stands in the Polish Bieszczady and the Ukrainian Beskydy Mountains. Study plots were set at forestry Procisne of Stuposiany Forest District (49º11’23’’N, 22º38’39’’E) in Poland, and at Jablunetske forestry of Nadsyansky Regional Landscape Park (49º09’47’’N, 22º45’15’’E) in Ukraine. Beech and fir stands in these regions were actively managed in last several centuries. After the World War II, tree stands at Bieszczady were less intensively exploited, but in the Beskydy part, belonging at that time to Soviet Union, the management of the forest was very intense. Therefore, neighbouring areas with the same ecological conditions have now different structure of tree stands and different accumulation of tree biomass. We forecasted potential changes in the biomass and the number of trees per area unit for next years with software FORKOME. We analysed a control scenario without climatic changes compared with four variants of changes: 1- warm and humid, 2- warm and dry, 3 – cold and humid and 4 - cold and dry. Obtained results confirmed a cyclic tendency for changes in the structure of tree stands, i.e. an exchange of proportions between beech and fir biomass. Temperature variation will not affect the direction of those changes. However they may disturb their rate and the proportion of admixture species. According to this prognosis, lower tree biomass can be expected at The Beskydy comparing to stands of Bieszczady. The output of those simulations is supported with field and literature data. Results of this study can be applied in long term forest management planning in the region. Key words: beech; fir; stands; biomass; prediction; temperature; precipitation. Introduction The analysis of succession in forest ecosystems has been of interest for ecologists for a long time (Clements, 1916). New perspectives have appeared due to a possibility of application the mathematic modelling, allowing for quantitative description of dynamics of forest ecosystems (Dale et al., 1985). Since 1970s, numerical computer technologies made possible for elaboration of individual-based tree models (Bugmann, 2001). The first ecological application of the model (Botkin et al., 1972), has become one of the citation classics in forest succession modelling. Recently, the gap models belonging to ecological models of processes were created in numerous centres of forest research. Those models consider the state of the forest and its changes that are resultants of natural processes and

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applied management measures. Because of stochastic character of process models, to obtain credible results, numerous repetitions of simulations are necessary (Bugmann et al., 1996). Alterations of beech and fir stands can be caused by climatic changes. According to forecast formulated under the framework of International Panel of Climatic Changes (IPCC), until the end of 21st century, a considerable increase of mean annual and quarterly temperatures, the sum of precipitation, and significantly more frequent extreme phenomena are foreseen (Lindner et al., 2010). In the zone of moderate continental climate, there is forecasted an increase of average temperature by the end of the century for about 2-3°C in spring and 3-4°C in remaining seasons of the year, as well as a change in the amount of precipitation - a decrease by about 10% in summer and an increase by about 10% in winter. Those changes will be accompanied with an increase of wind velocity, a decrease of cloudiness, an increase of frequency of torment precipitation, a drop in the length of the persistence of snow cover, and an increase of minimal air temperature, in winter even by 815°C (Räisänen et al. 2004). Diversified reactions of particular species for changes occurring in environment are main causal factors for structural alterations of tree stands. Climatic changes in Europe may induce shifts in the area of occurrence of many tree species (Brooker et al., 2007). Beech associations in Europe have proved to be susceptible for transformations resulting from climatic changes (Dale et al., 2010). The aim of this paper was to present results obtained with FORKOME model regarding the prediction of future development of beech and fir stands in the Polish Bieszczady and the Ukrainian Beskydy under various scenarios of climatic changes, to test the competitive relationships between beech and fir, and assess an influence of a change in the sum of effective temperatures and precipitation, upon the character of mutual relationship between both species. Materials and methods The present state of species composition of forest stands and their biomass, being a material of our studies in the Polish Bieszczady and the Ukrainian Beskydy, results from historical events. Depopulation of landscapes of the Polish Bieszczady resulting from the deportation of Ukrainians (Gil, 2004) in 1944-1947, has changed the character of this landscape, and caused a decrease of anthropogenic influence upon forests of this region. Effects of spontaneous secondary succession were reinforced by reforestation of former agricultural land at the area of abandoned villages, fields and pastures. Such was an origin of a considerable part of present forest cover: 34-36% in Baligród Forestry, 24% in Cisna Forestry, 35% in Komańcza Forestry, 45% in Lutowiska Forestry, 32% in Stuposiany Forestry, and 41% in Ustrzyki Dolne Forestry (Gielarek et al., 2011, Marszałek, 2011). However in the part of the Ukrainian Beskydy the anthropogenic influence upon forests had increased. In an effect, visible now is not only a difference in the proportion of forest cover between those neighbouring regions, having very similar ecological conditions (present maximal percentage of forests in Polish forest districts reaches 80%, while in the Ukrainian Beskydy only 47%), but also a difference in medium age of tree stands (78 years in Polish part and 56 in Ukraine), as well as the accumulation of biomass (on average over 300 m 3/ha and about 200 m3/ha in Polish and Ukrainian parts respectively) (Marszałek, 2011, Tretiak et al., 2011). For this analysis we selected 4 study plots (2 in the Polish Bieszczady and 2 in the Ukrainian Beskydy). Forest taxation parameters of selected plots confirm this visible tendency being a result of former changes in forest management. Standing crop of biomass in the zero year of the prognosis at plots „Beech 1” and „Fir 1” in the Polish part is higher comparing to values at plots „Beech 2” and „Fir 2” in Ukrainian part.

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Study plots – „Beech 1” and “Fir 1” were situated at forest compartment no. 13a of Stuposiany Forestry (49º11’23’’N, 22º38’39’’E) in the Polish Bieszczady. Study plots – „Beech 2”and „Fir 2” were situated at forest compartment no. 3 of Jablunetske Forestry in Nadsyansky Regional Landscape Park (49º09’47’’N, 22º45’15’’E) in Ukraine. The mean DBH (diameter at breast height) for the plot „Beech 1” was 48,8 cm (max 120, min 6, standard deviation 38,2), for „Beech 2” – 28,7 cm (max 82, min 4, standard deviation 27,8), for the plot „Fir 1” – 43,2 (max 72, min 7, standard deviation 16,2), for the plot „Fir 2” - 17,2 (max 38, min 6, standard deviation 8,7). All those plots were facing east, and the angle of the slope was 5–9° there. Statistical analyses of measurement of trees at studied plots were performed with STATISTICA.10 software. In all plots, DBH values displayed a normal, right-skewed distribution (Shapiro-Wilk test). Each study plot was a quadrate 50×50 m. According to literature, such size of a study plot (2500 m2) is appropriate for gap models (Shugart, 1984). Coordinates of particular trees (x,y,z) at studied plots we measured with GPS, their parameters (DBH, H- height, A- age) - with callipers, increment borer, and height meter LeissBL8, chemical reaction of soil and the litter with pH meter CP-401, and insolation with photometer LX-108. In order to estimate leaf area index (LAI), we took hemispherical photographs at every studied plot with camera Canon EOS 5D 12MP, Sigma 8mm f/3,5 DG FISH EYE lens with 180° viewing angle. Hemispherical photographs we analyzed with Gap Light Analyzer software (Frazer et al., 2000). Obtained data we stored in .csv format and subsequently supplied to the FORKOME model. In the FORKOME model, there is a possibility for simulation of unrestricted time periods (from short ones, serving for verification of management measures up to long time spans - for analysis of life strategies of tree species). In this paper conducted was a prognosis for next 500 years. That allowed for an analysis of cyclic changes between beech and fir, which life cycles are estimated for over 350 years (Piovesan et al. 2005). We obtained the climatic data (monthly values of average air temperature and its mean, and absolute minimal and maximal values as well as monthly sums of precipitation) from meteorological station situated possibly close to study area. Those climatic data allowed for introduction to the model FORKOME the simulation of climatic changes in a control variant – in present conditions and according to four hypothetical future scenarios, assuming an increase or decrease of mean annual temperature (by 2°C and precipitation by 200 mm per year): (1) warm and humid, (2) warm and dry, (3) cold and humid and (4) cold and dry. The original FORKOME model applied in this study, belongs to the group of gap models, and was verified for the conditions of the Carpathians (Kozak et al., 2012). Basic objects in FORKOME model are: the „Area” – representing actual, studied gap and „Tree” – representing particular trees. The model we constructed with an aim to simulate the dynamics of forest stands considering the fate of single trees (Fig. 1). In the model we applied a statistical method "Monte-Carlo", allowing for simulation up to 200 variants in every scenario. After performing the Monte-Carlo analysis, the program estimates the average number of trees and their mean biomass, and the standard deviation of those values per every year of simulation. After completion of all variants, the model performs a statistical analysis verifying obtained results (block STATISTICAL PROCEDURES), which includes not only the calculation of mean values and their standard deviation, but also serial calculations and cross-correlation functions (Kozak et al., 2014). We analysed all forested areas using the FORKOME model, considering the actual increase of DBH during the year of simulation (so-called DINC).DINC means an optimal increase of tree DBH in optimal conditions of environment. All traits of studied area can be modified in the model from the level of interface. In the model, the growth of a tree depends

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on the species and dimensions already achieved by the specimen in relation to maximal potential dimensions (Hmax, DBHmax and Amax values are: 60 m, 150 cm, 400 years for fir, and 45 m, 150 cm, and 300 years for beech). In annual growth of a tree, we considered an influence of external conditions through a parameter DINC. . The actual increase of trees DINC has to take into account environmental conditions (annual sum of temperatures effective for vegetation; water balance connected with precipitation and transpiration; an average level of light availability in the forest; availability of nitrogen in the forest litter) which may reduce an optimal increase of DBH. Depending on dominating environmental conditions, every factor decreasing the optimal growth may take values between 0 and 1. Every reducing factor is represented by an appropriate thematic block.

Fig. 1 Algorithm of FORKOME model Thermal conditions are in the model determined by the annual sum of effective temperatures (border values of the sum of effective temperatures DGD min and DGD max – are: 400°C and 2000°C for fir, and 400°C and 2750°C for beech), and temperature coefficient limiting the growth of a tree (Botkin, 1972). FORKOME model, while calculating an amount of light reaching a given tree, considers the loss of solar radiation caused by summarised shading by leaves of taller trees. The method for calculation the area of leaves we corrected by measurements of leaf area index (LAI). FORKOME model considers also the transpiration of tree leaves depending not only from meteorological parameters like in other gap models, but also from the species of a tree (Shugart, 1984). Rejuvenation of trees (a maximal number of young trees of a given species) is limited by a random factor, and by amount of light available at forest floor. Mortality of trees depends on age, minimal increment and external factors. 97

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The FORKOME model was already used in scientific projects in various regions of Poland, Ukraine and Sweden (Kozak et al., 2012, Kozak et al., 2014). The model has been supplemented with a risk factor reflecting a chance for the occurrence of forest fire. Important there, are climatic factors: the air temperature and amount of precipitation in particular (Kozak et al., 2014b). The model we also extended with additional blocks (logging, drying out of trees, and tree planting). Model FORKOME contains also continuously developed presentation layer, which allows for 3D visualization of components of the forest landscape, including the layer of ground vegetation, dried up, and fallen trees. Results and discussion Prognostic simulations with FORKOME model, show in the control scenario at the plot „Beech 1” in the Polish Bieszczady a short increase of beech biomass (Fig. 2a), followed by domination of fir (from 88 until 203 year of prognosis), and subsequently again the domination of beech. In the control scenario, cyclical changes between beech and fir are more visible in the Polish Bieszczady than in the Ukrainian Beskydy. In the Ukrainian Beskydy the biomass of beech at plot „Beech 2” will be dominating through the whole simulated period (Fig. 2b). Apart of beech and fir at both studied plots we foreseen insignificant (up to 70 t/ha) share of sycamore and spruce at the end of simulated period. a) b)

Fig. 2 Dynamics of tree biomass in the control scenario at „Beech” plots: a- Beech 1; b- Beech 2 At „Fir” plots, at which similarly like at „Beech” plots, at the beginning of the prognosis the biomass in the Polish part was higher than in Ukraine, we forecast an increase of fir biomass during 70 years in the first plot (Fig. 3a) and during 100 years at the second (Fig. 3b). Subsequently, the biomass of fir will decrease, and after 250 – 270 years plots will become dominated by beech. In the second half of simulated period, especially during the change of domination from fir to beech, the biomass of spruce increases insignificantly, to decline however by the end of simulated period. The model is also forecasting an appearance of pioneer species (birch, aspen) in the middle of simulated period, but their share in tree

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biomass is very small, and therefore not visible at the graph. At the end of simulated period possible is also an increase of sycamore (Fig. 3b) biomass. a) b)

Fig. 3 Dynamics of tree biomass in the control scenario at „Fir” plots: a- Fir 1; b- Fir 2 According to simulations at the plot „Beech 1”, some insignificant cyclic changes of biomass between beech and fir may occur. They are mostly visible for smaller patches, and changes at larger patches covered by beech, finally lead to the domination of this species. In conditions of possible climatic changes in the first scenario (warm and humid) at „Beech” plots notable was an increase of fir biomass, while in the second scenario (warm and dry) possible increase of beech biomass. In the third scenario (cold and humid) as well as in the fourth (cold and dry) the biomass of beech decreases in the second half of simulated period to less than 200 t/ha while the biomass of fir will grow up to 320 t/ha. Simulations of „Fir” plots in the scenario 1 show on average an increase of fir biomass up to 100 years, followed by it possible decrease and exchange of domination with beech. In scenario 2, an increase of fir biomass will be smaller and domination of beech will increase in the second half of simulated period up to 650 – 700 t/ha. Also the biomass of sycamore may build up to 75 t/ha at plot „Fir 1”. In scenario 3, fir will maintain its dominating position, with slight decrease in the second half of simulated period down to 200 t/ha at plot „Fir 1” and 260 t/ha at plot „Fir 2”. Additionally, a growth of spruce biomass is possible in the second half of simulated period (up to 200 t/ha). Fir will also dominate in the fourth scenario. In the second half of simulated period its biomass will increase up to 220 t/ha at plot „Fir 1” and up to 290 t/ha at plot „Fir 2”. Also the spruce biomass may increase in the second half of simulated period up to 200 t/ha. The main important difference between both study areas was the initial biomass (in zero year of simulation), which was 2.5 times higher per area unit in the Polish Bieszczady comparing to the Ukrainian Beskydy, due to a higher age of beech and fir stands in Polish part of study area. According to our prognosis, the tendency for changes in stand biomass was more similar at both fir studied plots than at both beech ones. For example, at both fir studied plots as was shown in control scenario during next 100 years expected is an increase of fir biomass followed by its decrease during next 200 years and its gradual replacement in domination by beech.

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The FORKOME model is a good enabler to obtain results that prove the thesis assumed in our paper. We carried out simulations with the FORKOME model in research areas of 50×50 m (1/4 ha). Therefore, within of the 2500 m2 of the area, 200 used simulation sequences correspond with equilibrium landscape of 50 ha. In other types of models, for instance the JABOWA model (Botkin, et al. 1972), 10×10 m areas were used with 100 simulations. The smallest area was 0,5 ha and was used in the FORSKA model (Prentice, Leemans, 1990).Simulation conducted in FORKOME model confirms that beech will exist in the Polish Bieszczady and the Ukrainian Beskydy regions on the east boundary of beech areal. Using computer models for such type of analysis is a current aimed perspective trend (Brooker et al., 2007). Important is the analysis of temperature and precipitation influence on beech and fir stands. Climate changes in the Carpathians can be estimated as a positive factor for forest productivity and biomass accumulations. Similar results were obtained for north and west Europe (Lindner et al., 2010). Beech stands in Carpathians have plausibility and delay in stand composition changes due to climate changes what was shown in the literature (Dale et al., 2010). Longer time of simulation (500 years) was used to show the cycle of biomass changes that were demonstrated in the literature (Shugart, 1984). In the other regions, the simulation time was 500 and 600 years (Kozak, Menshutkin, 2001). The changes in beech stands biomass in computer simulations with the use of FORKOME model define their directions regarding possible climate change conditions in the Carpathians and are important in forest management, both from the theoretical and practical perspective. Dynamics of forests changes in mountainous areas have a multitude of functions. Forests in the Carpathians have been managed for timber production over several centuries. After World War II, the forest use in the Polish Bieszczady was less active than in the Ukrainian Beskydy, what influenced on the future biomass accumulation. In both compared parts of the region, due to present management measures and spontaneous secondary succession, the foreseen change of tree stands at studied plots aims towards typical habitat for this region - climax forests dominated by beech with an admixture of fir, sycamore and spruce. Only the character of those changes will be different in Polish and Ukrainian parts, which is connected with different present state of tree stands, reflecting different management measures in the past. Conclusion In our study areas, the beech will maintain their domination regarding biomass in control scenario on beech stands and in the second half of simulated period on fir stands. Same cyclic changes between beech and fir are shown at the beech plot in the control scenario in Bieszczady. At beech plots in warm humid and warm dry scenarios, FORKOME model predicts possible weakening of cyclical changes between beech and fir biomass dynamic and increase in beech biomass. In cold humid and cold dry scenarios fir biomass increased and beech biomass decreased. At fir plots in the control variant, depending on initial stage, we forecasted the domination of fir for the first half of simulated period, followed by the domination of beech. In scenarios 1 and 2, the model shows an increase of fir biomass during first 100 years of simulation, followed by its decrease and change of domination towards beech. At beech plots in scenarios 3 and 4, the model predicts an increase of fir and its domination. Simulation conducted in FORKOME model confirms, that beech will continue to exist in the study regions at the eastern boundary of beech area of occurrence. Climate changes in the Carpathians can be seen as a positive factor for forest productivity and biomass accumulation. 100

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Changes in beech and fir stands biomass in computer simulations with the use of FORKOME model define their directions regarding possible climate change conditions in Carpathians, and are important issues in forest management, both from the theoretical and practical perspective. We would like to appreciate Polish Ministry of Science and Higher Education for financing research projects: No. N N 309 014638 and No. N N309 165937; Prof. Vasyl Parpan, Dr Jurij Szparyk, Dr Roman Viter from Institute of Mountain Forestry in Ukraine for assistance in data collection and consultations. References Botkin D.B., Janak J.F., Wallis J.R., 1972. Some Ecological Consequences of a computer Model of Forest Growth. Journal of Ecology 60, 3: 849–872. Brooker R., Travis J., Clark E.J., Dytham C., 2007. Species’ range shifts in a changing climate: The impacts of biotic interactions, dispersal distance and the rate of climate change. Journal of Theoretical Biology 245: 59−65. Bugmann H., 2001. A review of forest gap models. Climate Change 51: 259–305. Bugmann H., Fischlin A., Kienast F., 1996. Model convergence and state variable update in forest gap models. Ecological Modelling 89: 197–208. Clements F.E. 1916. Plant succession: An analysis of the development of vegetation. Washington, DC: Carnegie Institute. Washington Publ. No 242: pp.1–512. Dale V.H., Doyle T.W., Shugart H.H., 1985. A comparison of tree growths model. Ecological modelling 29: 145–169. Dale V.H., Tharp M.L., Lannom K.O., Hodges D., 2010. Modelling transient response of forests to climate change. Science of the Total Environment 408: 1888–1901. Frazer G.W., Canham C.D., Lertzman K.P., 2000. Gap Light Analyzer (GLA), Version 2.0: Image processing software to analyze true-colour, hemispherical canopy photographs. Bulletin Ecological Society of America 81: 191–197. Gielarek S., Klich D., Antosiewicz M. 2011. Forest cover change in Western Bieszczady Mts. in 19th and 20th century. Sylwan 155, 12: 835-842. (in Polish). Gil A. 2004. The deportation of Ukrainians from Poland in the years 1944-1946 as a problem in contemporary Polish-Ukrainian relations. Institute of East-Central Europe Press, Lublin, pp.1-30. (in Polish). Kozak I., Menshutkin V., 2001. Prediction of beech forests succession in Bieszczady Mountains using a computer model, Journal of Forest Science 47, 8: 333–339. Kozak I., Mikusiński G, Stępień A., Kozak H., Frąk R., 2012. Forest dynamics in a nature reserve: a case study from south-central Sweden. Journal of Forest Science 58, 10: 436– 445. Kozak I., Perzanowski K., Kucharzyk S., Przybylska K., Zięba S., Frąk R., Bujoczek L., 2014. Perspectives for the application of computer models in forest dynamics forecasting in Bieszczadzki National Park (Poland). Ekológia (Bratislava) 33, 1: 16–25. Kozak I., Węgiel A., Strzeliński P., Frąk R., Stępień A., Kociuba P., Kozak H., 2014b. FORKOME model application for prognosis of forest fires. Ekológia (Bratislava) 33, 4: 391–400. Lindner M., Maroschek M., Netherer S., Kremer A., Barbati A., Garcia−Gonzalo J., Seidl R., Delzon S., Corona P., Kolström M., Lexer M. J., Marchetti M., 2010. Climate change

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impacts, adaptive capacity, and vulnerability of European forest ecosystems. Forest Ecology and Management 259, 4: 698−709. Marszalek E. 2011. The forest management in the Carpathian part of Regional Directorate of the State Forests in Krosno and its influence on the protection of nature. Roczniki Bieszczadzkie 19: 59–77. (in Polish). Piovesan G., Di Filippo A.; Alessandrini A.; Biondi F., Schirone B., 2005. Structure, dynamics and dendroecology of an old-growth Fagus forest in the Apennines. Journal of Vegetation Science 16: 13–28. Prentice I. C., Leemans R., 1990. Pattern and process and the dynamics of forest structure: a simulation approach. Journal of Ecology 78: 340–355. Räisänen J., Hansson U., Ullerstig A., Döscher R., Graham L. P., Jones C., Meier H. E. M., Samuelsson P., Willén U. 2004. European climate in the late twenty−first century: regional simulations with two driving global models and two forcing scenarios. Climate Dynamics 22: 13−31. Shugart H.H., 1984. Theory of Forests Dynamics. New York, Springer, pp.1–278. Tretiak P., Pozynycz I., Sawitska A., 2011. Secondary succession of mixed forests in the Precarpathian Hills (Ukraine). Roczniki Bieszczadzkie 19: 47–59. (in Polish).

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UKRAINIAN PART OF THE DANUBE DELTA: LANDSCAPE CHANGES ISSUES Anna KOZLOVA1, Tamara DUDAR2, Mykhailo SVIDENYUK2 1

Scientific Centre for Aerospace Research of the Earth National Academy of Sciences of Ukraine, Kyiv, Ukraine; [email protected] 2 National Aviation University, Kyiv, Ukraine; [email protected]

Abstract Landscape changes for the period from 1985 to 2014 within the territory of the Ukrainian part of the Danube Delta were researched using satellite images from Landsat different missions. The classification of the landscapes was made. Two landscape maps of the Danube Delta were produced by performing a supervised classification and applying a Support Vector Machine algorithm. Key words: landscape; landscape change; multispectral images. Introduction As it is known, about 20 % (1,128 km2) of the Danube Delta – the major European wetland – is situated in Ukraine. High richness of the delta's wildlife, the natural state and processes which still take place at a large scale have a significant importance for nature conservation. The most important functions of the delta are buffer function, maintaining the climate in the area, maintaining the high biodiversity and high biological productivity (Davis et al., 2000). The Danube's wetlands and floodplains also provide local people with valuable ecosystem services. Climate change has also to be noted as inevitable environmental problem led to extinction of some species of flora and fauna listed in the Red Book of Ukraine. There is a general degradation of the fish fauna of the Danube Delta, associated with anthropogenic impacts and loss of spawning habitat for native fish species (Shekk, 2003). State-of-art Large areas of the delta floodplain on the territory of the former Soviet Union were heavily drained for industrial farming between the 1950s and the mid-1980s. Natural hydrological processes and retention areas of the Delta were interrupted and led to a reduction in runoff of sediment in the delta more than 2 times, particularly in Kiliya delta from 62.7 to 29.7 million tons (Lihosha et al., 2004). After 1990, the transition from a communist system to the centralised market economy led to significant restructuring processes of agricultural land, entailing a different type of land use, deterioration of irrigation systems and land degradation along some sectors of the alluvial Danube Plain (Corbu, 2012). All mentioned above, among others, have led to significant changes in landscape pattern. V. Starodubtsev and V. Stryk described landscapes changes in the Ukrainian part of the Danube Delta using satellite images from Landsat-2 and Landsat-5 in their popular scientific publication “Danube Delta: View from Space” in 2013. They stated that the area of hydromorphic landscape complexes (terrestrial, hygrophytic and hydrophytic ecosystems) was increased by 16,300 ha for the period 1977-2009, or about 500 ha per year. The causes of this phenomenon are: intensive agricultural activity and soil erosion, natural widening of the Delta into the Black Sea as well as overgrowing of lakes and channels with wetland vegetation.

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It is notable, that in the Bystroe channel mouth the Deep-water Navigation Pass DanubeBlack Sea was built. The area of shallow water was increased on 104.9 ha and a small island was formed because of deepening the channel for navigation, the flow accelerating and sediments inflow into the sea. The channel-clearing led to the reduction of the marshes area and increase of the area of riverine levees with thickets of hydrophytes, shrubs and sparse trees. Having regard to the above, the authors set a goal to investigate landscape changes within the territory of the Ukrainian part of the Danube Delta for the period from 1985 to 2014 using satellite images from Landsat different missions (Landsat Project Description, 2013). For this purpose the classification of the landscapes according to the CORINE land cover classification system has to be made, and two landscape maps of the Danube Delta have to be produced by performing a supervised classification and applying a Support Vector Machine algorithm. Materials and methods In the present study the Landsat-TM (acquired on 8 July 1985) (Fig. 1) and Landsat-OLI (acquired on 8 July 2014) (Fig. 2) multispectral images were used to obtain spatial distribution of the main landscapes of the Danube Delta.

Fig. 1 Landsat-TM5 (of 8 July 1985) Wavelengths 1.676, 0.84 and 0.66 µm in the Red, Green and Blue channels

Fig. 2 Landsat-OLI8 (of 8 July 2014) Wavelengths of 1.609, 0.86 and 0.65 µm in the Red, Green and Blue channels

Border of “DunayskiPlavni” reserve Multi-angle hyperspectral Proba-1/CHRIS data was applied to fill up the gap in ground ground-truth data (Fig. 3). Observations of wetlands and floodplains areas are taking advantage from the PROBA acquisitions due to the CHRIS spectral and spatial resolution features (Barducci et al., 2009). Pre-processing is especially critical in change studies because the detection of change assumes that the spectral properties of non-changed areas are stable, and inadequate preprocessing can increase error by causing false change in spectral space. In order to avoid such 104

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unwanted errors, radiometric and atmospheric corrections were performed for the both images.

Fig. 3 Proba-1/CHRIS at Nadir from 11 March 2005 false color composite using bands at wavelengths of 0.745, 0.664 and 0.563 µm in the Red, Green and Blue channels

For the current study, the Danube Delta landscapes were subdivided into ten classes in accordance with widely adopted CORINE land cover classification system (Tab. 1). Two landscape maps (Fig. 4, 5) were produced by performing a supervised classification and applying a Support Vector Machine algorithm. The validity of any analysis of landscape pattern changes depends on the classification accuracy of the landscape map. Classification accuracy is the extent to which a manual or automatic processing system identifies correctly selected classes. Overall classification accuracy of the landscape map 2014 comes to 90%. Since reference data were available only for 2005-2013, an overall classification accuracy of the landscape map 1985 is less and makes 82%.

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Tab. 1 The landscapes of the Danube Delta Class

5. Sparsely vegetated areas

Class Description Low-lying land usually flooded in winter and more or less saturated by water all year round. The reed plats and floating reed islands consisting of common reed (Phragmites communis), and near river banks mace reed/cattail (Typha latifolia, Typha angustifolia), sedge (Carex dioica, Carex stricta), Dutch rush (Scirpus radicans, Scirpus lacustris), brook mint (Mentha aquatica) etc. Reedbeds and other water-fringing vegetation by lakes substantially submerged Floating and submerse flora (Myriophyllum, Ceratophyllum, Vallisneria etc., floating plants with roots near the lakes borders; Salvinia natans, Stratiotes aloides, Spirogyra etc.). Woodland, forest and plantations dominated mainly by (Pinus) On the firm land of the delta and the riverbanks kept in natural state, groves of willow trees (Salix alba, Salix fragilis, Salix purpurea, Salix petandra, Salix triandra etc.) mixed with white poplar (Populus alba). Occasionally, the willow trees form corridors along the Danube arms and bigger channels. Herbaceous vegetation in pastures and hay meadows with significant areas of agriculture. Herbaceous vegetation significantly sparse on the firm land of the delta.

6. Beaches, sands, open soils

All firm non-vegetated lands of the delta.

7. Water bodies

Black sea, Danube river, channels and lakes

1. Inland marshes

1.1 Dense emergent

1.2 Sparse emergent 1.3 Submerged 2. Coniferous forests

3. Deciduous forests

4. Natural grasslands

7.1 SPM waters

Waters with significant amount of suspended particulate matter.

7.2 Clear waters

Clear freshwaters of lakes and salt ones of the Black sea

Tab. 2 Landscape classes of the Danube Delta and their corresponding proportions in 1985 and 2014 /%/ Landscape Class 1. Inland marshes 1.1. Dense emergent 1.2. Sparse emergent 1.3. Submerged 2. Coniferous forests 3. Deciduous forests 4. Natural grasslands 5. Sparsely vegetated areas 6. Beaches. sands. open soils 7. Water bodies 7.1. SPM waters 7.2. Clear waters 8. Clouds and shadows

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1985

2014

53.3 5.7 0.4 1.8 1.2 2.7 1.1 1.2

60.9 3.7 0.4 1.5 0.3 1.3 0.4 0.8

24.5 7.8 0.3 100

25.7 4.3 0.7 100

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Results and discussion To describe land cover character the maps of landscape changes were made. The satellite image Landsat-TM5 of the Danube Delta shows the territory as of 1985 under conditions of drying processes influence (Fig. 4). The satellite image Landsat-OLI8 demonstrates the changes that happened as a result of complex natural and anthropogenic impact caused with the delta's water regime for 29 years (Fig. 5). In Table 2 there are shown the delta’s main landscape classes and their corresponding proportions in 1985 and 2014.

Fig. 4 Landsat-TM5 (of 8 July 1985) Landscape map of Danube delta obtained from the classification of satellite image

Fig. 5 Landsat-OLI8 (of 8 July 2014) Landscape map of Danube Delta obtained from the classification of satellite image

LEGEND:

Inland marshes: Dense emergent Sparse emergent Submerged Deciduous forests Coniferous forests

Natural grasslands Sparsely vegetated areas Beaches, sands, open soils

Water bodies: Suspended particulate matter (SPM) waters Clear waters

As it is obvious from Tab. 2, the main territory is occupied with inland marshes. With time the area of wetlands is increased: dense emergent marshes occupied about 60.9% of all territory of “Dunayski Plavni” reserve in 2014 instead of 53.3% in 1985. The researched territory is also characterized by a process of overgrowing with aquatic vegetation. Some straits lost open surface. The process of the territory swamping is evident. In general, significant changes in proportional distribution of the protected area had not happened because of uniformity of most of the territory. The changes of the inner islands may be judged on the example of the Ermakov Island (Fig 6, 7). The Northern part of the Danube Delta has been changed due to the island Ermakov flooding. At first the island was flooded in spring 2010 on the basis of the "Restore the island Ermakov". This led to reduction of the

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water flows through water supply channel, and consequently reduction of majority of open water bodies. As it is seen from figures 6 and 7, the water regime change for 29 years led to significant changes in the island Ermakov land cover. All landscape classes had spatial changes, among the most sparsely vegetated areas, open soils, waters, which saved the occupied territory only on 24.6%. 10.6% and 24.2%. According to quantitative changes, only marshes with dense vegetation preserved occupied area on 97.8% (Tab. 3).

Fig. 6 Landsat-TM5(of 8 July 1985) Landscape map of island Ermakov obtained from the classification of satellite image

Fig. 7 Landsat-OLI8(of 8 July 2014) Landscape map island Ermakov obtained from the classification of satellite image

LEGEND

Inland marshes: Dense emergent Sparse emergent Coniferous forests

Natural grasslands Sparsely vegetated areas Beaches, sands, open soils

Water bodies: Suspended particulate matter (SPM) waters Clear waters

The vegetation types like reed, cattail, Dutch rush, brook mint, and etc. formed a dense vegetation surface of marshes. Around 76.2% of grasslands were flooded and unique plat species were replaced with the other types of vegetation. The powerful flow of river water reduced the salt content in soils, and made it impossible to actively overgrow the territory with the rare plants species. However, powerful water flows caused the erosion of island shores. It led to formation of open water bodies. Proportions of waters increased on 51.8% unlike open soils and grasslands area - decrease on 73.9% and 89.7% correspondently (Table 3). These landscape changes led to enrichment of biodiversity and increase of water eutrophication. These processes returned the state of the environment to its original condition and rare plant species.

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Tab. 3 Changes of the Ermakov island landscapes: dynamics from 1985 to 2014

Final State

Marshes: dense Marshes: sparse Marshes: submerged Deciduous forests Coniferous forests Grasslands Sparsely vegetated area Beaches. sands. open soils Waters Class Changes

97.8 75.6 33.3 95.8 1.2 17.7 33.3 1.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 1.7 16.7 0.8 0.5 0.2 0.0 0.8 0.1 0.0 0.0 0.0 0.0 0.2 16.7 0.0 0.1 4.6 0.0 0.8 2.2 82.3 100.0 100.0

Waters

Open soils

Sparsely veg. areas

Grasslands

Deciduous forests Coniferous forests

Sub-merged

Marshes: dense Marshes: sparse

Initial State

47.9 60.9 24.6 10.6 24.2 10.0 1.5 19.8 12.8 20.5 0.5 0.0 0.0 0.0 0.1 0.3 0.2 0.0 0.0 0.0 13.4 6.5 13.8 15.5 2.6 15.9 23.8 14.7 18.2 0.2 9.4 5.9 10.3 13.7 0.1 1.3 1.1 7.5 26.1 0.5 1.3 0.0 9.3 3.1 48.2 86.6 76.2 89.7 73.9 51.8

Conclusion The landscapes of the Ukrainian part of the Danube Delta have been significantly changed in the period from 1985 to 2014 because of natural and anthropogenic influence. As a result of our study, ten landscape classes were obtained. According to their corresponding proportions in 1985 and 2014, we conclude that the territory is mainly occupied with inland marshes. Their area was increased by 7.3% particularly because of the swamping process. The water regime change within 29 years led to significant changes in the land cover of the Island Ermakov. All landscape classes registered spatial changes, most of all the sparsely vegetated areas, open soils and waters. References Barducci A., Guzzi D., Marcoionni P., Pippi I., 2009. Aerospace wetland monitoring by hyperspectral imaging sensors: A case study in the coastal zone of San Rossore Natural Park. Journal of Environmental Management, 2278–2286. Corbu A.-M., 2012. Land use in the Danube floodplain and terraces. Giurgiu-Călăraşi sector, Romania, pp. 1. Davis D., Kleridge G., 2000. Wetlands characteristics features. Moscow. Russia, pp. 64 (in Russian). Diakov O., Tudor M., Drumea D., 2012. Vulnerability of the Danube Delta region to climate change. World Wide Fund for Nature. Moldova, Romania, Ukraine, pp. 34. Landsat Project Description. Available from: http://landsat.usgs.gov/about_project_descriptions.php, 2013 Lihosha L.V., 2004. Kiliya part of the Danube delta and its contemporary dynamics/Danube Journal ONU, pp. 56-67. Shekk P.V., 2003. Retrospective analysis and present-day state of fish fauna and fishery in the Danube Delta. Vestnik ONU, 8, 55 – 85.

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LANDSCAPE CHANGES IN SELECTED SUBURBAN AREA OF BRATISLAVA (SLOVAKIA) Piotr KRAJEWSKI Wroclaw University of Environmental and Life Sciences, Department of Spatial Management, Wroclaw; Poland; [email protected] Abstract An excellent example of high-intensity changes in the landscape are the suburban areas of big cities, especially those parts located in close proximity to areas of protected landscape. The biggest changes in the landscape structure can be observed there mainly due to location of new building areas. The main objective of the study is to determine from the case study of Bratislava the level and nature of changes in the landscape of selected suburban area. Study area is located at the northern border of Bratislava partly within Protected Landscape Area of Malé Karpaty. On the basis of the identification and evaluation of changes in landscape structure in different time periods is determined the indicator of landscape changes and types and intensity of changes in the landscape in the selected suburban area of Bratislava. The studies are based on the changes of percentage of main elements in the secondary landscape structure manifested by extent of urbanization level, resignation from land cultivation, forestation and other changes in the landscape. Key words: landscape changes; driving forces; landscape changes indicator; land-use and land-cover changes; Slovakia. Introduction Landscape is a dynamic interaction of natural and cultural aspects where effects of changing social needs become visible. Its characteristic feature is changeability under the influence of different forces called „driving forces of landscape change“ (Bürgi et al., 2004, Schneeberger et al., 2007) or „key processes“ (Marucci, 2000). They can be divided into five groups: cultural, socioeconomic, political, technological and natural. Synthesis of the changes in spatial structure taking place in the past creates unique character of each landscape (Krajewski, 2012). Its current condition has been created for ages as the result of the relationship between nature and man. At the very beginning the main reason of the changes was the development of settlement and the need for new cultivation land, further industrial revolution, the development of technical infrastructure, building technology and tourism. Current rural landscape condition is still mostly the effect of creating new land for agricultural production in last 200 years (Hernik, 2011). Therefore, one has to remember that any landscape research should be preceded by determining basic trends of landscape change. Spatial and ecologic features of landscape make it subject of interest of different groups of scientists – geographers, landscape ecologists, landscape architects or economists. It has been studied for a long time with different scientific methods in many countries for example in Spain (Serra et al., 2008), Switzerland (Herpserger, Bürgi, 2009), Brazil (Bertolo et al., 2012), Austria (Kraussmann et al., 2003) or United States (Brown, Schulte, 2011). However, there is still the need for further study of past landscape changes because of better understanding the reasons of current changes and the importance of it for sustainable landscape management (Antrop, 2006).

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In recent years we can see intensification of landscape changes as a result of strong socioeconomic forces for example changes in agriculture, transport and industry (Antrop, 2004). It was also recognized by the Council of Europe and in 2000 the European Landscape Convention was established to give the base for landscape protection in European countries. The signatories of the Convention consider the landscape as important part of quality of life and key element of cultural heritage. Increase of negative impacts on landscape in last ten years is particularly visible in Central Europe because of new possibilities which have been given by the membership of European Union. Current economic development connected with better technologies, more rich society and phenomenon of urban sprawl is the reason of increased land demands and negative impacts on the landscape in suburban areas. Most of the people want to live relatively close to the city in attractive landscape. That's why the discussion of landscape changes is particularly important in relation to areas with great landscape beauty where there are strong processes of urbanization. As excellent example of high-intensity changes in the landscape are those parts of suburban areas of big cities which are located in close proximity to areas of protected landscape. These parts are attractive locations for residential development. Materials and methods Study area The main objective of the study was to determine from the local-scale case study of Bratislava the level and types of changes in the landscape of selected suburban area located in attractive landscape in last ten years (from 2004 to 2014). The research should give some useful information about landscape changes indicator (LCI) which shows the scale of landscape change in different periods of time and types of changes observed in selected suburban area. The study area (Tab.1) – Zahorska Bystrica – was chosen because of diversified landscape structure where we can observe still growing population. It is located at the northern border of Bratislava partly within Protected Landscape Area of Malé Karpaty (Fig.1). Eastern part is mountainous and covered by forest. In the central part there is building area surrounded by flat agricultural land which dominate also in western part of study area.

Fig. 1 Location of study area.

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Tab. 1 Basic information about study area. Source: www.datacube.statistics.sk Name of Area study area [ha] Zahorska 3,229.78 Bystrica

Number of % of protected inhabitants landscape area 2,321 (2004) 42.9% 3,071 (2009) 3,999 (2014)

Landscape character Eastern part – mountainous, forested; central part – building area, western part – flat arable land

Methodology of assessing landscape changes indicator Landscape is a synthesis and physiognomic effect of changes taking place in spatial structure of the land. Therefore, assessment of landscape changes should be carried out on the basis of analysis the elements creating present and past spatial structure reconstructed from available ortophotomaps or cartographic materials. It will help to answer the question which period of time was crucial for landscape changes. The intensity of this phenomenon is defined by landscape change indicator (LCI). The essence of used method of relative data deviation is to compare the values obtained by quantitative area analysis of each element creating spatial structure in selected period of time, with value from analysis previous period of time called reference criterion. Deviation from the reference criterion gives information about changes of spatial structure components. A summary of all values allows determine the indicator of landscape changes. Methodology of assessing landscape changes indicator of study area was divided into four phases: 1) The first phase includes creating the maps in ArcGIS showing current and past spatial structure in 3 different time periods. On the basis of ortophotomaps and field inventory study area were divided into 15 components with different spatial functions: 1) highways and expressways; 2) main roads; 3) local roads; 4) single-family housing area; 5) multi-family housing area; 6) service area; 7) industry area; 8) sport and leisure area; 9) forest and forest succession area; 10) meadows and pastures; 11) large arable land; 12) small arable land; 13) old orchards; 14) allotment gardens; 15) closed quarry. 2) The second step is to create the database concerning the area of each element of spatial structure for each analyzed time interval. The maps created in first step form the basis for database. 3) The third phase consists of determining the level of percentage deviation between the reference criterion and data from next time interval, for each element of the spatial structure. The percentage for the reference criterion = 0, and the change from initial value by 1%, with reference to whole area of research, is equal to the deviation of +1 or -1. 4) The last step consists of summing the absolute values of obtained level of the deviation for all analyzed components of the spatial structure in all time sections, under the

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assumption that both increase and decrease of the value indicates the changeability of landscape. Resulting value is an indicator of landscape changes level (LCI). Assessing character of landscape changes Analysis of components creating spatial structure in three different time periods made possible the assessment of landscape changes. Character of them was the basis of division into types and subtypes of landscape changes. In the first type – transformations inside built-up area – were grouped all transformations from single-housing and sport and leisure area to multi-housing area. In this category two subtypes were determined: I1 – transformation from sport and leisure area to multi-family housing area; I2 – transformation from single-family housing area to multi-family housing area. The second type of changes – increase of housing area – represent transformations from large and small arable land, meadows and pastures, old orchards and allotment gardens to single and multi-housing area. Under increase of built-up area type there are seven subtypes: H1 – transformation from meadows and pastures to multi-family housing area; H2 – transformation from small arable land to multi-family housing area; H3 – transformation from small arable land to single-family housing area; H4 – transformation from meadows and pastures to single-family housing area; H5 – transformation from old orchards to single-family housing area; H6 – transformation fon from large arable land to single-family housing area; H7 – transformation from allotment gardens to single-housing area. The third type of changes – increase of service and industry area – is characterized by transformations from large arable land, meadows and pastures to service and industry area. This category contains three subtypes: C1 – transformation from large arable land to service area; C2 – transformation from meadows and pastures to service area; C3 - transformation from large arable land to industry area. The fourth type of changes – internal transformations of agricultural land – is represented by transformations between large and small arable land, meadows and pastures and old orchards area. Under this category four subtypes were identified: A1 – transformation from large arable land to meadows and pastures; A2 – transformation from small arable land to meadows and pastures; A3 – transformation from old orchards to meadows and pastures; A4 – transformation from small arable land to large arable land; The fifth type of changes – transformations of forested area – consists only one subtype – F1 – transformation from meadows and pastures to forest and forest succession area. The sixth t;ype of changes are transformations of transport system. Two subtypes were identified in this category: T1 – increase of highways and expressways; T2 – increase of main and local roads.

Results and discussion Maps prepared in ArcGIS show spatial structure of study area in 2004, 2009 and 2014 (Fig. 2). The dates cover the period of last ten years after becoming Slovakia a member of European Union. Analyses of landscape change indicator were performed for two 5-year periods – from 2004 to 2009 and from 2009 to 2014. Detailed information about area of each component of spatial structure and changes in percentage is given in Table 2.

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Analyses of spatial structure components in three different periods of time – 2004, 2009 and 2014 (Tab. 2) show that the systematic reduction of area covered by large arable land and old orchards, steadily increasing single housing area and amount of local roads are characteristic of the study area. In the first of the analyzed time periods – from 2004 to 2009 – more changes can be observed than in the other one. At this time significantly decreased area of old orchards, large and small arable land and meadows and pastures. A lot of new singlefamily, multi-family and service buildings were built, process of forest succession has began on a large area. Landscape changes indicator for this period of time is 6.18. Tab. 2 Landscape changes indicator (LCI) in 2004-2009 and 2009-2014 Spatial structure component

Area in Area in Area in % in 2004 [ha] 2009 [ha] 2014 [ha] 2004

% in % in 2004- 20092009 2014 2009 2014

Highways and expressways

6.00

6.00

10.29

0.19

0.19

0.32

0.00

0.13

Main roads

8.94

8.94

9.59

0.28

0.28

0.30

0.00

0.02

Local roads

14.61

17.78

20.86

0.45

0.55

0.65

0.10

0.10

138.85

165.49

179.81

4.30

5.12

5.57

0.82

0.44

0.37

4.91

5.82

0.01

0.15

0.18

0.14

0.03

Service area

12.54

30.15

30.9

0.39

0.93

0.96

0.55

0.02

Industry area

31.69

30.36

34.05

1.01

0.94

0.98

0.04

0.11

2.66

1.92

1.92

0.06

0.06

0.06

0.02

0.00

1,545.94 1,546.48 46.38 47.86 47.88

1.48

0.02

6.17

0.33

0.03

1,017.98 1,004.63 32.03 31.52 31.11

0.55

0.41

Single-family housing area Multi-family housing area

Sport and leisure area Forest and forest succession Meadows and pastures

1,498.08 208.85

198.25

197.03

6.49

6.14

Large arable land

1,034.61

Small arable land

96.37

71.24

70.67

2.98

2.21

2.19

0.78

0.02

Old orchards

97.17

52.83

41.06

3.01

1.64

1.27

1.37

0.36

Allotment gardens

75.66

75.66

74.34

2.34

2.34

2.30

0.00

0.04

2.35

2.35

2.35

0.07

0.07

0.07

0.00

0.00

Closed quarry

LCI = 6,18

1,67

The second time interval – from 2009 to 2014 – is characterized by much less changes. At this time increased the length of highways and expressways and single-family and multifamily housing area at the expense of area covered by old orchards and large arable land. Landscape changes indicator for this period of time is 1.67. The second part of research was represented by analyses of landscape changes types in 2004-2014 (Tab. 3). The most frequently observed transformations were those from meadows and pastures to single-family housing area (H4) and from large arable land to meadows and pastures (A4) but the biggest area of change is represented by transformations from old orchards to meadows and pastures (A3) and from meadows and pastures to area with forest succession process (F1).

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Fig. 2 Maps of spatial structure in 2004, 2009 and 2014.

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Tab. 3 Type of landscape changes of the study area. Type of change I1 I2 H1 H2 H3 H4 H5 H6 H7 C1 C2 C3 A1 A2 A3 A4 F1 T1 T2

Area of change [ha] 0.74 0.55 3.68 3.22 13.07 26.47 0.90 4.38 1.31 8.27 9.74 0.98 15.94 10.07 51.66 5.15 46.73 4.29 6.90

% of study area 0.02 0.02 0.11 0.10 0.40 0.82 0.03 0.14 0.04 0.26 0.30 0.03 0.49 0.31 1.60 0.16 1.45 0.13 0.21

Number of polygons 1 1 1 1 3 10 1 4 1 1 2 1 8 2 2 1 4 15 64

Conclusions The presented case study identifies a level of changes in two 5-years periods of time – from 2004 to 2009 and from 2009 to 2014. Different transformations scale of analyzed components of spatial structure allows to observe that landscape changes indicator is much bigger in first time interval. But this indicator gives general information about scale of landscape changes without any information about quality of changes. Only identification of types and subtypes of landscape transformations allows to assess changes from this point of view. Knowledge about level and character of landscape transformations is especially important for decision- making connected with management and future planning of landscape from the point of view of European Landscape Convention implementation References Antrop M., 2004. Landscape change and the urbanization process. Landscape and Urban Planning 67: 9-26. Antrop M., 2006. Sustainable landscapes: contradiction, fiction or utopia? Landscape and Urban Planning 75: 187-197. Bertolo L., Lima G., Santos R., 2012. Identifying change trajectories and evolutive phases on coastal landscapes. Case study: Săo Sebastiăo Island, Brazil. Landscape and Urban Planning 106: 115-123. Brown P.W., Schulte L.A., 2011. Agricultural landscape change (1937–2002) in three townships in Iowa, USA. Landscape and Urban Planning 100: 202-212.

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Bürgi M, Hersperger A.M., Scheenberger N., 2004. Driving forces of landscape change – current and new directions. Landscape Ecology 19: 857-868. Hernik J., 2011. Protecting of sensitive cultural landscapes of rural areas. Scientific Papers of Agricultural University in Cracow 351 (in Polish). Hersperger A.M., Bürgi M., 2009. Going beyond landscape change description: Quantifying the importance of driving forces of landscape change in a Central Europe case study. Land Use Policy 26: 640-648. Krajewski P., 2012. Landscape capacity assessment as a tool supporting spatial planning. Infrastructure and Ecology of Rural Areas 2/1/2012: 17-29 (in Polish). Krausmann F., Haberl H., Schultz N.B., Erb K.H., Darge E., Gaube V., 2003., Land-use change and socio-economic metabolism in Austria—Part I: driving forces of land-use change: 1950–1995. Land Use Policy 20: 1-20. Marucci D., 2000. Landscape history as a planning tool. Landscape and Urban Planning 49: 67-81. Scheenberger N., Bürgi M., Hersperger A.M., Ewald K.C., 2007. Driving forces and rates of landscape change as a promising combination for landscape change research—An application on the northern fringe of the Swiss Alps. Land Use Policy 24: 349-361. Serra P., Pons X., Sauri D., 2008. Land-cover and land-use change in a Mediterranean landscape: A spatial analysis of driving forces integrating biophysical and human factors. Applied Geography 28: 189-209.

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REMOTE LAND DEGRADATION ASSESSMENT IN THE VICINITY OF THE BORYSPIL AIRPORT Mykola LUBSKYI1, Alexander HUSIEV2, Kateryna BOLOT2, Kateryna ZHURBAS2 1

Scientific Centre for Aerospace Research of the Earth National Academy of Sciences of Ukraine, Kyiv, Ukraine; [email protected] 2 National Aviation University, Kyiv, Ukraine

Abstract The aim of this research is preliminary assessment of land resource degradation within a radius of approximately 30 km around the international airport Boryspil using a two-level model for land degradation mapping on multispectral satellite imagery of low and medium spatial resolution. The Boryspil area landscapes belong to forest-steppe zone, forest and marsh meadow floodplains and wetlands. They developed as a result of climate aridity increasing and deeper groundwater occurrence on medium-loamy loess rocks. The soil types are soddy-podzolic, ashed, turf gley sandy, meadow, slightly salty at surface chernozem. To assess the level of land degradation the authors performed preliminary processing of Landsat-5/TM and Landsat-8/OLI multispectral satellite imagery for the period from 1995 till 2013 obtained from the United State Geological Survey archive through the EarthExplorer data access portal (http://earthexplorer.usgs.gov). For the first model level two land degradation indicators – vegetation cover change and soil erosion – were mapped. Vegetation cover was evaluated using modified soil-adjusted vegetation index or MSAVI (J. Qi et al., 1994) and water and wind erosion were modelled using climate data from World Climate (http://www.worldclim.org) and soils specifications developed in the region: granulometric and hydrological numbers, average soil density, equivalent soil particles size, erodibility factor. Second level gives data fusion of specific thematic classifications of the first level into final thematic map to improve accuracy and reliability owing to joint interpretation. Algorithm for land degradation mapping was borrowed from Stankevich et. al. (2011). The areas of low, average and high degradation level are shown at the obtained map. More than 30% of the territory in the vicinity of the airport is subjected to anthropogenic impact of average and high level. The data demonstrate correlation between long-term agricultural and industrial impact and land degradation in the vicinity of the Boryspil airport. Key words: remote sensing; thematic mapping; land degradation; probabilistic model; geospatial data fusion. Introduction The present stage of human development is strongly associated with human impacts on the biosphere components. Particularly relevant issues for Ukraine are the processes of soil degradation, whereas 53.9% of the country is arable land. The problems of waste of the land use, extensive method of farming, availability of powerful sources of anthropogenic influence on soil quality are of primary importance for Ukraine and require prompt solution. One of the major factors of human activities that affect the environment is the development of air transport infrastructure. Airports are large and powerful anthropogenic systems that include a wide range of systems and performing a negative impact on the environment: soil contamination combustive-lubricating materials and products of its combustion, noise pollution, electromagnetic pollution, soil cover disturbance. 118

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There exist a significant number of papers related to the harmful effects on the soils within and beyond the airport area. Basing on a soil and crops sampling, Franchuk at. al. (2006) provided an assessment of a toxic substances impact which are containing in soils around the airport based on a soil and crops sampling. It was also provided an ecological estimation of the environment around the airport based on biological testing of selected soil and water samples and evaluation of heavy metals that are contain in soils and its spatial distribution around the airport. The estimation of soil contamination by petroleum products also provided. Estimation of the soil samples’ quality is an adequate and objective method of ecological assessment of harmful effect on the biosphere components. However, this activity is rather complicated and requires a lot of efforts to create the bulk statistical sampling. Therefore, this approach makes it impossible to obtain a rapid environmental assessment. Additional problems arise due to the necessity of obtaining the permission for the field works. Currently, a large amount of methods providing the prompt soil degradation processes’ assessment based on satellite imagery processing is developed. Multispectral images are the qualitative information basis for identifying processes causing the potential deterioration of the soils’ quality state. The main objective of multispectral image processing is the assessment of the land degradation indicators. For land degradation indicators’ mapping middle-resolution multispectral images are also involved as additional geospatial data including a digital relief model, terrain maps, soil type classification map, soils' hydrological characteristics and climatic conditions of the territory researched. The chosen object of this study is the agricultural territory around powerful anthropogenic source of harmful effects on the environment – the Boryspil Airport. Boryspil airport is the largest airport of Ukraine, thus its negative impact on the environment is assessed as the most significant among the man-made objects of this type. Materials and methods The main task of processing the remote sensing data and thematic maps is to evaluate temporal changes in key indicators of the degradation processes. The most representing indicators of soil degradation in Ukraine are modified soil-adjusted vegetation index (MSAVI) for the assessment of the soil cover changes during the research period and for the complete soil erosion (Groisman and Lyalko, 2012). There are some restrictions in multispectral imagery selection: 1) all multispectral images should be taken for the similar vegetation period; 2) absence of cloud cover, which strongly affects the quality of images. For estimating the MSAVI-index changes six multispectral images for the period from 1995 to 2014 were used. MSAVI-index estimation is performed by the following equation (Zhongming et al., 2010): Fv 

2En  1 

2En  12  8En  Er  2

,

(1)

where En and Er are the values of near infrared and red optical signals of satellite images respectively. Water and wind erosion are the destruction of the soil layer by the wind and water streams. Estimation of the water and wind erosion is performed separately. The basic material for the calculation of water and wind erosion is the percentage of the vegetation cover, obtained using multispectral imagery processing, SRTM digital elevation model, hydrological soil parameters (Tab. 1). Elevation model is used for creating a terrain

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slope map. Additionally, it is necessary to consider climatic parameters such as monthly average rainfall, wind velocity. Tab. 1 Table of hydrological parameters of soils No 1 2 3 4 5 6

Soil Size of the main Soil Runoff density structural components erodibility curve 3 [g/cm ] of particles [mm] [mm/month] number

Soil type Light grey podzolic Soddy-podzolic Grey Dark gray Chernozem Soddy-podzolic sandy loam soil Meadow slightly solonetzic chernozem

7

1.2-1.4 1.4-1.6 1.2-1.3 1.2-1.3 1.2-1.3

0.05-0.23 0.1-0.25 0.001-0.05 0.1-0.25 0.05-0.23

0.17 0.24 0.24 0.32 0.28

1.4-1.5

0.01-0.05

0.37

1.1-1.3

0.05-0.23

0.28

67

Water erosion is manifested in washing away of the upper soil layer or its dilution under the influence of snowmelt, rainfall or irrigation waters. Water erosion is estimated by the following equation (Hairsine and Rose, 1992):

z s  k s Q 2 tg 

1, 67

exp  0,07v  ,

(2)

where ks – soil erosion coefficient (mm per month), Q – overland water flow, α – terrain slope angle, v – vegetation coverage ratio. Overland water flow Q is determined by the following equation:

Q

P  0,2R 2 P  0,8R

,

(3)

where, Р – average monthly rainfall (mm per month), R – water retention (mm per month). Water retention (R) is calculated as follows:  1000  R  25,4  10  ,  Cs 

(4)

where Cs – tabulated runoff curve number (Woodward et. al., 2004). Wind erosion (deflation) is the destruction of a soil layer by the wind power. It occurs mainly on the cover lands which are insufficiently protected or not protected by vegetation. Wind erosion is modelled in the following way (Dolgilevic, 1997):

z w  0,059w  u d s3,67

(5)

where w – near-ground wind velocity (m per second), ds – is an equivalent diameter of soil structures, u – critical wind velocity (m per second): u  3,202  0,025d s ,

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Near-ground wind velocity is estimated by the following equation (Stepanenko and Voloshin, 2010): w  w0 exp  0,0139v  ,

(7)

where wo – dynamic wind velocity (determined on base of climatic characteristics of the region), v – vegetation cover coefficient (Carlson and Ripley, 1997):

v

NDVI  NDVI 0 , NDVI 1  NDVI 0

(8)

where NDVI – normalized vegetation index, NDVI0 – normalized vegetation index in the pixel with 0% vegetation (i.e. ploughlands), NDVI1 – value of normalized vegetation index in the pixel with 100% vegetation cover. The total wind and water erosion is determined by the sum of the results obtained from the equations (2) and (5). After formation of the corresponding maps the classification of the processes with conditional division that reflects the qualitative assessment of the index MSAVI (Fig. 1) and erosion condition changes (Fig. 2) are performed.

Fig. 1 Dynamic of the MSAVI index from 1995 to 2014

Fig. 2 Erosion risk assessment

Revaluation of quantitative MSAVI and erosion conditions indexes is performed using decision tree approach. Digital data was divided into seven conditional classes with its own value range. The first three classes reflect the negative values of indicators that signal about

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increasing of the risk of degradation processes. Next one, the intermediate class, demonstrates the absence of any negative tendencies, and another three classes indicate the reduction of the land degradation processes’ risks. The last step of degradation processes’ evaluation is the integration of the results of the previous first-level classifications in a resulting classification by means of the data fusion approach using multifactoral decision tree with merging of first-level qualitative data into a single unit resulting image (Fig. 3).

Fig. 3 Soil degradation risk mapping Results and discussion Landsat multispectral satellites images for the period from 1995 to 2014 were used for representation of the soil degradation. Processing of multispectral data allows calculating the dynamic changes of MSAVI vegetation index. For the second indicator of soil degradation assessment, (i.e. total erosion), soil classification based on the characteristics of different types of soil (e.g. structural particle diameters, soil erosion coefficient and others) was used. Additional data like SRTM digital elevation model, and microclimate conditions of the region were required. The resulting image estimated by Bayesian data fusion technique (Wenbo et al., 2008) shows the conventional assessment of the overall impact of changes in vegetation and soil erosion on the formation of degradation processes on the investigated area (Fig. 3). Conclusion The given two-level model of the degradation processes’ evaluation is a qualitative addition to the data obtained from the ground measurements. It allows examining the impact on landscapes of significant anthropogenic facilities like airports in more details. Subsequent researches should be directed at improving the model which is related to the creation of more qualitative and more detailed digital elevation model, the availability of the detailed climatic and soil characteristics, the development of more effective ways of the data

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fusion. This improvement will directly increase detailing of results and more accurately consider a variety of factors that affect on land degradation conditions. The presented method is relevant for rapid remote assessment of soil degradation conditions, which can give an approximate idea about it without in situ investigation. Also it can stimulate more detailed studies. References Carlson T.N., Ripley D.A., 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index, Remote Sensing of Environment, 62, 3, 241–252. Dolgilevich M.J., 1997. Extent and Severity of Wind Erosion in the Ukraine. Proceedings of International Symposium on Wind Erosion. Manhattan: Kansas State Univ. Franchuk G.M., Kipnis M.S. Majd S.M., Zagoruy Y.V., 2006. Biotesting technique of environmental assessment near airport. Messenger of National Aviation University, No. 2, 114–117. (In Ukrainian). Groisman P.Y., Lyalko V.I., 2012. Earth Systems Change over Eastern Europe. Kiev, Akadem periodyka, pp. 1–488. (In Ukrainian). Hairsine P.B., Rose C.W., 1992. Modelling water erosion due to overland flow using physical principles. Water Resources Research 28, 1, pp. 237–250. Popov M.A., Stankevich S.A., Kozlova A.A., 2012. Remote risk assessment of land degradation using satellite imagery and geospatial modelling. Proceedings of NAS of Ukraine, 6, 100–104. (In Russian). Qi J., Chehbouni A., Huete A. R., Kerr Y. H., Sorooshian S., 1994. A Modified Soil Adjusted Vegetation Index. Remote Sensing of Environment 48, 119–126. Stankevich S.A., Vasko A.V., Gubkina V.V., 2011. Two-level model for land degradation mapping on multispectral satellite imagery. Proceedings of the 8th International Conference on Digital Technologies (DT’2011). Žilina: University of Žilina, pp. 289–293. Stepanenko S.N., Voloshin V.G., 2010. Speed of wind are in the layer of vegetation. Ukrainian Hydro-Meteorological Journal 7, 6, 24–34. (In Russian). U.S. Geological Survey, 2014, Earth Explorer, accessed [October 16, 2014], at URL [http://earthexplorer.usgs.gov]. Wenbo W., Jing Y., Tingjun K., 2008. Study of remote sensing image fusion and its application in image classification, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII (B7), 1141-1146. Woodward D.E., Nielsen R.D., Kluth R., Plummer A., Mullem J. V., Conaway G., Moody H. F., 2004. National Engineering Handbook, Part 630 Hydrology, Chapter 9 Hydrologic Soil-Cover Complexes, USDA. WorldClim - Global Climate Data, 2014, Free climate data for ecological modelling and GIS accessed [October 16, 2014], at URL [http://www.worldclim.org/current]. Zhongming W., Leesa B.G., Feng J., Wanning L., Haijing S., 2010. Stratified vegetation cover index: A new way to assess vegetation impact on soil erosion. Catena 83, 1, 87–93.

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THE APPLICATION OF LANDSCAPE ECOLOGY TO THE PREDICTION OF CHANGES IN CLIMATIC CONDITIONS FOR GROWING AGRICULTURAL CROPS. A CASE STUDY FROM THE CZECH REPUBLIC Ivo MACHAR1, Antonín BUČEK2, Veronika VLČKOVÁ3, Vilém PECHANEC4, Jan BRUS5 1

Palacky University, Olomouc, Czech Republic; [email protected] 2 Mendel University, Brno, Czech Republic; [email protected] 3 Czech Technical University, Prague, Czech Republic; [email protected] 4 Palacky University, Olomouc, Czech Republic; [email protected] 5 Palacky University, Olomouc, Czech Republic; [email protected] Abstract Landscape ecology delimits vegetation zones in the Czech Republic using the method of bioindication. Vegetation zonation in Czech landscape is the expression of the dependence of potential natural vegetation on the long-term effects of altitude and exposure climate, which is determined by the combination of average and extreme air temperatures and the amount and distribution of atmospheric precipitation. Global warming will probably manifest itself in a gradual shift in vegetation zones to higher altitudes, and thus the overall change in vegetation zonation. A mathematical model of changes in vegetation zonation in the Czech Republic depending on predicted climate change allows the evaluation of possible climate change impacts on the growing conditions of agricultural crops in the Czech Republic. It is a correlation model, which can serve as an example of the application of landscape ecology in dealing with practical problems of landscape management. The model can be used as one of the tools supporting the development of strategies and adaptation measures to climate change. This article demonstrates the application of the model on the example of analytical prediction of future temporal and spatial changes in ecological conditions for growing sugar beet, which is a major and traditional agricultural crop in Central Europe. Key words: agriculture; climate change; crops; Czech Republic; geobiocoenology; model of vegetation zones shift. Introduction Increasing global temperature trends are indisputable (Flannery, 2005) although experts and scientists extensively discuss the importance of human activities in the objectively monitored increase in the amount of greenhouse gases in the atmosphere (Braniš, Hůnová, 2009). In Europe there is a very dense network of long-term measuring weather stations with a variety of complementary distance measurement systems and, therefore, the analyses of temperature trends are much more accurate in Europe than anywhere else in the world. The temperature of the European continent increased on average by 1.2 ºC during the 20th century and the upward trend doubled during the last 20 years. The average number of summer days doubled during the 20th century, and the average number of tropical days tripled. It can have important implications not only for changes in biodiversity (Hannah, 2011), but also for applied fields such as agriculture and forestry (Harrison et al., 1995). Trends in long-term meteorological measurements taken in the Czech Republic show not only the increase in average temperature but also a marked increase in the incidence of weather extremes – the numbers of tropical and summer days and nights increase and the numbers of frost days and ice days decrease. Linear trends in regional temperature and

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precipitation amounts in the Czech Republic (i.e. the modified values derived from data measured by station network, which take into account the position of individual meteorological stations) confirm the upward trend in average temperatures and the decrease in the total precipitation amounts in all seasons of the year except winter (Nátr, 2006). The results of ALADIN CLIMATE CZ model simulating future climate trends in the Czech Republic predict the ongoing increase in average temperatures by 0,24 ºC per decade, decrease in precipitation amounts in vegetation period, and at the same time dramatic increase in extreme weather events (e.g. flash floods, etc.) with adverse consequences for agriculture (Pretel, 2009). Vegetation zones (Zlatník, 1976) are a suitable spatial frame for the evaluation of possible climate change impacts on the growth conditions of agricultural crops. In the Czech Republic vegetation zones were defined by bioindication and are defined in detail in the characteristics of super-structural units of geobiocoenological landscape typology (Buček, Lacina, 1999). The distribution of vegetation zones in the landscape reflects the character of the orographically conditioned differences in climatic condition and their gradients. Changes in climatic conditions are manifested as a gradual shift of vegetation zones to higher altitudes, thus a change in the overall vegetation zonation of the landscape. Modelling of changes in vegetation zonation in the landscape is becoming increasingly important in the context of predicted climate change. Models of vegetation zones can be considered to be one of the fundamental knowledge bases to understand the significance of climate change for ecosystems. One of these models is the geobiocoenological model of vegetation zonation in the Czech Republic. The model is based on a general ecological relationship between the present vegetation zonation and the current climatic conditions and on the assumption that this relationship will be maintained also in the future. The projected climate change will, therefore, be manifested by a shift (change) in climatic conditions of the current vegetation zones. A mathematical model predicting changes in vegetation zones depending on climate change, which is based on geobiocoenological typing of the landscape, was developed at three Czech universities and in cooperation with the Czech Hydrometeorological Institute (Buček, Vlčková, 2012). The paper presents the application of this model to the prediction of climate change impact on future conditions for growing sugar beet in the Czech Republic. The aim of the paper is to contribute to the discussions about the future of sugar beet in Europe (Bittner, 2008) from the perspective of applied landscape ecology. Materials and methods Presented model of vegetation zonation changes belongs to the group of non-dynamic correlation models predicting the impact of climate change on terrestrial ecosystems (Walker, 1994) as it is based on the relationship between the current climate and vegetation types. The basis is the assumption that the general ecological relationship between vegetation zones and climatic conditions will be maintained also in the future. The projected changes in climate will therefore be manifested by a shift in the current vegetation zones. Vegetation zonation is the expression of the dependence of vegetation on the long-term effects of altitude and exposure climate, which is determined by the average and extreme air temperatures and the amount and distribution of precipitation (including horizontal precipitation). The current vegetation zones in the Czech Republic stabilized in older subatlantic about 800 - 500 BC and the shifts of vegetation zones in the landscape faithfully reflect the progress of climate change (Ložek, 2012). Therefore, the current vegetation zones represent the basic framework for the prediction of the impact of climate change on production and growth conditions of the vegetation and also agricultural crops, including sugar beet.

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Prediction climatic database of the Czech Hydrometeorological Institute (CHMI) was used as the source of climatological data for the model (Pretel, 2011); data on annual precipitation, annual average relative air humidity, annual average daily sums of global radiation, annual average air temperature, and annual average wind speed were used. This database links climatic data to a set of 131 points regularly spaced throughout the Czech Republic in the form of a regular trapezoidal network. Biogeography register (Kopecká, 1994) was used as the source of geobiocoenological data for the model. It contains the geobiocoenological classification of the landscape in the CR (vegetation zones, trophic and hydric series) projected on the selected type of administrative spatial units of the Czech Republic - cadastral territories. For various types of analyses, the biogeography register provides the description of geobiocoenological properties of approximately 13,000 polygons, fully covering the area of the CR. The definition points of the Biogeography register were assigned climatic characteristics using an analytic-geometric method designing a more detailed network of points in the area (250 m step), to which the values of climatic variables were recalculated by the gradient method using the values relevant to four nearest neighbouring points of the original CHMI climatic database. Projected climatic characteristics of the definition points, the corresponding vegetation zones, and the characteristics of natural climatic conditions were determined using the method of space-time analogies, for which Lang’s rain factor was used again as a relationship indicator. The output of the mathematical model for defined boundary conditions (climatic scenario for a specified time period, delimited geographic area, or algorithmized ecological conditions of a concrete plant species) provide scenarios of predicted future climatic conditions of vegetation zones in the landscape. This geobiocoenological database describes quite well the heterogeneity of natural conditions in the CR owing to the link to cadastral territories because the original Terezian cadastre (which is still more or less respected by the Land register) was delimited according to natural boundaries such as streams, forest borders, marked geomorphological features in the landscape, etc. (Semotanová, 2008). The computer model of the shift in vegetation zones due to climate change is designed as a set of special programs (FORTRAN programming language) and Arc/Info GIS applications. The definition points of the Biogeography register were assigned climatic characteristics using analytic-geometric method designing a more detailed network of points in the area (250 m step), to which the values of climatic variables were recalculated by the gradient method using the values relevant to four nearest neighbouring points of the original CHMI climatic database. Based on the characteristics of corn and sugar beet production areas in the Czech Republic, the current conditions for growing sugar beet were algorithmized into vegetation zones and geobiocoenological characteristics of hydric and trophic series. Projected climatic characteristics of the definition points, the corresponding vegetation zones and the characteristics of natural climatic conditions were determined using the method of space-time analogies (Kopecká, Buček, 1999) while Lang’s rain factor, which combines the average annual rainfall and average annual temperature into a single value (Rožnovský, Havlíček, 1999), was employed as a relationship indicator. Results Fig. 1 shows the current distribution of areas with different sugar beet growth conditions according to geobiocoenological algorithmization of ecological factors. The most suitable conditions are linked to the warmest areas of the Czech Republic in the first and the second vegetation zone.

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Fig. 2 shows a detailed trend in the predicted temporal changes in the proportion of the areas climatically suitable for growing sugar beet in agricultural production regions, depending on trends in vegetation zones in ten-year time horizons up to 2089. The graph clearly shows a marked enlargement of the areas suitable for growing sugar beet caused by predicted climate change. The map in Fig. 3 illustrates the predicted delimitation of areas suitable for growing sugar beet for the time horizon of 2050. This figure clearly shows that predicted climate change may have a positive effect on growing conditions of sugar beet in the Czech Republic – the areas suitable for growing sugar beet should become larger within the study area (Fig. 2).

Inappropriate conditions A few appropriate conditions Appropriate conditions The most appropriate conditions

Fig. 1 Cartogram of the current ecological conditions for growing sugar beet in the Czech Republic algorithmized into vegetation zonation of the landscape according to geobiocoenological landscape classification sensu Zlatník (1976) Discussion and conclusion Climate significantly affects both the distribution of organisms on the surface of the Earth and human activities such as agriculture (Lomolino, 2005). Possibilities of predicting the effects of climate change on growing conditions of agricultural crops are therefore sought for practical reasons (Kalvová, Moldan, 1995). In the Czech Republic the consequences of climate change will be most apparent in biocoenoses of the most common normal hydric

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range, which are bound to the hydric regime of soils and depend on the amount of atmospheric precipitation at individual sites (Trnka et al., 2011). The consequences of changes in climatic conditions will be less significant in biocoenoses of dry and limited hydric ranges at extremely hot and drying sites with a predominance of xerophilous S-strategists. Biocoenoses of waterlogged, wet and peat-bog hydric range with additional water will be also affected less dramatically.

The best conditions Appropriate conditions A few appropriate conditions Inappropriate conditions Completely non-compliant conditions

Fig. 2 Graph of the predicted temporal and spatial changes in the areas suitable for growing sugar beet in the Czech Republic in ten-year time horizons until 2089 The impact of climate change on vegetation will be first manifested at sites where ecological gradients form sharp boundaries between vegetation formations, e.g. at natural timberline. Significant changes can be expected on the border of the tundra and taiga; according to some models the area of the tundra biome may be reduced to two thirds of the current state when the amount of CO2 in the atmosphere doubles (Skre et al., 2002). A study of the shift of the timberline in the southern part of the Skanda Mountains in Sweden during the 20th century led to the conclusion that the line shifted 100 – 165 m upwards for individual tree species. In case of Scotch pine (Pinus sylvestris) it is now at its highest level in the last 4000 years. The shift of the timberline is explained by climate warming during the 20 th century, a major part of the shift took place before 1950, further progress was identified in the 1990s (Kullman, 2001). The analysis of possible consequences of global climate change for 128

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boreal forests in the 21st century led to the conclusion that in case of exceeding the limits of the current resilience of ecosystems, the changes in vegetation can be very fast and unexpected, often leading to the emergence of very different ecosystems (Chapin, 2004).

Inappropriate conditions A few appropriate conditions Appropriate conditions The most appropriate conditions

Fig. 3. Cartogram of the possible delimitation of areas suitable for growing sugar beet in 2050 Specialized experimental studies or mathematical modelling is most often used for the evaluation of potential impacts of ongoing and expected climate change on biota. Experimental studies can be either short-time experiments carried out in the laboratory (e.g. in microcosms), or long-time studies implemented in the field. However, their explanatory value is limited as the number of factors that can be simultaneously manipulated is small. While short-term experiments, in which only one environmental factor changes (e.g. CO2 concentration increases), do not represent a real picture of future climate change, a multi-year study with the simultaneous modifications of CO2 concentration, temperature, and the amount and availability of nutrients simulates the impact of expected climate change better (Betts, 2007). Mathematical modelling most often uses the following types of models to predict the future impact of climate change on biodiversity: a) models that take into account an individual specimen, b) models based on the ecological niche theory,

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c) models of global climate and combined ocean-atmosphere-biosphere models including dynamic global vegetation models, d) models based on species-area curve that take into account all species or large assemblages. When using mathematical models, however, it must be always taken into account that they do not represent predictions of future development. Models contribute to the prediction and must be carefully interpreted on the basis of knowledge of biology or ecology of organisms that are modelled. The vast majority of the hitherto proposed models are correlation models – they are based on the interdependence (a function or algorithm) between a certain environmental variable (or variables), usually bioclimatic variables such as temperature or precipitation, and the current range of a species. When you predict future changes in climatic conditions on the basis of climate scenarios, you can assign relevant biological species or communities to the changed variables. This procedure is known as bioclimatic envelope modelling (Botkin, 2007). The model predicting the effects of climate change on the growing conditions of sugar beet in the Slovak Republic was based on the evaluation of the current production potential of agricultural soils expressed by estimated pedologic-ecological units which were assigned environmental growth requirements and production parameters of sugar beet (Vilček, 2008). Although the research into the effects of climate change on biota has recently attracted the attention of many scientific teams composed of experts of various specializations, it is clear that our ability to predict changes in the abundance and distribution of both cultural and wild plant species due to climate change remains limited due to statistical and stochastic uncertainties in environmental data (Araújo et al., 2005). This study has been supported by a grants of Palacky University Olomouc No. CZ.1.07/2.3.00/20.0166 and No. CZ.1.07/2.2.00/28.0303 from European Social Fund and the Ministry of Education of the Czech Republic References Araújo, M. B., 2005. Reducing uncertainty in projection of extinction risk from climate change. Global Ecol. Biogeogr., 14, 529-538 Betts, R., 2007. Implications of land ecosystem-atmosphere interactions for strategies for climate change adaptation and mitigation. Tellus Ser. B Chem. Phys. Meteor., 59, 602-615. Bittner, V., 2008 Existují limity pro výnos cukrovky a jaká je její budoucnost v Evropě? Listy cukr a řepař., 124, (11), 296-297. Botkin, D. B., 2007. Forecasting the effects of global warming on biodiversity. BioScience, 57, 227-236. Braniš, M.; Hůnová, I. (eds.) 2009. Atmosféra a klima. Aktuální otázky ochrany ovzduší. Praha: Karolinum, 196 s. Buček, A.; Lacina, J., 1999. Geobiocenologie II. Brno: Mendel. univ., 156 s. Buček, A.; Vlčková, V., 2012 Scénář změn vegetační stupňovitosti na území ČR: deset let poté. Ochrana přírody, 64, suppl., 8-11. Chapin, L., 2004. Global change and the boreal forest: thresholds, shifting states or gradual change? Ambio, 33, 6, 361-365.Flannery, T., 2005. The Weather Makers: The History and future Impact of Climate Change. Melbourne: T Publishing Company, 270 s. Hannah L., 2011. Climate Change Biology. Amsterdam: Elsevier. Harrison, P. A.; Butterfield, R. E.; Downing, T. E., 1995. Climate change and agriculture in Europe: assessment of impacts and adaptations. Environmental Change Unit, University of Oxford, Oxford, UK, 411 pp.

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Kalv ová, J.; Moldan, B., 1995. Klima a jeho změna v důsledku emisí skleníkových plynů. Praha: Karolinum, 132 s. Kopecká V., 1994. Ekologická banka dat ISÚ, Roční situační zprávy pro Integrovaný informační systém o území ISÚ. Praha: TERPLAN - Státní ústav pro územní plánování. Kopecká, V.; Buček, A., 1999. Modelování možných důsledků globálních klimatických změn na území ČR. Praha: Agentura OPKaK ČR, 27 s. Kullman, L., 2001. 20th century climate warming and tree-limit rise in the Southern Scandes of Sweden. Ambio. 30, 2, 72-80. Lomolino, M.V. (ed.) 2005. Biogeography. Sunderland: Sinauer Association Inc.. 308 s. Ložek, V., 2012. Důsledky poznání vývoje přírody a krajiny ČR v holocénu. In Machar, I.; Drobilová, L. (eds.) Ochrana přírody a krajiny v ČR. Olomouc: Univ. Palackého, 58-64. Nátr, L., 2006. Země jako skleník: Proč se bát CO2?. Praha: Academia, 142 s. Pretel J., 2009. Současný vývoj klimatu a jeho výhled. Ochrana přírody, suppl., 46: 2-7. Pretel J. (ed.) 2011. Zpřesnění dosavadních odhadů dopadů klimatické změny v sektorech vodního hospodářství, zemědělství a lesnictví a návrhy adaptačních opatření (V) Závěrečná zpráva o řešení 2007–2011, Projekt VaV – SP/1a6/108/07. Praha: ČHMÚ. Rožnovský, J.; Havlíček, V., 1999. Bioklimatologie. Brno: Mendel. univ., 155 s. Semotanová, E., 2008. České země na starých mapách. Praha: Geografická služba AČR, 240 s. Skre, O., Barter, R., Ceaford, R.M.M., Callaghan, T.V., Fedorkov, A., 2002. How will the tundra-taiga interface respond to climate change? Ambio. Special report 12, Tundra-tajga treeline research, 37-46 . Trnka, M., Brázdil, R., Dubrovský, M., Semerádová, D., Štěpánek, P., Dobrovolný, P., Možný, M., Eitzinger, J., Málek, J., Formayer, H., Balek, J., Žalud, Z., 2011. A 200-year climate record in Central Europe: implications for agriculture. Agronomy for Sustainable Environment. 31, 4, 631-641. Vilček, J., 2008. Dopad klimatických zmien na možnosti pestovania cukrovej repy na Slovensku. Listy cukrovarnické a řepařské, 124, 3, 78-80. Walker, B.H., 1994. Landscape to regional scale responses of terrestrial ecosystems to global change. Ambio, 23, 67-73. Zlatník, A., 1976. Přehled skupin typů geobiocénů původně lesních a křovinných v ČSR. Zprávy GGÚ ČSAV, 13, 55-64.

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TRENDS IN INTER-COMPONENT RELATIONSHIPS DURING THE RECOVERY OF DISTURBED LANDSCAPES Ksenia MEREKALOVA, Alexander KHOROSHEV Faculty of Geography, Moscow Lomonosov State University, Moscow, Russia; [email protected]; [email protected] Abstract The paper focuses on the interdependencies between soil cover, plant cover and relief at the various stages of recovery succession after cutting in the East-European taiga landscape. By means of multiple regression modelling we come to the conclusion that the degree of adaptation of landscape components to each other is undergone to changes during succession. The leading role of nutrients supply at the early succession stages is replaced by the role of water supply at the late stages. Co-adaptation of soils and vegetation is getting better during landscapes recovery. At the same time plant cover becomes more independent of ecological processes controlled by landforms. Key words: landscape; component; inter-component relations; stability; linkage; scale; succession; digital elevation model; regression. Introduction Physical geography and landscape ecology demonstrate obviously increasing interest in climatic changes and short-term effects of natural and anthropogenic processes. Research efforts concentrate on the problem of ecosystems and landscapes sensitivity (Demek, 1995). The challenge to clarify forms of landscape stability in relations to exterior natural and anthropogenic factors seems to be critical. Multiplicity of kinds of stability is well-known (Grodzinsky, 1987; Huba, 1998; Holling, 2004). First, resistance is defined and an ability of a system to preserve parameters of structure and functioning under exterior impact. Second, resilience (elasticity) is defined as an ability to return to initial state after disturbance. Third, plasticity is defined as an ability to shift from one stable state to another one. Determination of the best, or coherent, scale for each phenomenon is highly needed (Wu, Qi, 2000; Hay et al., 2002; Ben Wu, Archer, 2005; McMahon et al., 2004). We propose the approach that focuses on assessment of landscape stability based on evaluation of density of linkages between landscape components: plant cover, soil, water, substrate and landforms. We believe that the urgent task for landscape ecology is to concentrate efforts on the forecast of possible chains of changes in landscape after anthropogenic impact. To solve this problem, temporal restrictions of forecast should be determined. We need to determine whether inter-component relations in a landscape follow more or less universal rules during transition from one evolution stage to another one. Our research focused on the following questions. Whether interdependencies between landscape components become stronger on this or that stage of landscape development after disturbance? Do plant and soil cover have “the purpose” to weaken their dependence on landforms and substrate during recovery? Landscape stability is a multidimensional and multi-scale notion. On the one hand, it depends on interior properties of a landscape structure or landscape element. On the other hand, according to system theory, landscape like any system exists in a framework imposed by the higher-order systems which provide so-called constants (Cushman, McGarigal, 2002; Burnett, Blaschke, 2003; Chang et al., 2006). Hence, explanation of spatial heterogeneity requires testing hypotheses to what extent landscape properties are controlled by properties of

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a higher-order system. Thus, we face the necessity to determine spatial parameters of higherorder systems imposing constants. Our point of departure is the geosystem concept which emphasizes the crucial importance of physical environment, landforms and substrate genesis in particular, for landscape differentiation with special focus on multiplicity of hierarchical orders (Khoroshev et al., 2007; Bastian et al., 2015). Interpretation of dense or loose linkages can differ and even be opposite in relation to different kinds of stability. Dense linkage means high degree of mutual adaptation of components at each patch of mosaic landscape. Biota affects soils and water aiming at forming the most favourable conditions for reproduction of the community and unfavourable for the competing ones. For example, acid litter in spruce forest favours progressing soil podzolization and by this suppresses penetration of broad-leaved trees and corresponding herb species occurring in the neighbouring landscape element. It worth noting that recovery is conditioned by inert soil properties and parent rocks with characteristic time scale larger than relaxation time of phytocoenoses. Chain reaction that can violate equilibrium state results from dense linkage between components with similar characteristic time scales. In that case the exterior signal is transferred rapidly from one property to another and causes structural transformation. We focus on dependencies between density of inter-component linkages and landscape development stages at the example of recovery succession after cutting in the taiga zone. The proposed multi-scale models enable us to test the hypotheses that: a) plant cover gradually becomes less dependent on physical environment and soils; b) dependence of landscape component on higher-order systems is not the same at different succession stages. The main objective is to reveal whether groups of correlating landscape properties remain constant at various stages of recovery succession. Materials and methods Case study was performed at the area of about 10 km2 located in middle taiga of North European Russia (southern Arkhangelsk region). Study area is located within the structural erosion-morainic plain composed by Permian marlstone which is covered by glacial and limnoglacial quaternary deposits (Khoroshev et al., 2013). The relief of territory has the block-and-joint structure generated by the neotectonic uplift. The contemporary erosion network and bog areas develop along the joints of various rank orders. Small-leaved (Betula pendula, Populus tremula) and coniferous (Picea abies, Pinus silvestris) forests on podzolic and sod-podzolic soils dominate. Oligotrophic bogs in the flat interfluve areas and agricultural landscapes in the dissected areas occur as well. Research material includes: 1) topographic map (1:10,000) and corresponding digital elevation model (DEM) with resolution 30 m; 2) topographic map (1:50,000) and DEM with resolution 400 m; 2) field data (more than 180 sample plots) which include descriptions of plant cover (species composition and abundance, canopy density, age of forest trees, coverage of each vegetation layer etc.), soils (thickness of horizons, colours of horizons according Munsell charts, texture etc.), deposits, landforms. The methodological approach of the assessment of landscape components relations consists of several steps (Khoroshev et al. 2007, 2013, fig.1). At the first step we used multidimensional scaling technique to reduce dimensionality of field data and to derive the independent differentiation factors (axes) of plant and soil cover. The calculation was performed separately for different groups of properties of landscape components – vegetation layers (trees, shrubs, low shrubs, herbs, mosses and lichens), soil horizons, soil colours and soil texture. The main factors of plant cover differentiation were interpreted as gradients of water supply, nutrients supply and anthropogenic disturbance. For soil properties main differentiation factors were identified as degree of podzolization, iron accumulation vs.

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gleyization, ratio of different types of organics sedimentation (humus vs. peat) and anthropogenic modifications.

Fig. 1 Methodical scheme of the evaluation of linkages between mobile landscape components (plant cover and soils) and landforms To identify the relations between, on the one hand, soil and plant cover properties and, on the other hand, the relief of surrounding higher-order geosystems we used nonlinear secondorder multiple regression models (below referred to as “Relief – Soils” and “Relief – Plant cover” models). Relief characteristics derived from DEM (standard deviation of altitudes, sum of stream lengths, vertical and horizontal curvatures) were computed in square moving window with linear dimension varying from 3 to 15 pixels. We assumed that the abovementioned morphometric indices characterize combination of landforms in various surroundings that determine physical conditions for ecological processes (water redistribution, nutrients migration etc.). In the regression models values of each vegetation and soil differentiation factor were used as response variable and four morphometric indices as predictors. To reveal the influence of grain size on the relationships between landscape components we used DEM resolutions 30 m (sensitive to microrelief) and 400 m (sensitive to mesorelief), derived from topographic maps with scale, respectively, 1:10,000 and 1:50,000. To determine the changes in landscape components relationships during the recovery succession we composed regression models separately for units disturbed less than 30 years ago, 30-45 and over 45 years ago with equal number of sample plots in each group.

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Comparing determination coefficients R^2 (i.e. proportion of explained variance) for three regression models enabled us to detect the changes in landscape components relations according to vegetation recovery stages for different grain and extent values of landforms characteristics. To determine density of “soil – plant cover” linkages at different succession stages we also used nonlinear multiple regression models with vegetation differentiation factor values as response variable and soil differentiation factors values as predictors. Determination coefficients provide information to what extent the structure of soil profile and soil processes control the plant cover. Finally, we calculated nonparametric Spearman correlation coefficients for every pair of differentiation factors of vegetation and soils. This allowed revealing the groups of interrelated properties of landscape components and to assess their stability during the recovery after disturbance. Results and discussion „Relief - Plant cover“ and „Relief - Soils“ models. The first group of hypotheses was based on fine-scale DEM with resolution 30 m which is able to describe almost all the microreliefgenerated landscape units. Multiple regression models showed that the plant cover is controlled by the relief-dependent ecological processes approximately until the age of 45 years, i.e. in young and middle-aged forests only. Proportion of explained variance accounts for 70-90 %, depending on size of moving window taken for calculation (see below). In contrast, vegetation layers in premature and mature forests become less dependent on relief (fig.2). The second group of hypotheses which tested the effects reflected by coarse-scale DEM (resolution 400 m) showed evidence that linkage density is much less related to the succession stage. Herb layer is undergone to the highest degree of the relief influence in comparison with the other layers, at early successional stages in particular. Combination of landforms in the square with linear dimension 90-210 m turned out to be significant for most factors of plant cover differentiation (sensitivity to water supply etc.) (fig.3, a, b). However, the higher-order geosystems with linear dimension 450 m are significant for the factors reflecting the ratio of forest and meadow species (anthropogenic factor obviously related to mesorelief) as well as ratio of xerophilous and hydrophilous oligotrophic species (e.g. Antennaria dioica and Lycopodium annotinum vs. Orchis mascula and Eriophorum vaginatum) (fig.3, b, c). Hence, the abundance of moisture-sensitive species is controlled by geosystems of at least two rank orders related respectively to microrelief and mesorelief. Regression models designed for the fine-scale DEM showed that the tree layer in the forest phytocoenoses is related to landform properties (for any linear dimension of surrounding geosystems ranging from 90 up to 450 m) at the early succession stages only (fig.3, d, e). The highest sensitivity to landforms is inherent for the factors responsible for the ratios of species with contrast preferences along gradients of nutrients and water supply. The phytocoenoses with the highest nutrients demand (e.g. Alnus incana with associated Filipendula ulmaria, Aegopodium podagraria) occur at the lowest sections of slopes and in the gully bottoms. The shrubs layer follows similar spatial pattern determined by the topography, mesorelief elements in particular. The low shrubs layer (Vaccinium myrtillus, V. vitis-idaea, V. uliginosum, Oxycoccus palustris etc.) is sensitive to the water supply determined by landforms variability within the surroundings up to 90-330 m (fig.3, f). Ratio of mosses with different water demand (Sphagnum sp. and Polytrichum commune) depends on the smaller range of hierarchical levels of surrounding geosystems with linear dimensions 150-270 m (mesorelief elements).

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Fig. 2 Determination coefficients of three age regression models for significant differentiation factors of plant cover (for landforms combination in surroundings: a – 90 m; b – 270 m). Here and below: „herbs“, „shrubs“, „trees“, „low shrb.“, „mosses“ – different vegetation layers; F1, F2 etc. – order number of the differentiation factor of certain vegetation layer.

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Fig. 3 Changes in the determination coefficients depending on the succession stage and the size of the surrounding geosystems (a-h – differentiation factors of plant cover and soils)

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Thickness of soil horizons is highly dependent on landforms combinations with proportion of explained variance up to 70-80 %. Inverse spatial pattern of peat accumulation and podzolization follows water supply gradients along the mesorelief elements (e.g. valley slope). It worth noting that degree of determination does not change as the recovery succession develops (fig.3, g). Obviously, this finding indicates that characteristic time scales of soil horizons and phytocoenosis differ much: soil preserves its dependence on relief while plant cover is undergone to rapid self-development with gradually weakening relief control. However this is not true for another factor of soil differentiation which describes the type of organic matter accumulation (peat, histic or humus horizons) depending greatly on soil texture and water regime: control of landforms is well-pronounced at early succession stages only (fig.3, h). Most likely, this is explained by previous agricultural activity in the areas presently covered by secondary forests. As for soil colour we found no clear evidence that relief is significant for its spatial variability except for Value and Chroma indicating podzolization or humus accumulation at early succession stages. Models designed for the coarse-scale DEM (resolution 400 m) confirmed that the determination coefficients decrease as the size of hypothetic higher-order geosystem increases. This holds true for most properties of landscape components despite the succession stage. However, statistically significant linkages between herbs and low shrubs layers and relief are manifested at various succession stages. „Soil - Plant cover“ models. The series of multiple regression models was composed to evaluate whether the density of linkages between properties of soils and plant cover changes during succession. Our analysis showed that soils colour indicating present-day soil processes is not clearly related to plant cover spatial pattern at any succession stage. In contrast, thickness of soil horizons is densely linked to plant cover at various succession stages. For example, nutrients-related factor of herbs spatial variability has the densest linkage to soil profile at the early stage of succession (proportion of explained variance accounts for 82 %). At the early succession stage nutrients-sensitive properties of tree layer are related to soil horizons thickness. Hence, nutrient supply in soils acts as a binding factor for both tree and herb layers at early succession stage. At the later succession stages (45 years after disturbance) clear linkage with soils are preserved by water-sensitive properties of low shrubs, mosses and lichens (proportion of explained variance 74-76 %). Thus, our data testify that as phytocoenosis recovers after disturbance change of principal ecological factor takes place with gradually growing contribution of water supply to spatial pattern of plant cover. To clarify interdependencies between landscape properties during recovery succession we compared non-parametric Spearman coefficients of correlations (S) between the factors of soil and plant cover differentiation. This enabled us to detect growing concordance in spatial variation of certain properties along the ecological gradients. For example, boreal low shrubs (Vaccinium myrtillus, V. vitis-idaea) and moss Polytrichum commune increase abundance with higher correlation at the late succession stages (S=0.53) than at the early ones (S=0.35). Correlation of abundance of hydrophylous herbs and shrubs increases as recovery succession develops as well. Increase of Munsell Value in soils indicating podzolization directly correlates with abundance of oligotrophic herbs but is inversely related to the peat thickness. Both correlations are more well-manifested in units with mature forests. At the same time, we detected another pairs of properties with decreasing compliance in sensitivity to ecological gradients at the late succession stages. The ratio of peat accumulation and podzolization in soils at early stages is related to the ratio of Sphagnum sp. and Polytrichum commune. In soils under mature forests Polytrichum commune totally dominates without any compliance with peat thickness. However, increasing density of linkages between soil and plant cover during recovery succession seems to be the more universal rule.

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Correlated response of soil and plant cover to ecological gradients enabled us to distinguish groups of interrelated landscape properties at various succession stages. At the initial stage of succession the first group consists of the water-sensitive properties described by corresponding factors: abundance of hydrophilous trees, shrubs, herbs and mosses, thickness of peat and gley soil horizons. Separate group is formed by the properties indicating the swamping process: abundance of bog trees and herbs and Sphagnum mosses, ratio of peat and podzolic horizons. The third group of properties consists of nutrition-sensitive properties: ratio of oligotrophic and mesotrophic herb and tree species, thickness of histic and humus soil horizons. As middle taiga landscape recovers after disturbance the groups of properties listed above can be changed. At the late stage of succession correlated spatial variability is preserved by water-sensitive properties of herbs, shrubs and low shrubs (ratio of hydrophilous and mesophilous species, ratio of Polytrichum and Pleurozium moss species). At the same time the above-mentioned properties vary in concordance with nutrition-sensitive properties: ratio of histic and humus soil horizons, color characteristics, ratio of herbs typical for the taiga and broad-leaved zones. The second group of properties correlating at the late succession stage consists of, on the one hand, nutrition-sensitive properties (ratio of oligotrophic and mesotrophic species of herbs, low shrubs and trees) and, on the other hand, proportion of typical boreal low shrubs and Polytrichum moss, ratio of podzolic and peat horizons, ratio of low and high Munsell Value in soils. Thus, our data demonstrate that a structure of intercomponents linkages become more complex as recovery succession occurs. Conclusion Our research shows that as a rule contributions of ecological factors to spatial variability of soil and plant cover are not similar at various succession stages. At the early stages contribution of nutrients supply dominates while later contribution of water supply grows gradually. Soil and plant cover become more well-adapted to each other during recovery succession. At the same time they gradually decrease their dependence on ecological processes controlled by landforms. Fine-scale models of intercomponent relations (resolution 30 m) show that the size of higher-order geosystem that impose control over elementary landscape units rarely exceeds 300 m. Plant cover is strictly controlled by the relief-dependent ecological processes approximately until the age of 45 years, later being more independent. Thus, disturbance of tree layer can cause rapid change in soil moisture content, nutrients supply, groundwater level, biochemical activity. However, inert properties (e.g. soil profile, soil texture, soil structure) preserve prerequisites for recovery of initial state of a landscape element. Dense linkages between properties with small characteristic time scale indicate low resistance but probably high elasticity. Dense linkages between property with large characteristic time scale and property with smaller one indicate in most cases high resilience of mobile property after disturbance (high elasticity). However, in the latter case dense linkage does not mean low resistance since signal from mobile component is insignificant for the inert property, at least if the signal has low duration. The research was financially supported by Russian Foundation for Basic Research (projects 14-05-00170, 13-05-00821). The contribution of G.M. Aleshchenko to elaboration of software for spatial analysis is greatly acknowledged.

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References Bastian O., Grunewald K., Khoroshev A.V., 2015. The significance of geosystem and landscape concepts for the assessment of ecosystem services: exemplified on a case study in Russia. Landscape Ecology 30, 7: 1145-1164 Ben Wu X., Archer S.R., 2005. Scale-dependent influence of topography-based hydrologic features on patterns of woody plant encroachment in savanna landscapes. Landscape Ecology 20: 733-742. Burnett C., Blaschke T., 2003. A multi-scale segmentation/object relationship modeling methodology for landscape analysis. Ecological modeling 168: 233-249. Cushman S.A., McGarigal K., 2002. Hierarchical, multiscale decomposition of speciesenvironment relationships. Landscape Ecology 17: 637-646. Chang C.-R., Lee P.-F., Bai M.-L., Lin T.-T., 2006. Identifying the scale thresholds for fielddata extrapolation via spatial analysis of landscape gradients. Ecosystems 9: 200–214. Demek J., 1995. Problems of landscape behaviour. Ecology (Bratislava) 14, Supplement 1/1995: 23-28. Hay G.J., Dube P., Bouchard A., Marceau D.J., 2002. A scale-space primer for exploring and quantifying complex landscapes. Ecological Modelling 153: 27-49. Holling C.S., 2004. From complex regions to complex worlds. Ecology and Society 9(1). http://www.ecologyandsociety.org/vol9/iss1/art11/ Huba M., 1998. Productivity – stability – sustainability. Ecology (Bratislava) 17. Supplement 1/1998: 34-42. Grodzinsky M.D., 1987. Stability of geosystems: theoretical approach to the analysis and methods of quantitative evaluation. Proceeding of the Academy of Sciences of the USSR, geographical series. No. 6, pp. 5-15. (in Russian). Khoroshev A.V., Merekalova K.A., Aleshchenko G.M., 2007. Multiscale organization of intercomponent relations in landscape. In: K.N. Dyakonov, N.S. Kasimov, A.V. Khoroshev, A.V. Kushlin (Eds.), Landscape Analysis for Sustainable Development. Theory and Applications of Landscape Science in Russia. Alex Publishers, Moscow, pp. 93-103. Khoroshev A.V., Eremeeva A.P., Merekalova K.A., 2013. Evaluation of intercomponents linkages in the steppe and taiga landscapes with account for modifiable areal unit. Proceedings of Russian Geographical Society. Vol. 145. No. 3, pp. 32-42. (in Russian). McMahon G., Wiken E.B., Gauthier D.A., 2004. Toward a scientifically rigorous basis for developing mapped ecological regions. Environmental management 34, Suppl. 1: 111-124. Wu J., Qi Y., 2000. Dealing with scale in landscape analysis: An overview. Geographic Information Sciences. Vol. 6. No. 1, pp. 1-5.

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QUANTIFYING LANDSCAPE CHANGES THROUGH LAND COVER TRANSITION POTENTIAL ANALYSIS AND MODELLING (ON THE EXAMPLE OF THE BLACK TISZA RIVER BASIN) Alexander MKRTCHIAN1, Daria SVIDZINSKA2 1

2

Ivan Franko National University of Lviv, Lviv, Ukraine; [email protected]; Taras Shevchenko National University of Kyiv, Kyiv, Ukraine; [email protected]

Abstract Ukrainian Carpathians being the significant regional biodiversity hotspot have experienced significant changes in land use / land cover during last decades. While the extensive farm abandonment favours the restoration of natural ecosystems, the widespread illegal logging, the decay of institutions and control mechanisms produced opposite effect. The research based on Landsat imagery and formal data analysis methods and techniques revealed some noteworthy trends in land cover dynamics for 1989-2009 period for the Black Tisza river basin. While the total forested area, experiencing decrease before 1998, have stabilized afterwards, the spatial structure of forest cover continues to deteriorate, as evident from the increased fragmentation and the shrinkage of core areas. The degree of influence of explanatory variables on land cover transitions vary significantly among time periods, suggesting the inconstancy of the drivers of land cover changes. Key words: land cover changes; Ukrainian Carpathians; transition potential models; Landsat imagery; hierarchical partitioning; forest cover fragmentation. Introduction Understanding landscape changes driven by multiple complex factors in space and time raises an important challenge to landscape ecology. These changes are imprinted in spatial landscape pattern dynamics which in turn can be recognized and measured through land cover changes modelling (Baker, 2009, Kolb et al., 2013). Ukrainian Carpathians, being an important regional biodiversity hotspot and an important refuge and corridor for plants and animals, have been experiencing dramatic changes in land use practices with corresponding transformations of land cover (LC) structure. Understanding the main factors and trends driving LC changes is crucial for the effective predictions of future landscape states, and for making informed policy assessments and management recommendations. Remote sensing provides an effective data sources for the analysis of land cover changes, while the development of abundant geospatial datasets and software tools for their processing and analysis paves the way for the studies that analyse the driving forces and influencing factors of LC changes through rigorous and reproducible scientific methods. Landsat imagery has long been regarded a valuable datasource for various ecological tasks: the good summary of Landsat applications for change detection and for characterization of landscapes with spatial metrics applied to Landsat data can be found in (Cohen W.B., Goward S.N., 2004). Ukraine, like a number of neighbouring countries, has experienced drastic political, societal, and economic changes during last several decades which have had powerful impacts on land management policies and land use patterns. However, the detailed spatial patterns and regional and local drivers of these changes remain under-researched. A series of studies have been carried out recently to evaluate the modern LC changes in Ukrainian Carpathians and to reveal their possible drivers using Landsat imagery and modern data analysis techniques. The

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study by Kuemmerle et al. (2008) used a change detection method based on support vector machines (SVM) to map farmland abandonment in the border triangle of Poland, Slovakia, and Ukraine in the Carpathians from Landsat TM/ETM+ images. It was found that for the Western part of Ukrainian Carpathians 13.3% of farmland have been abandoned between 1988 and 2000. In contrast to neighbouring regions of Poland and Slovakia, there appeared to be no increase in abandonment rates with the altitude and the slope and relatively lower reforestation rates mostly because of practically non-existent active forest planting. Another study, covering the entire Carpathian region of Ukraine and using rayons (district-level administrative units in Ukraine) as spatial units, has shown the lower abandonment rates at higher elevations and steeper slopes, and positive relations between infrastructure density and distance to local markets, and the abandonment rates. These results run contrary to what was expected and what is generally observed in the Western Europe, pointing to different mechanics of farmland abandonment, which in Ukraine is often connected with the demise of heavily industrialized collective farms while the traditional subsistence farming, often employing remote and marginal lands, has been relatively less affected (Baumann et al., 2011). The other study employing a similar methodology has focused specifically on forest cover changes in the Ukrainian Carpathians from 1988 to 2007, revealing that, while there has been a slight forest cover increase for the entire Ukrainian Carpathians, the forest cover increase in peripheral areas has been accompanied by forest loss in the interior Carpathians, and increased logging in remote areas. This may imply the continued loss of older forests and their services, and the ongoing fragmentation of some of Europe's last large mountain forests (Kuemmerle et al., 2009). The aim of our study is to analyze the recent LC changes in the study area in Ukrainian Carpathians, to reveal their main drivers and factors, to expound their consequences for habitat losses and fragmentations through a set of landscape metrics, and to predict impending landscape changes through simulation modelling. Materials and methods The Black Tisza river basin (24.14° – 24.55° E, 48.07° – 48.40° N) in the Ukrainian Carpathians was chosen as a study area. The watershed area is 567.72 km2 with absolute elevation range from 447 to 2 004 m a.s.l. The study area covers landscapes with the different types and intensity of disturbances: (1) highly transformed foothill area with beech forest patches, secondary grasslands and croplands; (2) medium height hills with mixed forest landscapes and secondary grasslands; (3) high hills covered mostly by coniferous forests; (4) highlands with subalpine and alpine meadows and krummholz. The data used in the research included processed Landsat 5 TM imagery; SRTM 1 ArcSecond (30 meters) Global Digital Elevation Dataset; OpenStreetMap vector data; WorldClim dataset containing current climatic data (1960 – 2000). The data manipulations, analyses, and graphics creation have been carried out using free and open-source GIS and statistical software – QGIS, SAGA, Fragstats and R software environment. Landsat Surface Reflectance Climate Data Record (CDR) products (Masek et al., 2006) for Landsat were used for the land cover analysis. The three scenes obtained on 08 Jul 1989, 02 Aug 1998, and 15 Jul 2009 have been used, each from Landsat 5 ETM+ sensor, Path 184, Row 026, 027. Water vapor, ozone, geopotential height, aerosol optical thickness, and digital elevation represent an input with Landsat data to Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer models to generate Top of Atmosphere (TOA) Reflectance, Surface Reflectance, Brightness Temperature, and masks for clouds, cloud shadows, adjacent clouds, land, and water. Thus, the procedure of digital numbers conversion to TOA and surface reflectance was omitted. However, the images are affected by the

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geometric distortion and shadowing due to topography, so in order to interpret them effectively these effects need to be minimized. Thus, prior to classification band layers were topographically corrected using Normalization method (after Civco, 1989 modified by Law, Nichol, 2004). The method applied here consisted of two stages. In the first stage, shaded relief models corresponding to the solar illumination conditions at the time of the image acquisition were computed using the SRTM DEM data. In the second stage, a transformation of each of the original bands of the satellite image was performed to derive topographically normalized images using a correction coefficient calculated from the spectral responses from large samples falling on the slopes facing to and away from the sun. After these, quality assurance masks from the original Landsat Surface Reflectance CDR were used to exclude water and cloud cover. Supervised classification with Maximum Likelihood algorithm has been performed for bands 1-4 (visible and NIR) using Semi-Automatic Classification Plugin v. 4 for QGIS (Congedo et al., 2013). There were nine spectral classes distinguished according to their spectral signatures separability, which afterwards were merged into six generic thematic classes corresponding to LC classes (see the descriptions below). Other mentioned data sources have been used mainly to characterize the factors and drivers of LC conversions, defined as the complete replacements of one cover type by another with the connected temporal changes in the LC spatial distribution. Two approaches have been applied to analyse and compare the impact of factors influencing LC conversions. The first one involved the modelling of land cover transition potential and simulating future land cover changes using algorithms realized in MOLUSCE (Modules for Land Use Change Evaluation) plug-in for QGIS released by Asia Air Survey Co., Ltd. (AAS) in 2013, designed to analyze, model and simulate land use/cover changes. Land use/cover change transition potential is modelled using four methods, namely Artificial Neural Networks (ANN), Logistic regression model (LRM), Multi-Criteria Evaluation (MCE) and Weights of Evidence (WoE). We have applied the first two of these methods as they are more suitable for the case of quantitative predictors. A simulated (projected) LC map can be produced by MOLUSCE based on a Monte Carlo and Cellular-automata modelling approach. In case the reference map exists, it can be used for the validation of simulation by comparing it with the simulated map and calculating the measures of agreement between the two. We used the LC maps for 1989 and 1998 to simulate the LC for 2009 and then compared the result with the LC map for 2009, using the Cohen's kappa coefficient and the % of correct predictions as the measures of agreement. Then we used the LC maps for 1998 and 2009 to make an extrapolation for 2020. The second approach consists in the application of the logistic regression model (LRM) and hierarchical partitioning (HP) methods to evaluate the relationships between the several most important transition types and their supposed drivers. The logistic regression is a type of regression analysis which estimates the occurrence probability of a binary variable as a function of a linear combination of predictors (Afifi, Clark, 1998). LRM, a multivariate evaluation of driver-variable relationships, provides a probability term that relates to the presence or absence of land-cover data with an explanatory variable that can be interpreted as a transition potential (Eastman et al., 2005). Binary LRMs have been widely applied to analyze variables involved in LC changes (Verburg et al., 2002). The LRM can be built using R glm( ) function with the parameter family = binomial (link = "logit"). Hierarchical partitioning is a promising approach that allows ranking the predictive variables by their independent contributions while taking into account the multicollinearity between predictors (Chevan, Sutherland, 1991). It allows isolating amounts of variance attributable to each predictor variable thus determining the most meaningful predictors among

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putative explanatory variables. The special R package ‘hier.part’ written by Chris Walsh has been used for the task. The software applies the hierarchical partitioning algorithm of (Chevan, Sutherland, 1991) to return a simple table listing each variable, its independent contribution (I) and its conjoint contribution with all other variables. The last stage of the study involved the analysis of the temporal dynamics of the fragmentation of forest class as the main habitat focus of the area. The forest fragmentation process involves changes in landscape composition, structure, and function over a range of scales (McGarigal, Cushman, 2002). Forest ecosystem fragmentation has a number of ecological effects on forest species and communities, introducing edge effects leading to habitat and forest biodiversity loss (Debinski, Holt, 2000). Some particularly sensitive species thrive only in the interior parts of forests patches (core habitat areas) sufficiently isolated from disturbances while some others favour the buffer (ecotone) zones at the forest edge. Thus forest fragmentation can differ in its effect depending on the ecology of a certain species, being especially problematic for area-demanding top carnivores and herbivores (Woodroffe, Ginsberg, 1998). Several landscape metrics characterizing forest habitat fragmentation have been calculated for the forest class using FragStats 4.2 (McGarigal, Marks, 1995), namely: 1) Total area of forest class (ha); 2) Percentage of landscape occupied by forest class; 3) Perimeter-area fractal dimension (PAFRAC); 4) Total core area, ha; 5) Percentage of core area; 6) Total class edge, km; 7) Mean patch area, ha. Perimeter-area fractal dimension (PAFRAC) equals 2 divided by the slope of regression line obtained by regressing the logarithm of patch area (m2) against the logarithm of patch perimeter (m). CORE equals the area (m2) within the patch that is further than the specified depth-of-edge distance from the patch perimeter. The depth-of-edge has been specified dependent on adjoining non-forest LC class, e.g. the maximum of 200 m for class 1 (artificial surfaces), the minimum of 50 m for classes 4 and 5. Results and discussion The classification allowed to single out six distinct LC classes. The class 1 represents artificial surfaces like buildings, paved roads, etc. The class 2 represents fresh clearances where forest has been recently removed. The class 3 relates to forests in general: while there are different types of forests in the study area, distinguishing between them is complicated because of the prevalence of forests where different deciduous and coniferous tree species are mixed with varied proportions. The class 4 is formed by the merging of three spectrally different primary classes each of which occurs almost exclusively on highland locations above 1600 m a.s.l. These classes correspond to highland grassland and krummholz communities, including also loose stone placers and the rocky outcrops. The class 5 corresponds to shrubs and tenuous and low tree stands, mostly – secondary communities formed on places of former clearances and abandoned agricultural lands. The class 6 represents the grassland vegetation (except of grasslands located above treeline) – natural meadows, pastures, and scattered plots of different annual crops. The three snapshots of the LC structure of study area have been obtained corresponding to the three classified Landsat scenes. Comparing these allowed to identify LC changes that have occurred for 1989-98 and for 1998-09 and to pick out the most significant LC conversions (Tab. 1). It can be noticed that the class 1 (artificial surfaces) has experienced a major drop in area during 1990-th when the economic conditions in Ukraine were the most harsh, but somewhat recovered its area later. The class 2 has experienced a major decline in area for both time spans, reflecting the decay of industrial clear felling. The area of forest class 3 declined before 1998, and stabilized later on. The highland class 4 experienced a major decline in area after 144

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1998, that might point to a rather disturbing ecological effect of recent climate changes (Mkrtchian, Svidzinska, 2014). The classes 5 and 6 representing mostly secondary and sere vegetation have expanded during both periods. Tab. 1 The LC class area distributions and changes Class ID 1 2 3 4 5 6

Class area /ha/ 1989 1998 371 285 336 252 31,434 29,357 2,080 2,174 11,691 12,396 10,468 11,916

2009 318 125 29,423 877 13,306 12,331

Changes in class area Changes in class area (1989/98), % (1998/09), % -23.18 -25.00 -6.61 4.52 6.03 13.83

11.58 -50.40 0.22 -59.66 7.34 3.48

To analyse the factors and reveal the driving forces of LC changes, the set of spatially distributed variables were suggested to serve as explanatory variables. The first obvious choice of predictor variable is the site elevation directly conveyed by DEM (elevation variable), closely correlating with the distribution of a number of ecological factors. Another important variable is the degree of terrain ruggedness that determines the amenability of the terrain to various anthropogenic disturbances, especially involving the construction works, the moving machinery, and the active tourism (ruggedness variable). Terrain ruggedness is often proxied by the slope values, but this can be deemed dubious, as rather steep yet straight and smooth slopes can be quite suitable for a few types of nature-disturbing human activities. Thus we used the Terrain ruggedness index (TRI) proposed by (Riley et al., 1999) that is computed as a sum of squared differences in elevation between a grid cell and its eight neighbour grid cells. We used TRI as a standalone predictor variable, and also as a cost grid to calculate the cost distances from the major roads (variable costd_roads), the artificial surfaces (costd_settl), the grasslands and arable class (costd_open), and the forests (costd_forests). Location measures such as distance to roads and distance to previously changed areas have been found especially important, since accessibility of roads and the distances to existing land uses can be the crucial drivers of LC changes (Kolb et al., 2013). Another important determinant of LC structure is the climate, thus the distribution of its characteristics is worth to be analyzed as a possible factor influencing LC changes. The WorldClim dataset, composed of the series of global maps of various climatic variables based on records for the 1960-90 period has been used to derive climate data (Hijmans et al., 2005). The warmest quarter mean temperatures variable was selected as a descriptor of heat conditions affecting ecological processes (variable temperature). The spatial detail of the dataset was refined with SRTM DEM data to 70 m resolution using technique based on geographically weighted regression, described in (Mkrtchian, Svidzinska, 2014). The results of the validation of two MOLUSCE simulations of LC structure produced by means of LRS and ANN methods using the mentioned predictors are shown on Tab. 2. According to (Landis, Koch, 1977), kappa values between 0.4 and 0.6 correspond to moderate degree of agreement. ANN produced more plausible prediction comparing to LRM method. To more specifically analyse the impact of explanatory variables, several important transition types (TT) have been identified, namely: deforestation (the transition of forest class 3 to any other LC class), reforestation (the transition of any LC class to forest), desolation (the disappearance of class 1), dealpinization (the disappearance of class 4). LRM and HP models were built for every TT (for desolation for 1989-98 only, for dealpinization for 1998-09 only). 145

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For every relevant explanatory variable the LRM z value has been calculated, showing the strength of the relationship, together with HP I value that represents its independent contribution to overall variability. The magnitude of these values reflects the strength of the relationship, and the sign of z value – its sign (positive or negative). AIC criterion characterizes the model containing all the variables. It should be noted that obtained z and AIC values cannot be used to determine the statistical significance of the relationships because of the highly inflated regression degrees of freedom due to spatial autocorrelation in data, and are used here for the mere comparison of different variables and relationships. Tab. 2 Validation results of simulations of 2009 LC, produced by MOLUSCE Simulation method LRM ANN

% of correct predictions 62.4 71.1

kappa 0.42 0.54

It can be seen that the drivers of deforestation are rather different for the two time spans. While in the 90-th forests disappeared mostly on low locations and steep slopes, in the 2000th the most significant factor has been the proximity to settlements and infrastructure. The reforestation drivers differed even more. For both periods the proximity to existing forests has been by far the most significant factor, yet in the 90-th the reappearance of forests was happening almost exclusively due to natural regeneration (the low AIC is another hint to this), while in the later period there are some indications to artificial afforestation as well.

Fig. 1 The forest and deforestation extents, 1989-98 (left), 1998-09 (right) There are several ways to refine the methodology we used in the study. Support Vector Machines (SVM) approach was shown to be well-suited for mapping farmland abandonment because it handles complex spectral classes well, thus allowing distinguishing farmlands from natural grasslands (Kuemmerle et al., 2008). Using images obtained in different phenological stages (e.g. summer and spring or fall images) can also improve distinguishing of different LC types. Explicitly accounting of spatial autocorrelation in data can allow making wellgrounded statistical inferences about relationships between different variables.

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Tab. 3 The impact of explanatory variables on forest cover changes Explanatory Deforestation, Deforestation, Reforestation, variable 1989-98 1989-98 1989-98 z I z I z I Elevation -100.4 1.7 -50.1 0.45 33.1 0.77 Ruggedness 57.7 1.58 -48.1 0.5 28.7 0.23 Costd_roads -69.5 0.54 -69.8 0.85 72.0 2.19 Costd_settl -22.9 0.12 -87.7 2.12 30.5 0.1 Costd_open -75.1 2 -56.7 0.59 Costd_forests -116.7 10 Temperature 99.8 1.53 46.1 0.35 -40.8 0.66 294667 298221 198257 Overall AIC

Reforestation, 1998-09 z I 6.8 0.47 89.0 3.42 46.7 1.15 26.5 0.13 -117.6 8.73 -10.7 0.33 287568

The main variables influencing the desolation have expectedly been the ruggedness, elevation, and costd_roads. For the dealpinization, the significant relations is observed with elevation and costd_open (negative sign) and with temperature (positive sign), suggesting that, while the ascension of treeline is rather sluggish, there is much quicker process of the substitution of (sub)alpine communities with grassland ones characteristic of lower elevations. To evaluate the impact of LC changes on ecosystem connectivity and integrity, a number of landscape metrics have been calculated for the forest class. Tab. 4 The temporal dynamics of forest class landscape metrics Year Total Area % of PAFRAC Total Core % of Core /ha/ Landscape Area /ha/ Area 1989 1998 2009

31,434 29,357 29,423

55.80 52.07 52.19

1.274 1.264 1.272

23,844 22,469 21,694

75.85 76.54 73.73

Total Mean Edge Patch /km/ Area /ha/ 1,762 77.9 1,663 65.4 1,763 49.4

It is notable from this table that, while the total forested area has increased a little from 1998 to 2009, the total core area in fact continued to decrease due to intensification of the fragmentation of forest patches. This is evident from the rise of PAFRAC metric, the increase in total edge lengths of forest patches, and the decrease in the mean patch area. The same metrics were calculated for the maps obtained through simulations done by MOLUSCE (Tab. 5). These relate to the projections for 2009 and can thus be compared to the last row of Tab. 4. As was expected, the simulation results reflect the tendencies observed in the period 1989-98, like the continued deforestation. When the general tendency changes e.g. as a result of changes in socio-economic context, the results of extrapolating simulations are not very plausible. In our case, the simulations significantly underestimated the total forested area. Even more noticeable is the greatly overestimated forest fragmentation, as is apparent from the simulated values of the appropriate landscape metrics (Tab. 5), suggesting that the applied technique might distort the configuration of patches thus introducing a systematic error to the assessment of metrics. ANN method produced distinctly better results over LR, consistent with validation results (Tab. 2). Tab. 5 The landscape metrics of forest cover simulation by MOLUSCE Simulation Total % of PAFRAC Total Core % of Core method Area / Landscape Area /ha/ Area ha/ LRM 26871 47.66 1.389 17046 63.44 ANN 28140 49.92 1.355 18123 64.4

Total Mean Edge Patch /km/ Area /ha/ 2917 21.4 2576 23.2 147

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Conclusion The study area like Ukrainian Carpathians at large has experienced major LC changes during last decades, effected by the widespread farmland abandonment, collapse in organized forestry and expansion of illegal logging. The revealed spatial pattern of LC changes in the Ukrainian Carpathians is generally consistent with that described in the previous studies. The drivers and factors are noticeably different from those operating in the Western Europe and also differ significantly between 1990-th and 2000-th time spans. While forest cuts in 1990-th were mostly observed on slopes near the bottoms of river valleys, in the latter period the proximity to settlements and roads has become the main correlate. While the decline in total forested area, observed in 1990-th, has stopped after 1998, there is a substantial deforestation in remote and core areas, accompanied by the lasting increase of the forest fragmentation, as indicated by the changes in appropriate landscape metrics. It is rather disturbing occurrence, signalling the continuous deterioration of the quality of forest habitats. Another disturbing phenomenon is a recent sharp decline of the area of alpine communities, that are mostly replaced with grassland communities characteristic of lower elevations; the recent climate changes may be the main driver of this process. The revealed trends point to a need of rethinking the ecosystem conservation strategies and priorities, emphasizing the protection of core forest areas and ensuring the proper connectivity of protected ecosystems. The proper conservation laws enforcement remains an urgent issue, e.g. concerning the illegal loggings inside the boundaries of protected areas. References Afifi A.A., Clark V., 1998. Computer aided multivariate analysis, Chapman Hall: London, pp. 1–455. Baumann M., Kuemmerle T., Elbakidze M., Ozdogan M., Radeloff V.C., Keuler N.S., Prishchepov A.V., Kruhlov I., 2011. Patterns and drivers of post-socialist farmland abandonment in Western Ukraine. Land Use Policy 28: 552–562. Chevan A. and Sutherland M., 1991. Hierarchical partitioning. The American Statistician 45: 90–96. Civco D.L., 1989. Topographic Normalization of Landsat Thematic Mapper Digital Imagery. Photogrammetric Engineering and Remote Sensing 55(9): 1303–1309. Cohen W.B., Goward S.N., 2004. Landsat’s role in ecological applications of remote sensing BioScience 54(6): 535-545. Congedo L., Munafo M., Macchi S., 2013. Investigating the relationship between land cover and vulnerability to climate change in Dar es Salaam. Working Paper, Sapienza University, Rome, Italy, pp. 1–48. Debinski D. M. and Holt R.D., 2000. A survey and overview of habitat fragmentation experiments. Conservation Biology 14:342–355. Eastman J.R., Van Fossen M.E., Solorzano L.A., 2005. Transition potential modelling for land cover change. In: Maguire D., Batty M., Goodchild M (Eds.) GIS, spatial analysis and modelling. ESRI Press, Redlands, CA, pp. 357–386. Hijmans R.J., Cameron S.E., Parra J.L., Jones P.G., Jarvis A., 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978. Kolb M., Mas J.-F., Galicia L., 2013. Evaluating drivers of land-use change and transition potential models in a complex landscape in Southern Mexico. International Journal of Geographical Information Science 27 (9): 1804–1827.

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Kuemmerle T., Chaskovskyy O., Knorn J., Radeloff V.C., Kruhlov I., Keeton W.S., Hostert P., 2009. Forest cover change and illegal logging in the Ukrainian Carpathians in the transition period from 1988 to 2007. Remote Sensing of Environment 113: 1194 – 1207. Kuemmerle T., Hostert P., Radeloff V.C., derLinden S., Perzanowski K., Kruhlov I., 2008. Cross-border comparison of post-socialist farmland abandonment in the Carpathians. Ecosystems 11: 614–628. Landis J.R., Koch G.G., 1977. The measurement of observer agreement for categorical data. Biometrics 33 (1): 159–174. Law K.H., Nichol J., 2004. Topographic correction for differential illumination effects on IKONOS satellite imagery. In: Orhan A. (Ed.) IISPRS Archives – Volume XXXV Part B3, XXth ISPRS Congress, Technical Commission III. Istanbul, Turkey, pp. 641–646. Masek J.G., Vermote E.F., Saleous N., Wolfe R., Hall F.G., Huemmrich F., Gao F., Kutler J., Lim T.K., 2006. A Landsat surface reflectance data set for North America, 1990-2000. IEEE Geoscience and Remote Sensing Letters 3: 68–72. McGarigal K. and Marks B.J., 1995. FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. General Technical Report PNW-GTR-351, USDA For-est Service, Pacific Northwest Research Station, Portland, Oregon, USA. McGarigal K. and Cushman S.A., 2002. Comparative evaluation of experimental approaches to the study of habitat fragmentation studies. Ecological Applications 12(2): 335–345. Mkrtchian A., Svidzinska D., 2014. Modeling the location of natural cold-limited treeline and alpine meadow habitats in Ukrainian Carpathians. In: Forum Carpaticum 2014: Local Responses to Global Challenges. Conference abstracts. September 16-18, 2014, Lviv, pp. 102–103. Riley S.J., De Gloria S.D., Elliot R., 1999. A Terrain ruggedness index (TRI) that quantifies topographic heterogeneity. Intermountain Journal of Science 5 (1-4): 23-27. Verburg P.H., Soepboer W, Veldkamp A, Limpiada R, Espaldon V, Mastura S.S., 2002. Modeling the spatial dynamics of regional land use: the CLUE-S model. Environmental Management 30 (3): 391–405. Woodroffe R. and Ginsberg J.R., 1998. Edge effects and the extinction of populations inside protected areas. Science 280: 2126–2128.

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LANDFORMS AND THE SHAPING OF CULTURAL LANDSCAPES IN MOUNTAIN AREAS: A METHODOLOGY FOR THE ANALYSIS OF PERMANENT PASTURES LANDSCAPE OF ALTO BARROSO REGION (NORTHERN PORTUGAL) Andreia PEREIRA Center of Studies in Geography and Territorial Planning, Arts Faculty of the University of Coimbra, Coimbra, Portugal; [email protected] Abstract Alto Barroso develops in a transitional strip located between the mountain ranges of Northwestern Portugal and the plateau of “Trás-os-Montes”. It contains a succession of uneven tablelands, divided by the entrenchment of the valleys of the main watercourses. The characteristics of the cultural landscape of Alto Barroso are clearly connected to its productive dimension, being mostly shaped by ancestral agro-silvo-pastoral systems. They are the outcome of the conjugation of hydrogeological and climatic constraints with the soil use options and technical solutions adopted by human communities. This research aims to contribute to the understanding of the landscape of Alto Barroso, demonstrating the relation of the soil use pattern with the major terrain features and medium scale landforms. The geomorphological analysis of the study area, supported by field work and by a geographic information system (GIS) with topographical, geological and hydrographical data, enabled the identification of the major terrain features and medium scale landforms. Then, the geomorphological data was simplified and converted into a map of homogeneous terrain units, which represents landforms categories, delimited accordingly to their topographic and morphostructural characteristics. Finally, the correlation of these landforms with the soil use patterns, particularly in what concerns to the distribution of the permanent grasslands, made evident the interactions between the geomorphology and the landscape organization. The results achieved enabled the definition of the morphostructural factors that influences the distribution of permanent grasslands and the identification of the landforms with major interest for landscape interpretation. Key words: landform; geomorphological heritage; cultural landscape; permanent pastures. Introduction Landforms may be subject of analysis in different geographic and time-scales, providing high-value data to several scientific branches. They hide time deepness and a set of chronological layers that only might be unfolded through the meeting of distinct disciplinary insights. If geomorphologists see the study of landforms as a way to understand Earth history and explain past and present environmental dynamics (Grandgirard, 1997); archaeologists and historians will consider them as a source of information to disclose the mankind’s history (Fig. 1). The interpretation of cultural landscapes is the field where these two approaches meet together. The different definitions associated with the concept of geomorphological heritage reflect different valuation criteria, which are derived from the theoretical and methodological frameworks of the different scientific fields and depends on the specific objectives of each research work. The goals and the scope of application of the most

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commonly used criteria for geomorphosites inventory, selection and assessment can be interpreted in diverse ways by different disciplinary domains or, preferably, within a multidisciplinary framework. From this point of view, it is questionable the prevalent division established between the so called ‘scientific criteria’ (sensu stricto) and the ‘additional criteria’, which relegates to a secondary level the ecological, scenic, aesthetic and cultural values. A holistic view of the natural and cultural heritage of a given territory will surely place in the forefront the landscape impact and the historical and cultural meanings of a geomorphosite. Cultural Geomorphology seeks to reveal the many dimensions, meanings and values of geomorphological heritage. This recent specialization of physical geography is defined by Panizza and Piacente (2003) as the “the discipline that studies the geomorphological component of a territory which embodies both a cultural feature of the landscape and its interactions with cultural heritage of the archaeological, historical, architectonic etc. type”. In this definition, landforms are equally considered as an element of cultural landscapes, as well as a support that interacts with built heritage.

Historical /cultural / economical value

Landform

Conditioning factor

Land-use options and strategic use of resources

Scientific / didactic value

Testifies Results from the combination of geo-structural and morphogenetic factors

Morphogenetic processes that took place in paleoenvironments, tectonic action and present dynamics

Appropriation of the resources associated to landforms

Understanding of geomorphological heritage as an evidence of Man – Nature interactions

Understanding of geomorphological heritage as a an evidence from the past and present geomorphologic processes

Landforms are a crucial variable in land planning, territory management and development strategies

Fig. 1 Dimensions and values of geomorphological heritage According to Panizza (2001) a geomorphosite corresponds to a landform to which value can be attributed and it may concern to geomorphological elements of variable dimensions, from a single (punctual) landform, as a sinkhole, to medium or macro-scale landforms, such as a slope or a valley (Reynard, 2005). The acknowledgment of a geomorphosite depends on the recognition of an intrinsic value. Panizza and Piacente (1993, 2003) defined five types of values assigned to geomorphosites– scientific, historical, cultural, symbolic, aesthetic and socio-economic –, which are grouped by Reynard (2005) in three major areas: natural heritage, cultural heritage and, finally, the economic resource.

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Therefore, taking into account the different approaches and objectives of each analysis, the term geomorphosite may not be “restricted to unique or spectacular geomorphological objects or groups of objects, but also includes ‘common’ sites in which people, animals and plants live” (Seijmonsbergen, De Graaff, 2009). These landforms that are not considered as being exceptional, in aesthetic or scientific sense, may be acknowledged as a heritage considering the role they play in landscape shaping, their cultural or symbolic meaning or their economic value, including direct and indirect functions. One should stress that many geomorphosites “are characterized by a high cultural value, due to their connection with human activities and settlements […], as well as by their geohistorical importance” (Pelfini, Bollati, 2014). This perspective of understanding and attributing value to geomorphological heritage expands the operational possibilities of this concept, strengthening its relevance to scientific research, territorial management, educational purposes and tourism development. This work underlines this broader outlook of geomorphological heritage, whose projection increased over the last decade, giving it a renewed leadership within the discussion about humanized territory and cultural landscapes. Geomorphology, conditioning decisively the distribution of natural resources and accessibilities, has a prevailing influence on the strategic occupation of the territory by communities. The geomorphological support grounds the land organization and landscape shaping. Terrain features interact with edaphoclimatic and phytosociological factors in the production of the biogeographic framework within which develops settlement patterns and productive systems. The conjugation of landforms and lithological formations influences the definition of locative options, settlement patterns, defensive strategies, communication networks and exploitation of natural resources. The characteristics of the cultural landscape of Alto Barroso are clearly connected to its productive dimension, being mostly shaped by ancestral agro-pastoral systems and techniques. It reflects the outcome of the combination of hydrogeological and climatic constraints with the soil use options and technical solutions adopted by human communities. This essay aims to analyse how major terrain features and medium scale landforms, as well as lithological formations, played a major role in the organization of the soil use pattern in Alto Barroso, with particular emphasis in the distribution of the different types of permanent grasslands. This research intends to contribute to the understanding of the landscape of Alto Barroso, following three specific goals: - define the conditions that influences the distribution of permanent grasslands, with particular emphasis in the morphostructural factors; - identify and characterise the landforms with major interest for landscape interpretation; - promote the acknowledgment of the landscape and productive values of the landforms and their potential heritage interest. Materials and methods Alto Barroso region develops in a transition strip located between the mountainous systems of North-western Portugal and the plateau of Trás-os-Montes, which extends to the border with Spain, being part of the Iberian Massif. Alto Barroso is a tableland, with an average altitude of 700 meters, which has resulted from the Alpine uplift of the region of Trás-osMontes. Its geomorphological delimitation is clearly defined by the fault alignments of NESW orientation, coincident with the main valleys, such as the Tâmega River. This plateau corresponds, thus, to an intermediate horst which is defined between the mountain range of Gerês, the graben of Tâmega River and the SW fault scarp of the ridge of Seixa. It may be considered as a tectonic step that makes the transition from the graben of Tâmega River and 152

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the mountain range of Gerês, a higher tectonic block, defined by the fault that affects the Beredo River. The main tectonic alignments are late-hercynian (graben Boticas-Chaves) and Alpine (N-S fracture that affects the Beredo River), with probable rematch of hercynian faults in the SW scarp. In a brief lithological characterization, the predominance of granitic rocks stands out. The Hercynian granites of medium to fine-grained occupy 60% of the study area. The properties of this bedrock favours the development of relatively deep alteration mantles, resulting in great water availability and in an effective supply of water tables, due to the easy infiltration and circulation of the water at the subsuperficial channels and base flow. In this way, the development of fertile soils is favoured. The soils of fine texture, rich in organic material, are dominant, considering that the rapid water infiltration in areas of granitic substrate and short warm season do not promote the biodegradation of the humic horizon. The specificity of the climate of Alto Barroso is conditioned by its macro-structural framework and morphological characteristics, with a particular influence of the altitude in the temperature behaviour and in the annual quantity of rainfall. Given that this region extends in a transition area between the Atlantic and the continental influences, the annual precipitation decreases from northwest to southeast. The mountain ranges that surround this plateau force the oceanic air masses to rise, producing orographic rainfalls, which affect mainly the eastern faces of the adjacent mountain range of Peneda-Gerês, where the average annual rainfall exceeds 2800 mm. In the eastern limit of Alto Barroso this value varies between 1200 and 1400 mm. Alto Barroso is characterized by cold winters and hot dry summers. The average annual temperature in Montalegre lies roughly in the 10ºC. Note the strong annual thermal amplitude, with very low temperatures during autumn and winter months (November to March). With monthly average temperatures of 4ºC in January and February and an average minimum of near 0ºC, snowfall is quite frequent and the number of days of frost can reach 80 per year. On the other hand, the summer months are extremely hot. In this geomorphologic and climatic context, the productivity of croplands and grasslands, essential to the self-sufficiency, was only possible thanks to the construction of agro-pastoral terraces and through the organization of a complex net of traditional irrigation channels, ruled by a communitarian management, resulting in a deeply humanized landscape. Geomorphological and climatic characteristics of Alto Barroso made of it a land of shepherds. Sampaio (1979, p.30) states that in the Barroso "... the pastoral regimen becomes so prevalent that, without exaggeration, one can say that the population lives mainly of the herds, which in a large part of the year are breed in the rich pastures of the hills and the collective use of the neighbours". The organization of the agrarian system of Alto Barroso still reflects the influence of the Roman model, expressing "a close relationship between the various components of the organization of rural space: the ‘domus’ with the house, the ‘hortus’ with the kitchen gardens, the ‘ager’ with the croplands, the ‘saltus’ with the pastures and ‘silva’ with the forest" (DevyVareta, Alves, 2007, p. 56). The complementarities between these spaces overpass the combination of functions and productive goals, including the exchange of energy and nutrients carried out by the collection and combustion of woody material and the production of animal manure. The cattle, bovine, sheep or goats, whose grazing areas vary throughout the year in altitude and typology, play a central role in this system, fertilizing the croplands and semi-natural grasslands with nutrients from the shrubs and woodlands. The morphological profile of a typical slope in Alto Barroso corresponds to a specific pattern of distribution of land uses, whose sequence in altitude is determined by the variation of soil properties, water availability and gradient. The forest and shrublands, the poor mountain pastures, the watermeadows, the upland cereal cultures and the extensive horticulture build a landscape mosaic

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sustained by an intricate network of functional interdependencies and complementarities. One of the key features of the landscape mosaic of Alto Barroso, which may be named as the ‘landscape of permanent grasslands’ (Fig. 2), is the association of the water-meadows with the poor mountain pastures. The water-meadows are permanent mountain grasslands that can be considered semi-natural, since they do not arise from the deliberate sowing of improved species (Pires et al., 1994; Moreira et al., 2001). Their origin dates back to the High Middle Ages (Moreira et al., 2001; Taborda, 1932), when the first agro-pastoral communities and rural settlements were established in the region, proving that we are in presence of a cultural landscape that have been shaped by Man throughout almost the last thousand Fig. 2 ‘Landscape of permanent grasslands’ years. Water-meadows occupy preferentially fields with high water availability and fine textured soils with big organic matter content which, when soaked and with no vegetation cover, are abundant in mud, fact that is at the origin of the designation as ‘lameiros’, a word that refers to a muddy grassland (Vieira et al., 2000). Therefore, they occur mainly on granitic substrates and on hillsides with slopes that goes from smooth to average gradient, so as to ensure a good drainage. The high amount of annual rainfall, associated with deep granitic alteration mantles, explain the great water availability, constituting a favourable factor to the implementation of water-meadows. This type of semi-natural permanent grasslands depends on ancient irrigation techniques and communitarian systems for water management. In order to satisfy the water requirements during the dry season and to protect the vegetation against frost during winter and early spring, the “water is derived from the mountain watercourses and/or springs, conveyed through small earth canals and spread over the fields through a cascade of small contour ditches” Fig. 3 Water-meadow with traditional (Poças et al., 2012), an irrigation technique irrigation channels that is called “rega de lima” (Fig. 3). In synthesis, the landscape character in Alto Barroso is conditioned by the morphological features of a table land, where stands out the medium scale landforms such as several plateaus developed in different elevation ranges, entrenched valleys, grabens and natural terraces, which has influenced the organization of a multi-century old agro-pastoral system. This traditional productive system, specifically adapted to the mountainous environmental conditions, is characterized by the importance of cattle breeding, based on extensive grazing, which takes advantage of the association of poor mountain pastures and water-meadows, frequently delimited by broadleaves, by the cultivation of poor dryland cereals at high altitudes and by the preserved groves of broadleaves, especially oaks. The concentrated settlement model conserves many mountain villages where it is still possible to observe the vernacular architecture, with the use of local building materials, and constructions of collective use (threshing floors, oil mills, ovens). 154

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Once completed this brief presentation of the cultural landscape of Alto Barroso we will focus on the contribution of Cultural Geomorphology to the understanding of its shaping. The selection of sites of geomorphological interest from a landscape point of view was conducted at a regional scale and the key criterion observed was the relation between landforms and the landscape organization and character. The followed methodology starts with the geomorphological analysis of the study-area. Extensive field work together with the development of a geographic information system (GIS), containing topographical, geological and hydrographical data layers, enabled the identification of the major terrain features and medium-scale landforms. Then, the geomorphological data was simplified and converted into a map of homogeneous terrain units, which represents landforms categories, delimited accordingly to their topographic characteristics and morphostructure (Guiné, 2014), taking also into account the facility of their recognition in the field and their landscape impact. Geomorphological cartography must be adapted in order to be more easily applied to the analysis of cultural landscapes. Firstly, one should define which information of the geomorphological map is most relevant to the interpretation of the landscape organization and evolution in a geo-historical time-scale. Besides this, the fact that the cartographic symbology combines punctual, linear and areal elements, as well as the complexity of the geomorphological legend, makes it difficult to use for landscape analysis.

Fig. 4 Relation of the homogeneous terrain units with the permanent grasslands (source: elaborated by the authors, based on the COS (Soil Use Map) 2007, Portuguese Geographical Institute, (IGP) and on the analysis of the IGP ortophotomaps, 2010) The map of homogeneous terrain units (fig. 4) represents the most important features of the regional morphology, such as the main valleys and related alluvial plains, grabens, plateaus, mountain ranges, hills and natural terraces. This cartography, centred in the identification and delimitation of the main morphological elements, reflects two methodological options: the focus on medium-scale landforms in the regional framework and a representation based on polygonal implementation. In this way, the conduction of different type of spatial analysis in

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GIS is easiest, namely in what concerns to the assessment of the influence of landforms over soil use and settlement patterns. Finally, the correlation of the landforms with the main soil use patterns, particularly in what concerns to the distribution of the permanent grasslands, demonstrated the relations between the geomorphology and the landscape organization. Results and discussion Landforms may be divided in three groups according to the importance of the water-meadows and the poor mountain grasslands (tab. 1) in the soil use. The first group includes the landforms with more than a half of their area is occupied by permanent grasslands. In this group the valleys and uplands are dominant. Only the graben of Amial is inserted here. In the second group, where the grabens stand out as the more frequent type of landform, these uses occupies from 25% to 50% of the total area. In the last group, the permanent grasslands represent less than 20% of the area of the landform. Here, there is a clear dominance of the mountain and hilly areas. Tab. 1 Percent of the soil use calculated in relation to the area of each landform Soil use area in % Landforms

A

B

C

D

E

F

Valley of Aires River Upper Valley of Rabagão River Upper Valley of Cávado River Upper Valley of Beça River Middle Valley of Beça River Lower Valley of Beça River Upper Valley of Assureira River Graben of Rabagão Graben of Dornelas Graben of Boticas Graben of Ardãos Graben of Amial Range of Seixa Range of Leiranco Range of Larouco Range of Cerdeira Range of Barroso Plateau of Pitões Plateau of Alturas Natural terrace of Solveira Natural terrace of Pinho

32.9 43.7 43.5 50.1 19.0 48.7 33.0 27.3 25.2 14.3 30.5 36.9 3.4 0.2 0.9 7.3 5.8 11.0 34.6 10.5 11.0

28.7 16.6 19.6 14.4 40.7 13.1 2.4 12.8 11.0 1.7 14.4 20.1 6.1 15.4 6.1 19.4 7.1 44.4 28.9 18.9 0.1

15.6 2.77 10.8 3.0 17.6 17.2 10.2 15.0 8.5 16.1 7.2 12.3 39.7 2.2 2.1 18.7 2.7 5.6 6.4 2.5 29.5

15.6 16.3 11.4 17.7 12.9 7.33 8.1 10.6 9.5 2.1 13.6 2.3 43.3 79.9 21.4 45.1 61.7 30.7 19.6 20.5 26.9

0.0 2.7 1.6 0.1 0.0 0.0 26.2 0.6 0.2 0.0 0.00 3.1 3.8 0.9 68.3 1.8 20.5 4.9 1.3 4.8 0.00

1.4 9.7 5.3 7.5 8.0 10.2 6.6 4.2 30.0 35.2 24.4 8.1 2.1 0.4 0.9 2.0 1.5 1.2 3.4 5.1 30.1

G 0.0 0.1 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 1.5 0.0

H

I

5.4 4.4 6.3 6.9 1.6 1.7 12.1 27.4 9.1 0.0 6.9 14.6 0.4 0.1 0.1 4.9 0.4 1.8 5.3 35.0 0.3

0.1 3.4 1.2 0.03 0.0 1.4 1.1 1.7 6.3 30.4 2.8 2.4 0.8 0.5 0.0 0.4 0.0 0.3 0.4 2.2 1.9

A - Water-meadows; B - Poor grasslands; C - Forest and shrublands; D - Shrubby and subshrubby vegetation; E - Areas with scarce vegetation cover; F - Complex agricultural areas; G - Permanent cultures; H - Annual cultures; I - Built area. In a more detailed analysis, some distinctions may be done regarding the distribution of the water-meadows and poor mountain grasslands. The upper valley of Beça River is the only 156

Halada, Ľ., Bača, A., Boltižiar, M. (eds.): Landscape and Landscape Ecology. Proceedings of the 17th International Symposium on Landscape Ecology

landform where more than a half of its area is occupied by water-meadows. The sum of both types of pastures abovementioned overpasses 64%. It is also interesting to highlight that the water-meadows are dominant over the poor mountain grasslands in the lower valley of Beça River, the upper valleys of the rivers Rabagão and Cávado (fig. 5), the valley of Aires River, the graben of Amial and the plateau of Alturas. In these landforms the permanent grasslands represent together always more than 60% of the area; however the water-meadows are strongly prevailing. Finally, we may distinguish the plateau of Pitões and the middle valley of Beça River where the permanent grasslands reach near 60%, being the majority of the area occupied by poor mountain grasslands. This may be explained by the combination of a higher elevation range together with the dominant geological formations that influence the edaphic characteristics. The low temperatures associated with bedrocks less prone to the alteration processes more frequent in this morphoclimatic context, led to the formation of less developed soils and with smaller water availability, not favouring the appearance of watermeadows. In the middle valley of Beça River, the presence of black schists is an obvious conditioning factor of the soil use.

Fig. 5 Importance of the permanent grasslands in the upper valley of River Cávado and its tectonic control Also in the second group some singularities must be pointed out. It is remarkable the prevalence of the water-meadows in the upper valley of Assureira River, in the grabens of Ardãos, Rabagão and Dornelas. In contrast, on the remaining landforms of the group, the natural terrace of Solveira and the range of Cerdeira, the poor mountain grasslands are predominant over the water-meadows. Once more, the elevation and the lithological substrate are the main conditioning factors of the land-use options. In the third group, where the permanents grasslands are less represented, other soil uses must be highlighted, particularly the importance of the complex agricultural systems in the graben of Boticas, as well as, the areas with scarce vegetation cover in the range of Larouco and the shrub, sub-shrub and herbaceous vegetation in the ranges of Leiranco, Seixa and Barroso. 157

Halada, Ľ., Bača, A., Boltižiar, M. (eds.): Landscape and Landscape Ecology. Proceedings of the 17th International Symposium on Landscape Ecology

Tab. 2 Morphological characterization of each landform and its relation with soil use Landform Valley of Aires River

Area /km2/

Morphologic features

6,50 Slope