BIODIVERSITY Cover - Biodiversity Information System - IIRS

NATIONAL ASSESSMENT

2012

AT LANDSCAPE LEVEL

BIODIVERSITY CHARACTERISATION

2012 NATIONAL ASSESSMENT

© Akshay Madan

AT LANDSCAPE LEVEL

BIODIVERSITY CHARACTERISATION

Published by :

Indian Institute of Remote Sensing Indian Space Research Organisation Govt. of India. Dehradun - 248001, Uttarakhand, India © IIRS 2012

ISBN No. 81-901418-8-0

Note : This publication may be reproduced in whole or in part and in any form for educational or non-profit purposes without special permission from copyright holder, provided acknowledgment of the source is made. IIRS would appreciate receiving a copy of any publication that uses this publication as a source. No use of this publication may be made for resale or for any other commerical purpose whatsoever without prior permission in writing from IIRS.

This publication is available in electronic form at: www.iirs.gov.in

Citation Roy, P.S., Kushwaha, S.P.S., Murthy, M.S.R., Roy A., Kushwaha, D., Reddy, C.S., Behera, M.D., Mathur, V.B., Padalia, H., Saran, S., Singh, S., Jha, C.S. & Porwal, M.C. (2012) Biodiversity Characterisation at Landscape Level: National Assessment, Indian Institute of Remote Sensing, Dehradun, India, pp. 140, ISBN 81-901418-8-0

Design & Realisation Xpressions Print & Graphics Pvt Ltd 9219552563

Acknowledgment The study was carried out under the overall guidance and support of Dr. K. Kasturirangan, Secretary Department of Space (1994-2003), Dr. (Mrs.) Manju Sharma, Secretary, Department of Biotechnology (1995-2004); Sri, G. Madhavan Nair, Secretary, Department of Space (2003-2009); Dr. K. Radhakrishnan, Secretary, Department of Space (2009 till date) and Dr. M. K. Bhan, Secretary, Department of Biotechnology (2004 till date). The team is indebted to Prof. J.S. Singh, Banaras Hindu University, Varanasi, Prof. A. K. Sharma, Calcutta Unviersity, Kolkata and Prof. Madhav Godgil, India Institute of Science, Banglore and Prof Sudhir Kumar Sopory, Vice Chancellor, JNU, New Delhi for their scientific guidance and critical review from time to time. Dr. Renu Swaroop, Advisor Department of Biotechnology and Dr V. S. Hegde, ISRO, Bangalore, have been tremendous strength behind this study. Last not the least, the entire team acknowledges the financial support provided by ISRO/DOS and DBT, Government of India.

National Assessment Contributors P.S. Roy1 S.P.S. Kushwaha1 M.S.R. Murthy2 Arijit Roy1 Deepak Kushwaha1 C. Sudhakar Reddy2 Mukund Dev Behera3 V.B. Mathur4 Hitendra Padalia1 Sameer Saran1 Sarnam Singh1 C.S. Jha2 M.C. Porwal1

1 Indian Institute of Remote Sensing (ISRO), Dehradun 2 National Remote Sensing Centre (ISRO), Hyderabad 3 Indian Institute of Technology, Kharagpur 4 Wildlife Institute of India, Dehradun

contents Foreword Preface Executive Summary

1. Introduction 1.1 1.2 1.3

1.4 1.5

001

General Biodiversity

001 003

1.2.1

005

Biodiversity Hotspots and Centers of Origin - India

Biodiversity assessment

006

1.3.1

007

Rapid Biodiversity Assessment (RBA)

1.3.2

Rapid Assessment Program (RAP)

008

1.3.3

Rapid Biological Inventory (RBI)

008

1.3.4

Rapid Ecological Assessment (REA)

008

1.3.5

Biodiversity Assessment and Mapping Methodology (BAMM)

008

Biodiversity Conservation priority: setting the right criteria for India and World

012

1.4.1

012

Baseline data on biodiversity at landscape level

Highlights of the study

2. Biodiversity Characterization Approach

013

015

2.1

Introduction

2.2

Geospatial Products

017

2.2.1

017

Vegetation Type Mapping

2.2.2

Phytosociological Analysis

023

2.2.3

Landscape Analysis

025

3. Landscape Analysis 3.1

015

Biogeography of India

031 031

3.2

Physical Features of India

031

3.3

Vegetation Types and the Land Uses

032

3.4

Fragmentation status

039

3.5

Disturbance status

045

3.6

Biological Richness Index

050

3.7

Biogeographic distribution of vegetation and landscape indices

055

3.7.1

055

Trans-Himalayas

3.7.2

Himalayan Region

057

3.7.3

Indian Desert

060

3.7.4

The Semi-Arid Zone

062

3.7.5

Western Ghats

064

3.7.6

Deccan Peninsula

067

3.7.7

Gangetic Plain

070

3.7.8

North-East India

072

3.7.9

The Islands

074

3.7.10

The Coasts

076

3.8

3.9

Protected Areas : National Parks and Wild life Sanctuaries

078

3.8.1

Sacred Groves

078

3.8.2

Mangroves

080

3.8.3

Alpine Pastures

081

Protected Aeas : National Parks and Wildlife Sanctuaries

083

4. Biodiversity Inventory

087

5. Biodiversity Information System

101

5.1

National Spatial Framework

102

5.1.1

105

NSF Parameters for 1:50k & larger scale state database

5.2

Conceptual Architecture

106

5.3

Components of BIS

108

5.3.1

108

BIOSPATIAL (Biodiversity Characterization at Landscape Level)

5.4

User Rights

111

5.5

Metadata Organization in Biodiversity Information Systems

111

5.6

Indian Bioresource Information Network

112

6. Biodiversity Conservation Planning : The Way Ahead

115

6.1

General

115

6.2

Monitoring Biodiversity

116

6.3

Predictive Distribution Modeling

118

6.4

Gaps in Taxonomic Knowledge

118

6.5

Ecological Corridor Modeling

119

6.6

Valuation of the Ecological Goods and Services Influenced by Biodiversity

120

7. Conclusion

121

7.1

Importance and need

121

7.2

Importance in conservation/prioritization and sustainable management

122

7.3

Identification of methodologies for sustainable use of ecological services

125

7.4

Fauna data integration

125

7.5

Data Utilization and Potential

126

7.6

Future direction

128

8. References

129

9. Appendix Appendix 1

i

Appendix 2

v

Appendix 3

ix

Appendix 4

xiii

Appendix 5

xvii

10. Image Chips

xxi

11. Biogeography wise vegetation, fragmentation, disturbance & biological richness Maps 12. Biodiversity Characterization at Landscape level Project Contributors (1998-2010) 13. Collaborating Organizations

Foreword Biodiversity is important for sustaining the ecosystem processes and services. Proper documentation of biological diversity is essential for conservation planning and its sustainable use for using it sustainably for the benefit of human kind. The Indian Space Research Organisation and Department of Biotechnology have implemented biodiversity characterisation using satellite derived parameters and geospatial modelling during 19982010 in different phases. I am happy to note that the work carried out in more than one decade has been compiled as a publication for national assessment. I am sure that the information generated will have a great value to the scientific community, bio-resource managers and research groups for biodiversity conservation and monitoring.

Dr. K. Radhakrishnan Chairman, ISRO

Indian Space Research Organisation Department of Space Government of India Antariksh Bhavan New BEL Road, Bangalore - 560231, India Telephone : +91-80-2341 5241 / 2217 2333 Fax : +91-80-2341 5328 e-mail : [email protected]

Preface Biodiversity is a natural wealth of a nation and its accounting is essential for intellectual property characterization and its conservation. Proper documentation of biological diversity enables conservation of the information about the nation natural wealth and its sustainable use for the benefit of mankind. The information of biodiversity in context of its spatial distribution, chracteristics, economic utility, traditional knowledge base, etc. is a important intellectual property of a nation. The program entitled "Biodiversity Characterization M.K. Bhan

at Landscape Level using Satellite Remote Sensing and Geographic Information System" is a pioneering effort to generate baseline data on biological richness from various regions of India. Department of Biotechnology and Department of Space have joined hands to handle this task collaboratively at the national level to cover the entire country. The Project has generated four primary products at national level i.e. vegetation type map, fragmentation map, disturbance index map and biological richness map. A total of 150 vegetation and land use classes have been delineated using

1857 1907

1947

lR;eso t;rs

visual interpretation technique at 1:50,000 scale. A total of 3,13,076 individuals comprising 7,761 plant species have been sampled across 16,518 sample plots and also includes 1897 medicinal species, 2,803 are economically important species, 648 endemic and 23 RET species. The outcome of the nationwide project have been quite significant, particularly the wall to wall and holistic database on the key inputs describing the quality and quantity of the vegetation and biodiversity at different spatial levels. This database is a baseline database on vegetation types, fragmentation status and biological richness of Indian landscape which is the key to biodiversity conservation planning and developing future management strategies for conservation efforts. The results generated will serve as a baseline database for various assessments of the biodiversity for addressing CBD 2020 targets. I am sure that the scientific contributions, valuable database generated in the present study will be utilized by the stakeholders and research groups for conservation and sustainable utilization of the bio-resources.

M.K. Bhan Secretary Government of India Ministry of Science & Technology Department of Biotechnology Block-2, (7th Floor) C.G.O. Complex Lodi Road, New Delhi-110003

t; fgUn

MINISTRY OF SCIENCE & TECHNOLOGY

BIODIVERSITY CHARACTERISATION AT LANDSCAPE LEVEL IN INDIA

Executive Summary

Biodiversity conservation necessitates not only preservation of single or multiple species but also the habitat as a whole. Prioritizing biological richness (BR) following landscape ecological approach has implications in management and planning. Six biodiversity attributes (i.e., spatial, phytosociological, social, physical, economical and ecological) were integrated to stratify BR of forest vegetation in India following a 'three-tier approach' with required field enumeration. Satellite image provided spatial distribution of vegetation types (corresponding to ecological habitats) which further subjected to landscape ecological analysis. Biotic disturbance buffers (i.e., proximity zones around roads and human settlements) along with landscape parameters were combined to calculate disturbance index (DI), which in turn became an intermediate surrogate for BR assessment. Both flora and fauna species diversity (Shannon's index); their ecosystem uniqueness (IUCN status), biodiversity value (BV) and total importance value (TIV) were enumerated for BR computation along with terrain complexity (TC). India being one of the mega biodiversity countries is also one of the most densely populated regions of the world. The country harbors two of the biodiversity hotspots out of the 34 global biodiversity hotspots: the Indo-Malayan which includes the eastern Himalayas, north-east India and Andaman Islands, and the Western Ghats. These regions harbor some of the important gene-pool of medicinal plants, wild varieties of cultivable crops and other species of economic importance as well as innumerable endemic and RET plant species. Conservation of this national wealth is of paramount importance in face of increasing pressure on the plant diversity in form of land use and land cover change, invasive species, global warming, nutrient deposition and climate change. The field inventory involving 16,518 geo-referenced 0.04 ha plots across India has recorded a total of 7,761 plant species, wherein 648 species are endemic, 23 are RET species, 1,879 medicinally important and 2,803 species are economically important. The geospatially tagged species database, created in the project, provides information on the endemic, rare, endangered, threatened and economically/medicinally important species. The database has been shared with organizations including State Forest Departments, has found extensive applications in policy planning, operational management etc. Majority of the forested areas in the country are under low fragmentation around 59%, moderately fragmented regions constitute 32% and high fragmented forest areas constitute 9% of the TGA of the forests. It has been observed that about 45% of the forested area is under high biological richness. The natural areas under low biological richness constitute 36% of the area. The website, (www.bisindia.org) allows identification of gap areas, species/ habitat relationship and helps in biodiversity conservation planning by setting priority areas. Detailed site-specific field inventories with this database can be used for identifying areas for bio-prospecting. The entire spatial and non-spatial data on Indian plant biodiversity has been organized and available in BIS (Biodiversity Information System), with its three major components viz., BIOSPATIAL for biodiversity

Biodiversity Characterisation at Landscape Level: National Assessment

spatial query shell, PHYTOSIS for plant species information system, and BioConsSDSS for biodiversity conservation spatial decision support system. The BioConSDSS is a unique application in web GIS environment which addresses the semi-structured problem with various degrees of uncertainty on biodiversity, where computer-based models can interact directly with the biodiversity experts to generate a knowledge base for biodiversity conservation and prioritizations. It provides multicriteria geospatial decision analysis for biodiversity conservation in web GIS environment. The information services implemented using OGC WMS under BIS are freely accessible by the users after formal registration while the digital spatial data is shared with user organizations for further value addition and scientific studies. The World Wide Web and high speed network access has given a new dimension to geospatial domain; thereby larger data sets are processed with more complex models and analysis for decision-making through better display and visualization. All the information is available in a web-enabled 'Biodiversity Information System' (www.bisindia.org) that contains the results including species-wise location and their ecological status. The geospatial biodiversity database prepared for the country will serve as (a) baseline data for forest managers and conservationists and (ii) will have implications for long-term biodiversity studies in lieu of 'climate change'; and thereby holds lots of promise to the International programs on studying of vegetation variables by providing the most comprehensive biodiversity information at a national level. Additionally, with the continuity of satellite data products by Indian Space Research Organization (ISRO) and availability various natural resources data repository, India is emerging as an important participant and contributor to the global monitoring of long and shortterm vegetation variables. There has been an urgent need of a quality database of the biological diversity at species, community, ecosystem and landscape levels that is now fulfilled by the study.

BIODIVERSITY CHARACTERISATION AT LANDSCAPE LEVEL IN INDIA

INTRODUCTION

1.1

General Why does biodiversity matter to us? There are at least three classes of reasons why it does. First, it provides us with a number of goods that have direct economic value, such as food, new pharmaceuticals, genes that improve crops, and organisms that perform biological control. Second, it is intricately linked to human well-being for aesthetic, ethical, cultural and scientific reasons. And third, it may contribute to the provision of ecological services that are generally not accounted for in economic terms, such as primary and secondary production, the regulation of climate, the maintenance of atmosphere quality, the regulation of the hydrological cycle, the maintenance of water quality, and the maintenance of soil fertility. Humans have extensively altered the global environment, changing global biogeochemical cycles, transforming 40-50% of the ice-free land surface, and converting grasslands, forests, and wetlands into agricultural and urban systems. Today, humans directly or indirectly consume about one-third of the terrestrial net primary productivity and harvest fish that contribute 8% of the ocean's productivity. They also use 54% of the available freshwater, and the projected use is likely to increase to 70% by 2050 (Chapin et al., 2000). Biologically diverse and resilient ecosystems are critical to human well-being, sustainable development, and poverty eradication. With the present rate of human consumption, and the resultant impacts on the environment, biodiversity on earth and in the oceans will be seriously compromised. It is already known that land use change is expected to have the largest global impact on biodiversity by the year 2100, followed by climate change, nitrogen deposition, species introduction, and atmospheric CO2 accumulation (Sala et al., 2000). Land use change is expected to be of particular importance in the tropics, while the effects of climatic change are likely to be important for temperate and polar regions; a multitude of interacting causes will affect other biomes. In short, the biological diversity on earth will be severely affected and therefore necessitates characterization for conservation and management. Biodiversity conservation necessitates not only preservation of single or multiple species but also the habitat as a whole along with its environment. Inventorying and analyzing vegetation cover is the most practical way of tracking biodiversity (Heywood and Watson, 1995). Article 7 of the United Nations Convention on Environment and Development (UNCED) requires signatory parties to 'identify components of biodiversity important for conservation and sustainable use and monitor, through sampling and other techniques, the components of biological units identified. Chapter 15.6 of UNCED document calls for the development of 'methodologies with a view to undertake systematic sampling and evaluation on a national basis of the components of biological diversity identified by means of country studies' and to initiate or further develop methodologies and begin or continue work on surveys at the appropriate level on the status of ecosystems and establish baseline information on biological resources. Studies of specimens collected during inventories yield


001

1 data useful to help prioritize areas for conservation and other management decisions. With little time to lose and limited conservation funds, we must implement programs and strategies that are effective at protecting biodiversity in situ. In order to do this, we need to have rapid and comprehensive methodologies that provide spatial distribution of important habitats for conservation and management. Inventory and assessment of existing levels and spatial patterns of biodiversity are essential for long and short-term management strategies. The understanding of the priorities of biodiversity conservation and management has resulted in a shift of approach from conservation of a single species to habitats through interactive network of species at landscape level. Landscape ecology sought to understand the ecological functions of larger areas and hypothesize those spatial ecosystems, habitats or communities had ecological implications in biological richness distribution. Vegetation is the main component of an ecosystem that displays the effects of other environmental conditions and historic factors in an obvious and easily measurable manner and its careful analysis can be used to reveal the information of other components of ecosystem. Any unit such as a vegetation type or an ecological habitat will have spatial, physical, social, phytosociological, ecological and economical attributes. Since the present day challenge is in-situ conservation, it is required to identify and prioritise biological richness of habitats or ecosystems for conservation planning. Biological richness is a cumulative property of an ecological habitat and its surrounding environment, which has emerging implications for conservation and planning. Appropriate assessment of biological richness is possible only when maximum number of biodiversity surrogates is considered. The present study on 'Biodiversity characterization at landscape level in India' judges the pattern of diversity (i.e., Shannon's index etc.) along fragmentation and biotic disturbance gradients. Satellite remote sensing was used for (i)

Classification of vegetation types and land cover units and

(ii) Estimation of vegetation cover. Geographic information system (GIS) was utilized for (I)

Spatial database development,

(ii) Analysis of spatial pattern of landscape units (i.e., patches), (iii) Generation of terrain complexity, (iv) Biotic disturbance buffer and, (v) Their integration along with economical, phytosociological and ecological attributes for qualitative labeling of biological richness in various ecological habitats.

INTRODUCTION

002

Global positioning system (GPS) was utilized for (I)

locating field sample plots,

(ii) gathering positional attributes of plant species and, (iii) Providing field-points for assessing the classification accuracy of vegetation type map. Phytosociological attributes of vegetation types viz., Shannon's diversity was calculated from field data. This is a promising approach for qualitative assessment of biological richness levels of various vegetation types and for making inventories and monitoring at landscape level at a shorter timespan. The spatial attributes i.e., location or extent of habitat or vegetation types and landscape properties were attached to the social attributes (i.e., biotic disturbance buffer) by adopting proportionate weights to derive an intermediate information layer of disturbance index (DI). Different biological richness levels of vegetation types were computed by integrating disturbance index with physical (i.e., terrain complexity), ecological (i.e., species diversity), phytosociological (i.e., species, endemism) and economical (i.e., species importance value) using a semi-expert package that developed through LAP, Bio_CAP and SPLAM.

1.2

Biodiversity Biodiversity is often considered synonymous with species richness, but the latter merely indicates the gross number of species in a given area, whereas the value of biodiversity may be linked to many more of its attributes than simply the number of species present. Of particular importance is the relative abundance of species. This may be of great importance to scientific values such as role of biodiversity in the functioning of an ecosystem. It is also very important for local values in terms of the utility of the species in meeting the community's needs for a certain commodity, or the risk if it is lost. Further, the actual composition of species present in any area is clearly of great significance to communities, scientists, and conservationists. Important special cases include the presence of globally rare and endemic species and large, vertebrates. The natural ecosystems are the repository of biodiversity, and the tropical ecosystems have the largest share of the world's vascular plant species, i.e., 45% of the total, as they provide niches for a large number of species and thus distinctly prevail as the most complex ecosystems. India, the second most populous country in the world, is the 11th mega-biodiversity center of the world and the third in Asia, with a share of about 11% of the total plant resources. The floral wealth of India comprises more than 47,000 species including 43% vascular plants. Nearly 147 genera are endemic to India (Nayar, 1996). The vast geographical expanse of the country has resulted in enormous ecological diversity, which is comparable with continental level diversity scales across the world. Twelve biogeographic provinces, five biomes, and three bioregions are represented in the country (Cox and Moore, 1993). Natural forests and forest plantations together cover 21.02% of the geographical area in India. India, one of the 12 Vavilovian Centers of Origin and diversification of cultivated plants, is known as the Hindustan Center of Origin of Crop Plants (Vavilov, 1951). About 320 species belonging to 116 genera and 48 families of wild relatives of crop plants are known to have originated in India (Arora and Nayar, 1984). We live in an era of rapid globalization with far-reaching consequences for human societies (Fig. 1.1). Globalization undoubtedly is also leading to impacts, for better or worse, through the equally rapid ongoing global environmental changes associated with the changes in biodiversity. Demographic, economic, socio-political, technology, lifestyle, and behavioral changes have made a significant impact on the proximate ecosystem drivers, causing perceptible perturbations in the structure and services of ecosystems (CBD, 2010). India's densely populated rural landscapes encompass some of the world's most intensively exploited ecosystems, including not only agroecosystems but also forests and other semi-natural ecosystems managed by populations in rural areas. Though these landscapes have been managed for centuries, the recent surge in population growth and adoption of fossil fuel-based energy sources use of synthetic fertilizers are likely to have a profound impact on ecosystems, especially the biodiversity.


003

Fig. 1.1 The components of global environmental change demonstrates relationships among human population and activity, the well-characterized components of change and changes in climate and biological diversity (adapted from Vitousek, 1994)

HUMAN POPULATION (Size and Resource Use)

AGRICULTURE

INDUSTRY

NITROGEN BIOGEOCHEMISTRY

INTRODUCTION

CO2 INCREASE

LAND USE / LAND COVER CHANGE

GLOBAL CLIMATE CHANGE

LOSS OF BIOLOGICAL DIVERSITY

004

The diversity of global species reached an all time high in the past geological period. The most advanced groups of organisms-insects, vertebrates, and flowering plants-reached their greatest diversity about 30,000 years ago. However, since then, the species richness has decreased, while human populations have grown (Leakey and Lewin, 1996). Between 1600 and 2000 (400 years), an estimated 4000 species of mammal, 9000 species of bird, 6300 species of reptile, 4200 species of amphibian, 19,100 species of fish, 1,000,000 species of invertebrate, and 250,000 species of flowering plant have become extinct (Primack, 2000). The United Nations has declared 2011-2020 as the United Nations Decade on Biodiversity, following the invitation of COP-10. It has requested the UN Secretary General to lead the coordination of the activities of the Decade on behalf of the UN, with the support of the CBD Secretariat and the secretariats of other biodiversity-related conventions and relevant UN funds, programs, and agencies. The strategic objectives of the Decade include the following: providing a supporting framework for implementation of the Biodiversity Strategic Plan 2011-2020 and the Aichi Biodiversity Targets at national, regional, and international levels; providing guidance to regional and international organizations; raising awareness of the value of biodiversity amongst the general public; and developing a broad consensus across society for the actions needed by individuals and communities. The COP-10, for the first time, attempted to assess the adequacy of global observation systems for monitoring biodiversity. They have set 20 targets organized under 5 strategic goals for the period 2011-2020. The goals and targets comprise both aspirations for achievement at the global level and a flexible framework for the establishment of national or regional targets. The five specific strategic goals (COP-10, 2011) are listed here. l Strategic Goal-A : Address the underlying causes of biodiversity loss by mainstreaming

biodiversity across government and society l Strategic Goal-B : Reduce the direct pressures on biodiversity and promote sustainable use l Strategic Goal-C : Improve the status of biodiversity by safeguarding the diversity of ecosystems,

species, and genes. l Strategic Goal-D : Enhance the benefits to all from biodiversity and ecosystem services. l Strategic Goal-E : Enhance implementation through participatory planning, knowledge

management, and capacity building.

1.2.1.

Biodiversity Hotspots and Centers of Origin - India Biodiversity hotspots, as proposed by Mayer (1988) are the regions characterized by exceptional plant endemism and plagued by serious level of habitat loss. There are 34 biodiversity global biodiversity hotspots, and India has four of them (Conservation International, 2012). They are

Fig. 1.2

(1) The Himalaya,

Distribution of biodiversity hotspots in the world (Source: Conservation International, 2012)

(2) Indo-Burma,


005

(3) The Western Ghats and Sri Lanka, and (4) Sundaland, including the Andaman and Nicobar Islands (Fig. 1.2). Although defining 'biodiversity hotspots' helps prioritize areas for conservation, overemphasis on such sites ignores the need for preserving adaptive variation across environments. However, a more comprehensive approach would be to include regions important to the generation and maintenance of biodiversity, regardless of whether they are 'species rich'. With climate change threatening large-scale shifts in species distributions and the habitats on which they depend, the hotspots of today are unlikely to be the hotspots of tomorrow. Only by maximizing adaptive variation can one hope to preserve the evolutionary response to changing climate and environmental conditions.

Fig. 1.3 Vavilovian Centres of Origin of crop plants (Vavilov, 1931).

According to Vavilov (1931), primitive agriculture originated in different regions of the world as a process of the domestication of local wild varieties of palatable plants (Fig. 1.3). Of the nine Vavilov's Centers of Origin of food crops, two fall within the Indian subcontinent-the Hindu-Kush and the Indo-Malayan region. Vavilov has described this region as an independent center of origin of crops, mainly rice. This makes the biodiversity of this region even more important as almost a quarter of the global population depend on the food crops, which have their genetic source in the region. There is an urgent need to conserve this invaluable agro-biodiversity and their wild genetic materials in situ.

Source http://archaeobotanist.blogspot.com)

North China : proso & foxtail millets soybean pig Eastern Tibet : buckwheat yak Crescent East : barley goat

Fertile West : Wheats barley sheep goat cattle pig Eastern North America : Sunflower sumpweed pitseed goosefoot squash Mesoamerica : Maize, beans, squash

North Japan barnyard millet burdock soybean North China : proso & foxtail millets soybean pig

West Sahel : pearl millet

rice (japonica)

Western Savanna: cowpea fonio African rice N. Peru / Equador : Lima bean sword bean squash

Eastern Savanna : Sorghum cattle

North / High Andes : potato, oca, llama

South India : mungbean horsegram browntop millet

rice (indica)

New Guinea : banana yams taro

Mid Andes : quinoa, amaranth, guinea pig Amazonia : manioc peanul

Archaeologically Better Documented Biogeographically Inferred, Poor Archaeology

1.3

Biodiversity Assessment The term biodiversity assessment in the literature covers a range of methodologies, mainly depending on the overall purpose of the assessment and the temporal and geographical scales at which it is applied. In general, three main purposes can be identified i.e., (i)

Conducting biodiversity inventories,

(ii) Biodiversity inventories are carried out to assess existing biodiversity and, (iii) In the sense of general stocktaking. This provides information on the biodiversity richness of a country or parts of a country. The second

INTRODUCTION

006

purpose is to carry out an inventory assessment to conduct a gap analysis. The objective of a gap analysis is to assess whether the protected area network of a country is sufficient in terms of its representation of the country's biodiversity. And thirdly, the term biodiversity assessment is used for monitoring biodiversity changes. Here the effect of human intervention on biodiversity is assessed. The latter has most relevance in the context of forest management and the assessment of its sustainability. Most of the criteria and indicators developed require all three aspects to be incorporated in forest management. Identifying, measuring and monitoring biodiversity is complex. An array of international and national initiatives has, therefore, sought to overcome this problem by trying to come up with simplified, yet significant, methodologies of biodiversity assessments. One way is the identification of indicators-a subset of attributes that could serve as surrogates for total biodiversity. These were developed for various levels-national, regional, and stand (e.g., Stork et al., 1997). Indicators of biodiversity can be divided into two broad groups: (1) Biological or taxon based indicators, particularly indicator species and guilds; and (2) Structure-based indicators-stand and landscape level (spatial) features such as stand structure complexity, plant species composition, and connectivity and heterogeneity (e.g., Bell et al., 1991; Weaver, 1995; Lindenmayer et al., 2000). The selection of indicators differs for biodiversity monitoring and biodiversity inventory. Various criteria have been developed for selection of indicators, taking into account biological as well as logistical aspects (Noss, 1990; UNEP, 1993; Pearson, 1994). Threatened species, endemic species, and economically or socially important species are often chosen as priorities for data collection (Groombridge and Jenkins, 1996). Extrapolation and prediction techniques are used to limit the number of sites to be assessed. The knowledge of species habitat requirements, coupled with baseline data on climate, altitude, soil type, and vegetation cover, is used to predict the species occurrence in areas not inventoried. Most of the biodiversity assessment techniques can be broadly categorized as baseline techniques, biodiversity modeling and monitoring techniques, or wildlife habitat assessment and monitoring techniques.

1.3.1

Rapid Biodiversity Assessment (RBA) A very rapid and cheap method to assess the relative biodiversity on different sites using the same indicator groups of species. RBA is based on the premise that certain aspects of biological diversity can be quantified without knowing the scientific names of the species involved. The main characteristic of RBA is the minimization of the formal taxonomic content in the classification and identification of organisms, which means that even non-taxonomists can attempt to use this technique. Data are gathered on certain groups of species. Appropriate groups are ones that are relatively abundant, have high species richness, contain many specialist species, are easy to sample, and have taxonomic traits amenable to RBA methods. Several groups, chosen as good predictor sets of biodiversity, are needed at each inventory location (Beattie et al., 1993; Kerr et al., 2000).


007

1.3.2

Rapid Assessment Program (RAP) An RAP conducts preliminary assessments of the biological value of poorly known areas. RAP teams usually consist of experts in taxonomically well-known groups such as higher vertebrates (e.g., birds and mammals) and vascular plants, so that ready identification of organisms to the species level is possible. The biological value of an area can be characterized by its species richness, the degree of species endemism, the uniqueness of the ecosystem, and the magnitude of the threat of extinction. RAPs are undertaken by identifying potentially rich sites from satellite images/aerial reconnaissance and then sending in ground teams to conduct field surveys. Such field surveys last from 2 to 8 weeks, depending on the remoteness of the terrain and the extent of the area to be inventoried (Parkar et al., 1993).

1.3.3

Rapid Biological Inventory (RBI) The goal of a rapid biological inventory is to catalyze effective action for conservation in threatened regions of high biological diversity and uniqueness. During rapid biological inventories, which typically take a month, scientific teams focus primarily on groups of organisms that indicate the habitat type and condition that can be surveyed quickly and accurately. These inventories do not attempt to produce an exhaustive list of species. They rather identify the important biological communities on the site or region of interest and determine whether these communities are of outstanding quality and significance in a regional or global context. Rapid biological inventory teams use protocols that are specific to the species groups under study and that are often modified to meet the demands of a particular expedition. Examples and field reports of RBI used in tropical forest regions exist from Bolivia, Ecuador, and Peru (Foster et al., 1998).

1.3.4

Rapid Ecological Assessment (REA) Rapid Ecological Assessment is a technique used to assess the biodiversity in large, poorly-studied, or exceptionally biologically diverse areas. The REA process consists of a series of increasingly refined analyses, defining further the sites of high conservation interest at each level. The levels involved are satellite observation; airborne remote sensing; aerial reconnaissance; and field inventory. Analysis of satellite images is used to produce maps of eco-regions, land cover, and priority areas. Integration of these with data from airborne sensors and aerial reconnaissance produces more detailed maps, extended to cover vegetation types and ecological communities. These are used to direct the costeffective acquisition of biological and ecological data through stratified field sampling. Such data are used to identify priority sites. Spatially referenced information is managed in GIS, allowing easy data handling and generation of maps (Grossman et al., 1992; Roger et al., 2000).

1.3.5

Biodiversity Assessment and Mapping Methodology (BAMM) The Biodiversity Assessment and Mapping Methodology (BAMM) was developed to provide a consistent approach for assessing biodiversity values at the landscape scale in Queensland. The BAMM is based on vegetation mapping from the Queensland Herbarium and incorporates a range

INTRODUCTION

008

of biodiversity related data. The BAMM is focused primarily on assessing terrestrial values. It is being used by the Department of Environment and Resource Management (DERM) to generate Biodiversity Planning Assessments (BPAs) for each of Queensland's bioregions. The BPAs are used by DERM staff, other government departments, local governments, or members of the community to provide advice on a range of planning (http://www.derm.qld.gov.au/register/ p00471aa.pdf) or decision making purposes. The BAMM is applied in two stages. The first stage uses existing data to assess ecological concepts such as rarity, diversity, fragmentation, habitat condition, resilience, threats, and ecosystem processes in a uniform and reliable way across a bioregion. These criteria are used to filter available data and provide a "first-cut" or initial determination of significance. This initial assessment is generated on a geographic information system (GIS). The second stage relies more upon expert opinion than on quantitative data to refine the results of the first stage. It uses expert knowledge to identify features such as wildlife corridors and areas with special biodiversity value (e.g., centers of endemism or wildlife refuges) and includes data that may not be available uniformly across the bioregion. The final BPA is a powerful decision support tool that can be comprehensively interrogated through a GIS platform and is supported by expert panel reports. BPAs have been completed for a number of bioregions within Queensland. Details of these assessments are listed below. Landscape is the best spatial scale to assess the response of biodiversity across different groups of organisms as it can commensurate with the alternations caused by humans. Analysis of landscape fragmentation (Turner et al., 1993) has been a common goal in the use of satellite data for landscape pattern analysis. The landscape properties were analyzed using various quantitative indices that measured the heterogeneity of landscape within a specific distance (Baker and Cai, 1992). They were ordinarily computed from samples of relatively homogenous cover types/patches (Turner et al., 1993) following geographic windows approach (Dillworth et al., 1994). The spatial arrangement of patches, their different quality, the juxtaposition and the proportion of different habitat types are elements that influence and modify the behavior of species populations and communities (Lidicker, 1995). Fragmentation increases the vulnerability of patches to external disturbance with consequences for the survival of these patches and of the supporting biodiversity (Nilsson & Grelsson, 1995). In landscape perspective, matrix and patches are the elements that have to be used when considering a landscape, fragmented or not (Wiens, 1994; Baudry, 1984). Heterogeneity is a sign of land patchiness, which is a product of disturbance (Lambeck and Saunders, 1993). Patchiness contributes to the diversity (Hansson, 1996) and controls the flow of materials, energy, organisms and information through the environment (Wiens et al., 1985; Wiens, 1994). Similarly, a landscape with higher porosity value indicates high-level of interaction among landscape elements and heterogeneous habitats with high degree of fragmentation (Forman and Godron, 1986). Interspersion and juxtaposition are measures of the connectivity of areas of similar type. Interspersion is a measurement of spatial intermixing of the vegetation types that represents landscape diversity and dispersal ability of the species (Mead et al., 1981). Juxtaposition is a measure


009

of proximity of habitat types and relative importance of adjacency (Mead et al., 1981; Lyon, 1983). Juxtaposition is a unique index that represents preference of one habitat with the other (Kamat, 1986). The calculation involves (i) the probability that a randomly selected pixel belongs to a given class, which is equivalent to the proportional representation of each class and (ii) the conditional probability that, given a pixel of one class, an adjacent pixel is of a differing class. The new ideas about heterogeneity (Kolasa and Pickett, 1991) and role of disturbance regimes in the ecological processes represent a further stage on which new paradigms such as ecotones and related processes like connectivity (Merriam, 1984) have been implanted. Landscape is a broad-scale area composed of a mosaic of patches or ecotopes, in to which we introduce physical, biological and social elements. McGarigal and Marks (1995) documented that the patch density and mean patch size serve as fragmentation indices for comparison between two time periods. The role of patch connectiveness on the dispersal and spatio-temporal distribution of a small tree dwelling bird revealed that the presence of birds significantly related to the length of suitable patches by Farina (1998). Patch size can influence floral and faunal composition and richness (Burgess and Sharpe, 1981). Small patches of forest tend to have a greater proportion of edge to interior than large patches have and thus are more likely to harbor exotic or weedy species (Levenson, 1981). Tilman et al., (1994) have demonstrated the negative effects of every slight increase in habitat fragmentation. By their study they correlated the environmental stochasticity decrease in genetic heterozygosity, edge effects and human disturbance. Zuidema et al., (1996) have studied the effect of forest fragmentation in the maintenance of biological diversity, wherein they pointed out that two key factors might cause the loss of biodiversity in forest fragments i.e., the effect of non-random isolation and edge effects. Forest fragmentation has been documented at the landscape scale using GIS to describe patch size, shape, abundance, spacing and forest matrix characteristics (Ripple et al., 1991; Ripple, 1994). Jean Paul (1991) has discussed the relation between spatial structure and the diversity of vascular plants in a fragmented tropical area. They found forest connectivity (provided by forest corridors and matrix stepping-stones) and landscape mosaic complexity (measured by boundary indices) was the key factors of spatial structure linked to the variation of tree species richness and evenness. Stoms and Estes (1993) have reviewed the biological specification of habitat and set the research agenda to monitor and mapping spatial distribution of species richness and ecological determinants to predict its response to global change using remotely sensed data and GIS. Patterns of species richness in biogeographical, ecological or habitat space have long been a central theme in biology (Pinaka, 1966; Richerson & Lum, 1980; Rohde, 1992). Species distributions patterns are exceedingly complex in space and time domain (Nagendra and Gadgil, 1999). Many workers have worked on the relationships between richness patterns and various ecological and geographical factors (Currie, 1993). Biodiversity and disturbance are hierarchical concepts. Disturbances may be either physical or biological, but their common action is to remove organisms. The intermediate disturbance hypothesis (Connell, 1978) proposes that the highest diversity is maintained at intermediate levels. Disturbance can be considered as a basic process responsible for many other processes, such as

INTRODUCTION

010

fragmentation, migration, local and regional extinction etc. Changes in frequency and intensity of natural disturbances constitute one of the major ways that humans have altered ecosystems and thus the biological diversity that occurs in them. Biodiversity often decreases with distance from source populations, and is most constrained by dispersal in areas that are surrounded by dissimilar habitats (Colinvaux, 1993). The decrease in diversity by decrease in distance may in part reflect the relative edge, geographical extent and different historical patterns of barrier formation and consequent biotic disruptions. Human settlements and roads are usually considered as the biotic source of disturbance and consequence decrease in species diversity. To predict the impact of a disturbance regime on communities and landscapes, it is necessary to understand at least the spatial and temporal architecture of the disturbance (Moloney & Levin, 1996). A few studies were done in India towards establishing the relationship between disturbance and the biodiversity without (Pandey and Shukla, 1999) and with (Roy and Tomar, 2000) landscape analysis. Menon and Bawa (1997) have discussed the role of remote sensing, GIS and landscape analysis for biodiversity conservation in Western Ghats using land cover modeling approach. Ramesh et al., (1997) attempted a vegetation-based approach for biodiversity gap analysis. Nagendra and Gadgil (1999) identified various landscape elements on the basis of field observations and found that the landscape elements significantly support distinctive sets of species of flowering plants. Roy and Tomar (2000) used geospatial techniques to characterize biodiversity at landscape level in Meghalaya state wherein the juxtaposition calculation was based on subjective judgment. Many authors have noted relationship between species diversity and habitat diversity (Kohn and Walsh, 1994). Others have called for a hierarchical approach for conservation based on ecosystem types and landscape units (Norton and Ulanowicz, 1992). Fig. 1.4. Potential remote sensing resolutions for biodiversity assessment and monitoring.

The potential use of satellite data of different spatial and temporal resolutions in generating inputs for assessing the biodiversity is illustrated in Fig. 1.4.

Coarse Resolution

60-500 M

Moderate Resolution

High Resolution

Hyperspectral

~ 30 M ~ 1 M to 5 M

Laser borne

220 bands

1ret/sq.m

Biogeochemical Interactions Habitat Condition/health

Ecosystem

Forest Type


Habitat/Fringe Structure

Fine Scale Structure

Species Discrimination

Community Boundaries

Automated Virutal 3-D model

System Nutrient Status

Community

WithinCommunity

Canopy

011

1.4

Biodiversity conservation priority : Setting the right criteria for India and World The complexity priority setting varies considerably due to complexity of biodiversity and the number of ways of valuing it. Among the biological criteria are richness (the number of species or ecosystems in given area), rarity, threat (degree of harm or danger), distinctiveness (how much a species differs from its nearest relative), representiveness (how closely an area represents a defined ecosystem) and function (the degree to which a species or ecosystem affects the ability of other species or ecosystems to persist). Some priority setting approaches use social, policy and institutional criteria as well. Utility, the most common non-biological criteria, points to biodiversity elements of known or potential use to humankind. Feasibility, often paramount in deciding how to allocate conservation resources, may be political, economic, logistical or institutional terms. Considering the biological criteria, areas can be identified where the actions are most likely to succeed. However, with increased recognition the social, policy and institutional factors are crucial for conserving biodiversity. Ecological approaches for setting priorities for biodiversity conservation generally seek to protect most of the species within conservation areas that are representative of a region's natural habitat. Ecosystem approaches for identifying conservation priorities use multiple criteria such as species richness, endemism, abundance, uniqueness and representativeness, as well as considerations of the physical environment, ecological processes and disturbance regimes that help to define the ecosystem.

1.4.1

Baseline data on biodiversity at landscape level of India The goals and scales of inventorying and monitoring programs may change with time. Hence, the baseline data at landscape level should be sufficiently robust to accommodate changes. It should be based on robust samples enabling calibration for future rapid biodiversity assessment. Landscapes contain all levels of the biological hierarchy, from ecosystem to species and genes that are targeted for biodiversity inventories and conservation. The present effort to characterize vegetation cover, fragmentation, disturbance and biological richness across the landscape is organized in the form of Biodiversity Information System (BIS). The field samples of key ecological characters have been used for geospatial extrapolation. The species database has been linked with above spatial details. The BIS allows identifying gap areas, species / habitat relationship and helps in biodiversity conservation planning by setting priority areas. Such database coupled with detailed site specific field inventories helps in conservation planning. The assessment of biological rich areas brings out distinctiveness of the landscapes as driven by pattern of richness, endemism, biological corridors, community composition and diversity. The analysis made also presents full range of distinct natural communities and ecological status at landscape level. The landscape capable of maintaining the viable population species, sustain important ecological processes and services that maintain biodiversity are also mapped. This information is of valued importance in rugged, inhospitable region throughout north-eastern region. Such areas remain by and large under explored. The results presented here could form the basic guideline to plan flora and faunal future inventories. The focus should be to cover varied landscapes differing based on vegetation types, disturbance regimes and BR. Such an approach allows building habitat factors like biophysical environment, landscape indices and disturbance regimes which allow monitoring changes taking place over a time in biodiversity regimes. Understanding of species habitat relationships, inventorying patterns, multivariate modeling of long-term datasets allows formulating and testing the hypothesis. The dataset could also allow monitoring and forecasting changes through extinction models using multi-temporal data. Such modeling can help in impact of global change in different landscapes. Finally the approach can be extended to study species diversity and genetic variability in biologically rich sites for effective conservation.

INTRODUCTION

012

1.5

Highlights of the Study The status of information and results of the integrated studies using geoinformatics and modeling tools provided following features : l Biodiversity is generally greatest in the oldest ecosystems. It changes across environmental

gradients like, latitude, altitude, depth, aridity etc. The habitat definitions in the form of vegetation cover types will allow 'what to look where'. The disturbance regimes assessed across the landscape will allow focusing on the ecosystems, which are under 'stress'. Hence if the field survey indicates that the region is important habitat for a species for bioprospecting, the 'stress' factor needs to be removed/reduced. l Biological Richness Index (BR) asserts the areas, which should be treated as priority in decision-

making and management level for conservation of biodiversity. The Gap Analysis carried out on maps will guide mangement and decision making. l All plant species have a basic requirement of its ecological optima in particular habitat or niche

within range of tolerance and requirement. Habitat identification and economic importance of the species can be useful input for biodiversity conservation. l Biological rich areas are those habitats where landscape ecological conditions are favourable for

natural speciation and evolutionary process. These areas can be expected to be in equilibrium where species can occur, grow and evolve in natural conditions. l Each species requires a specific ecological niche (minimum/optimum area for its survival,

evolution, gene exchange). Analysis of landscape parameters like habitat fragmentation, patchiness, interspersion and juxtaposition have shown impact on the definition of the limits in different habitats. Greater the variety of types of habitat, the greater is diversity of the species. Diversity also increases with expanding architectural complexity of the physical habitat. l Management of contiguous (large), intact and juxtaposed patches of high diversity in any

landscape should draw first attention for conservation. The ground inventories on species/ genetic diversity should further decide on priorities. The patches having higher biological diversity at landscape level will be subject for more intensive ground inventories for assessing species/genetic diversity. The patches with genetic and species diversity should draw first attention followed by patches of high species and/or genetic diversity.


013

l Most of the species growing in the natural conditions have some sociological association with

the species - environment complex and in general have fairly well defined niches. Similar ecological conditions in different geographical location bear similar biodiversity if not the same. But they will have differences at genetic level. The vegetation cover types, their composition, association, latitude, altitude, fragmentation levels, inferences on possible corridors and species database compliment the information needs. l Based on the existing literature about the occurrence of the valuable threatened species (BSI Red

Data Book and field data of the present and subsequent studies), its habitat can be examined in terms of its landscape requirements of the species. Once the comprehensive species database is established, potential species distribution and occurrence maps can be generated. Integrated gene marking techniques can help in preparing the location - species - environment complexes. Such information base can be of immense value for effective management. l It is expected that the maps will be strategically used for planning detailed ground level

inventories of flora and fauna by premier institutions like Botanical Survey of India, Zoological Survey of India, State forest departments and Wildlife Institute of India. The region-wise maps of the country can be used for redefining ecological zones for biodiversity conservation.

INTRODUCTION

014

BIODIVERSITY CHARACTERISATION APPROACH

2.1

Introduction In most parts of the world, the only spatially consistent information available is on higher-order surrogates such as vegetation type and the environmental classes. A map of vegetation types (communities or habitat types) and/or environmental classes provides spatial consistency across large areas. Higher levels in the biological hierarchy, such as species assemblages, habitat types, and ecosystems, lose biological precision but have other advantages. Such areas encompass more ecological processes that contribute to the maintenance of ecosystem function, and the relevant data are more widely and consistently available (Fig. 2.1). Due to these reasons, understanding the priorities of biodiversity conservation and management has resulted in a policy shift from conservation of single species to habitats through an interactive network of species at the landscape level. In this "top-down" approach, biodiversity can first be characterized at the landscape level, followed by a detailed inventory in the prioritized areas. This approach allows extrapolation of results to large landscapes and involves the development of a spatially explicit database with multiple scale amenability and helps in systematic monitoring (Fig. 2.1).

Fig. 2.1

Advantage : Precision As A Measure Of Character Diversity

Surrogates for measuring the biodiversity value (Paul Williams, 2000)

A Scale Of Surrogacy For A Value Currency Of Character Diversity

Advantage : Inexpensive Survey & Units More Inclusive Of Viability Enhancing Process

LOW

HIGH ‘Ecosystem richness’?

Environmental Surrogates

Climate class richness Terrain class richness

Environmental Assemblage / Surrogates

Substrate class richness Landscape class richness Habitat class richness

Assemblage Surrogates

‘Community class richness’ Vegetation class richness

Taxonomic Surrogates

Higher class richness Species / subspecies richness

Molecular Surrogates

Taxonomic / phylogenetic subtree length HIGH


Expressed gene richness

LOW

015

2 The present study tried to identify the biodiversity conservation priority zones at the landscape level using environmental complexity, disturbance index, and habitats (Fig. 2.2). The Indian Institute of Remote Sensing (ISRO), Dehra Dun has been working on landscape approaches using geospatial modeling for biodiversity since the late 1990s (Ravan and Roy, 1997, 2000; Roy and Tomar, 2000). The approach by Roy and Tomar (2000) was upscaled for a national initiative with funding support from the Department of Biotechnology, New Delhi and Indian Space Research Organization, Bangalore for nationwide biodiversity characterization at the landscape level using satellite remote sensing, landscape analysis, and field inventorying of actual diversity, including socio-economic valuation of its bio-resources. Satellite imagery was used for stratification of different natural and man-made vegetation strata (IIRS, 2003(a)-(d); NRSC, 2007(a),(b); IIRS, 2011(a)-(d)). The study, apart from providing an overview of the various methods and tools used in conducting biodiversity inventories in forested landscapes, also provides the first ever national baseline biodiversity characterization database for the entire country. The methods and tools described here draw upon the collective expertise of more than a hundred scientists, researchers, and forest managers and has evolved with more than a decade of working experience in the laboratory and the field. Fig. 2.2 Approach for Biological Richness Assessment

Biogeography

Social Interactions

Infrastructure/ Developmental activities

Ecosystem Uniqueness

Vegetation Type Map

Ecological Evaluation

Habitat

Disturbance Regimes

Biodiversity Characterization

Representative Ecosystems

Physical Environment Climate


Landscape Character

Social Driving Forces

Ground Inventories

016

2.2

Geospatial Products The study has generated vegetation type maps, forest fragmentation maps, disturbance maps, and biological richness maps using remote sensing and GIS based analysis. Around 16,518 field sample points (based on stratified random sampling design) encompassing the different strata were inventoried for plant species and have been utilized for biological richness modeling. A location and abundance spatial database of 7,761 species has been developed. A digital database on vegetation type distribution, the first of its kind in India, has been developed as a basic input for identifying habitats and will serve as a benchmark for further biodiversity related ecological studies. A fragmentation map provides insights into the effects of forest fragmentation on landscape patterns, the biodiversity, and ecological processes. Disturbance regimes assessed across the landscape flag "stressed" eco-systems and may highlight the causative factors in some cases. Biological richness maps (BR) lay emphasis on the areas that should be treated on a priority basis when formulating strategies for conservation of biodiversity.

2.2.1

Vegetation Type Mapping The vegetation type can be defined as an embodiment of unique physiognomy, structure, and floristics (intrinsic factors), influenced by the climate, topography, and anthropogenic factors (extrinsic factors). Champion and Seth's (1968) classification scheme follows a hierarchical approach wherein climatically driven forest ecosystems systems with distinct physiognomy and phenology are primarily classified as type groups. These type groups are further subdivided into subgroups based on dominant compositional patterns and region and location specific formations controlled by edaphic and disturbance conditions. Gadgil and Meher-Homji (1990) distinguished 42 forest types in India, based on the association and dominance of species and the prevailing bioclimate.

2.2.1.1

Classification Approach The existing classification systems precisely used ground data in deciphering the patterns of species assemblages but did not provide the explicit spatial boundaries of these assemblages. Such spatial explicit boundaries of vegetation types are important for studying the patterns of vegetation diversity and long-term monitoring. The delineation of such boundaries for larger spatial extents based on geospatial tools and field information have become time and cost effective. The satellite remote sensing data, in conjunction with spatial information on the topography, soils, climate, and ground floristic data, are also used to delineate detailed vegetation formations (Ravan et al., 1996). The on-screen visual interpretation technique has been used for vegetation type/land use mapping (Fig. 2.3). The biogeography and altitude zone maps were also used to define classes. Wherever necessary, field data were used to delineate the vegetation type and locale-specific classes. State level vegetation type maps were mosaiced to generate a national level map. Edge matching was performed to produce a seamless national vegetation type map.

2.2.1.2

Selection of Optimal Season Data Two-season IRS LISS-III satellite data of 2005-2006 were utilized optimally to map the vegetation types depending on the forest phenology, i.e., peak growth and leaf fall seasons. Satellite data pertaining to the time windows of November-early January and February-early April were used to take into account the phenological variations required for delineation of different vegetation types. In the case of grassland areas in Gujarat and Rajasthan, an additional data set covering AugustOctober was also used. The IRS P6 LISS-III sensor data was used. If no specified cloud free data were available, the best available archived data were used (Table 2.1).


017

Fig. 2.3 Approach used in classifying vegetation using multi-temporal remote sensing.

Start

IRS LISS III 2 Season Sat data available

IRS AWiFS 2 season sat data available

Published spatial input data

Intensive Field Information

15’ x 15’ grid overlay standards available

Vector preparation for 15’x15’ grid as per standard

Border Matching of scene as per standard

Undertake complete interpretation

Yes Undertake Georectification Temporal Registration

Yes

No

RMS and Planimetric accuracy standards met

No

Yes Prepare Scene Specific Image Chips Template

Yes Templates are exhaustive

Undertake reconn field work

Database in diff Scene per standard Yes

Mosaic state wise satellite scenes

Overlay state WGS84 boundaries

State Boundaries as per standard

National Boundaries as per standard

National WGS84 boundaries

Mosaic National sat Scenes

Extract National Seamless Mosaic


018

Table 2.1 Optimal seasons of satellite data for different parts of India

Region

Season of data selection

Western Ghats South

Nov-Dec and Mar-Apr

Central

Nov-Dec and Mar-Apr

North

Dec-Jan and April

Eastern Ghats South

Sept-Oct and Feb-Mar

Central

Oct-Dec and Mar-Apr

North

Nov-Dec and Mar-Apr

Central Plains

Nov-Dec and Mar-Apr

Eastern Himalaya

Dec-Jan and Apr-May

North-Western Himalaya

2.2.1.3

Subtropical temperate region

Nov-Jan and Mar-May

Cold desert

Aug-Nov and Apr-May

Indo-Gangetic Plains

Aug-Nov and Feb-Mar

Western Arid System

Aug-Oct, Nov-Dec and Mar-Apr

Radiometric Correction Radiometric corrections were carried out using dark pixel subtraction. Scene-to-scene matching was carried out using histogram equalization/matching. For missing lines or pixels, suitable interpolation techniques were used.

2.2.1.4

Reconnaissance Survey/Ground Truth Collection It is required to have a reconnaissance study of an area before attempting to classify the vegetation pattern. Traverses in the area of interest were made from the plains to the hill tops for collecting ground truth information. A survey of the published literature was carried out, and several interactions were held with the forest departments and educational/local institutions. At the end of the reconnaissance survey, an understanding was gained on the prevailing phenological, gregarious, locale-specific vegetation types of the study area. The information available in the forest working plans, published records, the tone and texture of satellite imagery, and the ground knowledge were used. The location specific data gathered on different vegetation types were utilized to prepare (a) A template for visual interpretation of satellite data and (b) Delineate training sets for digital classification of satellite data.


019

2.2.1.5

Image Interpretation Key An image interpretation key was developed prior to interpretation, which was further refined during the course of interpretation (Fig. 2.4.).

5 May 2002

24 December 2003

Terminalia alata, Sterculia urens, Holarrhena, Lannea coromandelica, Anogeissus latifolia Elevation : 200-300m Moist Deciduous Upper Sileru 24 December 2003

(elevation : 500-800m)

Dry Deciduous (elevation : 300-500m)

5 May 2002

Fig. 2.4 Use of tone and texture for on-screen digitization of vegetation types (IRS-P6 LISS III FCC images of part of Malkangiri district of Odisha showing phenological variability)

Hill top Savannah (elevation : 1100m)

Semievergreen (elevation : 800-1100m)

The vegetation classification scheme was framed that accommodates the natural and semi-natural systems were classified into forests, scrub/shrub lands, and grasslands based on the extent of green cover. The cultivated and managed systems were classified into orchards, croplands, long fallow/barren lands, and water bodies. The forest class was further sub-divided into mixed forest formations, gregarious formations, locale-specific formations, degraded/successional types, and plantations. The classification scheme and class details are given in Table 2.2.


020

Table 2.2 The vegetation / land use types and their respective classes under Champion and Seth's classification.

Class description Level-I

Level-II

Champion and Seth (1968) class with codes Level-III

Natural/semi-natural areas Mixed formations Evergreen

Tropical Wet Evergreen Forest (1)

Giant evergreen

Giant Evergreen Forest (1A/C1)

Andaman evergreen

Andamans Tropical Evergreen Forest (1A/C2)

Southern hill top

Southern Hilltop Tropical Evergreen Forest (1A/C3)

Secondary evergreen Subtropical broadleaved hill forest

Subtropical Broadleaved Hill Forest (8)

Subtropical dry evergreen

Subtropical Dry Evergreen Forest (10)

Montane wet temperate

Montane Wet Temperate Forest (11)

Himalayan moist temperate

Himalayan Moist Temperate Forest (12)

Himalayan dry temperate

Himalayan Dry Temperate Forest (13)

Sub-alpine

Sub-Alpine Forest (14)

Semi-evergreen

Tropical Semi-Evergreen Forest (2)

Moist deciduous

Tropical Moist Deciduous Forest (3)

Sal mixed moist deciduous

Moist Teak-Bearing Forest (3B/C1)

Teak mixed moist deciduous

Very Moist Sal-Bearing Forest (3C/C1)

Dry deciduous

Tropical Dry Deciduous Forest (5)

Sal mixed dry deciduous

Dry Sal-Bearing Forest (5B/C1)

Teak mixed dry deciduous

Dry Teak Bearing Forest (5A/C1)

Thorn forest

Tropical Thorn Forest (6)

Gregarious formations Sal

Moist Sal Bearing Forest (3C/C2)

Teak

Dry Teak Bearing Forest (5A/C1)

Dipterocarpus Mesua

Mesua Forest (1B/C2b)

Bamboo

Wet Bamboo Brake (2/E2), Moist Bamboo Brakes (2/E3), Secondary Moist Bamboo Brakes (2/2S1)

Pine

Subtropical Pine Forest (9), Siwalik Chir Pine Forest (9/C1a), Himalayan Chir Pine Forest (9/C1b), Western High-Level Dry Blue Pine (13/1S3)

Fir

Fir Forest (14/C1a)

Spruce Oak

Montane Bamboo Brakes (12/DS1)

Deodar

Moist Deodar Forest (Cedrus) (12/C1c)

Hardwickia

Hardwickia Forest (5/E4)

Red sanders

Dry Red Sanders Bearing Forest (5A/C2)

Cleistanthus Boswellia

Boswellia Forest (5/E2)

Acacia nilotica (babul)

Babul Forest (5/E3)

Butea

Butea Forest (5/E5)

Aegle

Aegle Forest (5/E6)

Acacia catechu (khair)

Khair-Sissu Forest (5/1S2)

Anogeissus pendula (kardhai)

Anogeissus pendula Forest (5/E1)

Acacia senegal

Acacia senegal Forest (6/E2)

Cypress

Cypress Forest (12/E1)

Alder

Alder Forest (12/1S1)


021


Level-II

Champion and Seth (1968) class with codes Level-III

Rhododendron

Dwarf Rhododendron Scrub (15/C2/E1)

Padauk Lagerstroemia Hollock (Terminalia myriocarpa) Locale-specific formations Mangrove

Tidal Swamp Forest (4B), Mangrove Forest (4B/TS2)

Avicennia Bruguiera Excoecaria Heritiera Lumnitzera Mangrove scrub

Mangrove Scrub (4B/TS1)

Phoenix (palm swamp)

Palm Swamp (4B/TS4/E1)

Rhizophora Xylocarpus-Rhizophora Littoral forest/beach forest

Littoral Forest (4A)

Freshwater swamp forest

Tropical Freshwater Swamp Forest (4C)

Lowland swamp forest

Tropical Seasonal Swamp Forest (4D)

Myristica swamp

Myristica Swamp Forest (4C/FS1)

Syzygium swamp

Syzygium cumini Swamp Low Forest (4D/SS3)

Shola

Southern Subtropical Broadleaved Hill Forest (8A)

Riverine

Tropical Riparian Fringing Forest (4E)

Dry evergreen

Tropical Dry Evergreen Forest (7)

Ravine

Ravine Thorn Forest (6B/C2)

Sacred groves Forest plantation Sal Teak Eucalyptus Acacia Pine Casuarina Cashew nut Padauk Red oilpalm Cryptomeria Alnus Mixed plantation Degradational formations Degraded forest Shifting cultivation Shifting cultivation (abandoned jhum) Shifting cultivation (current jhum) Degraded mangrove Woodland


Tree savannah

Low Alluvial Savannah Woodland (Salmalia-Albizzia) (3/1S1), Dry Savannah Forest (5/DS2)

Shrub savannah

Dry Savannah Forest (5/DS2)

022


Champion and Seth (1968) class with codes

Level-II

Level-III

Scrub Scrub/shrub land Open scrub Dry evergreen scrub Dry deciduous scrub

Dry Deciduous Scrub Forest (5/DS1)

Ziziphus

Southern Thorn Scrub (6A/DS1)

Euphorbia scrub

Euphorbia Scrub (6/E1)

Moist alpine scrub

Moist Alpine Scrub (15)

Dry alpine scrub

Dry Alpine Scrub (16)

Prosopis scrub Salvadora

Salvadora Scrub (6/E4)

Hippophae

Hippophae- Myricaria Scrub (13/1S1)

Desert dune scrub

Desert Dune Scrub (6/1S1)

Wet grasslands (upland grasslands)

Southern Montane Wet Grassland (11A/C1/DS2)

Grasslands

Riverine (lowland grasslands) Moist alpine pasture

Alpine Pastures (15/C3)

Dry alpine pasture

Alpine Pastures (15/C3)

Saline grassland

Saline/Alkaline Scrub Savannah (5/E8)

Dry grassland

Dry Grassland (5/DS4)

Man-made grassland Swampy grassland Cultivated/managed areas/others Orchards Tea Coffee Arecanut Coconut Rubber Citrus Agriculture Long fallow/barren land Water body Wetland Settlement Reject class

The species composition was recorded by field sampling in the respective mapped vegetation types, based on the stratified random sampling design described in section 2.2.2. The classes which were not amenable for delineation directly using remote sensing were grouped in the broad class. The hierarchical classification of the forest type will help in linking with different global classification systems and converging to the global scale. This has been undertaken to facilitate the migration of the database from the current classification system to any of the globally recognized classification systems for climate sensitive approaches and other research purposes. Descriptions of the different mapped vegetation types along with the satellite signatures and field photographs have been provided in Appendix 1.

2.2.2

Phytosociological Analysis The natural vegetation in the country has a long history of disturbance by way of grazing, fire,


023

logging, deforestation for raising forest plantations, etc., resulting in complex habitats. In order to understand the composition and species diversity pattern in these complex habitats, the landscape characterization in terms of patch size, shape, and neighborhood, coupled with phytosociological data, has been taken into account. Vegetation strata proportions were used for determining the sample points (plots). A sample intensity of 0.002 to 0.005 was aimed at, depending upon the state of the forests in the area. Stratified random sampling with probability proportionate to stratum size was used. Field information on cover type, locality, aspect, slope, geo-coordinates, signs of disturbance, and altitude was recorded. GPS receivers were used to determine the geo-coordinates and the altitude. Fig. 2.5 depicts the sample plots across the country.

Fig. 2.5 Distribution of field sample plots across India

Forest Scrub Grassland Managed Ecosystem

2.2.2.1

Sampling Design Sample plots of 0.04 (20 m x 20m) to 0.1 ha (31.62 m x 31.62 m) were randomly distributed across each stratum. Tree species were sampled using 20 m x 20 m and 31.62 m x 31.62 m plots, depending upon the within-stratum variability on the ground. For sampling the shrub species, two plots of size 5 m x 5 m size at two opposite corners of tree plot were taken. For herbaceous plants, five plots of 1 m x 1 m size (four at the corners and one at the center) were laid inside the tree plot (Fig. 2.6). GPS and ground bearings from SOI maps were used to reach the plots. A modified nested quadrat was used for laying the tree, shrub, and herb plots. Information on trees, shrubs, herbs, climbers, epiphytes, and lianas was recorded from these plots using field forms.


024

Fig. 2.6

2m .6

5m 5m 20

m

/

5m

N

1m

5m

1m

31

Tree, shrub, and herb sample plots in a modified nested quadrat.

In each sample plot, the circumference at breast height (cbh) of each tree with cbh > 30 cm was recorded. Trees with cbh > 17 cm and < 30 cm were treated as saplings, and those with cbh