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Proceedings of

National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 Sponsored by

TEQIP-II

(Technical Education Quality Improvement Programme-II)

Convener Dr. Padma Kumari K.

Organized by

DEPARTMENT OF CIVIL ENGINEERING UNIVERSITY COLLEGE OF ENGINEERING KAKINADA (Autonomous)

JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY KAKINADA Kakinada, A.P., India. www.jntuk.edu.in

Preface Evolution /Development of Science, Engineering and Technology can be attributed to that of mankind. Man strives to be more and more comfortable and simplifies his life through science and technology by exploring the natural resources, viz., minerals, water, oil, natural gas etc. Scientists, Engineers and academicians have been on research. Interdisciplinary research is order of the day out of need and necessity. Keeping this in view, this conference on multi theme has been envisaged and research papers from various disciplines have been called for. Wealth of a nation is its natural resources and exploring capabilities. A nation that equips and updates itself with recent advances in science and Engineering can develop further. Numerous researchers have been working in Geo-sciences, Engineering & Technology all over the globe. An application of Geo-sciences, Geospatial, remote sensing and GIS has direct bearing on life of mankind. As for as mapping of natural resources is concerned, the Resolution capability of Remote sensing satellites has been improved from 100 of metres to just a few metres. Geotechnical, structural, Environmental, Rock mechanics and water resources are more interwoven in civil engineering. construction of structures, ground water and soil conditions has become necessity due to due to paucity of suitable sites

this calls for either adoption of suitable

foundations to suit the soil conditions or, in the alternative, amelioration of the ground several examples can be sited which underscore the importance of all aspects as matter of study at macro level technologically. Image processing, digital image processing is required for the visual digital image interpretation techniques to know the changes to present from the past and predict the future. Hence papers on this theme are also an inclusive part of the conference theme. The Department of Civil Engineering has been on fore front for academic interaction through workshops, seminars and conferences ever since its establishment in 1946. The present National Conference on Recent

Advances in Geo-sciences, Engineering &Technology (NCRAGE12) with multi and inter-disciplinary research is unique and special feature. With great difficulty about 80 papers of relevance have been shortlisted out of115 papers. All the authors deserve to be acknowledged for their contribution and cooperation in this connection. I greatly acknowledge all reviewers Dr. K. Nageswara Rao, A.U. Vizag, Dr. S.V.B. Krishna Bhagavan, APSRAC, Hyderabad, Dr. GVR Prasada Raju, Dr. V. Ravindra, Dr. P. Subba Rao, Dr. K. Ramu, Dr. V. Srinivasulu, JNTUK, Kakinada. I gratefully acknowledge all the support and encouragement extended by our Hon’ble Vice-Chancellor, Dr. G.Tulasi Ram Das; Rector, Dr. E.V. Prasad; Registrar, Dr. G.V.R. Prasada Raju; Principal, UCEK, Dr. K. Padma Raju; Vice-Principal, Dr. P. Subba Rao; Head of the Dept. of Civil Engineering, Dr.V. Srinivasulu, and colleagues

in conducting this

conference under TEQIP-II. I will be failing in my duty if I fail to acknowledge the efforts of my beloved students without whose help this conference would be a grand success. I wish all the participants happy stay on the campus joyful and fruitful deliberations of the conference. It gives me immence delight to share my happiness mentioning my studens efforts to be acknowledged in this occasion. I wish a glorious future to my students Kasyap, Anil Kumar, Sunadh, Nandini, Pavan, Vinod, Nitin, Sateesh and many un-named individuals whose efforts rooted the pillar of success of this conference. I am sure that the deliberations of the conference would be meaningful and throw new light on the Geo-sciences, Engineering and Technology and its varied applications

(Convener)

Dr G Tulasi Ram Das

B.Tech.(EEE),M.E., Ph.D(IITM), F.I.E.(Ind),F.I.E.T.E.,M.I.E.E.E.,M.I.S.T.E.,MSESI

Vice-Chancellor

JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY KAKINADA Kakinada – 533 003, A.P. (INDIA)

MESSAGE

Today, in this constantly changing world, science and technology has reached its zenith. Advancements in every realm of engineering have taken its own shape and form. Thus, the engineers of today and visionaries of tomorrow are the real makers of the world we dream for. Thus I would like to wish the Department of Civil engineering a good luck on this endeavor of bringing the academicians and researchers of this country together on to a platform to share their ideas and to enrich the innovative aspects of Geo-Sciences, Engineering Technology. Since, accumulation and spreading of knowledge is a never ending process, I am very glad that the Department of Civil Engineering has taken a very wonderful step towards this. I sincerely appreciate the staff for their perseverance in organizing “NATIONAL CONFERENCE ON RECENT ADVANCES IN GEO-SCIENCES, ENGINEERING AND TECHNOLOGY(NCRAGE12) ” . I confidentially assure a big success to this event NCRAGE12 which would explore the research related topics and other advancements in Science and Engineering and encourage the engineers of tomorrow to dedicate themselves in wielding the future of India. I sincerely appreciate the efforts of the Principal Dr. K. Padama Raju, VicePrincipal Dr. P. Subba Rao, Head of the Department Dr. V. Srinivasulu, Convener Dr. K. Padma Kumari, staff, scholars and the students of Civil Engineering Department in organizing an event of such kind. I wish the event a grand success.

(Dr. G. TULASI RAM DAS)

Dr. E. V. Prasad

B.E. M.E. Ph.D.

Rector, JNTUK

MESSAGE

__________________________________________________________

It gives me an immense pleasure to see that “NCRAGE 12” a 2- day National Conference on Recent Advances in Geo-sciences Engineering & Technology (NCRAGE 12) is being organized by the Department of Civil Engineering, University College of Engineering, JNTUK Kakinada during 20th and 21st , December 2012. “NCRAGE 2012” is expected to create a platform for the Academicians and Researchers to get exposed to the state –of- art technologies in the discipline and to discuss the technological developments in this field of Geo-sciences. I am sure that the participants with their active interaction will help each other to broaden their vision and open their minds with a spectrum of new ideas for further innovative research. I am sure that “NCRAGE 12” creates an environment for the participants to present their views, ideas, and contributions, and opportunity to discuss the recent advances in the area of interest. At the outset, I congratulate the organizers and the department of Civil Engineering for their efforts to organize the conference.

I wish the conference a grand success.

(Prof. E. V. Prasad)

Dr. G. V. R. PRASADA RAJU B.E, M.E., Ph.D, Registrar JNTUK, Kakinada.

Message I am glad to note that the Department of Civil Engineering , JNTU College of Engineering , Kakinada is conducting a 2 day National Conference On Recent Advances in Geo-Sciences, engineering and technology NCRAGE 12 on 20th and 21st December , 2012. I extend my best wishes on the occasion as such a conference creates an opportunity for the Science and engineering Academicians and Researchers to update their knowledge on the latest areas. I look forward and wish that the department will conduct similar programmes in the future for the benefit of both students and staff. I extend my best wishes for the success of the event and all the participants a memorable stay.

Dr. K. PADMA RAJU B.Tech., M.Tech., Ph.D.

PRINCIPAL UCEK, JNTUK Kakinada

Message The advancements in Research, Engineering and Technology are progressing at a rapid pace especially in Science and Technology. The Department of Civil Engineering, JNTUK is always on the forefront in disseminating advances to various stake holders and is conducting seminars, workshops, symposiums at regular intervals. Now “NCRAGE 12”, a 2 day National Conference on recent advances in GeoSciences Engineering & Technology (NCRAGE 12) is being organized on 20th and 21st December 2012. “NCRAGE 12” is going to be another memorable and a common platform for the Researchers and Academicians of Civil Engineering to expose themselves and exchange their innovative ideas. I heartily congratulate the staff and students of the department of Civil Engineering for organizing and making this event a huge success.

(Dr. K.Padma Raju)

Dr. P. SUBBA RAO

B.Tech., M.E., Ph.D.

VICE-PRINCIPAL, University College of Engg. Kakinada (Autonomous), JNTUK , Kakinada-3.

Message I glad to note that a National Conference on Recent Advances in Geoscience, Engineering & Technology: (NCRAGE 12) is being organized on 20th & 21st December 2012 with spectrum of multi and inter-disciplinary themes; by the Department of Civil Engineering, University College of Engineering, Kakinada (Autonomous), JNTUK Kakinda under TEQIP-II. I am sure that the deliberations of this conference will definitely benefit the participants of this conference. I heartily congratulate the Department of Civil Engineering in general and the convener of the Conference in particular for their untiring efforts in making this event a grand success. I wish the conference a grand success. Kakinada, 17.12.2012

(Dr. P. Subba Rao)

Dr. V. Sreenivasulu B.Tech., M.Tech., Ph.D Chairman, NCRAGE-2012 & Professor and Head of Civil Engineering, University College of Engineering, JNTUK, Kakinada. Mobile: 9440107978, Email: [email protected].

MESSAGE Dear Participants! Taking this occasion, I would like to welcome each and everyone to our unique event of National Conference on Recent Advances in Geo-Sciences, Engineering and Technology (NCRAGE-2012) in University College of Engineering, Jawaharlal Nehru Technological University Kakinada (JNTUK), Kakinada, between 20-21 December 2012. This conference provides a venue for professionals in geographical information science, remote sensing and related disciplines to exchange their latest research and development. Today, the world faces many challenges: climate change, famine and drought, global epidemics, violent conflict and persistent poverty. Technology such as Geo-Sciences offers the possibility of visual analysis and allows us to see political boundaries, population trends, and socioeconomic differences. It also offers us the ability to acquire and verify facts. Technologies, like GIS, are important tools for understanding and supporting disaster resilient communities. Whether you're a city planner, development practitioner humanitarian responder, or crisis-mapper, information for decision-making is now at our fingertips to reduce risks and build disaster resilience. University College of Engineering, JNTUK, Kakinada is a premier Institute in the state of Andhra Pradesh. The Civil Engineering Department of this Institute has a greater reputation and has several distinguished alumni to its credit. The Department has been forefront in organizing many events like Seminars, Conferences and Workshops. NCRAGE2012 is one of such evens being organized by the Civil Engineering Department with the approval and financial assistance of TEQIP-II. I must place on record my deep appreciation for the untiring efforts put in by Dr. K. Padma Kumari, Convener of this event NCRAGE-2012. Publication of the volume has been made possible through sustained efforts of the organizing committee members. Their help is acknowledged with sincere thanks. On behalf the organizing committee, I would like to thank all the conference sponsors, authors and delegates besides all those who have worked behind screen and have helped in making NCRAGE – 2012 a grand success.

(Dr. V. Sreenivasulu) Chairman, NCRAGE- 2012.

Dr.Padma Kumari.K M.SC.,M.Phil.,Ph.D., Convenor,NCRAGE-12 Associate Professor Department of Civil Engineering. UCEK JNTUK, Kakinada.

Message At the outset I deem it a great privilege to be the Convener to the National Conference on Recent Advantages in Geo-sciences, Engineering & Technology (NCRAGE12) being conducted the Department of Civil Engineering, University College Engineering Kakinada (Autonomous), Jawaharlal Nehru Technological University Kakinada. India is one of the developing countries in the world; where in Andhra Pradesh is popularly known as store house of mineral wealth and rice bowl of India. India is highly potential in natural resources and geological features point of view together with industrial development in pace with Technology development. University College of Engineering Kakinada (Autonomous), JNTU Kakinada is one of the oldest institutes serving the nation by disseminating Technical Education. A lot of research is going on all over the world. This conference has been envisaged with an objective to identify the resources and how to utilize for further better benefit of mankind, by providing a common platform to all scientists and academicians. I suppose that science enables man realizes truth. Engineering & technology is manifestation of Science for the benefit of mankind. I am sure that this National Conference will provide a common platform for exchange of research findings and techniques for further development and how to solve the problems. I wish all the participants enjoy fruitful deliberations of the conference.

(Dr.Padma Kumari.K)

Organizing Committee Chief Patron

Dr. G. Tulasi Ram Das Hon’ble Vice-Chancellor, JNTUK, Kakinada. Patrons Dr. K. Satya Prasad, Rector, JNTUK, Kakinada Dr. E.V. Prasad, Registrar, JNTUK, Kakinada

Co-Patrons Dr. K. Padma Raju Principal, UCEK, JNTUK, Kakinada, Dr. M. Ramalinga Raju Vice-Principal, UCEK, JNTUK, Kakinada

Chairman Dr. P. Subba Rao, Professor & Head, Dept. of Civil Engg. UCEK, JNTUK, Kakinada

Convener Dr. Padma Kumari K. Associate Professor, UCEK, JNTUK, Kakinada

Treasurer Er. B. Krishna Rao Assistant Professor, UCEK, JNTUK, Kakinada

Members: •

Dr. KVSG Murali Krishna, Professor, UCEK, JNTUK



Dr. K. Ramu, Professor, UCEK, JNTUK



Dr. V. Srinivasulu, Professor, UCEK, JNTUK



Dr. D. Koteswara Rao, Professor, UCEK, JNTUK



Er. V. Lakshmi, Assistant Professor, UCEK, JNTUK



Er. G. Surya Rama, Lecturer, UCEK, JNTUK



Er. V. K. Raju, Lecturer, UCEK, JNTUK



Er. K.V. Ramana, Lecturer, UCEK, JNTUK



Er. P. Sridhar, Lecturer, UCEK, JNTUK



Er. P. Mynar Babu, Lecturer, UCEK, JNTUK

Technical Committee •

Dr. Nitin Tripathi, Asian Institute of Tech., Bangkok



Dr. KB Mahalakshmi, Acharya Nagarjuna Univ, Guntur



Dr. K. Nageswara Rao, Andhra University, Vizag



Dr. B. S. Prakash Rao, Andhra University, Vizag



Dr. V. Madhava Rao, Director, NIRD, Hyderabad



Dr. D.Venkat Reddy, NIT Suratkal



Dr. V. Sreenivasulu, JNTUK, Kakinada



Dr. K. Mruthyuanjaya Reddy, NRSC, Hyderabad



Dr. B. Subbu Nagulu, NRSC, Hyderabad



Dr. S.V.B Krishna Bhagwan, APSRAC, Hyderabad



Dr. V. Radha Krishna, APSRAC, Hyderabad



Dr. R. Pradeep Kumar, IIIT Hyderabad



Dr. Vazeer Mahammod, Andhra University, Vizag



Dr. G.Jaya Sankar, Andhra University, Vizag



Dr. V. Hemamalini, Andhra University, Vizag

Advisory Committee: •

Dr. P. Udaya Bhaskar, JNTUK, Kakinada



Dr. GVR Prasada Raju, JNTUK, Kakinada



Dr. V. Ravindra, JNTUK, Kakinada



Dr. K. Purnanandam, JNTUK, Kakinada



Dr. K. Ramu, JNTUK, Kakinada



Dr. D. Koteswara Rao, JNTUK, Kakinada



Dr. M. Anji Reddy, JNTUH, Hyderabad



Dr. G. K. Viswanath, JNTUH, Hyderabad



Dr. S. Srinivasulu, JNTUH, Hyderabad



Sri. J. Venkatesh, JNTUH, Hyderabad



Dr. C. Sasidhar, JNTUA, Anantapur



Dr. D. Dhanunjaya Rao, Andhra University, Vizag



Dr. N. Narendra, GITAM University, Vizag



Dr. P.V.S. Prasada Raju, ADRIN, Hyderabad



Dr. Pruthvi Raju, BHU, Varanasi



Dr. K. Hanumantha Rao, KL University, Vijayawada

Geo-Sciences

Table of Contents

1. GIS and Remote Sensing based Rajiv Awas Yojagana Project (RAY): A case study on

Kiron Ki Dhani Slum in Jaipur ............................................................................................... 3

2. Spectral Biomass Assessment of Yealgiri Hills .................................................................... 13 3. Land Slide Suitability Analysis............................................................................................. 21 4. Geo spatial based Computation of Morphometric Parameters –Kadam Reservoir Catchment

Area, Adilabad district, India ................................................................................................ 25

5. FOSS Geospatial Technology ............................................................................................... 33 6. Asset Mapping and Consumer Indexing under Restructed Accelerated Power Development

and Reforms Programme (R-APDRP) in Bellampalli, A.P. ................................................. 41

7. LIQUEFACTION STUDIES FOR VISAKHAPATNAM CITY ......................................... 53 8. A Case Study on Land Use /Land Cover Change Analysis Using Geospatial Technology in

the Jangaon mandal, Warangal District, Andhra Pradesh. .................................................... 61

9. Land Use/Land Cover Change Studies in Guntur Mandal Using RS and GIS Techniques . 69 10. Change Analysis of Land Use and Land Cover Pattern and NDVI as a Measure of Intensity

of Anthropogenic Activity and Micro-Watershed Health – A Study on Sarada River Basin, Visakhapatnam District, India............................................................................................... 77

11. Customized Property Tax Assessment Toolbar based on Digital Parcel Mapping using

ArcGIS .................................................................................................................................. 89

12. Identification of High Fluorosis in Certain Villages in Nalgonda District using Remote

Sensing & GIS .................................................................................................................... 101

13. Urban planning for the Industrial Hub of NCR - Faridabad: Using Geo-Informatics

Approach ............................................................................................................................. 107

14. Prediction of Land Surface Temperature from Land Use Land Cover Images using an

Artificial Neural Network Model ........................................................................................ 119

15. Detecting Urban Land Use Change and its Impact on the Surface Temperature of Greater

Hyderabad City using Landsat Images ............................................................................... 127

16. Geological and Geomorphological studies in part of Prakasam District, Andhra Pradesh,

India. Using Remote sensing Techniques. .......................................................................... 135

17. Development of Land and Water Resource Action Plan Using Remote Sensing, Gis, Gps

and Field Studies- A Case Study of Guntur City. ............................................................... 143

18. Earthquakes - Use of Remote Sensnig & GIS in Hazard Monitring and Mtigation ........... 155 19. Web – Based Village Information Systems for Effective Decentralisation of Administration

to Village Level ................................................................................................................... 165

20. Integrated Watershed Management using Remote Sensing and GIS a Case Study of

Dhanwada Watershed, Mahabubnagar District, A.P .......................................................... 175

21. Canal Alignment Using Geographic Information System .................................................. 181

22. Chemical Studies of Groundwater in And around Miryalaguda area, Nalgonda district, A.P.

............................................................................................................................................. 185

23. Development of a GIS Model on Groundwater Quality Assessment for Kurnool City ..... 193

Engineering 24. HYDROLOGICAL ANALYSIS FOR EFFECTIVE MANAGEMENT OF WATER

RESOURCES- A CASE STUDY ....................................................................................... 201

25. Study of Effects of Land Use Change on Hydrological Characteristics of Narava Gedda

Catchment Using RS & GIS in Visakhapatnam Urban ...................................................... 209

26. Evaluation of Groundwater Potential Index of Visakhapatnam Urban using GIS ............. 219 27. A STUDY ON MECHANICAL PROPERTIES OF RECYCLED AGGREGATE WITH

AND WITHOUT FIBRE REINFORCEMENT ................................................................. 231

28. Prediction of the uniaxial compressive strength of the Oporto granite using Relevance

Vector Machine ................................................................................................................... 241

29. Hydro Carbon Gaseous Anomolies Found

in Agriculture Field Around Sagar- Damoh, Southern Fringes of Bundelkhand Region, Madhya Pradesh. India. .................................. 247

30. INVESTIGATIONS ON EFFECTIVENESS OF GEOSYNTHETIC REINFORCED

PONDASH AS AN OVERLAY ON SOFT CLAYEY SUBGRADE ............................... 255

31. A Study on Desiccation and Hydraulic Conductivity of Modified Bentonite Amended With

Fly Ash and Silica Fume ..................................................................................................... 263

32. Evaluation of Reinforced Subbases on Sand Subgrades ..................................................... 271 33. Bearing Capacity of Circular footings on Reinforced Foundation Beds ............................ 279 34. Improvement of Subgrade characteristics using Lime–Cement combination .................... 287 35. Experimental Study on Partial Replacement of Fine Aggregate by Steel Slag .................. 295 36. Performance estimation of Stabilized Flyash Sub bases ..................................................... 307 37. ROBO SAND - The future of sand. .................................................................................... 319 38. Study of Geocell as a Basal Mattress for Improvement of Bearing Capacity of Soft Soils 325 39. Bearing Capacity of Square Footing on Geocell Sand Mattress Overlying Clay Bed ....... 333 40. Laboratory Study of Interaction Behaviour of Reinforcing Strips Embedded in Cement

Modified Marginal Backfill ................................................................................................ 341

41. Experimental Investigation for Determiniing the Suitability of Steel Slag as Fine Aggregate

in Concrete .......................................................................................................................... 351

42. Comparison of Pushover curves from ATC-40 procedure and SAP2000 Mode shapes

procedure ............................................................................................................................. 369

43. Experimental Investigation of Optimum Mix for High Strength Green Concrete ............. 377 44. Construction of Evergreen Dwellings using Geomembrane Liner System ........................ 381 45. Study of eccentrically loaded footing resting on Planer and Confined Geosynthetic

Reinforcement ..................................................................................................................... 389

46. Offshore compliant piled towers for oil & gas exploration ................................................ 399

Technology 47. Biometric Authentication for Robust Sparse Coding ......................................................... 407 48. Weighted Median Filtering Using Principal Component Analysis Techniques in Matrix

Decomposition .................................................................................................................... 417

49. Adaptive Gain Equalizer with Non linear Spectral Subtraction ......................................... 425 50. Automatic road detection and extraction for urban planning ............................................. 433 51. Technical Presentation on Artificial Neural Networks Fuzzy Logic (Automated

Automobiles)....................................................................................................................... 445

52. Enhancing the Digital Images by using Curvelet Transform .............................................. 453

National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

Geo-Sciences

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National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

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National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

GIS and Remote Sensing based Rajiv Awas Yojagana Project (RAY): A case study on Kiron Ki Dhani Slum in Jaipur Dr. N. Darga Kumar1, D. Hari Prasad2 1. Assistant Professor, Civil Engineering Department, JNTUH, Hyderabad 2. Ph.D. Scholar, JNTUH, Hyderabad

Introduction: Rajiv Awas Yojana (RAY) Scheme Rajiv Awas Yojana (RAY), a path breaking scheme for the slum dwellers and urban poor envisages a ‘Slum-free India’ through encouraging States/Union Territories to tackle the problem of slums in a holistic manner. It calls for a multi-pronged approach focusing on: Bringing existing slums within the formal system and enabling them to avail of the same level of basic amenities as the rest of the town. Slum-free City Planning: The preparation of Slum-free City Plan will broadly involve Slum Development/ Rehabilitation Plans based on Survey of all slums – notified and non-notified; Mapping of slums using the state-of-art technology; Integration of geo-spatial and socio-economic data; and Identification of development model proposed for each slum. Base maps to an appropriate scale would be a pre-requisite for the preparation of Slum Development Plan/Slum-free City Plan. States/UTs may need to proceed in the following steps for the preparation of Slum-free City Plans. Securing CARTOSAT II/latest satellite images from NRSC/ISRO and preparation of base maps for the whole city and its fringes using the images; Identification and inventory of all slum clusters of all descriptions in the urban agglomeration with the help of satellite image and other available data; Inventory of all possible vacant lands in each zone of the urban agglomeration that could be used for slum development/ rehabilitation development purposes; Development of Slum Map of every slum within the city and its fringes using GIS with CARTOSAT II images, ground level spatial data collected through total station survey, collating spatial information with respect to plot boundaries, network of basic infrastructure like roads, sewerage, storm drainage and water lines, etc and superimposing this on the satellite image and importing them into GIS platform as the first step towards the preparation of Slum Development Plans and Slum Free City Plan. This may be undertaken with the help of technical partners of NRSC/ ISRO/other technical institutions/agencies; Identification and engagement of Lead NGO/CBO to guide and anchor community mobilization for the purpose of slum survey, (May be more than one NGO/CBO in different slum zones) of the city. These Lead NGOs/CBOs should also be associated in slum survey operations and dialogues for preparation of slum level development plans;

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National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

Conduct of Slum Survey based on the detailed formats (with or without changes) prepared by the Ministry of Housing & Urban Poverty Alleviation with the help of National Buildings Organisation (NBO) - after due training of trainers, training of survey personnel /canvassers and canvassing. It would be helpful for community mobilisation to pick as many canvassers from the sourced slum or nearby slum pockets; Collection of bio-metric identification data of slum dwellers based on the above survey (subject to guidelines issued by Unique Identity Authority of India (UIDAI)); Entry of data from Slum Surveys in the web-enabled MIS application (to be provided by Ministry of HUPA), compilation and collation of data, preparation of Slum-wise, City and State Slum Survey Database and Baseline Reports. The MIS will assist in developing a robust Slum and Slum Households Information System. (Guidelines and software for development of the MIS will be issued by the Ministry of HUPA); Integration of Slum MIS with GIS Maps to enable the preparation of GIS-enabled Slum Information System that is to be used for the preparation of meaningful Slum Development Plans and Slum-free City Plan using a city-wide/zone-based approach.(Guidelines and software for development of GIS platform and its integration with the MIS will be issued by the Ministry of HUPA); For each slum identified, Slum Development Plan to be decided based on models like PPP development, infrastructure provision only, community-based development through Rajiv Aawas Housing Societies, etc. This decision-making should necessarily be done with the involvement of the community after community mobilization and dialogue for deciding the model tobe adopted. Each slum development plan should have the timeline against each of the activities; and Preparation of Slum-free City Plan should be based on the development plans for all slums and strategies for the prevention of future slums, including reservation of land and housing for the urban poor. The Plan should contain timeline of activities for achieving slumfree city, phasing information and financial estimates against each of the activities Project Definition: The rising urban population has given rise to increase in the number of urban poor. The ever increasing number of slum dwellers causes’ tremendous pressure on urban basic services. Thus the main objective of Rajiv Awas Yojana is to render cities and towns slum free. This requires the existing slums in a city or town to be remodelled so that the residents are provided with a minimum level of housing and access to basic urban services with a standard that the residents of the rest of the city/town enjoy. Objectives To implement the RAY guidelines and make the city of Jaipur, a slum free city. The specific objectives are: To study the existing condition of the slums in the City

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National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

To prepare slum free city action plan including prioritization of slums to be taken up for development in a phased manner To support JDA/JMC in designing various models for slum development(modes and mechanisms)based on the analysis of land values and socio economic attributes, land use, risk mapping and focus group discussion with the community i.e., to identify the failures or loop holes in the existing formal system which lie behind in the creation of slums To prepare detailed project reports for all slums SLUM FREE CITY PLAN & ITS PHASING Introduction: The Slum Free City Plan of Action would include two strategies – improvement of existing slums (curative strategy) and prevention of formation of new slums (preventive strategy) by organizing supply of affordable housing for the urban poor. Thus the broad methodology of the planning listing out the various steps and activities are Methodology of Slum Free City Plan and Slum DPR

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National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

Methodology Flow Chart Selection of cities for the 1st Phase of RAY Identification of Slum Pockets in the City

Slum Level

Problem Identification in the Slum Pockets

Zone Level

Surveys

GIS-enabled Slum MIS

City Level

Land use Total Station DGPS GPS Socio-economic Household Survey

Creation of thematic maps in Auto CAD Generating error free maps in Arc INFO (Defining Topology)

Database Creation (Spatial & non-spatial) in Arc GIS

Decision for Slum Development Model Relocation

Development in situ

Slum Free City Plan (with Time lines & Phasing)

Review and Changes in Master Plan & Framing of Appropriate Regulations to facilitate Slum Free Cities

Jaipur Profile of Jaipur Jaipur, also popularly known as the Pink City, is the capital and largest city of the Indian state of Rajasthan. Founded in 1727 by Maharaja Sawai Jai Singh II, it is one of the first planned cities of India designed by a Bengali Architect, Vidhyadhar Battacharya.

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National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

City Profile of Jaipur Name of the City Geographical Location Average elevation Area Population No. of house holds No. of Slums Slums under JMC area Slums under JDA Source: CDP Jaipur

Jaipur 26°55′N 75°49′E / 26.92°N 75.82°E / 26.92; 75.82 431 metres (1417 ft) 1464 Sq. kms 23, 74,180 4,74,751 236 190 46

Jaipur City: Jaipur City is divided into 3 distinct jurisdictions - the Walled City, Jaipur Municipal Corporation (JMC) and the Jaipur Development Authority (JDA) area. Area and Population of Jaipur Region No. 1 1A 1b. 2. 3.

Area

Total Area (sq. km)

TotalPopulation (millions)

JMC Walled City Rest of JMC Rest of JDA Total JDA

1991 218.3 6.7 192.3 1220 1464

1991 1.52 0.5 1.02 0.35 1.87

2001 288.4 6.7 281.7 1149.9 1464

2001 2.32 0.4 1.92 0.36 2.68

Kiron Ki Dhani has been selected as a Pilot Project under RAY Scheme by the Jaipur Development Authority. It is a path breaking approach being taken up Central Government, State Government and JDA, as there is a felt need to embark on this project with the aim of evolving, demonstrating and establishing models that can thereafter be scaled with a key objective to incentivize innovation and encourage new approaches and solutions that can demonstrably improve the quality and quantity of shelter and services for the poor. DESCRIPTION OF THE PROJECT Introduction Kiron Ki Dhani is one among the notified slums under Jaipur Development Authority Area. It is located in Municipal Ward No. 31, with an extent of 11.36 hectares in Sanganer Zone which has the 2nd highest slum population after Vidhyadhar Nagar Zone. Though Vidhyadhar zone has the largest Kachi Basthi in Jaipur, Sanganer is not far behind and thus Kiron Ki Dhani needs to be taken up as a pilot project to improve the quality and quantity of shelter and services for the slum dwellers. Zone Wise Distribution of Slum Household

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National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

Thus Kiron Ki Dhani project makes a critical case for the issue under the RAY- Slum Free city Planning. The Slum started with a small settlement of 42 households in 1991 which rose to 72 in 2004 and had a drastic increase as shown in the present size as that of 920 households. It can also be proved from the information collected during the socio economic survey, where the senior people of the slum have told that they have been residing at the place namely Kiron Ki Dhani since last 20 years. Location

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National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

Location Map of Kiron Ki Dhani Kiron Ki Dhani Slum falls under the Zone VIII of Jaipur Development Authority (JDA). Kiron Ki Dhani Slum is located in the core area, near Muhana Mandi, Sanganer. Base Map of the slum

Existing Base Map enclosed as Annexures GIS BASED MAPS: Based on the Total Station Survey and Socio Economic Survey, GIS based Thematic Maps have been generated for Kiron Ki Dhani Slum. It includes the existing Base Map Of Kiron Ki Dhani Slum and also the existing condition with demographic and infrastructure and Land Tenure analysis. Thematic Maps: Slum Base Map Land use Map Map showing Caste Details Map showing Household Size Map showing House Type/Structure Map showing Minority Status Socio Economic Analysis 9

National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

Total Population in Slum BPL Population in Slum No. of Households in Slum No. of BPL Households No. of Women- headed household No of Persons older than 65 Years ** No. of Child Laborers No. of Physically challenge person No. of Mentally Challenged person

SCs

STs

OBCs

Others

Total

2136 615 527 108 52 7

38 6 8 1 0 0

1315 270 314 43 24 9

276 64 71 12 10 2

3765 955 920* 164 86 18

Minorities (out of total) 121 24 31 4 6 0

2 4 --

0 0 -

1 5 -

0 0 -

3 9 -

0 0 -

Physical Infrastructure: Infrastructure is the basic requirement of urban life and its adequacy and accessibility are two important ingredients and key contributors in the up gradation and enrichment of quality of urban life which is the primary objective of any planned development effort. The extent and the nature of problems faced by different towns vary by size, geographical conditions, and local natural resources, state/regional differentials in the resource availability and the policies, resource base on local authorities and several such factors directly or indirectly affecting the population of cities/towns. Social amenities and infrastructure fall under the social welfare objectives of the urban development programmes, as distinct from economic development objectives and especially in the context of the rapidly developing liberalized and competitive economic scenario. These infrastructure facilities are broadly classified into two aspects

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National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

Water Supply Existing Water Supply -1 bore well and 1 Cistern. All the households will collect water from the cistern only. The households have informed about the long queues which go upto 2 to 3 hours before their turn for collecting water. Some of the households have constructed sump individually. Existing Pipelines are available in a few houses near Water Tank in the Slum. A SWOT analysis must start with defining a desired end state or objective. A SWOT analysis may be incorporated into the strategic planning model. Strengths: characteristics of the business, or project team that give it an advantage over others Weaknesses (or Limitations): are characteristics that place the team at a disadvantage relative to others Opportunities: external chances to improve performance (e.g. make greater profits) in the environment Threats: external elements in the environment that could cause trouble for the business or project SWOT Analysis Strengths Good Accessibility with Arterial Roads i.e 30 mtr wide road

Weakness All the existing roads are Kucha Roads

High Land Value

Lack of Civic amenities

Muhana Mandi

Most of the Slum Dwellers are unskilled labours

Cotton Industries Sanganer

Opportunities As the land ownership is with JDA, there is no issue with Land Acquisition so that in future there can be a huge development like ORR(Outer Ring Road) Swarn Vihar Layout covers the existing Slum of Residential Land use as per sector map. In future there is scope for developing Housing Layouts & upcoming Commercial Complexes. Slum Dwellers to be trained in Packaging, Pickle making e.t.c

Ground Water Employment in Pollution due to Opportunities. chemicals. Skill Up gradation. 11

Threats No scope for further development as Kiron Ki Dhani Slum exists

No scope for further development as Kiron Ki Dhani Slum exists. The JDA land surrounded by the Slum is already auctioned no venture has been grounded. Open spaces are being used as Garbage dumping yards Being a whole sale market retail customers are lagging. Seasonal fluctuations are affecting their livelihood. Health Problems occur due to Chemicals regular usage.

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Strengths

Weakness

Opportunities

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Threats

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Spectral Biomass Assessment of Yealgiri Hills Dr. V. E. Nethaji Mariappan1, T. Nagaraju2 1Scientist-D, 2Junior Research Fellow Centre for Remote Sensing and Geoinformatics, Sathyabama University, Rajiv Gandhi Road, Jeppiaar Nagar, Chennai – 600 119 [email protected] Mob: 9444226029 ABSTRACT Forest, one the vital element for sustainment of life on earth, is a terrestrial component of the earth ecosystem and inter –linked with all other components of ecosystem. Forest combat climate change storing carbon and sucking in carbon di-oxide from the atmosphere and locking it in the biomass (UNEP & FAO, 2011). Yelagiri hills is one among forest is lies in Vellore district in Tamil Nadu. A study was taken up to assess biomass of Yelagiri hills using AWIFS satellite data and through field survey. A total of nine grids each gird size (1km*1km) were identified through stratified sampling technique from AWIFS image for the February 2009. Such grids were subsetted from the entire study area scene. Normalized Difference Vegetation Index (NDVI) approach was used to identify spatial variability of vegetative cover of Yelagiri hills. Remote sensing techniques such as image enhancement, geometric correction, feature extraction and statsistical analysis were performed during the course of investigation. A method of grade NDVI change (Peng et al., 2012) was used in this study. High vegetation cover occupied more than 50 per cent of the total area, medium vegetation cover reached 35 per cent area and the rest comes under low vegetation area. Above ground biomass estimation for nine plots are carried out using standard procedures. An approach is attempted for integrating field methodology and remote sensing estimates in order to minimize biomass error. Corresponding author Key words: Biomass assessment, NDVI, filed survey, grade change & integration

INTRODUCTION The Eastern ghats constitute an important biogeographic region in the Indian region and is a major center of plant diversity with a high endemism. Ranging from Orissa Andhra Pradesh to Karnataka and Tamil Nadu, the Eastern ghats are spread over an area of about 75,000 sq.km through a chain of fragmented and disjunct hill ranges. The fragmented nature of the Eastern ghats mountain ecosystem include a rich assemblage of floral, faunal wealth including many endangered and endemic species. The southern part of the ‘Eastern Ghats, particularly the Nallamalai – Cuddapah – Tirupati – Chittoor - Shevaroy – hill ranges, are known to be major centers of plant and animal diversity (Areendran et al., 2010). In Tamil Nadu is covered by North and South Arcot hills, Salem (Yercaud hills, Kollimalai hills, Villupuram (Kalrayan Hills) and Coimbatore (Satyamangalam ranges). Our study area focus upon Yelagiri hills is situated in Jolarpet Panchayat Union of Tirupattur taluk, have a district geographical unit and have an elevation of about 1411m. It has an area of 51 square km. of which 3297.68 ha. are under reserve forest. The hill villages are situated mostly at an elevation of 1889 meters. The richness of bio cover could be measured to assess the biomass of the regions in sequence to carbon sequestration potential of the study site. Such measurements are carried out in two approaches in vegetation coverage detection, i.e., field survey and remote sensing. Since vegetation coverage shows significant spatial and temporal variation, it is difficult to 13

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estimate the vegetation coverage through traditional field sampling at a broad scale with high accuracy. On the contrary, in view of these defects, the approach of remote sensing is competent for its good temporal and spatial continuity (Wang et al. 2008). Remotely sensed image has become a reliable and effective data source in vegetation dynamic studies (Hu et al. 2010), especially for rapid monitoring at global or regional scale. Furthermore, there are mainly two methods in dealing with remotely sensed images in quantifying vegetation coverage, i.e., mixed pixel decomposition and vegetation index (VI). Generally speaking, the former is based on spectral mixture model, which considers that the size of ground objective is often smaller than the spatial resolution of remotely sensed images and that the pixel characters of remotely sensed image can reflect the comprehensive information of land cover features, but the accuracy of measurement is directly restricted by the technological maturity of mixed pixel decomposition; the latter is extensively accepted Objectives  To map NDVI cover in Yelagiri hills and to estimate Bio mass using AWIFS data.  To assess the temporal bio cover of the region  To compare sequentially grid wise bio mass density from satellite data STUDY AREA Yelagiri hills falls within the district boundaries of Vellore District. Yelagiri hills is situated in the Jolarpet Panchayat Union of Thirupattur taluk, surrounded in the north, west and south by Vaniambadi taluk, This hill is situated 92 km. East of Vellore and 30 km. West of Tirupattur. It has only one revenue village namely Athanavoor with thirteen hamlets. It has an area of 51 square km. The study area is represented in figure 1. Climate The temperature of Yelagiri hills during Summer (April) is of 27⁰C and the minimum temperature goes down in Winter between December and January to 11⁰C. It has comparative dry climate with low humidity of 45-50 %. The mean annual rainfall for Elagiri hills is 1026.16mm. and a maximum of 131.8 mm received during South West monsoon and 333.7 mm during Northeast monsoon. Soils About 50 percent of the land area is red loam clay and sandy soil that roughly constituting 13 and 12 percent of the total area. This type of soil is derived basically from feldspar and hormablend. It has been observed that mineral resources such as Sulphides, Quartz, Haryte, Apatite and Vermiculite occur in areas adjoining Tirupattur of these hills.

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Figure 1. Index map of the Yelagiri Hills METHODOLOGY Satellite data Processing AWiFS (Advanced Wide Filed Sensor data) data of path and row corresponding the date of acquisition 16 February 2009 and 144 / 52 and of 10 January, 2010 comprising band 2, 3 and 4 were acquired from National Data Centre, National Remote Sensing Centre, Hyderabad. The data comprise four bands as blue, green, red and NIR bands were subjected to geometric corrections to minimize the geometric distortions introduced by extraneous factors. Individual bands were layer stacked and mosaicked in order to get entire study area in Universal Traverse Mercator (UTM) projection in Leica's Erdas Imagine 8.7 software. A vector dataset in shape file format of Yelagiri forest was created, derived and overlaid above the composite image, thus study area boundary was subset from the entire scene (figure 2) was used for further analysis. The hills are bounded by thick forest cover at the boundary, an open forest and mixed forest towards the centre of Yelagiri Hills. Such spatial variation showed an insight of bio spatial variability of forest resources. The entire forest area was gridded to 1km*1km at a spacing of 3 km between two grids at both the axis. A total of nine grids fall within the study area as represented in figure 3.

Figure 2 & 3. Satellite data of Yelagiri Hills of the year 2010 and sampling grids 15

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Grid Generation The study area possess complex vegetation coverage that can be construed as Very High, High vegetation, Moderate Vegetation and Poor Vegetation. In order to harmonize the biomass assessment, a sequential grid sampling strategy was adopted for our study. Grids area generated at as size of 1*1Km and the spacing distance between the grids are kept at 3 Km in ERDAS IMAGINE software. Since vegetation coverage shows significant spatial and temporal variation, the girds are selected in such a way that vegetation coverage of all categories is covered through these grids. A total of nine grids fall under this category of this 4 grids fall on the border of the study area. Among the four 2 grids occupy an area of more than 90 per cent and other 2 grids cover an extent of more than 70 per cent. The geographic coordinates of the grid study site and its nature of vegetation are listed in Table 1. Table 1. Study area grid details and its nature of vegetation Plot Latitude Longitude Site name No. (N) (E) 1 12.62242 78.6437 Mandalavadi 2 3 4 5 6

12.62240 12.62198 12.58576 12.58615 12.58573

78.6797 78.7175 78.6063 78.6434 78.6803

7 8 9

12.54969 78.6067 12.54968 78.6436 12.54946 78.6799

Land_mark

Distance Nature of Vegetation

Near by small village 5.2km

Alangayam Near by forest Alangayam forest Ponneri forest Yelagiri lake Near by yelagiri lake Mangalam Hilly area road Fakir dharga Yelagiri village road Perumapattu Forest Andiyappanoor Forest

8.39 km 4.38 km 3.08 km 0.85 km 2.36 km

Agriculture with forest Agriculture with forest Forest Forest Agriculture Thick forest

3.94 km Forest 3.66 km Forest 3.96 km Forest

NDVI analysis Normalized Difference Vegetation Index (NDVI), is the most widely adopted vegetation index proven to be highly sensitive with the vegetation coverage ratio of 25–80%, and Lijiang County happens to have the similar vegetation coverage features. Thus, NDVI is used to quantify the change of vegetation coverage in the study area. In detailed calculation as shown in Eq. 1, NDVI is defined as the difference of the spectral reflectance between nearinfrared band and visible red band normalized by the summation of these two bands, where NIR is near-infrared band, R is visible red band. In the case of Landsat TM data, NIR is represented by TM4 band with TM3 band for R. Generally speaking, NDVI ranges from −1 to 1. The negative value indicates land cover without chlorophyll, such as cloud, rock, water, storm, etc. The positive value represents vegetation coverage, with the higher value for the more dense coverage and healthy of green vegetation. RED and NIR stand for the spectral reflectance measurements acquired in the red and near-infrared regions, respectively. NDVI = (NIR –Red) / (NIR+Red) …………………………………. Eq. 1. 16

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Vegetation coverage change matrix analysis Vegetation coverage of Yelagiri hill is dynamic due to climate change and human activities, the value of NDVI is always found to be changeable in numerous case studies. Therefore, it is significant to assess the change of vegetation coverage, besides the change detection. At present, the method of grade change is widely used, which focuses on the distinct change of vegetation coverage and ignores the small change. AWIFS satellite data pertaining to 16th February 2009 and 23rd January 2010 was employed for NDVI analysis considering previous year as reference data for NDVI change matrix analysis corresponding for the year 2010. According to natural conditions of the study area, vegetation coverage is divided into four grades with NDVI value, i.e., full vegetation coverage (FVC, 1≥ NDVI≥0.9), high vegetation coverage (HVC, 0.9> NDVI≥0.5), medium vegetation coverage (MVC, 0.5>NDVI≥0.26), and low vegetation coverage (LVC, 0.26>NDVI≥−1). RESULTS AND DISCUSSION NDVI Analysis NDVI is the one of the favorite index universally adopted to understand the vegetative vigour of biome especially tree, crops etc. Analysis was carried for AWIFS data corresponding the year of 2009 and 2010 respectively. Spatial generated NDVI map has been represented in figure 4 & 5. NDVI values ranged from -1 to 1 value. In this study, water bodies (e.g., oceans, seas, lakes and rivers) which have a higher absorption in both spectral bands resulted in negative to very low positive NDVI values. Bare rock, soils and sand generally exhibit a low near-infrared spectral reflectance thus tend to exhibit a rather lower NDVI values in the range of 0.1 to 0.2. The central part of the study area possess a moderate values as represented more of open spaces intermixed with shrub and grassland with a NDVI values of 0.2 to 0.3. in figure 6. Dense vegetation was observed all along the boundary of Yealgiri hill that contain the NDVI values in the range of 0.3 to 0.8. Dynamics of NDVI between the images of 2009 and 2010 seem to be predominant exclusively at the lower and higher values.

Figure 4 & 5. NDVI image of Yelagiri Hills for the year 16th Feb. 2009 & 23rd Jan.2010 17

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Study area grid of 1km*1km and proximity look up of NDVI pixel values and vegetation distribution Figure 6. Characteristic NDVI Signatures of 2010 AWIFS image In order to study temporal and spatial variability of NDVI values, the two NDVI images containing the attributes of both the NDVI values and the corresponding number of pixels in the attribute table of the images were exported to MS-Excel format. A graph was plotted with NDVI values in X-axis and Number of pixels in Y-axis. Results of this study exhibited that there was not much variation between the images for the negatives values. A significant variation was observed for the values between 0.3 - 0.4 as well for the values 0.7 0.8. in figure 7. Such variation might be due to the transition of pixels form the higher values to lower NDVI values.

Number of Pixels

Spatial and Temporal Variation of NDVI Values of year 20092010

-0.6

700 600 500 400 300 200 100 0 -0.4

-0.2

0

0.2

2009

0.4 2010

0.6

0.8

1

NDVI VALUES

Figure 7. Quantity of pixels and corresponding NDVI values for the year 2009-2010 Such NDVI variation matrices between the two images especially lower and medium NDVI values have provided insight to carryout in-depth NDVI metrics analysis of the study area. One such analysis was the gird generation and selection of the study site based on the spatial variability of NDVI matrices.

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Grid Generation A sequential grid sampling strategy was adopted for our study. Grids area generated at as size of 1*1Km and the spacing distance between the grids are kept at 3 Km in ERDAS IMAGINE software through frame sampling tools through grid generation techniques in shape file format. A total of nine grids were generated under this process. Some grids lie on the border of the study area, 2 grids occupy an area of more than 90 per cent and other 2 grids cover an extent of more than 70 per cent of the study area. Grids in shape file format were overlaid above the satellite image and subset for the analysis purpose. NDVI images of all the girds were subset for further spatial variability analysis. Comparison of NDVI images of the entire study scene is quite difficult in contrast to the grid wise corresponding images. The grid images for the two dates were given in figure 7. Grid wise NDVI Analysis NDVI images of the grids were subset from the Yelagiri study area and preliminary analysis on NDVI fractionation was performed at eleven intervals at an average value of 0.1 between each grid. Enormous data on pixels with respect to each fraction lead to a complexity in investigating of NDVI variability metrics and therefore an aggregation of the NDVI values was aggregated into four types as per (Peng et al., 2012), later modified for NDVI range value according to Yelagiri bio-climatic factor, i.e., full vegetation coverage (FVC, NDVI≥0.75), high vegetation coverage (HVC, 0.55> NDVI≥0.75), medium vegetation coverage (MVC, 0.22>NDVI≥0.55), and low vegetation coverage (LVC, -1>NDVI≥0.22). Table 2. Vegetation Coverage in Yelagiri hills during 2009 Yelagiri Satellite acquisition date: 16-02-09 Class Pixels Area Sq. Km. Percentage LVC 142 445.312 0.45% MVC 22161 69496.896 69.86% HVC 9230 28945.28 29.10% FVC 187 586.432 0.59% Total 31720 99473.92 100.00% Table 3. Vegetation Coverage in Yelagiri hills during 2010 Yelagiri

Satellite acquisition date: 23-01-10

Class LVC MVC HVC FVC Total

Pixels 20 9416 18656 3628 31720

Area Sq.Km. 62.72 29528.576 58505.216 11377.408 99473.92

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Percentage 0.06% 29.68% 58.81% 11.44% 100.00%

National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

Classification on image depicted the actual picture of spatial variability of the grids between two dates. The class intervals, number of pixels, proportionate area and its percentage are listed in Table 2 & 3. Form the table it is construed that there was 40 % decline in MVC, 30 % increase in HVC and 10 % increase in FVC. The resultant increase in vegetative cover from medium value cover to high and full value cover during 2009-2010 might be due substantial increase in rainfall lead to higher vegetative growth of MVC to HVC and FVC. Conclusion Vegetation change in terms of NDVI for the year 2009 and 2010 for the Yelagiri hill was studied using AWIFS satellite data. Spatial and temporal variation for the entire scene was performed. Drawing the conclusion on dynamic nature of vegetative cover seems to be complex and hence sequential gird approach was performed for the Yelagiri hill. A total of nine grids were studied for the temporal variation. Annual variation of NDVI values in the MVC, HVC and FVC were higher in terms of respective proportional area. This study has demonstrated that grid wise approach is best methodology for analyzing inter annual variations of vegetative coverage especially in the forest region. Reference •

Jian Peng & Yinghui Liu & Hong Shen & Yinan Han & Yajing Pan 2012. Vegetation coverage change and associated driving forces in mountain areas of Northwestern Yunnan, China using RS and GIS Environ Monit Assess (2012) 184:4787–4798.



Hu, C. J., Fu, B. J., Liu, G. H., Jin, T. T., & Guo, L. (2010). Vegetation patterns influence on soil microbial biomass and functional diversity in a hilly area of the Loess Plateau, China. Journal of Soils and Sediments, 10, 1082–1091.



Wang, J., Meng, J. J., & Cai, Y. L. (2008). Assessing vegetation dynamics impacted by climate change in the southwestern karst region of China with AVHRR NDVI and AVHRR NPP time-series. Environmental Geology, 54, 1185–1195.

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Land Slide Suitability Analysis P.Balamurugan1, V.Sampathkumar2, Dr. Illantharaiyan3 1. Assistant professor, Department of Civil Engineering, Erode Sengunthar Engineering College 2. M.E. Environment Engg., Department of Civil Engineering, Erode Sengunthar Engineering College 3. Assistant professor, Department of Geography, Government Arts College, Karur.

INTRODUCTION Landslide is the major disaster event occurring in the hilly regions. It is an event occurs slowly and rapidly. It classified differently as per its types. Landslides create vast damage to the mankind and infrastructure. These events are associated with pre and post of earthquake, soil erosion, rainfall and anthropogenic activities. The combination of Remote sensing and GIS can able to prepare Landslide Zonation map to minimize the loss of humans and associated assets. Aim The aim of the exercise is to prepare Landslide Zonation map for a given area. Study Area The Shervarayan hill is the study area. This study area lies from11°42΄59”N to 11°57΄20”N Latitude and from 78° 5΄ 51”E to 78° 21΄ 59” E Longitude. The study area falls in the Survey of India toposheets no.58 I/1, 2, 5 and 6. It covers an area of 469 sqkm. It consists of 55 revenue villages. The main types of landslides occurring in the study area are: Falling, Subsiding, Sliding, and Flowing. In the study area, it has been observed that natural slopes are disturbed due to cutting of roads for construction purposes, prevention of natural drainage and the changing land use pattern are the factors contributing to landslides and landslips. Most of the times it is triggered by high intense down pour. Data Used Satellite imagery IRS-ID LISS III, with 23.5 m resolution, SRTM imagery of 90m resolution. Survey of India Toposheets 58I/2 on 1:50000 scale, Geological map published by Geological Survey of India, soil map NBSS. Software Used ArcGIS 9.1 and Erdas 8.7

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National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

Lineaments Gneissic rock is the major rock type found in this region. There are three major lineaments found in this study area. A major shear zone on the western part of the study area, which runs north to south. The second major lineament found in the eastern part of the study area, it runs north to south and the third major lineament found in the southern part. It runs west to East. There are many small lineaments that are cutting the major lineaments. The structure of this study area plays an important role in the formation of drainage pattern and influences the landslide activity. The lineaments of the area analyzed in two methods such as frequency and intersection. Lineament frequency calculated by 1cm*1cm grid of the study area which covers 25 hectares similarly lineament intersection also identified for each grid. Further to establish the lineament influence 0.5km and 1km buffers were created, the near buffer got high rank values than the far buffer. These buffer zone intersections are also used for ranking. After identifying the lineament frequency and intersection kernel using density in spatial analysist, frequency map was generated. The high frequency got high rank value. Those areas doesn’t have lineaments “0” was allotted. The weight and rank of the lineaments are shown in the table 2.1.

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Land Use The land use/ land cover map derived from the hybrid use of SOI toposheets and IRS1C-Liss III image. The image classified using supervised classification and the area is classified into following land use/land cover categories. They are Waterbody, Settlements, Land without scrub, Land with scrub, barren upland, Fairly dense vegetation, Dense vegetation and Querry. By understanding the land use class and landslide suitability the ranks were given it is shown in the table 2.1 and the landuse map shown in fig. 2.2. Stream Frequency The stream network of the Shervarayan hills digitized from SOI toposheet. Stream frequency calculated for each 25 hectares grids. The higher ranks were given for higher frequency. The stream frequency map shown in fig 2.3. Weightage and rank shown in table 2.1 Slope The slope map is generated from the SRTM, DEM of the study area. In this area, there are 6 categories of slope were found, they are 1°-15°, 15°-25°, 25°-35°, 35°-45°, 45°65° and more than 65°.Slope of 35°-45° have higher rank than the other slope categories. The slope map shown in fig 2.8.

Analysis GIS is the best platform for analyse the spatial and non spatial data. Before the integration phase weightage and ranks were assigned based on the suitability nature. In of each theme, the analysis weightage and ranks were multiplied for each theme, then the themes are integrated by Union operation. The Landslide suitability is the addition of the sum of the weightage *ranks of the individual theme. Mean and standard deviation is calculated for the landslide field in the table of cumulative map. Based on the mean and standard deviation the Landslide zonation are classified into High suitable, Moderate suitable, slightly suitable and Not suitable. 23

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Result The landslide map shows the area under high suitable, moderate suitable, slightly suitable and not suitable. The very high suitable area covers 20.64 sq.km, high suitable area occupy 60.44 sq.km., moderate suitable found 100.45 sq.km. and low suitable area occupies 290.60 sq.km. The fig.2.9 shows the landslide suitability zonation.

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National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

Geo spatial based Computation of Morphometric Parameters –Kadam Reservoir Catchment Area, Adilabad district, India P.Sridhar,1 K.Padma Kumari,2 K.Srinivasa Reddy3 1. Research scholar, Civil Eng. Department, JNTUK, Kakinada [email protected] 2. Associate Professor of Geology, JNTUK Kakinada 3. Research Associate, CRIDA, Santoshnagar, Hyderabad ABSTRACT Geo spatial based computation of Morphometric Parameters play key role in the watershed planning and management of the hydrological behavior, such as runoff, soil erosion, sediment yield etc. The study area semi-arid region of Kadam watershed was covered in Godavari River basin and delineates twenty one sub-watersheds and marphometric parameters were analyzed in GIS environment. The Geospatial database is captured from Survey of India toposheets. Using the geospatial database various morphometric parameters were computed at subwatershed level. The drainage density, drainage frequency, bifurcation ratio, elongation ratio, circularity ratio and form factor 2.45 to 6.38, 3.7 to 13.15, 2.86 to 8.17, 0.56 to 0.71, 0.16 to 0.64 and 0.25 to 0.40 respectively, these are appearing significantly lower to higher values. Results are dissimilar of each distinct subwatershed are higher to low runoff, sediment yield and infiltration capacity and also prove the Geographic Information System (GIS) techniques are competent tool in marphometric analysis. KEY WORDS: Kadam watershed, GIS, Marphometric analyses

INTRODUCTION The quantitative analysis of morphometric parameters is found to be of immense utility in river basin evaluation, watershed prioritization for soil and water conservation and natural resources management. Morphometric analysis of a watershed provides a quantitative description of the drainage system which is an important aspect of the characterization of watersheds (Strahler, 1964).Geospatial based techniques are now days used for assessing various quantitative morphometric parameters of the watersheds, as they provide a flexible environment and a powerful tool for the manipulation and analysis of spatial information. In the present study Kadam watershed is delineated twenty one sub-watersheds and each subwatershed morphometric parameters were computed, derived and tabulated. STUDY AREA The study area Kadam catchment is one of the tributary to Godavari River; it is located lies between latitudes 19005' to 19035’ N and longitudes 78010’ to 78055’ E, it is falling in Survey of India toposheet no.56 I-3, 6, 7, 8, 10, 11, 12, 14, 15 and 56 I-16. The areal extent of the area is 2656.25sq.km and politically placed in Adilabad districts of Andhra Pradesh (Fig.1).The climate is semi arid with an average annual rainfall is 765mm. The minimum and maximum temperatures range from 180 to 43.60C respectively. The watershed elevation ranges between 180 to 650m above the Mean Sea Level (MSL), slightly undulating terrain with slight to moderate slopes (2 to 3%) and dendritic to sub dendritic drainage pattern. Soils are covered with black cotton and red soils. Geologically the area is consisted with consolidated rocks of granites, gneisses, schists, dolerites and basalts rock formations. These groups of rocks are occupying the entire eastern and central part and granites, gneisses and schits occur in the areas of Nirmal, Khanapur, Utnoor and Luxettipet. Basaltic rocks (Deccan Traps) occur in the western and central part of the areas of near Mudhole, Boath, Ichoda and north of Utnoor. 25

National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

MATERIAL AND METHODS All streams of different extents and patterns were digitized from Survey of India toposheets (1:50,000 scale), entire analysis of watershed morphometry using GIS software (Arc GIS 9.3). The each stream order was given by following Strahler (1964) stream ordering technique. The attributes were assigned to create the digital data base for drainage layer of the watershed. Various morphometric parameters such as linear and areal aspects calculated. The input parameters for morphological studies such as perimeter, area, elevation, stream length etc. were obtained directly in Arc Info GIS software using query based algorithm. Other morphometric parameters were calculated using formulae based on input values. Various morphometric parameters such as linear and areal aspects were computed in GIS environment as shown in following Table.1. RESULTS AND DISSCUSSIONS Drainage Density: ` Horton (1932&1945) has introduced drainage density is defined as the total length of streams of all orders per drainage area. Smith (1950) and Strahler (1957) described the range of various drainage density values in the manner of, the values less than 5 are considered as coarse, values between 5 and 13.7 as medium, between 13.7 and 15.53 as fine , while values greater than 15.53 as ultra fine. In the study area the drainage density ranges 2.45 to 6.38 (Table-2&3), its indicating medium drainage density. Elongation Ratio Elongation ratio (Schumn,1956) is defined the ratio between the diameter of the circle of the same area as the drainage basin and the maximum length of the basin. The elongation ratio values generally exhibit variation form 0.6 to 1.0, a wide variety of climate and geologic types. Values close to 1.0 are typical of regions of very low relief, whereas values in the range 0.6 to 0.8 are usually associated with high relief and steep ground slope (Strahler, 1964). Analysis of elongation ratio indicates that the areas with higher elongation ratio values have high infiltration capacity and low runoff. In the study area, Elongation Ratio ranges 0.56 to 0.71(Table-2&3), indicates sub watersheds to be elongated with strong relief and steep ground slope. Drainage texture Drainage texture is the total number of stream segments of all orders per perimeter of that area (Horton, 1945), according to Smith (1950), five different drainage texture have been classified based on the drainage density. The drainage density less than 2 indicate very coarse, between 2 and 4 is related to coarse, between 4 and 6 moderate, between 6 and 8 is fine and finally greater than 8 is very fine drainage texture. The drainage texture depends upon a number of natural factors such as climate, rainfall, vegetation, rock and soil type, infiltration capacity, relief and stage of development (Smith, 1950). The soft or weak rocks unprotected by vegetation produce a fine texture, whereas massive and resistant rocks cause coarse texture. In the study area, drainage texture ranges 2.86 to 9.28 (Table-2&3), its indicating that the area with course to very fine texture. 26

National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

Form factor Form factor may be defined as the ratio of the area of the basin and square of basin length (Horton, 1932, 1945). The value of farm factor would always be less than 0.78 for a perfectly circular basin. Smaller the value of form factor, more elongated will be the basin. The basins with high farm factors have high peak flows of shorter duration; whereas, elongated sub-watershed with low farm factors have lower peak flow of longer duration. In the study area, Form factor ranges 0.25 to0.40 (Table-2&3), its indicating that the area with lower peak flow of longer duration. Stream Frequency Stream frequency / channel frequency is the total number of stream segments of all orders per unit area (Horton, 1932&1945).In the study area, stream frequency ranges 3.7 to 13 (Table-2&3). Circulatory Ratio It is the ratio of the area of the basin to the area of the circle having the same circumference as the perimeter of the basin (Miller, 1953). It is influenced by the length and frequency of streams, geological structures, land use / land cover, climate, relief and slope of the basin, described the basin of the circularity ratios range 0.4 to 0.5 which indicates strongly elongated and highly permeable homogenous geologic materials and circulatory ratio is helpful for assessment of flood hazard. Bifurcation Ratio Bifurcation Ratio is the ratio of the number of streams of a given order to the number of streams of the next higher order (Schuman, 1956). Horton (1945) considered bifurcation ratio as an index of relief and dissections. Strahler (1957) demonstrated only a small variation for different regions on different environment except where powerful gelogical control dominates. In the study area, bifurcation Ratio ranges 2.86 to 8.17(Table-2&3). CONCLUSIONS The drainage density, drainage frequency, bifurcation ratio, elongation ratio, circularity ratio and form factor 2.45 to 6.38, 3.7 to 13.15, 2.86 to 8.17, 0.56 to 0.71, 0.16 to 0.64 and 0.25 to 0.40 respectively, these are appearing significantly lower to higher values. Results are dissimilar of each distinct sub-watershed are higher to low runoff, sediment yield and infiltration capacity and also prove the GIS techniques are competent tool in marphometric analysis.

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National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

REFERENCES 1. Strahler, A.N. (1964). Quantitative Geomorphology of Drainage Basins and Channel Networks. A Handbook of Applied Hydrology, McGraw Hill Book Company, Newyork, Section 4-11. 2. Horton, R.E. (1932). Drainage Basin Characteristics. Transactions of American Geophysical Union. 13, pp. 350-361. 3. Horton, R.E. (1945). Erosional Development of Streams and their Drainage Basins: Hydrophysical Approach to Quantitative Morphology. Geological Society of America Bulletin, 56, pp. 275370. 4. Smith,K.G.(1950). Standards for Grading Texture of Erosional Topography, Amer. Jour.Sci.,248,pp 655668. 5. Strahler, A.N. (1957). Quantitative Analysis of Watershed Geomorphology. Trans. Am. Geophys. Union, 38: pp.913-920. 6. Schumn,S.A.(1956), Evaluation of Drainage Systems and Slopes in Badlands at Perth Amboy, New Jersy”, Bull. Geol. Soc. Amer,67,pp 597-646. 7. Miller (1953). A Quantitative Geomorphic Study of Drainage Basin Characteristics in the Clinch Mountain Area, Varginia and Tennessee”, Project NR 389-042,Tech. Rept.3.,Columbia University, Department of Geology, ONR, Geography Branch, New York

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National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

Fig.2: Delineated of sub-watersheds of Kadam watershed Table-1: Formula Used for Computations of Marphometric Parameters Morphometric Parameter

Formula

Description

Reference

Re = 2 A/ L

(Re) was as the ratio between the diameters of the circle of the sane area as the drainage basin and the maximum length of the basin, the values of Re generally vary from 0.6 to 1.0 over a wide variety of climatic and geologic types. Where: Re= elongation Ratio, 2 = constant, A= area of the basin, L= maximum of the length ratio

Schumn(1956)

Circularity Ratio (Rc)

Rc= 4π A / P2

(Rc) Was calculated as the ratio of the basin area to the area of the circle whose perimeter is equal to the perimeter of the basin. The ‘P’ is the perimeter of the basin. It is also influenced by the length and frequency of stream, geological structures, land use and cover, climate, relief and slope of the basin expressed as, Where: Rc = Basin circularity, A = Area of the basin P = perimeter of the basin.

Miller (1953)

Form Factor (Rf)

Rf = A/(Lb)2

(Rf) was computed as the ratio between the basin area and square of the basin length. Where: Rf= Form Factor, A=Area of the basin, (Lb)2=Square of basin length.

Horton (1945)

Elongation Ratio (Re)

Bifurcation Ratio (Rb)

Rb = Nu/(Nu + 1)

Drainage Frequency (Df)

Df = N/A

Drainage Density (Dd)

Dd L/A

=∑

(Rb) was computed as the ratio between the numbers of streams of any given order to the number of streams in the next higher order. It is shown a small range of variation for different regions or different environment except where the powerful geological control dominates. The bifurcation ratio is not same form one order to its next order. Where: Nu= numbers of streams, (Nu + 1) = number of streams in the next higher order. (Fu) was computed as the ratio between the total number of streams and area of the basin. Where: Fu = Drainage Frequency, N= total number of streams. A= area of the basin. (Dd) was measured as the length of stream channel per unit area of drainage basin. Density factor is related to climate, type of rocks, relief, infiltration capacity, vegetation cover, surface roughness and run-off intensity index. The amount and type of precipitation such as thundershowers, loses greater percentage of rainfall as run-of resulting in more surface drainage lines. Where: Dd = Basin circularity, A = Area of the basin, P = perimeter of the basin.

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Schumn(1956) Horton(1945)

Horton (1945)

Horton (1945)

National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

SN o

Sub Watershed

1

Sikkumanu

2

Gajjalavagu

3

Dorla vagu

4

10

Lothuvara Peddamma Vagu Allampalli Vagu Batkamma Vagu Gangapuram Vagu Kaddam Reservoir Balli Vagu

11

Palukeru Vagu

5 6 7 8 9

12 13 14

Kaddam Reservoir-1 Kaddam River Kaddam River above

15

Gundi Vagu

16

Ragidobanala

17

Datki Vagu

18

Wankedi

19

Dasnapur

20

Pedda Vagu

21

Pochera

Table-2: Results of Marphometric Parameters of Sub-watersheds Lengt Drain Drain Far Area Perime Elongat Stream h age age m (sqk ter ion Freque of Densit Textu Fact ms) (kms) Ratio ncy Basin y re or 440.4 163.62 23.74 2.83 0.57 7.63 0.25 4.62 3 166.9 65.74 14.14 2.88 0.61 7.32 0.29 5.18 1 184.7 86.77 16.55 3.56 0.60 7.59 0.29 6.75 8 55.89 33.18 9.59 3.51 0.65 5.91 0.34 6.03

Circula rity Ratio

Bifurca tion Ratio

0.21

5.22

0.49

6.41

0.31

4.32

0.64

5.34

72.23

57.14

13.06

3.16

0.64

3.99

0.32

4.51

0.28

7.08

39.87

34.67

9.83

3.06

0.67

3.52

0.35

5.02

0.42

6.61

27.06

29.13

8.91

3.07

0.69

2.86

0.37

5.25

0.40

4.86

41.10

30.51

9.14

3.39

0.67

4.57

0.35

6.13

0.55

6.83

18.79

30.67

9.17

6.38

0.70

3.91

0.39

13.15

0.25

2.86

32.43 209.4 4

25.25

8.21

2.48

0.68

3.19

0.36

3.76

0.64

6.66

68.58

14.48

2.91

0.60

8.90

0.28

4.62

0.56

4.17

47.75

55.52

12.85

4.53

0.66

3.90

0.34

6.68

0.19

8.17

15.44 488.7 7 113.1 4 148.1 4 34.36 194.0 4 80.24 169.7 7 58.17

22.94

7.78

5.56

0.71

3.74

0.40

8.23

0.37

2.99

193.34

26.10

2.90

0.56

7.32

0.25

5.36

0.16

5.91

56.99

13.04

2.45

0.62

4.87

0.31

3.70

0.44

4.40

77.70

15.55

2.98

0.61

5.68

0.29

4.59

0.31

4.83

36.81

10.17

3.26

0.68

3.04

0.36

5.04

0.32

4.27

64.13

13.94

3.07

0.60

9.28

0.28

5.09

0.59

5.52

46.39

11.60

2.94

0.64

5.08

0.32

4.71

0.47

4.29

73.08

15.02

2.83

0.61

6.58

0.29

4.86

0.40

3.98

38.13

10.38

3.15

0.65

4.80

0.33

4.99

0.50

4.28

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National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

Table-3: Summary Statistics of the Morphometric Parameters of Sub-watersheds Lengt Drain Drain Far Elongat Stream Circula Area Perime h age age m ion Freque rity (sqkm ter of Densit Textu Fact Parameters Ratio ncy Ratio s) (kms) Basin y re or Minimum 15.44 22.94 7.78 2.45 0.56 2.86 0.25 3.70 0.16 488.7 Maximum 193.34 26.10 6.38 0.71 9.28 0.40 13.15 0.64 7 125.6 Mean 61.44 13.01 3.38 0.64 5.41 0.32 5.63 0.40 5 Median 72.23 55.52 12.85 3.07 0.64 4.87 0.32 5.04 0.40 Standard 129.5 43.39 4.74 0.97 0.04 1.97 0.04 2.01 0.15 Deviation 5 Standard Error 28.27 9.47 1.03 0.21 0.01 0.43 0.01 0.44 0.03 16783 1883.0 Sample Variance 22.48 0.94 0.00 3.86 0.00 4.05 0.02 .42 5 Kurtosis 3.21 4.51 2.53 4.67 -0.88 -0.88 10.00 -0.98 0.77 Skewness 1.85 2.11 1.55 2.21 -0.12 0.54 0.03 2.88 0.02 Confidence Level 58.97 19.75 2.16 0.44 0.02 0.89 0.02 0.92 0.07 (95.0%)

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Bifurcat ion Ratio 2.86 8.17 5.19 4.86 1.38 0.30 1.92 -0.40 0.34 0.63

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National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

FOSS Geospatial Technology Santhi Swarup Manugula & Vineet Chandan

Faculty in Centre for Spatial Information Technology, JNTU, Hyderabad-500085

[email protected]

ABSTRACT: The daily use of geospatial technology, such as the global positioning system (GPS), geographic information systems (GIS), and remote sensing (RS), is increasing. Increase in the number of commercial and open source tools for geospatial applications have resulted in confusing environment among students / researchers, institutions and consultants while selecting software tools. Also geospatial software applications require more time and money in design and development. Though number of tools are available for web based spatial information system, each one of them have some restrictions, such as license, availability of skilled person, etc. To overcome such problems, open source tools are better solutions. Open source geospatial tools provide facilities such as cost effectiveness and it can be customized according to user needs. Also, various spatial queries can be made for better decision making. They are helpful in monitoring various rural development schemes run by government. To demonstrate the use of free available software i.e. free open source software (FOSS) for geospatial data management, a system was designed for extracting geospatial information content from spatial database and practical issues during development are described in this paper. Keywords: FOSS, Free Open source software, geospatial technology.

INTRODUCTION: The free software movement was launched in 1983. In 1998, a group of individuals advocated that the term free software should be replaced by open source software (OSS) as an expression which is less ambiguous and more comfortable for the corporate world. With open source software, generally anyone is allowed to create modifications of it, port it to new operating systems and processor architectures, share it with others or, in some cases, market it. Even though of tools are available for web based spatial information system, each one of them have some restrictions, such as license, availability of skilled person, etc. To overcome such problems, open source tools are better solutions. Open source geospatial tools provide facilities such as cost effectiveness and it can be customized according to user needs. In India most of the rural area people cannot subscribe commercial software. Though number of tools are available for web based spatial information system, each one of them have some restrictions, such as cost, license, internet connectivity, availability of skilled person, etc. To overcome such problems, open source tools are better solutions. Open source geospatial tools provide facilities such as cost effectiveness and it can be customized according to user needs. Also, various spatial queries can be made for better decision making. They are helpful in monitoring various rural development schemes run by government. This paper is mainly for the requirement of two purposes •

Cost optimization 33

National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada



Satisfy the basic requirements in GIS/Image Processing activities

Advantages of Open-source Geospatial Tools • • • • • • • • • • • •

Access to source. Enables development of highly customized applications based on client’s needs. Development priorities are driven by end-user needs. No licensing fees. Resources are allocated for building the applications. No licensing multiple machines. Interoperability, adoption of open specifications. Developers listening to users directly. Issues can be resolved in-house. Affordable and high quality. Open source software has fewer defects, because if defects are present, they get repaired faster Free as in freedom.

OPEN SOURCE GEOSPATIAL TOOLS: Integrated Land and Water Information System (ILWIS) ILWIS is PC-based GIS & Remote Sensing software, developed by ITC up to its last release (version 3.3) in 2005. ILWIS comprises a complete package of image processing, spatial analysis and digital mapping. It is easy to learn and use; it has full on-line help, extensive tutorials for direct use. Key features: Integrated raster and vector design Import and export of widely used data formats On-screen and tablet digitizing Comprehensive set of image processing tools Orthophoto, image georeferencing, transformation and mosaicing Advanced modeling and spatial data analysis 3D visualization with interactive editing for optimal view findings Rich projection and coordinate system library Geo-statistical analyses, with Kriging for improved interpolation. Production and visualization of stereo image pairs Spatial Multiple Criteria Evaluation Set of operations on DEMs/DTMs and hydrological processing

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National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

Tech support available ILWIS has extensive user documentation and help files GeoServer GeoServer is an open source software server written in Java that allows users to share and edit geospatial data. Designed for interoperability, it publishes data from any major spatial data source using open standards. Being a community-driven project, GeoServer is developed, tested, and supported by a diverse group of individuals and organizations from around the world. GeoServer is the reference implementation of the Open Geospatial Consortium (OGC), Web Feature Service (WFS) and Web Coverage Service (WCS) standards, as well as a high performance certified compliant Web Map Service (WMS). GeoServer forms a core component of the Geospatial Web. It can access databases like PostGIS, in addition to the dozens of other vector and raster formats. It supports feature labeling and has a great user community. Quantum GIS (QGIS) Quantum GIS (QGIS) is a user friendly Open Source Geographic Information System (GIS) licensed under the GNU General Public License. QGIS is an official project of the Open Source Geospatial Foundation (OSGeo). It runs on Linux, Unix, Mac OSX, and supports numerous vector, raster, and database formats and functionalities. Natural Resources Database (NRDB) NRDB Pro NRDB Pro is a free GIS tool for developing and distributing environmental databases. It was designed to provide people in developing countries with a powerful yet simple tool to assist in the managing of their own resources.

Database: The Natural Resources Database is a spatial database. As well as storing data of type text or numbers you can also store point, polyline or polygon data.

The data structure of NRDB Pro is hierarchical. This means that, for example, you can represent the administrative structure for your project area and calculate statistics based on this. NRDB Pro is also a time-series database, all data has a date associated with it. You can therefore observe changes over time in the data. All data for the NRDB Pro is stored in a single database file which can be redistributed with the software. The database structure consists of features and attributes. You define these in the Data Dictionary. You can define a structure that meets the needs of your project. NRDB Pro is therefore is applicable to a wide range of environmental / socio-economic projects in developing countries.

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National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

Queries: As well as simple selection of features and attributes to display on maps, graphs and reports you can also use queries. These give you more control over what is displayed. With queries you can apply conditions, e.g. only select data for 2002 or display only households that are not formal settlers and are living in makeshift housing. Queries can also be used for calculating statistics. You can for example count the number of households below the poverty threshold by municipality. You could also normalize this by dividing it by the number of households in each municipality. In the same way that you can add layers to maps, by selecting values or using queries, you can also produce reports.

Digitizing, Import and Export Data can be encoded directly into the NRDB Pro software. You can also encode data into a spreadsheet and then import it into the NRDB Pro software.

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National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

Spatial data can be imported from shape files or text files. NRDB Pro also includes a utility for georeferencing image files scanned from paper maps. You can then digitize directly using the NRDB Pro software. we can also export map layers to shape files. Uses  NRDB software can assist with the following sorts of projects:  Monitoring the effect of management and impacts on environmental resources.  Collating information on environmental management and organisations, so as to coordinate effort more effectively.  Storing socio-economic information spatially, so as to relate services to need.  Production of thematic maps of environmental concerns to assist in advocacy work.  As a tool for training in environmental management and spatial mapping. POSTGRESQL PostgreSQL is an object-relational database management system (ORDBMS) based on POSTGRES, Version 4.2, developed at the University of California at Berkeley Computer Science Department. POSTGRES pioneered many concepts that became available only in some commercial database systems much later. PostgreSQL is an open-source descendant of this original Berkeley code. It supports a large part of the SQL standard and offers many modern features: complex queries, foreign keys, triggers, views, transactional integrity, and multiversion concurrency control. PostGIS PostGIS adds support for geographic objects to the PostgreSQL object-relational database. In effect, PostGIS "spatially enables" the PostgreSQL server, allowing it to be used as a backend spatial database for geographic information systems (GIS), much like ESRI's SDE or Oracle's Spatial extension. PostGIS follows the OpenGIS "Simple Features Specification for SQL" and has been certified as compliant with the "Types and Functions" profile. Recent advances in the domain of spatial technology: Recent advances in the domain of spatial technology are making considerable impact in planning activities. This type of planning is more important in countries like India where rural population is more and is having variations in geographic patterns, cultural activities, etc. One of the strongest points in favor of open source geospatial tools is that they are cost effective. By using open source geospatial tools, planning work can become simpler and cost effective. It is helpful in monitoring the rural development schemes and various spatial queries that can be run for analyzing the problem. Various maps can be generated using open source geospatial tools, which provide an added dimension to data analysis by Geo-visualization. •

Administrative Map, Village Location Map, State Highways Map, Major District Roads. 37

National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

• •

• • •

Map of Village Roads, Light Vehicle Roads (Matelled, Un-matelled), Roads Under construction, Prime Minister Roads under Construction. Map showing Child and Maternity Welfare Centre’s, Ayurvedic, Allopathic, Homeopathic Hospitals, Community Health Centres, Public Health Centres, Base Hospital, District Hospital, Civil Hospital, Women Hospital etc. Perennial Water bodies Non Perennial Water bodies of District, Forest Cover of District, Agriculture Land map, etc. Service area map, showing accessibility of service facility from villages or to villages. Map showing drought affected area, flood affected area can be prepared.

The main advantages of the system are: •

• • • •

Centralized control over data & model resulting in lower costs for hardware, software, distribution, maintenance and training as well as more efficiency in model improvement and data update, particularly, for models with dynamic and real time information. The simple GUI and user friendly way to query/extract the village level information. Users do not need professional GIS knowledge, training or expensive and complex hardware and software as web based systems are platform independent. Allow public and stakeholders to access and participate in planning and decision making processes. Other facilities such as education, market changes, agriculture information and information of government schemes can be made available through the same system.

CONCLUSION: This paper also deals with the following issues. •

• • •

Open source software can solve some of rural India's social, political, and administrative challenges and create a viable & cost effective technology for the provision of health, education, and other social services. Open source geospatial tools provide an added dimension to data analysis which helps in visualizing the real world complex problem. It’s easy to modify using source codes of software tool rather than building new which facilitates the conservation of financial resources. Voluntary organizations can get better software tools for spatial and non spatial study helping in planning and management for development work with less cost.

REFERENCES: • • •

ILWIS, (2008), (The Integrated Land and Water Information System), website. [Online]. Available: www.itc.ni/ilwis/default.asp. Intermap’s Product Handbook and Quick Start Guide, Version 3.3, 2004.

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• • • • •

NRDB, Natural Resource Data Base, 2010, website [Online], Available: http://www.nrdb.co.uk PostGIS, 2008, Supporting object for PostGRESQL, Website [Online]. Available: http://postgis.refractions.net PostGRESQL, 2008, ORDBMS Sql server for spatial data, website [Online].Available: http://www.postgresql.org GeoServer, 2008, Geographical Information System Web Server, website [Online]. Available: http://www.geoserver.org QGIS, (2008) website. [Online]. Available: www.qgis.org

Author: - Santhi Swarup Manugula B.Tech.,(Civil).M.Tech (Remote Sensing) and having 14 years of experience (Civil.,GIS/ Photogrammetry/ Remote Sensing) worked with National & International Clients in various MNC in various roles from GIS Engineer to Dy. General Manager .

.

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National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

Asset Mapping and Consumer Indexing under Restructed Accelerated Power Development and Reforms Programme (R-APDRP) in Bellampalli, A.P. Srikanthi, P1, Srinivasulu.V2 1. Spatial Information Technology, Institute of Science & Technology, JNTU Kakinada, Kakinada. 2. Professor, Dept., of Civil Engineering, UCEK, JNTUK, Kakinada. Abstract Any country development is completely based on the level of infrastructure of vivid sectors. When it comes to know the factor that having huge infrastructure does not solve the problem, proper maintenance and efficient utilisation is utmost important priority. The power sector is one of the most important infrastructural aspects of the Indian economy. But of late, it has been facing some serious problems such as old worn out and poor distribution network leading to frequent outages, skewed tariff structure, huge Transmission & Distribution (T&D) losses largely due to outright theft & unmetered supply, high LT/HT line ratio, overloaded DT/ Lines, lack of Accountability at feeder level and in distribution setup of State Electricity Boards (SEBS). Government of India is stepping towards strengthening the power sector through RestructuredAccelerated Power Development Reforms Programme (R-APDRP) during the XI Plan with revised terms and conditions as a central sector scheme. The focus of the programme shall be on actual, demonstrable performance in terms of sustained loss reduction. Establishment of reliable and automated systems for sustained collection of accurate base line data, and the adoption of Information Technology in the areas of energy accounting will be essential before taking up the regular distribution strengthening projects.The thesis project is an attempt understand use of spatial technologies like ArcGIS, DGPS/GPS in creating the database for the project to achieve the desired objective.

Introduction The power sector is one of the Important Infrastructural aspects of the Indian Economy. But of late, it has been facing some serious problems such as old worn out and poor distribution network leading to frequent, skewed tariff structure, huge distribution (T&D) losses largely due to outright theft& unmetered supply, high LT/HT line ratio, overloaded DT/lines, lack of accountability at feeder level and in distribution set up of State Electricity Boards.Hence, the govt identified distribution reforms as the key area to bring about the efficiency& commercial viability into the power sector. The Government took various initiatives in this direction, one of them is the “Introduction of Accelerated power Development programme (APDRP)”in Feb 2000.The project was accepted and continued for further development . The Govt. of India has proposed to continue Restructured-Accelerated Power Development Reforms Programme (R-APDRP) during the XI Plan with revised terms and conditions as a Central Sector Scheme. The focus of the Programme shall be on actual, demonstrable performance in terms of sustained loss reduction. Establishment of reliable and automated systems for sustained collection of accurate base line data, and the adoption of Information Technology in the areas of energy accounting will be essential before taking up the regular distribution strengthening projects.

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National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

We can use Geographic Information tools such as Arc GIS, Global Mapper, GPS track marker, GPS path finder to generate Asset Mapping and consumer indexing. The main aimof the project is to prepare a spatial database for Asset Mapping and consumer indexing using GIS Introduction to R-APDRP The Restructured - Accelerated Power Development Program is planned in the 11th plan with revised terms and conditions as a central sector scheme. The focus of the program shall be on actual, demonstrable performance in terms of sustained loss reduction .Establishment of reliable and automated systems for sustained collection of accurate base line data, and the adoption of Information Technology ,in the areas of energy accounting will be essential before taking up the regular distribution strengthening projects, for this, we can use Geographic Information Tools such as, ARCGIS, Global mapper ,GPS track Maker, GPS path finder to generate Asset Mapping and Consumer Indexing. R-APDRP Program Coverage: It is proposed to cover urban areas-towns and cities with population of more than 30,000.In addition, in certain high load density rural areas with sufficient loads, works of separation of Agricultural Feeders from Domestic and Industrial ones , and of High Voltage Distribution System(11KV) will also been taken up. Proposed scheme: Projects under this scheme shall be taken up in two parts : Part-A: Preparation of Base-line data for the project area covering Consumer Indexing, GIS Mapping, Metering of Distribution Transformers and Feeders, and Automatic Data Logging for all Distribution Transformers and Feeders and SCADA / DMS system (only in the project area having more than 4 lacs population and annual input energy of the order of 350 MU). It would include Asset mapping of the entire distribution network at and below the 11kV transformers and include the DistributionTransformers and Feeders, Low Tension lines, poles and other distribution network equipment. It will also include adoption of IT applications for meter reading, billing & collection; energy accounting & auditing; MIS; redressal of consumer grievances; establishment of IT enabled consumer service centres etc. The base line data and required system shall be verified by an independent agency appointed by the Ministry of Power. The list of works is only indicative. Part-B: Renovation, modernization and strengthening of 11 kV level Substations, Transformers/Transformer Centers, Re-conductoring of lines at 11kV level and below, Load Bifurcation, feeder separation, Load Balancing, HVDS (11kV), Aerial Bunched Conductoring in dense areas, replacement of electromagnetic energy meters with tamper proof electronics 42

National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

meters, installation of capacitor banks and mobile service centres etc. In exceptional cases, where sub-transmission system is weak, strengthening at 33 kV or 66 kV levels may also be considered. Study Area & Methodology Location Bellampalli or Bellampalle is a town, mandal and municipality in the Adilabad district, in the state of Andhra Pradesh, India. Located at 16 km north of Mancheral and lies between West: 79.468080 East: 79.5 04355 North: 19.079806 South: 19.017605 History: Bellampalli is noted for its Coal mines; the first coal mine was established in 1936 by the British government. Later the town developed very rapidly with the discovery and excavation of many coal mines. Demographics As of 2001 India census, Bellampalli had a population of 94,070. Males constitute 51% of the population and females 49%. Bellampalli has an average literacy rate of 65.65%, higher than the national average of 59.5%, with 57% of the males and 43% of females literate. 11% of the population is under 6 years of age. Politically the town and mandal are represented by the Bellampalli Assembly Constituency. Bellampalli mandal has 12 villages in it. Coal production has been very important to the economic history of Bellampalli and it is thus known as an industrial town. A chemicals and fertilizer factory also lies in the town. However, the average incomes of Rs. 700/- per capita according to 1989 figures mean that the average person is living in poverty. Infrastructure- Social  Temples: Kodanda ramalayam, Shiva, Ganesh, Sai baba temple, Hanuman temple, Krishna temple, Ayyappa temple, Pochamma gudi, etc.  Education: 20+ Secondary education institutions, 5+ Intermediate colleges, 3+ Degree colleges, 1 Polytechnic college,etc.  Sports Grounds: Tilak Stadium, Ambedkar ground & AMC Ground  Parks: Childrens Park(Thilak Stadium), AMC park, Rose garden, Booja bangla garden, etc.  Scope & Limitations: The present study covers an understanding of literature related to RAPDRP and mapping of electrical poles & consumers. Methodology Geographic Information System solution consists of capturing, storing, checking, integrating, manipulating, analyzing and displaying Geo-data related to positions on the earth's surface and data related to attributes of the entities/customers in a utility. This is

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achieved through GIS mapping to pre-defined scale, generation of intelligence electrical network maps and super imposing them on the land base GIS maps. Customer Indexing is defined here as a unique coding of index process for all types of customers into a data base structure, created with pre determined attributes connected to a uniquely coded electrical network including locations with a facility using GIS tools to query and retrieve information. The customer indexing and asset management system is essential for gearing of Electric Distribution utilities to maintain the system in a dynamic mode to meet the day-today imperative changes The objectives, which are expected to be achieved by way of this project, are as follows: Creation of base maps of project area using suitable satellite imagery and georeferencing the same. • Preparation of physical area maps for the areas, based on collected information, digitization of important landmarks, overlaying of features on the base map with predefined scale for viewing graphically with the aid of offered GIS software. • Involve DGPS survey (Sub-meter accuracy) for finding latitude-longitude of utility’s network entities and land base features, base map preparation, entity • data collection and geo-coding, uniquely indexing each customer based on the electrical system network information and • Customer data (66KV/33kv & below) collected through door to door survey and physically link of each customer on the map with the network. • Developing and collecting attribute data and create network entities. Thereafter, creation of digital map of network through GIS application package • Carry out customer indexing through door-to-door survey and identifying the code numbers of the Customers and source of supply to the customers and develop customer database. • Asset codification of all assets and other electrical system, power t ransformers, EHV/HV Line, towers, poles, DTs, Breakers and other electrical assets and legibly painting on the asset as per approved color scheme. • Imposition of all the assets on the GIS maps including all attribute data of various components as per requirement. • The indexed customer database, when created and operational, shall be capable of being 'on line' connected to other business process software without any limitation. The database shall be based on established open database (ODBC) architecture suitable for linkage to other databases. The database shall be capable of updating through user-friendly form entries and through file transfer modes. •

Methodology Overview: The methodology followed is simple and as follows: • •

Collection of Existing hardcopy maps and Digital files Catalog / Indexing (metadata) of the data collected. 44

National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

• • • •

Georeference the hardcopy and softcopy datasets on the base map. Digitize the network details from the georeferenced hard copy maps Adjusting / orienting the existing vector data to the base map based on the association of existing vectors Establish the total network and data validation based on the collated dataset

Project Process Boundary Demarcation & Satellite Data Procurement Process Boundary data is collected town wise respectively from APNPDCL department. With the availability of data satellites are identified for which the time is allotted is 6 months duration. Accurate satellite data is collected from NRSC (National Remote Sensing AgencyData Centre) with 100 percentage of advance payment. Then Demarcation of Town Boundary with respect to the local department officials is done within the given time. The next step is identification of Ring Fencing Points. The main factor to be followed is clear demarcation of boundary (feeder limits within town boundary) for asset mapping and consumer indexing .Thus, it is visualized to have two types of boundaries as follows • •

Boundary for base mapping and Boundary for asset mapping

Survey would be carried out as follows The first step is identification of the last DT location within every feeder inside the town boundary. All the consumers connected to the identified last transformer within the demarcated town boundary would be surveyed. No survey outside the town boundary would be done, in case of feeder network going outside the town boundary. GPS Control Survey – Planning One Control point is fixed for every 5 sqkm for Georeferencing of satellite data. Minimum 5 points per town even if the town area is less than 20 sqkm is followed. Base Control station to be monument using concrete – preferably on Electrical office buildings. The base control station should follow the condition like Clear sky, no overhead obstruction, 45

National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

not to be disturbed. Control point location should be preferably on well identifiable points on satellite images like compound walls, pillars etc. the Base station control points should established on the buildings. Few controls along the overlapping region would be planned for large areas Existing Data Usage and Issues Existing data gives a pictorial representation of assets and its relative position. Existing data is not geo-referenced. Existing data does not represent the exact shape of real features (Ex. Road which is curve in nature is represented as straight line from one junction to other junction which is not the real case Creation of Land base Table 3: Representing creation of land base 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Towns ProjectArea Landmarks Road CenterLine Trans Structures Road Railways District Boundary WaterBody Building Other Features Trans Features State Boundary Division Boundary Town Project Area Boundary Sub Division Boundary Circle Boundary Company Name Boundary Zone Boundary

Polygon Point Polyline Polyline Polyline Polyline Polygon Polygon Polygon Polygon Polygon Polygon Polygon Polygon Polygon Polygon Polygon Polygon

Asset Mapping The location of assets marked on the map as per the correct location which is based on satellite data / base map. Then marking of asset numbers using paint / permanent markers is done. HT poles in sequence – starting from Feeder DT’s in sequence – for a feeder The unique feeder code in a town to be given by the Zonal Coordinator Asset Number – temporary number with a paint marker to be written The color and symbology practiced on field is standardized The standardized numbering and color schema Paper based data collection / PDA based data collection – as per the design doc / format. DT’s having meters need to be recorded with separate sheet for DT meters. Usage of digital camera of minimum of 4 mega pixels is mandatory. The DGPS co-ordinates as surveyed need to be given in the attribute in the table in the respective shape file. • • • • • • •

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National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

When a structure holds more than asset (switch / transformer / isolator etc), attributes has to be collected separately. The asset location has to be placed in the same location which should be in logical sequence. Steps followed in Software The image of the respective place is collected from client and the image is georeferenced. The georeferenced image is then converted to raster format and is used as a final image background for the process • • • •



• • • • • •

The data collected from surveying from through DGPS device is converted to shape file using GPS track maker software Using Arc GIS conversion tool box the .kml format is converted to shape file format. Now the data for mapping is ready. Data of Distribution Transformers (DTR) are also note using GPS and converted into separate shape files. The HT Poles and LT poles are given proper numbering depending on distribution transformers (DTR). The information such as pole number, location, height of the pole, type of the disc, number of shackles present , presence of consumer etc are added through attribute table of respective shape file. LT and HT lines are drawn in sequence to the respective pole. The information such as line number, location, length of line, type of conductor, size of conductor, phase of wire, etc are added through attribute table of respective shape file Now DTR wise LT poles and LT lines are taken and exported into .jpeg image format. Similarly on whole all DTR’s along with HT poles and HT lines are exported into jpeg image format. These images are sending to the office of APNPDCL for 1st validation. This image is taken in A3 size and it is being used for consumer indexing. With respect to the HT and LT poles consumers are noted for the respective DTR. After it is approved by APNPDCL, consumer indexing begins.

Consumer Indexing: Consumer indexing captures all users of electricity and connects them up to distribution transformers and feeder level. This helps in identification of overloading of equipments, non billed consumers and helps in better load management, better maintenance of equipments, better billing and revenue collection. The process results in reduction of technical as well as commercial loss and improves quality & reliability of power supply Steps followed in software: • • •

After the first validation from APNPDCL the consumer indexing begins in manual format. From the verification sheet, the consumer belongs to each LT pole is noted manually. The manually collected information is converted to digital format using arc GIS software.

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National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

From each LT pole two or more consumers will be there. All the consumers are marked in sequence with service line. The consumer shape file is given with all necessary information like consumer name, address, mobile number, service number, meter make, meter model, voltage, power, last reading of meter, number of electronic appliances etc in the attribute table. •

Snapshots Once data imported from the DGPS hand receiver sets, data converted to shape file and spatial location of Distribution Transformer as below

Softwares and Methods Information Technology and Geographic information system plays a vital role in the implementation process of Restructured Accelerated Power Development and Reforms Programme. The usage of geographic information system tools and softwares are the most important factor in this project and mainly for submitting the output. The most used softwares we used in this program are GPS track maker, GPS path finder, Global Mapper and Arc GIS GPS Path finder Data can be imported to the GPS pathfinder office software from a number of GIS and database formats, allowing previously collected data to be taken back to the field for verification and update. The software's data dictionary editor creates custom lists of features and attributes for field data collection and supports the development of conditional attribute data capture forms in Trimble Terra Sync™ software that dynamically adapt to previously entered attribute values for maximum data collection efficiency. The software makes it easy to manage, correct, and update GIS data collected in the field. Data are collected in GPS device and stored in rover on date wise. A base device is fixed in one particular place without any disturbances. Daily data are collected w wizard is used in which base data is added with rover data and final data for mapping is obtained.

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GPS Track Meter The professional version of GPS Track Maker® is mainly used for area calculation, data transfer to Microsoft Excel®, import and export to Auto Cad® and Arc View®, and other advanced functions. The program uses a hardware key or dongle, an electronic plug that works as an unlock password for GTM PRO®. The plug is easily connected to the USB port, and must be present when GPS Track Maker Professional is being executed. The plug doesn't interfere in the operation of the printer, scanner or othe devices. It creates complete projects of Maps (MAP and PJC files). It provides Image rotation for an accurate map calibration. It allows Expand Zone function that allows calibrating maps located in two or more zones. The software supports GeoTiff (Geocoded TIFF), Cartographic and Topographic Surfaces. Data which is done correction is then opened with GPS Track maker software. This software displays the position of each pole and when clicked on the pole the complete information along with latitude and longitude value it is displayed. Then the file is saved in .kml format . ARC GIS: ArcGIS is a complete system for designing and managing solutions through the application of geographic knowledge. It enables you to perform deep analysis, gain a greater understanding of your data, and make more informed high-level decisions. Results & Conclusion After completion of Asset Mapping and Consumer Indexing the output has been send for final validation. As soon the validation got over the sample final output image of the Restructured Accelerated Power Development and Reforms Programme under Asset mapping and Consumer Indexing will be as mentioned below.

Figure 15 : Representation of R-APDRP in Bellampalli 49

National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

References 1. Alekhya data, Shome varma & Ashish kumar gupta “APPLICATION OF GIS TECHNOLOGY IN ELECTRICAL DISTRIBUTION NETWORK OPTIMIZATION” Symbiosis Institute of Geoinformatics, Pune 2. Adekunle , A.A.(1995),”Utility Mapping Using GIS technique :A case study of University of Lagos ,”unpublished B.SC , project submitted to the department of Geography and planning , University of Lagos . 3. Adeoye , A.(1998) , “Geographical /Land Information Systems:Principles and Applications ,”Information Management Consultants , Ebutte- Metta , Lagos ,Nigeria. 4. Adetro ,S.A.(2002) ,”Developing Geographical Information System for Utility Management: A case study of Electricity Distribution Lines and T/F stations in Obafemi AWOLWO University :Ile-Ife “, proceeding of the technical session of the 37th Annual General Conference and Meeting of Nigeria Institution of Surveyors , Owerri ,Imo state ,Nigeria. 5. Alamu , E.O.,and H.C. Ejibolin(2002),”Utility Information Infrastructure Needs in Antenucci ,J.C.(1988),Technical Trends in AM/FM and the Institutional factors Driving them”, paper presented at the IBM GFIS Users Group Work-shop ,Kentucky ,usa Utility Organisation in Nigeria :A case study of Nigeria state Wter board. 6. Ayeni , O.O., Kufoniyi , o, and Akinyede (2003) ,”Towards a National Geospatial Information Policy for Nigeria ,” proceeding of the Technical session of the 38th Annual General Conference and Meeting of Nigeria Institution of Surveyors , Lokoja , Kogi state , Nigeria . 7. Emengini ,E.J. (2004),”Application of Geographic Information System (GIS) to Utility Information Management : A case study of Onitsha-North , L.G.A., Anambra state ,Nigeria ,unpublished ,M.SC .Thesis submitted to the Department of Surveying and Geo informatics ,Nnamdi Azikwe University ,Awka ,Anambra state ,Nigeria 8. Ezigbo , C.U. (1998) ,”Application of Geographic Information Systems (GIS) to Utility Mapping” , in C.U. , Ezeigbo ,Principles and Applications of Geographic Inform Jones , C.B.(1997), “Geographical Information Systems and Computer Cartography “ Essex , Addison Wesley Longman Ltd . 9. Igbokwe , J.I,and Emengini (1997),GIS in Management of Electricity Distribution Network ,stusy of onithsa-North L.G.A , Anambra state ,Nigeria ,submitted to E.G. Department of surveying and Geoinformatics , Nnamdi Azikiwe University ,Awka, Anambra state , Nigeria . 10. Jones , C.B.(1997), “Geographical Information Systems and Computer Cartography “ Essex , Addison Wesley Longman Ltd . 11. Kufonyl ,Olajide (1998) , “Data base Design and Creation “,in C.U.Ezigbo , Principles and Applications of Geographic Information Systems , Lagos . 12. Brig M.Gopal Rao Land Information system in India was published by,Director ,Digital Mapping Centre ,Survey of India . 13. Mohammad Moinuddin Afroz , T. Vijaya Lakshmi (2010), Creating the GIS based Electrical Asset Mapping and Consumer Indexing of Gudiyattam Town, Tamilnadu was refered in Review of literature pg no 3-4 . 14. Maguire , D.J.(1989),”Computers in Geography “ Essex ,Longman Group UK Limited. 15. .Mukro ,M,I, S.A Adetoro , and H.C. Ejoblin (2002) , “Evaluation of National Electric Power Authority (NEPA) Utility Information Infrastructure Towards steady power supply by the end of 2001: A case study of Bida NEPA “,Proceedings of the Technical session of the 37th Annual General Conference and Meeting of Nigerian Institution of Surveyors ,Oweni ,Imo state ,Nigeria.

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16. Mapping Sciences in Power Sector form UNESCO Training Module on the Applications of Geographical Information Systems (GIS) for power sector . 17. NRSA(2004-2005) , Manual of Land Use and Land Cover mapping using Multi temporal AWiFS Data, Department of space government of India Hyd, A.P. 18. .NRC( 2012), Manual for National Geomorphological and Lineament mapping on 1:50,000 scale, ISRO Department of space , Government of India, Hyd 19. Sun,Y.C.,J.Van Westen and E.J.Sides (2001),”Spatial Data Analysis” ,in A.D.B.Rolf ,Principles of Geographic Information Systems 20. Saheed Salawudeen and USMAN Rashidat,(2006) Nigeria in XXIII FIG Congress Munich Germany, October 8-13. 21. The punch , (2001) ,NEPA Ensure Stable power supply to Sokoto”,punch Nigeria Ltd ,Lagos . 22. This Day (2002) ,”Obasanjo Gives NEPA Fresh Mandate:orders 10,000 mw by 2005 “,Leaders and company Ltd,Lagos. 23. Vijay,Kumar and Anjuli Chandra (2001 ),”Role of Geographic Information Systems in Distribution Managementt “ ,www.gisdevelopment .net/application/urban/overview/power/index.html .

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LIQUEFACTION STUDIES FOR VISAKHAPATNAM CITY Swarna lath, Ch.1, Vazeer Mahammood2 1,2. Department of Civil Engineering Andhra University College of Engg., Visakhapatnam- 530 003. Email:[email protected] ABSTRACT During an earthquake, soil can fail due to liquefaction devastating effects such as land sliding, lateral spreading, or large ground settlement. The damage due to liquefaction for the ports and harbour structures was of appreciable magnitudes in the Andaman port due to 2004 Sumatra earthquake. The Visakhapatnam port is a semi natural harbour and is working and upgraded. Visakhapatnam coast is erosional type, fault controlled and hence vulnerable to tremors of low to moderate intensity (Banerjee et al. 2001). Therefore it is essential to take up seismic hazard studies at micro level in order to improve safety norms for the port structures and industrial structures. In the present study, the factor of safety against liquefaction is evaluated for Visakhapatnam city which has an area of 533 sq.km. Geotechnical borehole data is prepared and used for liquefaction analysis for different spectral accelerations. The factor of safety against liquefaction (FL) varies in the range of 0.3 to 10 for corresponding Peak Ground Accelerations from 0.1g to .3g.

1.0

Introduction

During an earthquake, soil can fail due to liquefaction with devastating effects such as land sliding, lateral spreading, or large ground settlement. The phenomenon of liquefaction of soil had been observed for many years, but was brought to the attention of engineers after the Niigata earthquake (1964) and Alaska earthquake (1964) (Kramer). Though Visakhapatnam falls in the area with low seismic probability, as specified IS:1893 (Part I) – 2002 still there is need for carrying out seismic hazard studies at micro level in order to improve safety norms for the rapidly growing structures. In the present study evaluation of factor of safety against liquefaction for Visakhapatnam city which has an area 533 km2. Geotechnical borehole database has been prepared after collecting more than 300 boreholes at various locations of Visakhapatnam. Using this collected borehole data an attempt is made to assess in detail the liquefaction potential of soils in the study area using SPT-based method and also to present a factor of safety against liquefaction map. 2.0

Study Area

The study area Visakhapatnam city is located in the state of Andhra Pradesh, India along the East coast, at latitude 17o 47’ North and longitude 83o 20’ East, the second largest city in Andhra Pradesh with an area of 533 km² covering a wide range land use/land cover like industrial built-up land reserved forests. According to the seismic zonation map of India, Visakhapatnam is classified in the category of earthquake prone zone (II), low damage risk zone MSK160 cm) Dumping area: The area facing one of the most important is that the industrial dumping area is near the residential areas and in the busy region of the city in spite of outside of the city. Thus it affects both the health as well as the environment of the city. This problem requires quick solution for welfare of both man and environment. They cover 0.56% of Faridabad AOI. CONCLUSIONS: The study demonstrates the importance and potentiality Satellite Remote Sensing technique for preparation of more consistent, accurate and up-to-date baseline information on urban land use for future planning, management and development of any area, The present study is derived on the basis of interpretation of Faridabad with the help of downloaded satellite data i.e. Google Earth- The study together with satellite data incorporated with ground truth data and secondary data revealed different layers in altogether created in 3 Datasets of Geodatabase, namelyA) LU/LC, b) Soils, c) Base Layers REFERENCES: •

B.Ramesh, Paliwal Rakesh, Jayanthi C. Satish, Bhavani S.V.L, Raghaswamy & S. Surendra: Urban Growth & Land use Change in the NCR of Delhi using Remote Sensing & GIS Techniques” pp 1119-1124, ISPRS by NRSC.



J. Kaiser, David R. Godschalk & F. Stuart Chapin “Urban Land use Planning, Journals of Urban Land use Planning”, pp-4.



Holmberg S.C (1994), “Geo-Informatics for Urban & Regional Planning: Environment, Planning & Design”, Volume-21(1), pp 5-19. H.S Sudhira, T.V Ramchandra, Karthik S. Raj & K.S. Jagdish “Urban Growth Analysis using Spatial & Temporal Data”, pp 90-105

• •

National Urban Information System (NUIS) – Design and Standards (July 2006), Town & Country Planning Organization, Govt. of India, Ministry of Urban Development, New Delhi.

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Ravindra Kumar Verma, Sangeeta Kumari, & R. K. Tiwary “Application of RS and GIS Technique For Efficient Urban Planning In India”



William P. Anderson,”Urban Form, Energy & the Environment”- A Review of Issue by Evidences & Policy- University of Hamilton, Ontario, Volume-33, pp 7-35.



UN (2001): World Urbanization Prospects.



Urban Infrastructure, Housing, Basic Services and Poverty Alleviation pp-394 11th 5 year plan.

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Prediction of Land Surface Temperature from Land Use Land Cover Images using an Artificial Neural Network Model 1

K.Sundara Kumar*, 2K.Padma Kumari & 3P.Udaya Bhaskar Research Scholar, Dept. of Civil Engineering, JNT University-Kakinada, A.P, India 2 Associate Professor, Dept. of Civil Engineering, JNT University-Kakinada, A.P, India 3 Professor, Dept. of Civil Engineering, JNT University-Kakinada, A.P, India *corresponding author e-mail: [email protected], Ph: +91 9440112013 1

Abstract Estimation of land surface temperature (LST) is important for urban climate studies particularly for the study of intensity of urban heat island and its spatial distribution. LST is primarily depends on the land use/land cover (LULC) of the area and changes with extent of urbanization. For LST retrieval, remote sensing satellite images of high resolution with thermal band are required which are scarce. This paper deals with the development of artificial neural network model for prediction of LST image from LULC image. The advantage of the model is that model requires only LULC image to get LST image. A feed forward back propagation network is developed with LM training algorithm. For training the model LULC image and LST image of 2001 was used. For testing the model LULC and LST image of 1990 was used. The model was found to give good results. The outputs of the model were converted in to images and presented. Keywords: Land surface temperature, Remote sensing data, Land use/Land cover, Artificial neural network

1. INTRODUCTION The urban air temperature is gradually rising in all cities in the world. One of the possible causes is the drastic reduction in the greenery area in cities. The distinguished climatic condition termed ‘Urban Heat Island’ (UHI) is developing in the rapidly urbanized cities. Understanding the distribution of Land Surface Temperature and its spatial variation will be helpful to decipher its mechanism and find out possible solution. The development of LST images requires Landsat imagery of high resolution with thermal band. The availability of Landsat imagery is limited. This paper deals with the development of an artificial neural network model for prediction of LST from land use land cover images which can be developed by a variety of satellite data available. Several researchers used the Landsat imagery to develop land use/cover images as well as temperature images. K. C. Seto, C. E. Woodcock, C. Song, X. Huang, J. Lu And R. K. Kaufmann, have monitored the land-use change in the Pearl River Delta using Landsat TM.[1] J. Li and H.M. Zhao have studied the Urban Land Use and Land Cover Changes in Mississauga using Landsat TM images.[2], Land use land cover images were developed from Landsat imagery for Vijayawada city by K. Sundara Kumar, M. Harika, Sk. Aspiya Begum, S. Yamini, & K. Balakrishna.[3] Javed Mallik, Yogesh Kant and B.D.Bharath estimated land surface temperature over Delhi using Landsat-7 ETM+.[4] LST images were developed from Landsat data using ERDAS for Vijayawada city by K. Sundara Kumar, P. Udaya Bhaskar, K. Padmakumari.[5] K. Gobakis et al have developed an artificial neural network model to predict urban heat island based on experimental investigation.[6] Mehmet Şahin, B. Yiğit Yildiz, Ozan Şenkal & Vedat Peştemalci have developed a model using artificial neural 119

National Conference on Recent Advances in Geo-Sciences, Engineering & Technology (NCRAGE) 20th & 21st December 2012 JNTU Kakinada

network for the estimation of land surface temperature (LST) using meteorological and geographical data in Turkey.[7] 2. STUDY AREA AND DATA SOURCES Vijayawada is a historical city situated at the geographical centre of Andhra Pradesh state in India on the banks of Krishna River with latitude 160311 N and longitude 800 391 E. Vijayawada city of Andhrapradesh is experiencing rapid urbanization that has resulted in remarkable UHI. Urban Heat Island is one of the upcoming urban climate related problems developing in the city. For the present study Landsat images were procured from USGS website. The details of the imagery collected are given in Table.1. Table 1: Details of Imagery procured from USGS Sl.No

Date

Satellite/Sensor

No of Bands

Reference system/ Path/Row

1

10-11-1990

Landsat5/TM

7

WRS2/142/49

2

31-10-2001

Landsat7/ETM+

8

WRS2/142/49

3. METHODOLOGY The present research work involves image processing of Landsat data and development of land use and land cover images. This was done by the unsupervised classification method using ERDAS Imagine software. Normalized Difference Vegetation Index (NDVI) image was developed from bands 2, 3 & 4 of Landsat images. Using the thermal band of Landsat image LST has been retrieved by using the model maker of ERDAS. The detailed procedure can be referred by the author’s research paper given in references 3 and 5. DERIVATION OF NDVI The Normalized Difference Vegetation Index (NDVI) is a measure of the amount and vigour of vegetation at the surface. The reason NDVI is related to vegetation is that healthy vegetation reflects very well in the near infrared part of the spectrum. The value is normalized to -1