Nov 19, 2004 - Site : Tadoba-Andhari Tiger Reserve, Maharashtra. Dr. M.S. R. ... Period of engagement. S. No. Name. Designation Site. From. To. 1. Ms. Ambica ..... Pabla (1998) using IRS 1B produced spatial database in GIS domain for.
WII-MoEF-NNRMS Pilot Project ‘Mapping of National Parks and Wildlife Sanctuaries’
FINAL TECHNICAL REPORT 2004-2008
Volume I (Project Background, Objectives, Salient Outputs and Conclusions)
December, 2008
The Team
Project Leader Mr. P.R. Sinha Director, Wildlife Institute of India, Dehradun
Project Coordinator & Principal Investigator Dr. V.B. Mathur Dean, Wildlife Institute of India, Dehradun
Co-Principal Investigators Dr. S.P.S Kushwaha
Site : Kaziranga National Park, Assam
Dr. P.K. Mathur
Site : Dudhwa Tiger Reserve, Uttar Pradesh
Dr. Afifulah Khan
Site : Corbett National Park, Uttarakhand
Dr. V. B. Mathur
Site : Tadoba-Andhari Tiger Reserve, Maharashtra
Dr. M.S. R. Murthy
Site : Indira Gandhi Wildlife Sanctuary, Tamil Nadu
Dr. S. Sudhakar
------ do ------
Dr. V.K. Srivastava
------ do ------
Dr. C. Sudhakar Reddy
------ do ------
The Research Team
S. Name No.
Designation
Site
Period of engagement From
To
Ms. Ambica 1. Paliwal
Junior/ Senior Tadoba-Andhari Tiger Research Reserve, Maharashtra Fellow, WII
Ms. Neha 2. Midha
Junior/ Senior Dudhwa Tiger Reserve, 22nd Nov, 2004 31st Dec, 2008 Research Uttar Pradesh Fellow, WII
3.
Shri Shijo
Junior
Joseph
Research Fellow, WII
Shri Amit 4. Kumar Srivastava
Junior Research Fellow, WII
5.
Shri Athar Noor
Junior Research Fellow, WII
Indira Gandhi Wildlife Sanctuary, Tamil Nadu
Corbett Tiger Reserve, Uttarakhand
Corbett Tiger Reserve, Uttarakhand
19th Nov, 2004 31st Dec, 2008
1st Dec, 2004
13th Aug, 2007
22nd Nov, 2004 1st Sep, 2006
1st Oct, 2006
30th Sep, 2007
Shri Pebam 6. Rocky
Junior Research Fellow, WII
Kaziranga National Park, th 6 Dec, 2004 Assam
Shri Mohit 7. Kalra
Junior Research Fellow, WII
Kaziranga National Park, 16th May, 2006 1st Aug, 2007 Assam
9th Sep, 2005
8.
Dr. Hitendra Padalia
Research WII-GIS Lab, Dehradun Associate, WII
22nd Nov, 2004 19th Jan, 2007
9.
Sh. Ved Prakash Ola
Technical Assistant
NRSA, Hyderabad & WII-GIS Lab, Dehradun
08th Aug, 2008 31st Dec, 2008
10.
Ms. Sweta Sahi
Technical Assistant
IIRS, Dehradun
25th Aug, 2008 31st Dec, 2008
11.
Sh. Arun Technical Kumar Thakur Assistant
AMU, Aligarh & WII-GIS 08th Aug, 2008 31st Dec, 2008 Lab, Dehradun
Table of Contents Volume I :
Project Background, Objectives, Salient Outputs and Conclusions
Acknowledgements---------------------------------------------------------------------------1. Project Background -----------------------------------------------------------------------1 1.1 Role of Remote Sensing and GIS-------------------------------------------4 1.2 PA/Biodiversity Spatial Database ------------------------------------------5 1.3 The Pilot Project-----------------------------------------------------------------6 1.4 Large Scale Mapping Using High Resolution Data ---------------------7 1.5 Development of Digital Topographic Sheets by the Survey of India ---------------------------------------------------------8 2. Objectives -----------------------------------------------------------------------------------8 3. Project Steering Committee -------------------------------------------------------------9 4. Methodology --------------------------------------------------------------------------------9 5. Report Layout----------------------------------------------------------------------------- 11 6. Salient Outputs at the Pilot Sites----------------------------------------------------- 11 6.1 Indira Gandhi Wildlife Sanctuary, Tamil Nadu ---------------------11-17 6.2 Tadoba-Andhari Tiger Reserve, Maharashtra ---------------------18-25 6.3 Dudhwa Tiger Reserve, Uttar Pradesh ------------------------------26-34 6.3 Kaziranga National Park, Assam--------------------------------------35-40 6.5 Corbett National Park, Uttarakhand ----------------------------------41-43 7. Conclusions ------------------------------------------------------------------------------- 44 Volume II
Technical Report: Indira Gandhi Wildlife Sanctuary, Tamil Nadu
Volume III
Technical Report: Tadoba-Andhari Tiger Reserve, Maharashtra
Volume IV
Technical Report: Dudhwa Tiger Reserve, Uttar Pradesh
Volume V
Technical Report: Kaziranga National Park, Assam
Volume VI
Technical Report: Corbett National Park, Uttarakhand
:: i ::
Acknowledgements
We would like to gratefully acknowledge with thanks the following organizations and individuals for their advice, assistance and suggestions that have helped us accomplish the assigned task:
Ministry of Environment and Forests, Government of India Dr. Prodipto Ghosh, Ms. Meena Gupta, Mr. Vijay Sharma, Mr. J.P.L.Srivastava, Mr. P. R. Mohanty, Mr. B.R. Parsheera, Mr. M.B. Lal, Dr. R.B.Lal, Dr. G.V. Subrahamanyam, Dr. R.K. Suri
Indira Gandhi Wildlife Sanctuary, Tamil Nadu Mr. K. Sridharan, Dr. Sukh Dev, Mr. R. Sundararaju, Dr. H. Basvaraju, Dr. S.K. Srivastava
Tadoba-Andhari Tiger Reserve, Maharashtra Mr. B. Majumdar, Dr. S.H. Patil, Mr. U. Dhottekar, Mr. Vashist
Dudhwa Tiger Reserve, Uttar Pradesh Mr. Mohd. Ahsan, Mr. D.N.S. Suman, Mr. B.K. Patnaik, Mr. M.P. Singh, Mr. U.S. Singh, Mr. P.P. Singh, Mr. R. Pandey
Kaziranga National Park, Assam Mr. S. Doley, Mr. M.C. Malakar, Mr. B.S. Bonal, Mr. Suresh Chand, Mr. N.K. Vasu, Mr. D.M. Singh, Mr. S.N. Buragohain, Mr. Utpal Bora, Mr. D.D. Goyal, Mr. R. Garwal, Mr. L.N. Baruah, Mr. Rabindra Sharma, Mr. P.K. Deka, Mr. Ikramul Majid, Mr. Salim, Mr. Trilok Bhuinya and Mr. D. Boro
Corbett Tiger Reserve, Uttarakhand Mr. R.B.S. Rawat, Mr. S.K. Chandola, Mr. Rajiv Bhartari, Mr. Vinod Singhal
Survey of India, Dehra Dun Brig. Girish Kumar, Mr. Shamsher Singh, Mr. S.V. Singh
Department of Space, Government of India Dr. V.Jayaraman, Dr. V.K. Dadhwal, Dr. P.S. Roy, Dr. R.S. Dwivedi, Dr. Ajai, Dr. Sarnam Singh, Dr. M.C. Porwal, Dr. I.J. Singh, Dr. D.N. Pant, Mr. G. Rajasekhar, Mr. G.S. Pujar
Wildlife Institute of India (WII), Dehra Dun Mr. S. Singsit, Mr. V.B. Sawarkar, Dr. A.J.T. Johnsingh, Mr. A.K. Bhardwaj, Mr. A.Udhayan, Mr. Qamar Qureshi, Mr. Rajesh Thapa, Dr. Panna Lal, Mr. S.K.Khantwal, Mr. P.K. Agarwal, Mr.Y.S. Verma, Mr. M.M. Babu, Mr. A.K. Dubey, Mr. Naveen Singhal, Dr. Manoj Agarwal, Mrs. Manju Bishnoi, Mr. H.C.S. Rajwar, Mr. Ravindra Nath, Mr. Madan Uniyal, Mrs. S. Uniyal, Mr. Rajeev Thapa, Mr. J.P. Nautiyal, Mr. Virender Sharma, Mr. M. Verrappan, Mr. Kehar Singh, Mr. Bhuvan Chand, Mr. Birender Kumar, Mr. Saklani, Mr. Rajinder
Last but not the least, the hospitality, cooperation and knowledge provided by field staff of five project sites is gratefully acknowledged.
-The Team
1.
Project Background
India’s altitudinal, terrain and diverse climatic variations support a wide array of species and habitats. Over the years, the populations of many wild animal species have declined due to intensive and unwise human activities. Destruction of natural ecosystems and habitats of large number of species is one of the biggest threats to the planet earth. Increasing human interventions and excessive exploitation of resources have resulted in great modification of natural habitat and accelerated loss in biodiversity. Overall, the IUCN Red List now includes 44,838 species, of which 16,928 are threatened with extinction (38 percent). Of these, 3,246 are in the highest category of threat i.e. Critically Endangered; 4,770 are Endangered and 8,912 are Vulnerable to extinction (IUCN Red List 2008). Worldwide destruction of natural environment is reducing the number of wild species and biodiversity in general. Therefore, to protect species of wild animals from extinction, inter-alia a regional conservation planning is required which needs basic information on the status and distribution of habitat of animals, plants and various geophysical components throughout the region of interest. Though India has well defined programme on in-situ biodiversity conservation through Protected Area Network (PAN), but to effectively manage protected areas reliable baseline data and spatial database is needed. Remote Sensing and GIS are effective tools that could be used to put forth management solutions through interdisciplinary studies with an integrative approach and in a perspective way.
It has been realized that efforts towards conservation and management of wildlife are often hampered due to non-availability of good quality data on species, habitats and suitability of the habitats for different species. The solution to conserve biodiversity in-situ requires major investments and multidisciplinary approaches sustained by a foundation of sound scientific and technological information with careful analysis. Recent advances in the understanding of ecological processes and technological understanding have made management of wildlife more scientific. Spatial and non spatial databases are becoming available to wildlife managers and decision makers to look at species-habitat relationships in a much better way. Better :: 1 ::
integration of technology with more sophisticated modelling of species-habitat requirements is required to evaluate current and potential impacts of management practices on landscape composition and structure, the availability of ecological resources, habitat quality and the viability of species populations. Such tools and models have to be flexible and should include appropriate analytical techniques for evaluating the effects of management practices on the conservation of biological diversity among multiple scales of time and space.
India’s remote sensing programme has made rapid strides and high resolution data at relatively low cost is now being made available to a variety of users by the National Remote Sensing Centre, Hyderabad. In order to utilize the satellite data for applications in wildlife conservation and management, the MoEF had constituted a Task Team in February, 2003 under the Chairmanship of Director, Wildlife Institute of India. Based on the recommendation of this Task Team, the NNRMS Standing Committee on Bioresources and Environment in its 19th Meeting held on 31.12.2003 sanctioned a pilot project ‘Mapping of National Parks and Wildlife Sanctuaries’ at a total cost of Rs. 1,38,63,500/- to the Wildlife Institute of India.
This pilot study was initiated in five sites namely Tadoba-Andhari Tiger Reserve
(TATR)
in
Maharashtra,
Corbett
Tiger
Reserve
(CTR)
in
Uttarakhand, Dudhwa Tiger Reserve (DTR) in UP, Kaziranga National Park (KNP) in Assam and Indira Gandhi Wildlife Sanctuary (IGWS) in Tamil Nadu (Fig.1).
:: 2 ::
Corbett Tiger Reserve, Uttaranchal Uttarakhandl
Dudhawa Tiger Reserve, Uttar Pradesh
Kaziranga National Park, Assam
Tadoba-Andhari Tiger Reserve, Maharastra
Indira Gandhi Wildlife Sanctuary, Tamilnadu
Figure. 1 Location of five pilot project sites 1). Tadoba-Andhari Tiger Reserve (TATR), Maharashtra 2). Corbett Tiger Reserve (CTR), Uttarakhand, 3). Dudhwa Tiger Reserve (DTR), UP 4). Kaziranga National Park (KNP), Assam 5). Indira Gandhi Wildlife Sanctuary (IGWS), Tamil Nadu. :: 3 ::
1.1
Role of Remote Sensing and GIS
The quickest possible way for inventory and evaluation of the natural resources is through application of Remote Sensing and Geographic Information System (GIS). These technologies provide vital geoinformation support in terms of relevant, reliable and timely information needed for conservation planning. The advancement in science and technology has revolutionalised the process of data gathering and map making and their application in habitat inventory, evaluation and wildlife census. Wildlife habitat mapping is similar to any type of land cover mapping. Both biotic and abiotic surface features including vegetation composition, density and landforms can be mapped. Interspersion of habitat components, the extent of habitat types and the distance to other critical habitat components can be measured. The NOAA (National Ocean and Atmospheric Administration), IKONOS, SPOT (Le systeme pour l’Observation de la Terre) and IRS (India Remote Sensing Satellite) series of satellites have added a temporal dimension to habitat mapping and change detection. The potential of using high resolution satellite data in wildlife habitat characterization is essentially required for intensive and effective management of park resources. This can often be achieved in real-time, which minimizes the amount of data entry that is required by a large cohort of experts. In addition, the GIS provides experts with a spatial context when providing data through the inclusion of other data layers such as digital elevation model, road network or vegetation distribution. Recently, India has placed a satellite RESOURCESAT in 2003 in the orbit equipped with high resolution LISS-IV sensor (5.8 m spatial resolution). High resolution data provides information on vegetation cover type and area, land cover diversity, size of open spaces and vegetation units, landscape heterogeneity (as indices of fragmentation and form complexity), indivisibility etc. which are useful parameters for habitat suitability analysis with more information and with higher levels of accuracy. IRS P 6 LISS IV data facilitates better discrimination of different forest types and detailed micro level information by delineating crown density levels due to high spatial resolution. Therefore, this project has been conducted using of IRS P6 LISS IV data.
:: 4 ::
1.2
PA/Biodiversity Spatial Database
In recent times, advanced technologies of RS and GIS have been widely used to develop spatial database for protected areas. Dubey (1999) developed GIS based spatial database for Tadoba-Andhari Tiger Reserve, Maharashtra using IRS 1B LISS II at the scale of 1: 50,000 to facilitate decision making process. Pabla (1998) using IRS 1B produced spatial database in GIS domain for Bandhavgarh National Park at the scale of 1: 50,000. The project entitled “Biodiversity Characterisation at Landscape Level Using Satellite Remote Sensing and GIS” was one of biggest project for the development of national database in India. The Department of Biotechnology and the Department of Space together took initiative to study biodiversity hotspot regions in India using satellite remote sensing. During Phase-I, the regions studied were North-eastern, Western Ghats, Western Himalayas and the Andaman and Nicobar islands. Phase-II which included Central India, Eastern Ghats and mangrove landscape of East Coast has also been completed. The output was GIS database with maps at the scale of 1:2,50,000 depicting biodiversity status of landscape (National Remote Sensing Agency, 2007).
All the above databases and many more are on the scale of 1:50,000 or on smaller scale. The basic management unit to work for any wildlife manager is a forest range/beat/compartment. The medium scale database cannot provide the information to the desired extent for that level. Adoption of any management strategy requires the identification and demarcation of small patches, their areal extent and boundary especially of important swamps or water bodies, plantations. Detailed information on the management infrastructure i.e. network of forest roads, firelines, building, check posts, barriers, watch tower etc is also very important. This baseline data is prerequisite for management and monitoring and for the better understanding of various conditions of important habitats and attributes of any protected area.
Till recent past meager efforts were made in India to prepare spatial database for any protected area at the larger scales. In other parts of the world, such endeavours started in last 2-3 decades. In one such effort, Welch et al. (2002) :: 5 ::
developed vegetation database and associated maps on a large scale of 1:15,000 using aerial photographs for the Great Smoky Mountain National Park in Eastern United States. The output included GIS database of both overstorey and understorey vegetation communities for the entire park, and hardcopy maps at the scale of 1:15,000. The database could assist park managers in identification of particular patch, in assessing vegetation patterns related to management activities, and in quantification of forest fire fuels by GIS modelling. In another study, Welch et al. (1995) utilized the combination of satellite imaging, aerial photographs, Global Positioning System, and GIS technologies to develop a spatial database in GIS domain for over one million hectares of South Florida’s National Parks and Preserves. The digital GIS database and associated hardcopy map on a scale 1:24000 aimed to provide up-to-date spatial information needed by parks managers in evaluating the status of vegetation and the threats caused by urban expansion.
1.3
The Pilot Project
In response to the above management requirement of PAs in the country, for the first time, a decision was taken by the Bio-Resources and Environment Committee of National Natural Resources Management System (NNRMS) to make an attempt through this project to develop spatial database for all PAs at the large scale of 1:25,000. The project aimed to generate accurate, reliable, and latest baseline spatial information on forest types, density, topographic features on the scale of 1:25,000. In addition, as value addition to the maps, vital information on plant and animal diversity, density, and richness information was also visualized. Such maps not only provide basic record of forest biodiversity in the country but also have immense utility in the preparation of forest management plans and in various scientific researches. This was a multi-institutional project and involved various lead organizations like the Wildlife Institute of India, Dehradun; Survey of India, Dehradun; Aligarh Muslim University, Aligarh and various specialized remote sensing centers as the Indian Institute of Remote Sensing, Dehradun and National Remote Sensing Centre, Hyderabad. Initially, four pilot sites– Corbett Tiger Reserve, Uttarakand; Kaziranga National Park, Assam; Tadoba-Andhari Tiger Reserve, Maharashtra; and Indira Gandhi National Park, were selected for :: 6 ::
gaining sufficient experience of large scale mapping, which could be extrapolated to all PAs of the country. Later, Dudhwa Tiger Reserve in Uttar Pradesh was also included as the fifth pilot site. These five sites, located in four different biogeographical zones are important from wildlife point of view. They represent wet, humid to dry tropical and sub-tropical wildlife habitats and possess numerous and obligate species of wild animals. Thus, primarily this project was the first step to achieve the goal of ‘Resource Mapping at 1:25000 scale’ at the national level for five pilot PA sites.
1.4
Large Scale Mapping Using High Resolution Data
Remote
Sensing
(RS)
and
Geographical
Information
System
(GIS)
technologies, in recent times have revolutionized the process of inventory of natural resources, its quality, and pace of surveying and thus collectively have emerged as an ideal tool for database development.
A new generation of satellites with improved temporal frequency of data acquisition, better spatial and spectral resolution has considerably enhanced the potential of remote sensing in the development of spatial database. Improved spatial resolution allows better textural identification of ground features and helps to produce maps at a fine scale with clearly identifiable information on forest type, physical infrastructure, and boundaries. Thus, the availability of high resolution satellite imagery now makes it possible to perform large scale and accurate mapping.
Today, India has an impressive array of remote sensing satellites meeting the national need for management of natural resources. One of the high resolution satellites in the family is IRS P-6, also known as Resourcesat–1. It was launched into polar orbit on 17 October, 2003 from Satish Dhawan Space Centre by the Indian PSLV C5. The present project has attempted to utilize one of its high resolution sensor i.e. Linear Imaging Self Scanner IV (LISS IV) with spatial resolution of 5.8 m to develop spatial database at the scale of 1:25,000.
:: 7 ::
1.5
Development of Digital Topographic Sheets by the Survey of India
Historically, Survey of India (SoI) - the designated national mapping agency has produced topographical sheets for the entire country on a 1:50, 000 scale which have been extensively used by all line agencies including the State Forest Departments. In order to use the High Resolution LISS-IV satellite data and to prepare a spatial database in GIS domain it was critical to have topographical sheets on 1:25,000 scale. Thus, Survey of India was assigned the responsibility of providing digital topographic sheets on 1:25,000 scale for all the five pilot sites of the project. A provision of Rs 54 lakhs was made in the project budget and an advance amount of Rs 26.10 lakhs was given to SoI in April, 2006. Since the task involved fresh topographic surveys and creation of spatial database, the SoI was able to provide the product for one site viz. Indira Gandhi Wildlife Sanctuary, Tamil Nadu only after 12 months of payment of advance. For two sites viz. Kaziranga National Park, Assam and Corbett National Park, Uttarakhand
the digital topographic sheets were
provided after 29 months; for Tadoba-Andhari Tiger Reserve, Maharashta after 32 months and for Dudhwa Tiger Reserve, Uttar Pradesh after 33 months. This inordinate delay in production of digital topographical sheets on 1:25,000 scale affected the development of spatial database for the pilot sites and led to repeated extension of the project duration, from an initial 36 months project period to a final 60 months project duration. Moreover, the digital toposheets
are of variable quality and consistency and this has led to
differential spatial databases in the five project sites.
2. Objectives 1. Prepare a spatial database in GIS domain on 1:25,000 scale using LISS-IV satellite data for 5 project sites. 2. Train the wildlife staff in the project sites in the process of collection, collation and use of spatial database for management and monitoring of PA resources.
:: 8 ::
3.
Project Steering Committee
In order to steer the activities of the project, the MoEF also constituted a Project Steering Committee (PSC) under the Chairmanship of Inspector General of Forests (Wildlife), Ministry of Environment & Forests. During the project duration 5 meetings of the Project Steering Committee were organized, which provided valuable oversight to the project activities.
4.
Methodology
The broad methodology for field sampling is given in Fig. 2 and for preparation of spatial database is given in Fig. 3.
Field Sampling Field Sampling – Line Transects with circular plot, laid in the smallest administrative unit (Beat) based on the major vegetation types, Elevation, Temperature and Precipitation.
10m
200 m
200 m 3m
Transect Shrubs Tree Species
Figure 2. Field Sampling Design
:: 9 ::
Topographic Mapping
New Survey on 1:25,000 scale
Thematic Mapping
Existing Topographic Map Updation Using Satellite Imagery
IRS P6 LISS-IV
Ground Truth
TOPOGRAPHIC MAP Contour Road & Railway Firelines Watch Tower/Chauki/Post Village Location & Boundary Drainage & Waterbody Slope, Aspect & Elevation Reserve Forest Boundary Division, Range, Block & Compartment Boundary
THEMATIC MAP Forest Type & Density Maps Champion & Seth’s Level III & IV classes Five Density Classes Integrated Type & Density Map
Maps of Species Distribution/Abundance Maps
Landuse/Landcover, Forest Type, Density & Biodiversity Map on 1:25,000 scale with Topographic Features
Spatial Database on 1:25,000 scale
Figure 3. Methodology for collection and collation of data and preparation of spatial base
:: 10 ::
5. Report Layout
The final technical report is presented in 6 separate volumes. Volume I provides the project background, objectives and salient outputs at the five pilot sites. Volume II, III, IV, V and VI provide a detailed account of the project activities in the 5 sites as per details given below: VolumeI
: Project Background, Objectives and Salient Outputs
Volume II
: Indira Gandhi Wildlife Sanctuary, Tamil Nadu
Volume III
: Tadoba-Andhari Tiger Reserve, Maharashtra
Volume IV
: Dudhwa Tiger Reserve, Uttar Pradesh
Volume V
: Kaziranga National Park, Assam
Volume VI
: Corbett National Park, Uttarakhand
A Compact Disc (CD) containing spatial databases and the technical reports of all 5 project sites has been prepared and is enclosed in Volume I of the report.
6. Salient Outputs at the Pilot Sites
6.1
Indira Gandhi Wildlife Sanctuary, Tamil Nadu
6.1.1 Based on the digital toposheets provided by the SoI comprehensive infrastructure
and administrative (Range and Beat boundary) maps have
been prepared (Fig. 6.1.1 and Fig. 6.1.2)
6.1.2 Using LISS-IV satellite data Forest Type and Land Use map has been prepared having 15 classes (Fig. 6.1.3)
:: 11 ::
76°50'0"E
76°55'0"E
77°0'0"E
77°5'0"E
77°10'0"E
77°15'0"E
77°20'0"E
10°30'0"N
10°30'0"N
INFRASTRUCTURE MAP OF INDIRA GANDHI WILDLIFE SANCTUARY
POLACHI
10°25'0"N
10°25'0"N
ULANDY UDUMALAIPETTAI
10°20'0"N
10°20'0"N
VALPARAI
MANAMPALLY
AMARAVATHI
INDIA
10°15'0"N
MANAMBOLY
Forest Rest-House & Office
Settlements
Forest Genetic Research Centre
Roads Fire Lines
Watch Tower TAMIL NADU
Outside Sanctuary
Check Post
0
1.5
3
6 Km
Rain Guage
SC-B\NNRMS, MoEF, GoI
76°55'0"E
Range Boundary
Anti Poaching Shed
National Remote Sensing Centre Forest Department, Tamil Nadu Wildlife Institute of India Survey of India Funding Agency
76°50'0"E
Boundary of IGWLS
Elephant Camp Participating Organizations
10°15'0"N
Legend
77°0'0"E
77°5'0"E
77°10'0"E
77°15'0"E
Figure 6.1.1. Infrastructure Map of Indira Gandhi Wildlife Sanctuary :: 12 ::
77°20'0"E
76°50'0"E
76°55'0"E
77°0'0"E
77°5'0"E
77°10'0"E
77°15'0"E
77°20'0"E
ADMINISTRATIVE MAP OF INDIRA GANDHI WILDLIFE SANCTUARY
10°30'0"N
10°30'0"N
POTHAMADA BEAT
ARTHANRIPALAYAM BEAT AYIRAMKAL BEAT ALIYAR BEATPARUTHIYUR BEAT
POLACHI PACHATHANNIR BEAT MANGARAI BEAT KARATTUR BEAT TOPSLIP BEAT VILLONNIE BEAT ATTAKATTY BEATVALLAKONDAPURAM BEAT VARAGALIYAR BEAT Unsurveyed
CHINNAR BEAT
ANALI BEAT
KURUMALAI BEAT UPPER ALIYAR BEAT
Unsurveyed
UDUMALAIPETTAIEASAL
THITTU EAST BEAT
THIRUMURTHI MALAI BEAT
KOMBU EAST BEAT
KAVURKAL BEAT IYERPADI BEAT
MANAMPALLI BEAT
KALLAPURAM BEAT
VALPARAI
MANAMPALLY
URULIKAL BEAT
KARATTUR BEAT
KOMBU WEST BEAT
AKKAMALAI BEAT
10°20'0"N
SHEIKALMUDI BEAT
10°20'0"N
10°25'0"N
ATTAKATTY BEAT
10°25'0"N
EASAL THITTU WEST BEAT
ULANDY
AMARAVATHI
Legend GRASSHILLS BEAT
TALINGI BEAT
Boundary of IGWLS Range Amaravathi
INDIA
PERIYA KALLAR BEAT CHINNAKALLAR BEATMANAMBOLY
Manampally
10°15'0"N
10°15'0"N
KILANAVAYAL BEAT MANJANPATTI BEAT
Manamboly Polachi Udumalaipettai
TAMIL NADU
Ulandy
Participating Organizations
Valparai
National Remote Sensing Centre Forest Department, Tamil Nadu Wildlife Institute of India Survey of India Funding Agency
Beat Boundary
SC-B\NNRMS, MoEF, GoI
76°50'0"E
76°55'0"E
0
1.5
3
6 Km
Outside Sanctuary 77°0'0"E
77°5'0"E
77°10'0"E
77°15'0"E
77°20'0"E
Figure 6.1.2. Administrative Map of Indira Gandhi Wildlife Sanctuary showing Range Boundary and Beat Boundary
:: 13 ::
76°50'0"E
76°55'0"E
77°0'0"E
77°5'0"E
77°10'0"E
77°15'0"E
77°20'0"E
10°30'0"N
10°30'0"N
FOREST TYPE & LAND-USE MAP OF INDIRA GANDHI WILDLIFE SANCTUARY
10°25'0"N
ULANDY RANGE
UDUMALAIPETTAI RANGE
VALPARAI RANGE
10°20'0"N
MANAMPALLY RANGE
10°20'0"N
10°25'0"N
POLACHI RANGE
AMARAVATHI RANGE
Legend Non-Forest
Forest Type
10°15'0"N
MANAMBOLY RANGE
TAMIL NADU Participating Organizations
Administrative Units
National Remote Sensing Centre Forest Department, Tamil Nadu Wildlife Institute of India Survey of India Funding Agency
76°55'0"E
0
1.5
3
6 Km
Boundary of IGWLS Range Boundary Outside Sanctuary
SC-B\NNRMS, MoEF, GoI
76°50'0"E
10°15'0"N
Scrub Evergreen Grassland Semievergreen Barren land Moist Deciduous Water Dry Deciduous Plantations Shola Cinchona Plantation Savannah-Woodland Eucalyptus Plantation Degraded Forest Tea Plantation Teak Plantation
INDIA
77°0'0"E
77°5'0"E
77°10'0"E
77°15'0"E
Figure 6.1.3. Forest Vegetation type and Land-Use map of Indira Gandhi Wildlife Sanctuary
:: 14 ::
77°20'0"E
6.1.3 The analysis of species/community–environment relationships has always been a central issue in ecology. The importance of climate to explain animal and plant distribution was recognized early on. Climate in combination with other environmental factors has been much used to explain the main vegetation patterns around the world (Holdridge 1967; Ashton 1969; McArthur 1972; Tilman 1982). More recently, studies have revealed species’ associations with topography, water and nutrient availability on local scales in tropical forest worldwide (Clark et al. 1998; Cannon and Leighton 2004; Valencia et al. 2004). These observations led to a variety of hypotheses to account for high diversity at local scales (Hubbell et al. 2001; Wright 2002); many of these hypotheses invoke density and frequency dependent mechanisms. The fundamental principle to these hypothesis are resource allocation and thereby niche differentiation with respect to available resources. The climate on a broad scale and topography on a fine scale are two dependent parameters which decides the resource availability and structure of climax community. Therefore, efforts have been made to characterize
the
vegetation
communities
in
response
to
different
environmental gradients and to identify the most important predictors of diversity in Indira Gandhi Wildlife Sanctuary.
The temperature and rainfall data collected from WORLDCLIM website (Hijmans et al. 2005) were used to analyze the role of rainfall and temperature gradients in the distribution of species diversity. The altitude, slope and aspect were generated from SRTM (Shuttle Radar Topographic Mission) data. The temperature and rainfall data collected from WORLDCLIM website (Hijmans et al. 2005) were used to analyze the role of rainfall and temperature gradients in the distribution of species diversity.
In order to
investigate the relationships between species richness and environmental variables, a canonical correspondence analysis (CCA) was employed (ter Braak 1987), using the software PC-ORD 4.0 (McCune and Mefford 1999). As required by CCA, data was set into two distinct matrices: the species matrix and the matrix of environment variables. The species matrix contained number of species per plot. The environmental variables matrix included are
:: 15 ::
elevation, slope, aspect, temperature and precipitation. Multiple linear regression analysis was conducted to identify the best predictor of diversity. A stepwise backward elimination approach was adopted in which the analysis started with all the continuous variables and eliminated the least significant variable in each progressive step. The variables were removed if the probability of ‘F’ exceeded 0.05. The species richness was the dependent variable and elevation, slope, aspect, rainfall and temperature were the independent variables.
Canonical correspondence analysis was performed for 169 species on 206 plots with 5 environmental variables. The eigenvalues for the first three CCA axes were 0.749, 0.523 and 0.304 respectively. The cumulative percentage variance accounted for those axes was 4.0% (1.9, 1.3 and 0.8 respectively), indicating that a considerable amount of ‘noise’ still remained unexplained. However, ter Braak (1995) considers low percentage of unexplained variance as normal in vegetation data, and this fact does not weaken the significance of species–environment relationships. In fact, the CCA produced high correlations between species and environmental variables for these axes (0.943, 0.883, and 0.740 respectively). The first ordination axis was highly correlated, in descending sequence, with precipitation, temperature, elevation and slope (Table: 6.1.1). The second ordination axis has shown high correlation with elevation and temperature while the third ordination axis is correlated with slope. The weighted correlations between environmental variables showed strong interrelationships, especially between elevation and climatic variables (temperature and precipitation). Segregation of vegetation communities along the noted gradients was also observed. The left side of the ordination space is dominated with communities which are primarily evergreen species whereas the right side is occupied by deciduous species (Fig. 6.1.4). The details of the communities are further explained below.
:: 16 ::
P2 3 0
P2 2 9 P2 2 7
Montane shola forest communities
P2 2 2P2 2 5
P6 8
P2 2 8
P156
P51 P50
P4 3
P6 3
P4 9
P16 8 P4 6
P4 8
P4 5P4 4
P16 7
Moist deciduous communities
P4 7
P171 P170
P16 0 P16 9 P159 P16 5 P16 4 P16 3 P155 P16P154 1 P153 P16 2 P152 P150
Elevatio P54
P53
P157
P13 9
Dry deciduous communities
P174 P14 9 P14 8
P114
P19 2
P15
P1760 P18
P2 0 6 P2 0 4 P2 0 2P2 0 1 P19 6 P19 8
Precipit
P56 P16
P19 3 P2 0 5 P19 5 P19 4
P55
Slope
P2 0 9
P2 3 1
P19 7 P2 0 0
P2 0 7 P2 0 8
P18 5 P179 P18 6 P178 P18 2 P57 P18 1
P14 7 P119
P112 P115 P116
P58
P2 3 3 P2 4 0
P9
P2 3 2
P110
P18 4
P10 3 P9 7
P74 P13 5 P8 6 P8 9 P13 6 P9 3 P9 6 P13 4 P9 2
P13 3
P9 4
P13 2
P9 1
P9 8
P59 P10 5
Evergreen communities
P6 4
P71
P14 1
P12 2 P10 P12P12 0 P1327 3 P113 6 P12P12 P12 5 4 P12 1 P13 8 P12P111 8 P10P10 9 6 P12 7
P18 9
P18 3 P18 8
P2 4 1
P19 9
P70 P8 5P6 P8492 P8
P14 5 P14 2 P12 9 P10 0
P14 6
P13 0
P6 6 P6 7
P10 1
P117 P10 7
P172
P52
P14 0
P14 3
P151
P2 0 3
Axis 2
P175
Scrub forest communities P78 P8 0 P79 P13 1 P8 1 P75 P72 P76 P77
P73
P14
P8 P12 P1
P7
Temperat
P2 2
P3 P2 1
P2
P11 P19
P4 04 P2 3P2P2 P2 9 P2 5P3 4 P2 3 9
P2 3 7 P3 0 P2 5 P3 7 P2 3 8
P3 1 P3 3
P10
P18
Semi-evergreen communities
P2 3 4
P2 6
P17
P2 3 5 P3 6
P3 2
P4 0 P2 3 6 P4 1 P3 7
P6 2
P2 8
P4 2 P13
P6 1
P3 9 P3 8
P6 0
Axis 1
Figure 6.1.4.. CCA ordination diagram (Axis 1 by Axis 2) with plots (scattered points) and environmental variables (lines) in Indira Gandhi Wildlife Sanctuary. Each circle represents partitioning of vegetation communities along environmental gradients.
Variable
Axis 1 Axis 2 Axis 3 Elevation Slope Aspect Precipitation Temperature
Elevation
-0.662 0.734 0.063
1 0.422
0.181
0.757
-0.946
Slope
-0.542 0.016 0.806
0.422
1
0.027
0.48
-0.436
Aspect
-0.186 0.268 -0.123
0.181 0.027
1
0.186
-0.175
Precipitation
-0.986 0.139 -0.052
0.757
0.186
1
-0.805
Temperature
0.715 -0.619 -0.131
-0.946 -0.436 -0.175
-0.805
1
0.48
Table 6.1.1. Canonical Correspondence Analysis of 169 species in 206 plots in Indira Gandhi Wildlife Sanctuary. Matrix presents intraset correlation between environmental variables and first three axes and weighted correlations between environmental variables.
:: 17 ::
6.2. Tadoba-Andhari Tiger Reserve, Maharashtra
6.2.1 Using LISS-IV satellite data Landuse/Landcover map has been prepared having 10 classes (Fig. 6.2.1) along with a Canopy Density map having 5 density classes (Fig. 6.2.2).
6.2.2 Landscape characterization using Fragstat software was carried out in TATR and various metrices were calculated (Table 6.2.1 and Table 6.2.2).
Table 6.2.1. Landscape metrics for TATR landscape Landscape Metrics No. of Patches (NP) Patch Density (PD) Largest Patch Index (LPI) Interspersion and Juxtaposition (IJI) Simpson Diversity Index (SIDI) Simpson Evenness Index (SIEI)
Values 2307 1.7/km² 32.53% 50 0.38 0.42
Table 6.2.2 Class level metrics for landscape of TATR Vegetation Types
PLAND
NP
(%)
PD
MPS
LPI
IJI
(No./100ha)
(ha)
(%)
(%)
Mixed Bamboo Forest 77.9
340
0.25
136.1
32.5
68.2
Mixed Forest (MF)
6
671
0.49
5.3
0.6
5.8
Teak forest (TF)
2
182
0.13
6.6
0.6
61.6
1
42
0.03
13.7
0.2
14
Riparian Forest (RF)
0.3
35
0.03
2.3
0.02
62.8
Grassland (GL)
4.1
225
0.16
7.2
0.6
42.8
(MBF)
Teak Mixed Bamboo Forest (TMB)
:: 18 ::
Fig. 6.2.1. Landuse/ Landcover Map of TATR :: 19 ::
Fig.6.2.2. Canopy Density Map of TATR
:: 20 ::
6.2.3 Based on 702 km walk on 50 transects, density estimates of 5 wild ungulate species were made and are presented in Table 6.2.3.
Table 6.2.3. Density estimates of wild ungulates in TATR Tadoba National Park (northern zone)
Andhari Wildlife Sanctuary (central & southern zones combined)
Central Zone
Southern Zone
Overall TATR
Density/km²(SE), Group density/km²(SE) All ungulate (Pooled data)
50.11(±7.1) 19.1(±1.98)
44.7(±6.2) 11(±0.8)
35.4(±5.7) 9.7(±0.99)
33.43(±4.6) 9.2(±1.1)
Chital
29.15(±7.2) 7.2 (±1.7)
15.2(±5.06) 3.17(±0.63)
19.31(±6.9) 3.2(±0.82)
6.1(±2.4) 2.1(±0.7)
21.2(±4.1) 4.9(±0.87)
Sambar
9.4(±2.2) 5.5(±1.06)
3.1(±0.91) 2.03(±0.51)
4.76(±1.4) 2.6(±0.64)
1.4(±0.44) 1.2(±0.33)
7.67(±1.3) 3.8(±0.66)
Nilgai
3.9(±1.2) 1.5(±0.57)
3.2(±1.09) 1.5(±0.45)
1.69(±1.28) 1.7(±1.2)
2.1(±0.97) 1.6(±0.75)
3.2(±0.75) 1.5(±0.35)
Wild Pig
13.72(±3.8) 2.4(±0.59)
11.7(±3.8) 3(±0.8)
8.5(±4.5) 2.3(±0.9)
7.6(±3.9) 2(±1)
10.3(±2.5) 2(±0.41)
Gaur
1.27(±0.86) 0.6(±0.29)
10.7(±3.4) 2.4(±0.48)
4.9(±4.12) 1(±0.65)
11.5(±4.3) 2.5(±0.55)
7.04(±1.65) 1.1(±0.29)
40.2(±4.3) 12.13(±1.2)
6.2.4 An attempt was made in this study to develop habitat models for five major ungulate species i.e. Chital, Sambar, Nilgai, Gaur, Wild pig using Ecological Niche Factor Analysis (ENFA) and GIS. The environment envelope approach was opted because absence of evidence cannot be equated with evidence of absence. The objective of the exercise was to assess the current status of these species and to explore the species-specific ecological habitat requirements to devise sound management practices which may be applied for effective management. The Habitat Suitability Maps developed developed for five major ungulate species i.e. Chital, Sambar, Nilgai, Gaur, Wild pig are given in Fig. 6.2.3 to Fig. 6.2.7 respectively.
:: 21 ::
Fig 6.2.3. Habitat Suitability Map of Chital in TATR
Fig 6.2.4 Habitat Suitability Map of Sambar in TATR
:: 22 ::
Fig. 6.2.5 Habitat Suitability Map of Gaur in TATR
Fig 6.2.6 Habitat Suitability Map of Nilgai in TATR
:: 23 ::
Fig 6.2.7 Habitat Suitability Map of Wild Pig in TATR 6.2.5 The presence of canopy was one of the main determinants of habitat utilization by large ungulates in TATR, with all species associating with various canopy classes. The key finding here is that ungulates separated themselves ecologically by canopy density classes. All canopy classes except non-forest were favoured by ungulates. Canopy density below 30% was most favoured (Table 6.2.4) The burnt area had the positive influence. High elevation was generally avoided with the exception of Sambar. It is inferred from the models that a majority of ungulates respond negatively towards habitations. Ungulates showed the proximity towards open areas and interspersion of habitat types which provide good blend of food and cover values. Leopold (1961) recognized greater habitat interspersion as a favourable facet for most ungulates.
:: 24 ::
Table 6.2.4. Scores of marginality factors for all ungulates studied in TATR Species EGVs
Chital
Sambar
Gaur
Nilgai
Wild Pig
Canopy60% **
0.308
0.502
0.099
-0.155
0.518
Non-forest
-0.029
-0.001
-0.205
0.373
-0.006
Elevation **
-0.334
0.421
-0.41
-0.009
-0.234
Area Burnt
0.262
0.051
0.194
0.153
0.305
Open Forest
-0.099
-0.121
0.069
0.101
-0.037
Riparian Forest *
0.207
0.347
-0.224
0.238
0.22
Distance from road ****
-0.502
-0.296
-0.493
-0.312
-0.451
Scrub
-0.06
-0.129
-0.081
0.424
-0.011
Teak Forest **
0.195
0.467
-0.195
0.257
0.348
Teak Mixed Forest
0.197
0.214
-0.097
0.23
0.096
Distance from village
0.296
0.137
0.185
-0.078
-0.132
Distance from water
0.001
-0.099
0.034
0.021
-0.034
* Determinant variables, greater the number of asterix narrower the range
6.2.5 Based on the digital toposheets provided by the SoI, a spatial database for TATR was developed which has 12 thematic layers viz. Roads, Drainage, Water Sources, Well and Springs, Powerlines, Countours, Slope, Aspect, Elevation, Landuse/Landcover,
Canopy and Settlements. Rangewise
thematic layers have also been prepared which provide valuable information for management and monitoring of resources. See Volume III for details.
:: 25 ::
6.3. Dudhwa Tiger Reserve, Uttar Pradesh
6.3.1
Using LISS-IV satellite data Landuse/Landcover map have been
separately prepared for Dudhwa National Park (DNP) (Fig. 6.3.1), Katerniaghat Wlidlife Sanctuary (KAT)
(Fig. 6.3.2) and Kishanpur Wildlife
Sanctuary (KWS) (Fig. 6.3.3). LISS IV allowed delineation of 21 Landuse/ Land cover classes. This included 14 forest types, two grassland types, three wetland types, and two other land use/land cover classes.
6.3.2 Visual analysis of images of the sample sites in KAT extracted from LANDSAT ETM+, IRS 1D LISS III and IRS P-6 LISS IV revealed more contrast amongst features in LISS IV compared to other datasets owing to its high spatial resolution. The boundaries were more precise and easy to delineate in LISS IV. Examples of more accurate boundary delineation and possible identification of small important patches of otherwise a suppressed vegetation type within other surrounding vegetation types are presented in Fig. 6.3.4. In case of LISS IV, presence of contrast and discernible bank line were evident (Fig. 6.3.4 a). High resolution imagery of LISS IV allowed better demarcation of grassland boundaries and delineation of a plantation patch within, which was otherwise invisible in ETM+ and LISS III (Fig. 6.3.4 b). Similarly, contrast tone and texture of Dense Sal Forest was conspicuous within other forest types in case of LISS IV (Fig. 6.3.4 c). Delineation of boundaries of Dense Sal Forest in medium resolution datasets (ETM+ and LISS III) was confusing.
All linear features such as metalled road, forest road, railway line, etc were very clear and easy to extract in LISS IV, except in some places where the contrast was relatively low. In case of both the medium resolution datasets, it was difficult even to identify the adjacent railway and metalled road. However, point features such as water wells and single trees were impossible to be detect in any of the datasets.
:: 26 ::
Fig. 6.3.1 Land Use/Land Cover of DNP Developed from IRS P-6 LISS IV at the Scale of 1:25,000 :: 27 ::
Fig. 6.3.2 Land Use/Land Cover of KAT Developed from IRS P-6 LISS IV at the Scale of 1:25,000
:: 28 ::
Fig 6.3.3 Land Use/Land Cover of KWS Developed from IRS P-6 LISS IV at the Scale of 1:25,000 :: 29 ::
ETM+
LISS III
LISS IV
a
b
c
a: Arrow indicates contrast and discernible bank line in LISS IV b: Circle indicates distinctive grassland boundary and added information on the patch of eucalyptus plantation within grassland as indicated by arrow c: Arrow indicated contrast tone and texture of Dense Sal Forest Fig. 6.3.4 - Images of Land Use Features for Visual Comparison between Landsat ETM+, IRS 1 D LISS III, and IRS P-6 LISS IV 6.3.3 The results demonstrated that the extent of three linear features i.e. metalled road, forest road, and railway line mapped from LISS IV was much more than other datasets. Statistics of the length of the features mapped is given in Table 6.3.1. The comparison indicated that the extent of the railway line mapped from three datasets was almost identical. Likewise, the length of metalled road extracted from LISS IV and LISS III was also almost equal. On the contrary, a significant difference in the extent of the main road mapped from LISS IV and ETM+ was recorded (Table 6.3.1). Forest roads mapped using three datasets allowed remarkable distinction in length. Fig. 6.3.5 also illustrates the distinction in extent of extraction in forest roads. The metalled road was not at all clear in ETM+ data and got merged with adjacent railway line. In case of forest roads, difference in the extent of mapping between three datasets was apparent. The length of the forest road extracted in LISS IV was much higher, being 112% in comparison to ETM+. The enhancement of such
:: 30 ::
extraction was only to the extent of 16.5% from ETM+ (30 m) to LISS III (23.5 m) and enhancement from LISS III to LISS IV was to the extent of 82% (Table 6.3.1; Fig. 6.3.5).
Table 6.3.1 - Length of Linear Features Extracted from Landsat ETM+, IRS 1 D LISS III, and IRS P-6 LISS IV (Values in km) Category
LISS IV
LISS III
ETM+
Railway line
12.92
12.82
12.84
Main Road
2.52
2.51
0.00
Forest Road
49.70
27.29
23.42
The comparison of land cover maps derived from LISS III and LISS IV revealed that in both the datasets, seven vegetation classes were delineated (Fig. 6.3.6). To compare the concordance area (mutual agreed area of a vegetation type deciphered from two datasets – LISS III and LISS IV), a confusion matrix was generated (Table 6.3.2). Accordingly, the major diagonal of the matrix (running from upper left to lower right) indicates concordance. For example, out of 483.9 ha area of Dense Sal Forest delineated by LISS IV, the concordance area with LISS III was 148.8 ha i.e. 30.7% coincidence (Table 6.3.2). The remaining area (335.1 ha) of Dense Sal Forest was misclassified by LISS III into three different classes (Moderately Dense Sal Forest, Terminalia alata Forest, and Teak Plantation). The maximum mismatch was with Moderately Dense Sal Forest indicating that LISS IV was able to segregate two most close classes accurately. The values of % coincidence for other six forest classes ranged from 30.7% to 100% in case of Dense Sal Forest and Upland Grassland, respectively. The values of % coincidence were found to be high for Mixed Deciduous Forest and Teak Plantation being 89.7% and 89.2%, respectively. Higher values indicated that the both datasets classified them near equally due to their distinct tone and texture. Only Upland grassland obtained a value of 100% coincidence. The overall % coincidence was found to be 66.4%.
:: 31 ::
Fig. 6.3.5 - Linear Features (Metalled Road, Forest Road, and Railway Line) Extracted from Landsat ETM+, IRS 1D LISS III, and IRS P-6 LISS IV
LISS III
LISS IV
Fig. 6.3.6 - Land Cover Maps Derived from IRS 1D LISS III and IRS P-6 LISS IV
:: 32 ::
Table 6.3.2 - Concordance Area (ha) of Land Use Classes Based on IRS 1D LISS III and IRS P-6 LISS IV Land Cover Classes from LISS IV
Land Cover Classes from LISS III Dense Sal
Tropical Moderately Mixed Teak Upland Terminalia alata Seasonal Dense Sal Deciduous Plantation Grassland Swamp
Dense Sal
148.8
68.8
40.9
0.3
19.5
Moderately Dense Sal
229.1
483.4
146.3
1.8
Terminalia alata
73.3
86.6
254.8
13.6
Mixed Deciduous
98.1
2.5
18.8
Tropical Seasonal Swamp
6.7
13.3
6.3
4.0
0.8
500.9
Teak Plantation
32.6
10.0
Upland Grassland
6.6
Total
483.9
648.9
442.1
109.3
16.7
561.2
6.6
% Coincidence
30.7
74.5
57.6
89.7
79.5
89.2
100.0
6.3.4 Sharda River exhibited pronounced changes during the assessment period (53 years: 1977-2001). It showed increased instability with its west bank line more unstable. Within 53 years, the period of 1990-99 was found most influential as notable alteration in river channel were documented. The increasing instability of Sharda River is threatening the prime habitat (Jhadi taal) of endangered swamp deer in KWS.
6.3.5 The Locational Probability Model developed for Sharda river revealed that 51% of the study area had a low probability of the channel remaining in that location, indicating channel instability. Forty-five per cent of the study area had moderate probability of being continuously occupied by the river channel, thus evincing moderate stability. Only 4% of the area had a high probability of being continuously occupied by river channel, indicating channel stability.
The only stable area of river channel was in segment ‘A’ (Fig. 6.3.7) upstream from Jhadi taal. Unstable channel was identified in all the segments, and the
:: 33 ::
unstable west bank line in segment ‘B’, in particular, indicates continuing instability in the Jhadi taal area. Segment ‘C’ had its maximum area under moderately stable category. Two major configuration changes in terms of direction of flow had occurred in segment ‘C’ during the assessment period; otherwise it had occupied the same area in all the years with minor changes.
Segment ‘A’
3-33-66% (Moderately stable) >66-100% (Stable Area)
Jhadi taal Segment ‘C’
Fig. 6.3.7 - Probabilities of Channel Stability Based on a Locational Probability Model for the Sharda River Channel Adjacent to Katerniaghat Wildlife Sanctuary Stable and unstable areas also differed in their size and shape. Unstable areas were elongated and located mostly along periphery whereas the lone stable area was spatially distinct and occupied a small area. Areas classified as moderately stable were of large size and spatially contiguous, but located within two peripheral unstable areas (Fig. 6.3.7).The Locational Probability Model developed for the Sharda River channel in the present study supports the argument of threat to Jhadi taal by sudden inundation or choking of swamp by heavy siltation in the near future. The river also depicted enhanced flooding and silt deposit. The floodplain was found to be encroached and pronounced conversion of newly found abandoned areas to agriculture was noticed, thus, hampering succession to natural vegetation.
6.2.6 Based on the digital toposheets provided by the SoI a spatial database for DTR was developed which has 10 thematic layers viz. Roads, Railway, Drainage,
Powerlines, Countours, Slope, Aspect, Elevation, Landuse/
:: 34 ::
Landcover and Canopy Thematic layers have also been separately prepared for Dudhwa National Park, Katerniaghat Wlidlife Sanctuary and Kishanpur Wildlife Sanctuary, which provide valuable information for management and monitoring of resources. See Volume IV for details.
6.4 6.4.1
Kaziranga National Park, Assam Using ASTER satellite imagery Landcover type
map has been
prepared having 11 categories (Fig. 6.4.1) The largest cover class was river sand (38.67%), followed by river water (20.09), tall grass (19.99%), semievergreen forest (11.77%), short grass (3.08%) and water bodies/beels (Fig.6.4.2).
6.4.2 The new 1:25,000 scale maps provided by Survey of India did not depict any park boundary. The only forest type of Kaziranga i.e. semievergreen forest was categorised into three canopy density classes viz., 1040% (open), 40-70% (medium dense) and >70% (dense) based on visual interpretation of the satellite imagery. The exercise revealed that 55.40 percent forest had dense canopy (55.40%), 24.62 percent had medium dense canopy and 19.97 percent had open canopy (Fig.6.4.2) .
6.4.3 Kaziranga is divided into 28 tiger compartments, of which 10 compartments are large (area >18 km2) and the remaining are smaller. The largest compartment covers 20.80 km2 area while smallest compartment occupies 8.21 km2 (Fig. 6.4.3 and Table 6.4.2).
6.4.4 Kaziranga has 122 forest protection camps inside the park and they are more or less quite evenly distributed within the park area (Fig. 6.4.4). Twenty five more camps are proposed for an effective anti-poaching strategy. With 147 camps in place, Kaziranga will have nearly one camp for every 7 km2, the highest density of protection camps in any national park in India.
:: 35 ::
93°0'0"E
93°10'0"E
93°20'0"E
93°30'0"E
93°40'0"E
26°50'0"N
26°40'0"N
26°40'0"N
26°50'0"N
.
Semi-evergreen 10-40% Semi evergreen 40-70% Semi-evergreen >70 % 26°30'0"N
Tall grass 26°30'0"N
Short grass Agriculture Tea garden Fallow land 0
3.5
7
14
21
Kilometers 28
Waterbody River sand River
93°0'0"E
93°10'0"E
93°20'0"E
Fig. 6.4.1: Forest / land cover map.
:: 36 ::
93°30'0"E
93°40'0"E
93°20'0"E
26°40'0"N
26°40'0"N
.
Semi-evergreen 10-40% Semi evergreen 40-70% Semi-evergreen >70 % Tall grass Short grass Agriculture Tea garden Fallow land Waterbody 0
0.25
0.5
1
1.5
Kilometers 2
River sand River
93°20'0"E
Fig. 6.4.2: Forest / land cover map (a part on 1:25,000 scale).
:: 37 ::
93°0'0"E
93°10'0"E
93°20'0"E
93°30'0"E
93°40'0"E
26°50'0"N
26°50'0"N
. TE3 TE2 TEC5
26°40'0"N
TC9 TCW21
TC19
TC10
TE1 TE7
TC11 TC12
TC18 TW22
River
TE6 TEC8
26°40'0"N
TC4 TC20
TC15
TC14
TC13
TCW17 TBP28
TW26
TW25
TW24
TW23
TCW16
0
3.5
7
93°0'0"E
14
21
26°30'0"N
26°30'0"N
TBP27
Kilometers 28
93°10'0"E
93°20'0"E
Fig. 6.4.3: Tiger compartment map.
:: 38 ::
93°30'0"E
93°40'0"E
93°10'0"E
93°20'0"E
93°30'0"E
93°40'0"E
26°50'0"N
26°50'0"N
93°0'0"E
Lahorijan ")
Debeswari HatichoraErasuti ") ") Naobhangi Hathiguri ") ") Bhengrai Alubari ) " Maklung ") ") Rajamari") ") KholkholiTajeng") ") Muamari Arimora ") Dhanbari BahumariTinibeel") ") ") ") ") ") ") ") Dhekiatol Sohola") Dhuba Gobrai Tilaidubi ") ") Duramari") Dusuti ") ") Holalpath Kartika") Mohkhuti ) " ") ") ") Gorpal Chiga ") ArikatiSukani Panpurghat ") ") ") Kathonibari Biswnathghat Chitalmari ") ") ") ") ") ") Noloni") Agratoli RO Baruntika ") Kathpara ) " Bornoloni") Jamuguri Baghmari Naromora ") ") ") ") Difaloomukh beat ") ") Lengtajan Ajogor Goroimari ") ") ") Bimoli ") Amkathoni ") Laudubi ") Panbari") ") Rajapukhuri ") Kerasing ") Bokpora ") ") ") ") ") Benga Bherbheri") ") DaflongBorbeel ") ") Rowmari") Thungru Buloni ") ") Nalamukh ") ") ") ") ") ") ") ") ") Difaloo ") ") Deopani Donga Bokabeel Solmora Gotonga ") ") ") ") ") ") ") Kohra RO BaneswarSundari Borghup ") Bahubeel ") Haldibari Janata ") ") ") ") ") Malani") Dusuti ") ") ") Tunikati ") ") ") ") ") ") Gerakati ) " ) " Bagori RO ") ) " Amguri Hatidandi Panijuri")
Moriahola ")
26°40'0"N
26°40'0"N
")
26°30'0"N
26°30'0"N
")
93°0'0"E
93°10'0"E
93°20'0"E
Fig. 6.4.4: Forest protection camps
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93°30'0"E
93°40'0"E
Table 6.4.1: Tiger compartments.
Compartment River TE1 TE2 TE3 TC4 TEC5 TE6 TE7 TEC8 TC9 TC10 TC11 TC12 TC13 TC14 TC15 TCW16 TCW17 TC18 TC19 TC20 TCW21 TW22 TW23 TW24 TW25 TW26 TBR27 TBR28 Total
Area (km2) 551.52 18.91 16.90 13.77 18.54 11.85 14.98 14.22 16.45 14.67 8.21 16.15 10.00 13.26 15.34 20.89 20.45 14.07 14.33 15.84 20.21 20.58 18.68 20.77 13.80 9.30 18.41 10.57 20.59 993.27
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Area (%) 55.53 1.90 1.70 1.39 1.87 1.19 1.51 1.43 1.66 1.48 0.83 1.63 1.01 1.34 1.54 2.10 2.06 1.42 1.44 1.60 2.03 2.07 1.88 2.09 1.39 0.94 1.85 1.06 2.07 100.00
6.5
Corbett National Park,
6.5.1 Using LISS-IV satellite data Landuse/Landcover map has been prepared having 9 classes (Fig. 6.5.1) along with a Canopy Density map having 5 density classes (Fig. 6.5.2).
6.5.2 As part of the study, bird species diversity and richness was studied. The bird species richness varied between habitat types. The highest mean bird species richness was recorded in riverine forest (1.857). It was then followed by Dry deciduous mixed forest (1.553) and mixed forest with plantations (1.506). The mean bird richness was 1.430 and 1.427 in scrub and sal mixed forests respectively. The lowest bird species richness was recorded in Sal forest and it was 0.990. The overall bird species richness was 1.456. The mean species richness differed significantly between the habitats F 5 & 317 = 9.109, P < 0.05. The spatial distribution of bird species richness is given in Fig.6.5.3
6.5.3 A geo-spatial database has been created which has thematic layers of Corbett National Park and its Ranges, details of which are given in Vol.VI.
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Fig. 6.5.2. Spatial distribution of various LULCs in CTR
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Fig.6.5.3. Spatial distribution of mean bird group density in CTR.
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7. Conclusions The project has been able to meet its intended objectives. Spatial database for all 5 project pilot sites have been created, which would be very valuable in both management and monitoring of resources and especially in revision of the management plans. The availability of spatial information at the Forest Range level is an important contribution of the project which would help in improving the efficacy of protected area management. During the project duration the PA staff has also been trained in collection and collation of ecological data.
As part of the project activities, the spatial database would be transferred to the 5 PAs and it would be imperative upon the PA management to use as well as update the database periodically. In addition to the above, the spatial databases would be maintained by the Computer/GIS Cell of the Wildlife Institute of India for use in training and research.
One of the significant outputs of the project has been the preparation of two doctorate theses viz. Geospatial Modeling of Ungualte Habitat Relationships in Tadoba-Andhari Tiger Reserve, Maharashtra by Ms. Ambica Paliwal and Landuse, Forest Fragementation and River Dynamics in Dudhwa Landscape and Their Conservation Implications by Ms. Neha Midha, the two project researchers who worked for the entire duration of the project (2004-2008) at the Wildlife Institute of India (WII). These theses provide comprehensive information on the spatial database development in GIS domain including spatial modelling of species-habitat relationships and habitat attributes especially river dynamics. These theses, available in the WII library, would serve as a valuable reference material for the scientific community and park managers interested in the application of remote sensing and GIS in protected area management and wildlife conservation.
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The capacity building of eleven researchers to conduct ecological surveys and to build spatial databases using satellite data has also been a major achievement of this project.
Undoubtedly, the project has demonstrated the immense utility of LISS-IV satellite data in Landuse/ Landcover and infrastructure mapping. However, it is learnt that as a policy decision, the Survey of India would be involved in the development of digital topographical sheets on 1:10,000 scale from XI Plan onwards and therefore the effective use of high resolution satellite data would be contingent upon the timely availability of topographical data.
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