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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
Period of engagement S.
No. Name Designation Site
From To
1. Ms. Ambica Paliwal
Junior/ Senior Research Fellow, WII
Tadoba-Andhari Tiger Reserve, Maharashtra
19th Nov, 2004 31st Dec, 2008
2. Ms. Neha Midha
Junior/ Senior Research Fellow, WII
Dudhwa Tiger Reserve, Uttar Pradesh
22nd Nov, 2004 31st Dec, 2008
3. Shri Shijo Joseph
Junior Research Fellow, WII
Indira Gandhi Wildlife Sanctuary, Tamil Nadu
1st Dec, 2004 13th Aug, 2007
4. Shri Amit Kumar Srivastava
Junior Research Fellow, WII
Corbett Tiger Reserve, Uttarakhand
22nd Nov, 2004 1st Sep, 2006
5. Shri Athar Noor
Junior Research Fellow, WII
Corbett Tiger Reserve, Uttarakhand
1st Oct, 2006 30th Sep, 2007
6. Shri Pebam Rocky
Junior Research Fellow, WII
Kaziranga National Park, Assam
6th Dec, 2004 9th Sep, 2005
7. Shri Mohit Kalra
Junior Research Fellow, WII
Kaziranga National Park, Assam
16th May, 2006 1st Aug, 2007
8. Dr. Hitendra Padalia
Research Associate, WII
WII-GIS Lab, Dehradun 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 Kumar Thakur
Technical Assistant
AMU, Aligarh & WII-GIS Lab, Dehradun
08th Aug, 2008 31st Dec, 2008
:: i ::
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
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 ::
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 multi-
disciplinary 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
:: 2 ::
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).
:: 3 ::
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.
Corbett Tiger Reserve, Uttaranchal
Dudhawa Tiger Reserve, Uttar Pradesh
Tadoba-Andhari Tiger Reserve, Maharastra
Kaziranga National Park, Assam
Indira Gandhi Wildlife Sanctuary, Tamilnadu
Corbett Tiger Reserve, Uttarakhandl
Dudhawa Tiger Reserve, Uttar Pradesh
Tadoba-Andhari Tiger Reserve, Maharastra
Kaziranga National Park, Assam
Indira Gandhi Wildlife Sanctuary, Tamilnadu
:: 4 ::
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.
:: 5 ::
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)
:: 6 ::
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
:: 7 ::
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.
:: 8 ::
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.
:: 9 ::
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.
Figure 2. Field Sampling Design
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.
200 m 200 m 3m
10m
Shrubs
Tree Species
Transect
:: 10 ::
New Survey on 1:25,000 scale
Existing Topographic Map Updation Using Satellite Imagery
TOPOGRAPHIC MAPContour
Road & RailwayFirelines
Watch Tower/Chauki/PostVillage Location & Boundary
Drainage & WaterbodySlope, Aspect & Elevation Reserve Forest BoundaryDivision, Range, Block & Compartment Boundary
IRS P6 LISS-IV
Ground Truth
THEMATIC MAPForest Type & Density Maps
Champion & Seth’s Level III & IV classes
Five Density ClassesIntegrated Type & Density Map
Landuse/Landcover, Forest Type, Density & Biodiversity Map on 1:25,000
scale with Topographic Features
Maps of Species Distribution/Abundance
Maps
Topographic Mapping Thematic Mapping
Spatial Database on 1:25,000 scale
New Survey on 1:25,000 scale
Existing Topographic Map Updation Using Satellite Imagery
TOPOGRAPHIC MAPContour
Road & RailwayFirelines
Watch Tower/Chauki/PostVillage Location & Boundary
Drainage & WaterbodySlope, Aspect & Elevation Reserve Forest BoundaryDivision, Range, Block & Compartment Boundary
IRS P6 LISS-IV
Ground Truth
THEMATIC MAPForest Type & Density Maps
Champion & Seth’s Level III & IV classes
Five Density ClassesIntegrated Type & Density Map
Landuse/Landcover, Forest Type, Density & Biodiversity Map on 1:25,000
scale with Topographic Features
Maps of Species Distribution/Abundance
Maps
Topographic Mapping Thematic Mapping
Spatial Database on 1:25,000 scale
New Survey on 1:25,000 scale
Existing Topographic Map Updation Using Satellite Imagery
TOPOGRAPHIC MAPContour
Road & RailwayFirelines
Watch Tower/Chauki/PostVillage Location & Boundary
Drainage & WaterbodySlope, Aspect & Elevation Reserve Forest BoundaryDivision, Range, Block & Compartment Boundary
IRS P6 LISS-IV
Ground Truth
THEMATIC MAPForest Type & Density Maps
Champion & Seth’s Level III & IV classes
Five Density ClassesIntegrated Type & Density Map
Landuse/Landcover, Forest Type, Density & Biodiversity Map on 1:25,000
scale with Topographic Features
Maps of Species Distribution/Abundance
Maps
Topographic Mapping Thematic Mapping
Spatial Database on 1:25,000 scale
New Survey on 1:25,000 scale
Existing Topographic Map Updation Using Satellite Imagery
TOPOGRAPHIC MAPContour
Road & RailwayFirelines
Watch Tower/Chauki/PostVillage Location & Boundary
Drainage & WaterbodySlope, Aspect & Elevation Reserve Forest BoundaryDivision, Range, Block & Compartment Boundary
IRS P6 LISS-IV
Ground Truth
THEMATIC MAPForest Type & Density Maps
Champion & Seth’s Level III & IV classes
Five Density ClassesIntegrated Type & Density Map
Landuse/Landcover, Forest Type, Density & Biodiversity Map on 1:25,000
scale with Topographic Features
Maps of Species Distribution/Abundance
Maps
Topographic Mapping Thematic Mapping
Spatial Database on 1:25,000 scale
Figure 3. Methodology for collection and collation of data and preparation of spatial base
:: 11 ::
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)
:: 12 ::
VALPARAI
AMARAVATHI
UDUMALAIPETTAI
POLACHI
ULANDY
MANAMBOLY
MANAMPALLY
77°20'0"E
77°20'0"E
77°15'0"E
77°15'0"E
77°10'0"E
77°10'0"E
77°5'0"E
77°5'0"E
77°0'0"E
77°0'0"E
76°55'0"E
76°55'0"E
76°50'0"E
76°50'0"E10
°30'
0"N
10°3
0'0"
N
10°2
5'0"
N
10°2
5'0"
N
10°2
0'0"
N
10°2
0'0"
N
10°1
5'0"
N
10°1
5'0"
N
INFRASTRUCTURE MAP OF INDIRA GANDHI WILDLIFE SANCTUARY
0 3 61.5 Km
INDIA
Participating Organizations
National Remote Sensing CentreForest Department, Tamil Nadu
Wildlife Institute of IndiaSurvey of India
Funding Agency
SC-B\NNRMS, MoEF, GoI
TAMIL NADU
LegendForest Rest-House & Office
Forest Genetic Research Centre
Watch Tower
Elephant Camp
Anti Poaching Shed
Check Post
Rain Guage
Settlements
Roads
Fire Lines
Boundary of IGWLS
Range Boundary
Outside Sanctuary
Figure 6.1.1. Infrastructure Map of Indira Gandhi Wildlife Sanctuary
:: 13 ::
VALPARAI
AMARAVATHI
UDUMALAIPETTAI
POLACHI
ULANDY
MANAMBOLY
MANAMPALLY
TALINGI BEAT
IYERPADI BEAT
KOMBU WEST BEAT
KALLAPURAM BEAT
PERIYA KALLAR BEAT
GRASSHILLS BEAT
TOPSLIP BEAT
AKKAMALAI BEAT
VARAGALIYAR BEAT
ALIYAR BEAT
VILLONNIE BEAT
CHINNAR BEAT
ANALI BEAT
MANAMPALLI BEAT
KOMBU EAST BEAT
MANJANPATTI BEATKILANAVAYAL BEAT
UPPER ALIYAR BEAT
ATTAKATTY BEAT
EASAL THITTU EAST BEATUnsurveyedTHIRUMURTHI MALAI BEAT
CHINNAKALLAR BEAT
KURUMALAI BEAT
KARATTUR BEAT
EASAL THITTU WEST BEATVALLAKONDAPURAM BEAT
Unsurveyed
KARATTUR BEAT
SHEIKALMUDI BEAT
PARUTHIYUR BEAT
MANGARAI BEAT
URULIKAL BEAT
KAVURKAL BEAT
ARTHANRIPALAYAM BEAT
POTHAMADA BEAT
AYIRAMKAL BEAT
PACHATHANNIR BEAT
ATTAKATTY BEAT
77°20'0"E
77°20'0"E
77°15'0"E
77°15'0"E
77°10'0"E
77°10'0"E
77°5'0"E
77°5'0"E
77°0'0"E
77°0'0"E
76°55'0"E
76°55'0"E
76°50'0"E
76°50'0"E
10°3
0'0"
N
10°3
0'0"
N
10°2
5'0"
N
10°2
5'0"
N
10°2
0'0"
N
10°2
0'0"
N
10°1
5'0"
N
10°1
5'0"
N
ADMINISTRATIVE MAP OF INDIRA GANDHI WILDLIFE SANCTUARY
0 3 61.5 Km
INDIA
Participating Organizations
National Remote Sensing CentreForest Department, Tamil Nadu
Wildlife Institute of IndiaSurvey of India
Funding Agency
SC-B\NNRMS, MoEF, GoI
TAMIL NADU
Legend
Range
Boundary of IGWLS
Amaravathi
Manamboly
Manampally
Polachi
Udumalaipettai
Ulandy
Valparai
Beat Boundary
Outside Sanctuary
Figure 6.1.2. Administrative Map of Indira Gandhi Wildlife Sanctuary showing Range Boundary and Beat Boundary
:: 14 ::
VALPARAI RANGE
UDUMALAIPETTAI RANGE
AMARAVATHI RANGE
POLACHI RANGE
ULANDY RANGE
MANAMBOLY RANGE
MANAMPALLY RANGE
77°20'0"E
77°20'0"E
77°15'0"E
77°15'0"E
77°10'0"E
77°10'0"E
77°5'0"E
77°5'0"E
77°0'0"E
77°0'0"E
76°55'0"E
76°55'0"E
76°50'0"E
76°50'0"E10
°30'
0"N
10°3
0'0"
N
10°2
5'0"
N
10°2
5'0"
N
10°2
0'0"
N
10°2
0'0"
N
10°1
5'0"
N
10°1
5'0"
N
FOREST TYPE & LAND-USE MAP OF INDIRA GANDHI WILDLIFE SANCTUARY
0 3 61.5 Km
INDIA
Participating Organizations
National Remote Sensing CentreForest Department, Tamil Nadu
Wildlife Institute of IndiaSurvey of India
Funding Agency
SC-B\NNRMS, MoEF, GoI
TAMIL NADU
LegendForest Type
Degraded Forest
Dry Deciduous
Evergreen
Moist Deciduous
Savannah-Woodland
Semievergreen
Shola
Non-Forest
Plantations
Administrative UnitsBoundary of IGWLSRange BoundaryOutside Sanctuary
Barren landGrasslandScrub
Water
Cinchona PlantationEucalyptus PlantationTea PlantationTeak Plantation
Figure 6.1.3. Forest Vegetation type and Land-Use map of Indira Gandhi Wildlife Sanctuary
:: 15 ::
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
:: 16 ::
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.
:: 17 ::
P1
P2
P3
P4
P7P8
P9
P10
P11
P12
P13
P14
P15
P16
P17
P18
P19
P20
P21
P22
P23 P24
P25
P26P27
P28
P29
P30P31
P32
P33
P34
P35
P36
P37
P38
P39
P40
P41P42
P43
P44P45P46
P47P48
P49P50
P51
P52
P53 P54
P55
P56
P57
P58
P59
P60
P61
P62
P63
P64
P66P67
P68
P69P70
P71
P72
P73
P74
P75P76P77
P78
P79P80
P81
P82P84P85
P86P89
P91
P92
P93
P94
P96
P97
P98
P100
P101
P102
P103
P105
P106
P107
P109
P110
P111
P112
P113
P114
P115P116
P117
P119 P120P121
P122P123P124P125P126
P127P128
P129
P130
P131P132
P133P134
P135
P136
P137P138
P139
P140
P141P142
P143
P145P146P147
P148P149
P150P151
P152
P153P154P155
P156
P157
P159P160
P161
P162
P163P164
P165
P167
P168
P169 P170P171
P172
P174
P175
P176
P178P179
P180
P181P182
P183
P184
P185P186
P188P189
P192
P193
P194P195P196
P197
P198
P199
P200
P201P202
P203
P204
P205
P206
P207P208
P209
P222P225
P227
P228
P229
P230
P231
P232
P233
P234
P235
P236
P237P238
P239
P240
P241
Elevatio
Slope
Precipit
Temperat
Axis 1
Axi
s 2
Evergreen communities
Montane shola forest communities
Semi-evergreen communities
Moist deciduous communities
Dry deciduous communities
Scrub forest communities
P1
P2
P3
P4
P7P8
P9
P10
P11
P12
P13
P14
P15
P16
P17
P18
P19
P20
P21
P22
P23 P24
P25
P26P27
P28
P29
P30P31
P32
P33
P34
P35
P36
P37
P38
P39
P40
P41P42
P43
P44P45P46
P47P48
P49P50
P51
P52
P53 P54
P55
P56
P57
P58
P59
P60
P61
P62
P63
P64
P66P67
P68
P69P70
P71
P72
P73
P74
P75P76P77
P78
P79P80
P81
P82P84P85
P86P89
P91
P92
P93
P94
P96
P97
P98
P100
P101
P102
P103
P105
P106
P107
P109
P110
P111
P112
P113
P114
P115P116
P117
P119 P120P121
P122P123P124P125P126
P127P128
P129
P130
P131P132
P133P134
P135
P136
P137P138
P139
P140
P141P142
P143
P145P146P147
P148P149
P150P151
P152
P153P154P155
P156
P157
P159P160
P161
P162
P163P164
P165
P167
P168
P169 P170P171
P172
P174
P175
P176
P178P179
P180
P181P182
P183
P184
P185P186
P188P189
P192
P193
P194P195P196
P197
P198
P199
P200
P201P202
P203
P204
P205
P206
P207P208
P209
P222P225
P227
P228
P229
P230
P231
P232
P233
P234
P235
P236
P237P238
P239
P240
P241
Elevatio
Slope
Precipit
Temperat
Axis 1
Axi
s 2
Evergreen communities
Montane shola forest communities
Semi-evergreen communities
Moist deciduous communities
Dry deciduous communities
Scrub forest communities
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 TemperatureElevation -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 0.48 0.186 1 -0.805
Temperature 0.715 -0.619 -0.131 -0.946 -0.436 -0.175 -0.805 1
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.
:: 18 ::
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 Values No. of Patches (NP) 2307 Patch Density (PD) 1.7/km² Largest Patch Index (LPI) 32.53% Interspersion and Juxtaposition (IJI) 50 Simpson Diversity Index (SIDI) 0.38 Simpson Evenness Index (SIEI) 0.42
Table 6.2.2 Class level metrics for landscape of TATR
Vegetation Types PLAND
(%) NP
PD
(No./100ha)MPS (ha)
LPI (%)
IJI (%)
Mixed Bamboo Forest (MBF) 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
Teak Mixed Bamboo Forest (TMB) 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
:: 19 ::
Fig. 6.2.1. Landuse/ Landcover Map of TATR
:: 20 ::
Fig.6.2.2. Canopy Density Map of TATR
:: 21 ::
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) 40.2(±4.3)
12.13(±1.2)
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)
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.
:: 22 ::
Fig 6.2.3. Habitat Suitability Map of Chital in TATR
Fig 6.2.4 Habitat Suitability Map of Sambar in TATR
:: 23 ::
Fig. 6.2.5 Habitat Suitability Map of Gaur in TATR
Fig 6.2.6 Habitat Suitability Map of Nilgai in TATR
:: 24 ::
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.
:: 25 ::
Table 6.2.4. Scores of marginality factors for all ungulates studied in TATR Species
EGVs Chital Sambar Gaur Nilgai Wild Pig Canopy<30% *** 0.448 0.183 0.234 0.581 0.412 Canopy40-60% 0.224 0.058 0.548 0.081 0.114 Canopy>60% ** 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.
:: 26 ::
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.
:: 27 ::
Fig. 6.3.1 Land Use/Land Cover of DNP Developed from IRS P-6 LISS IV at the Scale of 1:25,000
:: 28 ::
Fig. 6.3.2 Land Use/Land Cover of KAT Developed from IRS P-6 LISS IV at the Scale of 1:25,000
:: 29 ::
Fig 6.3.3 Land Use/Land Cover of KWS Developed from IRS P-6 LISS IV at the Scale of 1:25,000
:: 30 ::
ETM+ LISS III LISS IV
a
b
c
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
:: 31 ::
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%.
:: 32 ::
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
Fig. 6.3.6 - Land Cover Maps Derived from IRS 1D LISS III and IRS P-6 LISS IV
LISS III LISS IV
:: 33 ::
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
Moderately Dense Sal Terminalia alata Mixed
Deciduous Tropical Seasonal Swamp
Teak Plantation
Upland Grassland
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
Teak Plantation 32.6 10.0 4.0 0.8 500.9
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
:: 34 ::
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.
3-<33% (Unstable Area)
>33-66% (Moderately stable)
>66-100% (Stable Area)Jhadi taal
Segment ‘A’
Segment ‘B’
Segment ‘C’
3-<33% (Unstable Area)
>33-66% (Moderately stable)
>66-100% (Stable Area)
3-<33% (Unstable Area)
>33-66% (Moderately stable)
>66-100% (Stable Area)Jhadi taal
Segment ‘A’
Segment ‘B’
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/
:: 35 ::
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 Kaziranga National Park, Assam 6.4.1 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%), semi-
evergreen 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. semi-
evergreen forest was categorised into three canopy density classes viz., 10-
40% (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.
:: 36 ::
93°0'0"E
93°0'0"E
93°10'0"E
93°10'0"E
93°20'0"E
93°20'0"E
93°30'0"E
93°30'0"E
93°40'0"E
93°40'0"E
26°3
0'0"
N
26°3
0'0"
N
26°4
0'0"
N
26°4
0'0"
N
26°5
0'0"
N
26°5
0'0"
N.
Semi-evergreen 10-40%
Semi-evergreen >70 %
Tall grass
Short grass
Agriculture
Tea garden
Fallow land
Waterbody
River sand
River
Semi evergreen 40-70%
.
0 7 14 21 283.5Kilometers
Fig. 6.4.1: Forest / land cover map.
:: 37 ::
93°20'0"E
93°20'0"E
26°4
0'0"
N
26°4
0'0"
N
.
0 0.5 1 1.5 20.25Kilometers
Semi-evergreen 10-40%
Semi-evergreen >70 %Tall grassShort grassAgricultureTea gardenFallow landWaterbodyRiver sandRiver
Semi evergreen 40-70%
Fig. 6.4.2: Forest / land cover map (a part on 1:25,000 scale).
:: 38 ::
RiverTE1
TC4
TE2
TC15
TC20TE6
TW23
TC9 TE7
TE3
TW22
TW26
TC11
TBP28
TC19
TEC8
TC14
TCW21
TCW16
TC18TC13
TW24
TEC5
TCW17
TC12
TC10
TW25TBP27
93°0'0"E
93°0'0"E
93°10'0"E
93°10'0"E
93°20'0"E
93°20'0"E
93°30'0"E
93°30'0"E
93°40'0"E
93°40'0"E
26°3
0'0"
N
26°3
0'0"
N
26°4
0'0"
N
26°4
0'0"
N
26°5
0'0"
N
26°5
0'0"
N.
0 7 14 21 283.5Kilometers
Fig. 6.4.3: Tiger compartment map.
:: 39 ::
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Benga
ChigaDhuba
Donga
Sukani
Janata
Noloni
Amguri
Dusuti
Ajogor
Gobrai
Buloni
Tajeng
SoholaGorpal
DusutiMalani
Bimoli
Borghup
Panbari
Difaloo
Sundari
Laudubi
Alubari
Solmora
BokporaThungru
Kartika
Arimora
Arikati
MuamariMaklung
Erasuti
Rowmari
Gotonga
BorbeelDaflong
Deopani
Bhengrai
PanijuriTunikati
Baneswar
NaromoraBaghmari
Kerasing
Bokabeel
Jamuguri
Kohra RO
Mohkhuti
Dhanbari
DuramariTinibeelBahumari
Rajamari
Kathpara
Bahubeel
Nalamukh
Gerakati
Moriahola
Haldibari
Baruntika
Dhekiatol Tilaidubi
Bherbheri
Holalpath
Kholkholi
Naobhangi
Goroimari Lengtajan
HathiguriHatichora
Debeswari
Bornoloni
Lahorijan
Hatidandi
Amkathoni
Bagori RO
Panpurghat Chitalmari Kathonibari Agratoli RO
Rajapukhuri
Biswnathghat
Difaloomukh beat
93°0'0"E
93°0'0"E
93°10'0"E
93°10'0"E
93°20'0"E
93°20'0"E
93°30'0"E
93°30'0"E
93°40'0"E
93°40'0"E
26°3
0'0"
N
26°3
0'0"
N
26°4
0'0"
N
26°4
0'0"
N
26°5
0'0"
N
26°5
0'0"
N
Fig. 6.4.4: Forest protection camps
:: 40 ::
Table 6.4.1: Tiger compartments.
Compartment Area (km2) Area (%) River 551.52 55.53 TE1 18.91 1.90 TE2 16.90 1.70 TE3 13.77 1.39 TC4 18.54 1.87 TEC5 11.85 1.19 TE6 14.98 1.51 TE7 14.22 1.43 TEC8 16.45 1.66 TC9 14.67 1.48 TC10 8.21 0.83 TC11 16.15 1.63 TC12 10.00 1.01 TC13 13.26 1.34 TC14 15.34 1.54 TC15 20.89 2.10 TCW16 20.45 2.06 TCW17 14.07 1.42 TC18 14.33 1.44 TC19 15.84 1.60 TC20 20.21 2.03 TCW21 20.58 2.07 TW22 18.68 1.88 TW23 20.77 2.09 TW24 13.80 1.39 TW25 9.30 0.94 TW26 18.41 1.85 TBR27 10.57 1.06 TBR28 20.59 2.07 Total 993.27 100.00
:: 41 ::
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.
:: 42 ::
Fig. 6.5.2. Spatial distribution of various LULCs in CTR
:: 43 ::
Fig.6.5.3. Spatial distribution of mean bird group density in CTR.
:: 44 ::
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.
:: 45 ::
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.
****