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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


Corbett Tiger Reserve, Uttarakhand

22nd Nov, 2004 1st Sep, 2006

5. Shri Athar Noor



1st Oct, 2006 30th Sep, 2007

6. Shri Pebam Rocky


Kaziranga National Park, Assam

6th Dec, 2004 9th Sep, 2005

7. Shri Mohit Kalra



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


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


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


Indira Gandhi Wildlife Sanctuary, Tamilnadu

Corbett Tiger Reserve, Uttarakhandl

Dudhawa Tiger Reserve, Uttar Pradesh

Tadoba-Andhari Tiger Reserve, Maharastra


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







IRS P6 LISS-IV

Ground Truth







Maps









IRS P6 LISS-IV

Ground Truth







Maps









IRS P6 LISS-IV

Ground Truth







Maps



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




Funding Agency


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




Funding Agency


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’



>66-100% (Stable Area)



>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.

****

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