Photonirvachak Journal of the Indian Society of Remote Sensing, Vol. 33, No. 2, 2005

HABITAT EVALUATION FOR SARUS CRANE IN THE K E O L A D E O NATIONAL PARK USING IRS LISS III AND

PAN MERGED DATA AND GIS

SARVESH PALRIA@, AKANKSHA S1NGH*, J.R. SHARMA ~176 AND SUPARN PATHAK** "Department of Remote Sensing,

Maharshi Dayanand Saraswati University, Ajmer-305 009, India "*Regional Remote Sensing Service Centre, CAZRI Campus, Jodhpur-342 003, India

@Corresponding author : spalria@rediffmail.com

ABSTRACT

The Keoladeo National Park, Bharatpur, a man-made fresh water wetland carved out of a natural depression on the floodplain of two minor tributaries of the Yamuna-Gambhir and the Banganga is the country's finest waterfowl habitat. This important wetland was set aside as a bird sanctuary in 1956 and it was elevated to the status of a National Park in 1981. It was also designated a Ramsar site- a wetland of international importance under the Ramsar convention. This important wetland has distinction of being the only Indian wetland to be included under both the Ramsar and the World Heritage convention.

The attempt has been made to evaluate the habitat of Sarus crane in the Keoladeo National Park using satellite data - IRS LISS III and PAN merged product and GIS. Geocoded data of IRS - IC LISS III of 21 March 1999 on 1:50,000 scale and PAN data of March 17, 1999 were used to generate the vegetation cover type map and open water. The maps showing drainage, human habitations, contours, roads, etc. were prepared using the Survey of India topographical sheets and contour map of park area. Information regarding habitat parameters was collected from the existing literature and field observations. The Sarus crane mainly fed in the wetland on the rhizome of Nymphaea sp., Scirpus tuberosus and Eleocharis plantaginea. As there were changes in their habitat requirements at different seasons, the sighting of Sarus crane in each habitat were recorded along with the time and activity during observation. The most utilized habitat for the entire period of study was moderately wet grassland followed by pools. The pools were used mainly during the summer. The water depth requirement observed was between 30-40 cm and 20-40 cm. The suitability maps for Sarus crane were then generated using all remote sensing based and conventional information using rule based equations in the GIS within the Keoladeo National Park.

Presented at ISRS National Symposium on "Advances in Remote Sensing Technology with ,Special Emphasis on High Resolution Imagery", December 10-11, 2001, Ahrnedabad.

Received in final form 15 July, 2004

260 Sarvesh Palria et al.

Introduction

The Keoladeo National Park (KNP) is a wetland of international importance as a waterfowl habitat. Recently it has been listed as a "World heritage site". The Sarus crane (Grus ant igon ant igone) is a residential bird in the park, is a habitat-specific and selective feeder. Its habitat in KNP has been adversely affected by the proliferation of the grassy weed Paspa lum dist ichum. Further, increased agricultural and domestic water requirements have caused a reduction in the flow of the water to the park. The KNP wetland has thus suffered from human intervention and mismanagement. Habitat loss is one of the reasons for the extinction of many bird species. Therefore, the study of the birds in relation to their habitats would give valuable information for effective park management.

Habitat evaluation by developing remote sensing based quantitative models has been carried out by Roller (1978); Roller and Colwell (1978) and Pettinger et al. (1978). Roller's study developed to construct computer based habitat quality rating model for the quantitative evaluation of deer habitat can be useful for the study of other wildlife species in other geographical areas. The model developed by Pettinger et al. (1978) is based upon the Habitat Evaluation Procedure (HEP). The grid cells were used as field sampling plots. The specific characteristics (like food and cover) were measured at each sampling plot. Thus, a Habitat suitability Index Score (HIS) was obtained from the graphical parameter- HIS relationships for it.

The use of satellite Remote Sensing and GIS is a quick way to generate habitat suitability maps. Thematic Mapper TM data was used for mapping of food availability for Whitetail deer (Ormsby and Lunette, 1987). In recent years models for habitat evaluation were developed using the satellite data derived from information on land cover types and their spatial distribution (Mead et al., 1981; Lyon, 1983; Ormsby et aL, 1985). Using remote sensing data habitat assessment of Kaziranga National Park

was done by Parihar et al. (1989). A case study of Nal Sarovar (Guj arat) for management of waterfowl habitat and wetland monitoring has been carried out. (Tatu et al., 1999). Roller, (1978), Roller and Colwell (1978); Pettinger et al. (1978) have carried out habitat evaluation by developing remote sensing based quantitative Models. However, Roller's study did not pertain to the wetland or waterfowl. But the techniques developed by him to construct computer based habitat quanti ty rating Model for the quantitative evaluation of deer habitat can be useful for the study of other species and geographical areas. The habitat assessment model of Roller (1978) and Roller and Colwell (1978) quantified in terms of rate (rating) while our habitat assessment model quantify wetland waterfowl habitat in terms of'Score' (Habitat Suitability Score). Further, unlike the model developed by Roller (1978), habitat availability has been given maximum importance by keeping it in multiplicative relationship with Interspersion, Juxtaposition and Abundance (extent) of habitat.In the present study, habitat evaluation for Sarus Crane in the KNP area has been under taken using remote sensing and GIS technology.

Study Area and Methodology

The Keolodeo National Park (KNP) is situated between 27 ~ 7' 06" to 27 ~ 12' 02" N latitude and 77 ~ 29' 05" to 77 ~ 33' 09" E longitude. It is 2 km south east of Bharatpur City and 50 km West of Agra. There are about 14 villages around the park. The total area of the KNP is about 29 km z, out of which wetland covered 8.5 km z.

The Satellite data oflRS-1C LISS II, Path/Raw 96/52, date of pass March 21, 1999 and IRS PAN data Path/Raw 96/52, D 1 for date of pass March 17, ! 999 of the study area were used. The Survey ot" India (SOI) toposheet (54E/8, 54E/12) at 1:50,000 scale were used. Bimonthly field data were collected of Sarus crane habitat from the KNE The Vegetation Type Map ofKNP, Bharatpur at 1:9000 scale and Contour Map at 1:15,000 scale were used.

Habitat Evaluation for Sarus Crane in... 26 !

Population of Sarus crane was estimated by simultaneously counting the birds from various blocks using the dykes as transacts. Regular monthly data of the number of individuals and habitat, in which they were sighted, were done following Weller (1975).

The analysis was performed at RRSSC, Jodhpur on IBM-RS 6000 37T workstation using image processing S/W EASI/PACE 6.3 version and ARC/ INFO 8.0 GIS software. Supervised and unsupervised classification techniques were used for vegetation type classif icat ion and wetland area. For the nomenclature of vegetation classes and for finding the relative importance of diversity of vegetation, Bombay Natural History Society (BNHS) Map for wetland was used.

The study of waterfowl needed a parameter for water depth for which 1 meter contour interval data pertaining to park area was digitised and interpolation was performed. The water depth was categorised based on requirement, liking and observed depth. Further, shelter and food habits of Sarus Crane were taken in to account.

Sarus Crane Habitat assessment models

Digital Elevation Model (DEM) was generated using the interpolation of exiting 1-meter contour intervals and spot elevation data with in park area through TOPOGRID command in GIS. A 100 m x 100 m grid was prepared on entire park area and the wetland boundary. The analysis was performed for waterfowl habitat assessment on grid cell basis. Each complete cell of grid represents 100 m x 100 m Main Sampling Site (MSS) and incomplete cell on either side (< 100 m) are referred to Marginal Grid Sampling Site (MRSS).

Habitat Availability Code (HAC) was generated based on Weighted area under each grid cell. These were ranked from 0 to 5 based on percentage area (Table 1). Preferred Habitat Availability (HA) in grid cell of the wetland was determined from following equation:

HA = (100\N) n (1)

Where, N = Total number of preferred habitats within

cell. n = Number of preferred habitats available per

unit area of the wetland.

Table 1: Habitat availability code

Percentage area in Ranking each grid cell

0 to 25 0

25 to 50 1

50 to70 2

70 to 80 3

80 to 90 4

90 to 100 5

Habitat Quality Code (HQC): The quality of the habi ta t has been cons ide red to determine Interspersion, Abundance and Juxtaposition by the following method:

HQC = RE x H + AE + RE x E (2)

Where,

HQC = Habitat Quality Code

Weighted area of the available preferred habitat in sampling unit

RE x H =. Area of sampling unit

AE= Weighted number of edge of available preferred habitat in sampling Unit

Weighted perimeter of the available preferred habitat in sampling unit

RE xE = Total extent or perimeter for the sampling unit

The Interspersion, i.e. the complexity of habitat has been measured by counting number of edges of different habitat present in each grid. AE is measure of spatial intermixing of habitats in non-specific

262 Sarvesh Palria et al.

manner. The Juxtaposition, i.e. the accessibility of habitat has been measured by measuring the length (extent) of respective edges with in each cell (RE x H). Both, extent of habitat and extent of edge are determined relative to the extent of the grid (100 m x 100 m) and extent of the perimeter of the sampling plot. Thus the final number available is an absolute number i.e. 'score'.

Habitat Suitability Model (HSM) was formulated by combining vegetation cover, terrain, proximity disturbance and preferred water depth with field setting. Final Habitat Suitability Model was generated keeping in mind the above concentration and the following formula was used:

HS = K (HA x HQC) 0)

Where,

HS

K

K

= Habitat Suitability

= Suitability constant

HA =

HQC=

lor 0, if water exists or does not exits in the wetland's area, at least in one MRSS or one MSS

Equivalent score of percent availability.

Habitat Quality Code.

The habitat assessment models of Roller (1978) and Roller and Colwell (1978), quantifies in terms of 'rate' (rating). Our habitat assessment models quantify wetland as waterfowl habitat in terms of 'Score' (Habitat Suitability Score). Habitat Suitability Score can be defined as the number derived for and assigned to each grid (sampling site) of wetland area. This reflects the suitability of the unit for fulfilling the habitat requisites (like feeding, resting and roosting) of waterfowl. The model developed by Roller (1978) on habitat availability has been given maximum importance by keeping it in multiplicative relationship with extent of habitat (Abundance), interspersion of habitat and juxtaposition of habitat.

R e s u l t s a n d D i s c u s s i o n

The field observations on population and distribution of Sarus crane were carried out. The results are compiled and tabulated block-wise (Table 2). The variability observed in the number of species and number of individuals within population resulted from differences in basic habitat elements like food cover, water, shelter and other qualities of the habitat. The population and distribution of Sarus Crane revealed its presence in blocks D, E, F, K and L but it was less in numbers in blocks F and L because of lack of water in these blocks.

Habitat requirements of Sarus crane for feeding, resting, roosting and breeding had been determined by making monthly field observation from July 1999 to June 2001. Ali and Ripley's (1983) work was referred for assigning the weightages. The weightage values of different habitat classes are presented in Table 3.

Satellite remote sensing has been used for wetland classification with reliable accuracy through digital techniques. The digital Landcover mapping of wetland consisted of six vegetation categories. The area under each cover type is given in Table 3.

The total suitability area of Sarus crane was calculated to be 772.04 ha. The ratings for each aquatic vegetation cover, terrain, proximity to water sources, proximity to disturbance (road, tourism and villagers), terrain and proximity to weeds as food and other values were carried out on the basis of bimonthly field observation and are tablulated.

The sources and their corresponding GIS layers of various habitat parameters of Sarus crane are given in Table 4. Habitat suitability score for the Sarus crane was determined on the criteria of Interspersion, Juxtaposition and Abundance (extent) of the available habitats. These were ft/rther grouped together on the basis of equali ty of habitat availability to form habitat suitability classes

Habitat Evaluation for Sarus Crane in... 263

Table 2: Population and Distribution o f Sarus Crane (Grus antigon antigone) in the Keoladeo National Park wetland block

* data n o t avai lab le

Year Month of study

July

August

September

October

November

1999-2000 December

January

February

March *

April

May

June

July

August

September

October

November 2

2000-200 ! December *

January

February

March

April

May

June

B D

3

3

4

10

45

39

7

7

9

10

56

3

4

5

8

25

17

Wetland Block

E F K L N

2 4 4 2 11 26

4 4

4 4 3 10 24

4 5 2 11

3 3 4 14

15 4 7 36

3 7 6 4 65

3 4 2 - 48

15 4 4 2 20 52

12 4 4 2 10 39

2 4 4 2 10 22

7 8 4 2 30

4 2 4 6 26

7 4 2 6 77

4 3 10

3 3 10

3 3 11

11 3 22

I0 4 39

7 3 2 29

Total

considering that the availabil i ty o f the habitat in a

unit area is the most impor tan t parameter . The

c lasses shown (F ig . l a , b , c ) for I n t e r spe r s ion ,

Juxtaposition and Abundance (extent) are based on

quartile classification o f these values. The number

of class 1-12 are g roup ing o f quint i le form for

presentat ion purpose. DEM and Vegetat ion type

map of park using IRS LISS lII dated March 21, 1999

are shown in Fig. 2 and 3 respectively. The results

o f the study indicate that large portion o f the KNP

wetland 541.80 ha was found to be suitable for Sarus

crane. Based on the obse rva t i ons , four habi ta t

suitability score classes were demarcated in to 1-4

(0-25% c l a s s - l , 25-50% c l a s s - 2 , 5 0 - 7 5 % class-3

and 75-100% class--4). The level o f preference for

class 1 comes to not prefer red , for c lass 2 low

264 Sarvesh Palria et al.

Table 3: Habitat classes, use and their area

Habitat classes Weightage Area Habitat for use value - in ha.

Open water (pools) 0.35 43.40 Mainly used in summer for flocking, breeding and feeding also.

Forest 0.00 150.93 They do not prefer.

Aquatic vegetation 0.15 224.72 They preferred rhizomes of Nymphoea spp., Scirpus tuberosis" Eleocharis spp. for feeding.

Moderately wet grassland 0.04 202.40 Primarily provide food, roosting and resting to the crane.

Flooded grassland 0.04 134.81 Provide feeding.

Grass removed area and barren dry soil 0.01 16.12 Providing grass grains for feeding.

Table 4: Sources, GIS layers and habitat parameters used for analysis

Sources GIS Layer Sarus crane habitat parameters

Remotely sensed data Wetland classification Food, shelter, feeding and breeding area

Contour map Digital elevation model Elevation

& Buffer Zone Disturbance

Survey of India toposheets Drainage Water

BNHS Maps Vegetation classification Food, shelter and feeding area

Fig. la. Interspersion Fig. lb. Juxtaposition Fig. lc. Abundance

Habitat Evaluation for Sarus Crane in... 265

Fig. 2. Digital Elevation Model (DEM)

preferred, class 3 moderately preferred and for class 4 highly preferred for the Sarus crane. It was found that out of the total preferred area of 541.80 ha, 186.43 ha low preferred, 169.38 moderately preferred and 185.99 ha highly preferred for Sarus Crane in the park.

It should be noted that waterfowl habitat assessment Modelling developed here for Sarus crane may hold true for other waterfowl species requiring similar habitats in the KNP wetland. However, some modifications would be required according to the waterfowl's life requisites.

Acknowledgement

The work was carried out und/~r project "Remote sensing for the Keoladeo National Park Wetland" sanctioned by the Ministry of Environment and Forests, New Delhi. Mr. Akanksha Singh is thankful to the Ministry of Environment and Forests, New

Fig. 3. Vegetation types

Delhi for providing fellowship to carry out this research work.

References

Ali, S. and Ripley, S.D. (1983). A Pictorial Guide to the Birds of Indian Subcontinent, Oxford University Press, Delhi.

Lyon, J.G. (1983). Landsat-derived land cover classification for locating potential kestrel nesting habitat. Photogramm. Engg. and Remote Sensing. 49(2): 245-250.

Mead, R.A., Sharik,-T.L., Prisley, S.P. and Heinen, J.T. (1981). A computerised spatial analysis system for assessing wildlife habitat from vegetation maps. Can. J. Remote Sensing. 7(1): 34-40.

Ormsby, J.P., Geroin, J.C., Lunetta, R.S. and Nickeson, J.E. (1985). Habitat evaluation and landcover analysis using Landsat-4. TM data. Proc. 19th International Syrup. Remote Sensing of Environ. ERIM, Ana Arbor, Michigan. pp. 415-421.

266 Sarvesh Palria et al.

Ormsby, J.P. and Lunetta, R.S. (1987). Whitetail deer food availability maps from Thematic Mapper data. Potogramm. Engg. and Remote Sensing. 53(8): 1081-1085.

Parihar, J.S. (1989). Wildlife Habitat Studies Using Remotly Sensed data. In : Perspectives in Ecology. (Eds.: LS. Singh and B. Gopal). Jagmander Book Agency, New Delhi, pp. 335-351.

Pettinger, L.R., Farmer, A. and Schamerger, M. (1978). Quantitative Wildlife Habitat Evaluation using High- altitude Colour infrared Aerial Photographs. In: PECORA IV. Proc. of the Symposium on Application of Remote Sensing data to Wildlife Management, National Wildlife Federation, South Dakota.

Roller, N.E.G. and Colwell, J.E. (1978). Wetland Inventory and Condition Evaluation Technique. In :

PECORA IV, Proc. of the Symposium on Application of Remote Sensing Data to Wildlife Management, National Wildlife Federation, South Dakota, pp. 227-234.

Roller, N.E.G. (1978). Quantitative Evaluation of Deer Habitat, Ibid. pp. 137-146.

Tatu, K., Parihar, J.S. and Kimothi, M.M. (1999). In: Remote Sensing for wetland monitoring and water fowl habitat management - A case study of Nal sarovar (Gujarat). APH Publishing Corporation, New Delhi, pp. 48-50. ISBN 81-7648-124-6.

Weller, MW. (1975). Management of freshwater marshes for Wildlife. In: Waterfowl Ecology and Management, Selected Readings. (Eds. J.T. Ratti, L.D. Flake and W.A. Wentz), The Wildlife Society. Inc., Bethesda, Maryland, pp. 942-954.