GISdevelopment.net ---> Application ---> Environment

Geoinformatics for wildlife habitat characterisation


S P S Kushwaha
S. P. S. Kushwaha
Head, Forestry & Ecology Division IIRS, Dehradun
spskushwaha@yahoo.com



Abstract
The application of geoinformatics for wildlife habitat characterisation, evaluation and management is relatively younger discipline. Several studies have been carried out on utility of geoinformatics in gathering information on physical parameters of the wildlife habitat as well as analysis of spatial data through geospatial modeling. The results indicate profound advantage of these technologies over traditional techniques in terms of accuracy of the information, timeliness and graphic nature of the data. Over time this has largely improved evaluation significantly. Besides facilitating geospatial data integration, the GIS also has proved to be a highly valuable tool in alternate scenarios creation, before selecting the best suited one for a particular wildlife area. On cost-coverage analysis, these techniques have been found to be cost and time-effective. This paper discusses developments on use of remote sensing and GIS in wildlife habitat evaluation and management.

Introduction
A natural environment is self-renewing, self-perpetuating and stable one, in which every organism contributes in some way, howsoever, small to the overall stability. In natural ecosystems, the plants and animals have evolved at their own pace and in their own way under the influence of natural selection to fit in the constellation of certain environmental factors or niches. In the process, they help to sustain others, each species controlling its own population growth and at the same time limiting of other species, so that a reasonable ecological balance may be achieved and maintained for hundreds of years (Bhargava et al., 1988).

The term wildlife applies to all biotic elements that comprise every species of plant and animal in the world, excluding man and his domesticated pets. But in practice, the term has become a fashionable coin giving value to a certain limited number of species of spectacular animals that enjoy public recognition (Chakraborti, 1990). India has a rich heritage of wildlife as well as a long history and tradition of the conservation. Human beings and wildlife co-existed in nature as long as man was not a dominant organism. Wildlife all over the world is vanishing rapidly presently due to growing influence of humans. The pressure of modernisation, along with an unprecedented growth of the human population and commercial exploitation have been the prime causes for the decline of wildlife in almost all the countries. Man has ruthlessly destroyed nature in search of food, water and shelter. As a result, the formerly secure wildlife habitats have been depleted and fragmented. This has caused wild animals to enter into human habitations and destroy crops in search of food. Damage to property, dwellings and human life is not uncommon. This leads the rural people to ponder whether it is reasonable to conserve wildlife. In reality, it is extremely necessary to have a peaceful co-existence with nature and natural life for the development and progress of human beings. The destruction of any form of life affects the human race itself. We must not reduce the earth into a dreary wilderness. During the past centuries, man has never walked such a path of self-destruction as in the latter half of the 20th century. The situation worsened during the 1950s and 1960s, which witnessed depletion of the country’s biological heritage on a massive scale.

Table 1. Wildlife sanctuaries and national parks in India
Station/Union Territory  Sanctuaries  National Parks
Total No. Area (km2) Total No. Area (km2)
A & N Islands 94 455.56 8 1153.34
Andhra Pradesh 21 11832.54 4 372.23
Arunachal Pradesh 9 6177.45 2 2468.23
Assam 8 990.58 2 930.00
Bihar 19 3881.75 2 567.32
Goa 4 355.78 1 107.00
Gujarat 21 16970.16 4 479.67
Haryana 10 342.65 1 1.43
Himachal Pradesh 30 4702.87 2 1295.0
Jammu & Kashmir 15 10157.67 4 3900.07
Karnataka 20 4238.21 5 2471.98
Kerala 12 2143.36 3 536.52
Madhya Pradesh 32 10567.05 11 6485.72
Maharashtra 25 13995.49 5 958.45
Manipur 1 184.85 2 81.00
Meghalaya 3 34.21 2 267.48
Mizoram 3 560.00 2 250.00
Nagaland 3 24.41 1 202.02
Orissa 18 6214.96 2 1212.70
Punjab 6 294.82 0 -
Rajasthan 22 5662.87 4 3856.53
Sikkim 4 92.1 1 850.00
Tamil Nadu 17 2671.03 5 401.63
Tripura 4 603.62 0 -
Uttar Pradesh 29 8107.52 7 5429.83
West Bengal 15 1055.55 5 1692.65
Daman & Diu 1 2.18 - -
Delhi 1 13.20 - -
Chandigarh 1 25.42 - -
Dadra & Nagar Haveli - - - -
Lakshadweep - - - -
Pondicherry - - - -
Total 448 112,357.86 85 36,171.60


Modern civilisation, by clearing the forests for settlements, agricultural activities and communication purposes, and by setting up large scale hydroelectric projects and industries has done irreparable damage to the environment that it makes any attempt to salvage the diversity of wildlife a difficult undertaking. Way back in March 1980 five international organisations viz., International Union of Conservation of Nature and Natural Resources (IUCN), United Nations Environment Programme (UNEP), Worldwide Fund for Nature (WWF), Food and Agriculture Organisation (FAO) and United Nations Educational, Scientific and Cultural Organisation (UNESCO) had come out with combined “World Conservation Strategy” document to minimize the destruction of world’s wildlife. All these efforts express concern of the people to dwindling wildlife resources.

Table 2: Biosphere reserves in India
Biogeographic
Region
Name of the Biosphere Reserve
& State/Union Territory
Area
(km2)
Date of
set up
Western Himalaya Nanda Devi (Uttaranchal) 2236.74 18.1088
North East India Nokrek (Meghalaya)
Manas (Assam)
Dibru Saikhowa (Assam)
Dihang-Dibang (Arunachal Pradesh)
80.00
600.00
765.00
01.9.88
14.3.89
28.7.97
Gangetic Plains Sunderbans (West Bengal) 9630.00 29.3.89
Coastal Gulf of Mannar (Tamil nadu) 10500.00 18.2.89
Western Ghats Nilgiri (Karnataka, Kerala & Tamilnadu) 5520.00 01.8.86
Islands Great Nicobar (A. & N. Islands) 885.00 06.1.89
Deccan Peninsula Simlipal (Orissa) 2750.00 21.6.94


Wildlife management scenario in India
It was only in the early 1970s that action was taken to arrest the declining trend and that concern for nature conservation has been reflected to a certain extent in the planning and development processes. The enactment of Wildlife (Protection) Act 1972 and subsequently Forest (Conservation) Act 1988; the inclusion of the subject in the Concurrent List of the Constitution; the enlargement of the network of National Parks and Sanctuaries; the launch of Project Tiger in 1973; the Crocodile Breeding Project in 1975, Project Elephant in early 1991, Project Hangul in 1970, Manipur Brow-antlered Deer Conservation Project in 1973 and other schemes for the protection of species and their habitats; the regulation of wildlife trade and commerce; the strengthening of education and training facilities, culminating in the establishment of Wildlife Institute of India; and the efforts to create a general awareness for nature conservation, are all important initiatives taken in recent years in this direction. To the prevailing and future challenges, there is an urgent need for a long-term strategy based on three specific objectives of living resources conservation:
  • To maintain essential ecological processes and life support systems,
  • Preserve genetic diversity, and
  • Ensure the sustainable utilisation of species and ecosystems.
These concerns led to the adoption of the National Wildlife Action Plan by the Indian Board of Wildlife under the Chairmanship of the Prime Minister. It is apt to recall the address made by late Prime Minister, Indira Gandhi at the Plenary Session of the United Nations Conference on Human Environment at Stockholm on June 14, 1972:


Fig 1. Paradigm of this study

Table 3 Wetlands of India
Wetland State Area in ha
*Chilka Orissa 114,000
*Harike Punjab 4,100
*Keoladeo Ghana Rajasthan 2,873
*Loktak Manipur 27,600
*Sambhar Rajasthan 7,200
*Wular Jammu & Kashmir 18,900
Ashtamudi Kerala 3,200
Bhoj Madhya Pradesh 33,000
Kabar Bihar 6,738
Kanji Punjab 3,000
Kolleru Andhra Pradesh 90,000
Nalsarovar Gujarat 18,400
Pichola Rajasthan 1,000
Renuka Himachal Pradesh 670
Sasthamkotta Kerala 375
Sukhana Chandigarh 170
Ujni Maharashtra 35,700


“It is said in country after country that progress should become synonymous with an assault on nature. We who are part of nature and dependant on her for every need, speak constantly about “exploiting” nature …”.

Broadly, the National Wildlife Action Plan is a prospectus of action to be taken with regard to wildlife conservation in India and its main components are: (i) establishment of a representative network of protected areas, (ii) management of protected areas and habitat restoration, (iii) wildlife protection in multiple use areas, (iv) rehabilitation of endangered and threatened species, (v) captive breeding programme, (vi) wildlife education and interpretation, (vii) research and monitoring, (viii) domestic legislation and international conventions, (ix) national conservation strategy and (x) collaboration with voluntary bodies in the conservation effort.

Nature has endowed India with such abundant and varied flora and fauna that it compares favourably with that of any other region in the world. A scientific assessment of all the endangered and threatened species of wild fauna and flora on countrywide basis has not been carried out in India so far. However, a total of 253 species and sub-species of wild fauna (mammals, avies, reptiles, amphibians and invertebrates) have been included in Schedule I of the Wildlife (Protection) Act 1972 in order to afford total protection to these species. As regards flora, the subject has been under intensive study for the last few years and about 2000 species of flowering plants are reported to suffer from some kind of threat. An inventory of 135 threatened species and sub-species of rare and endangered plants has been prepared by Botanical Survey of India. Further scientific studies and explorations are desirable in this respect.

Although the first wildlife sanctuary was established in India at the turn of century, the progress up to 1970s was not substantial. By 1975, there were only 5 national parks and 126 sanctuaries, whose total area was only 25,000 sq.km. By 1983, the number of national parks was increased to 19 and sanctuaries to 210. Today we have a total of 85 national parks and 448 sanctuaries covering approximately 4.5 per cent of the total geographical area of the country. This works out to be 21.8 per cent of the total forest area in India (Table 4). In addition to that 12 large areas have been declared as biosphere reserves among national parks and sanctuaries. (Table 2) Five national parks have been declared as World Heritage Sites. India is also very richly endowed with a large number of wetlands of national and international importance (total number–17), of which 6 viz., Chilka, Harike, Keoladeo, Loktak, Sambar and Wular happen to be Ramsar sites. (Table 3) Project Tiger areas include 25 tiger reserves in 4 states of India (Anon., 1999). (Table 4) The Wildlife Protection Act, 1972 has been revised recently and a new notification is awaited in near future.

Role of remote sensing and GIS in wildlife characterisation and management
Wildlife management is much more than the preservation of certain plant and animal species; it involves management of a complete ecosystem (De Wulf et al., 1988). Quantification and analysis of current impacts on wildlife habitat such as logging agriculture, road development etc. are vital phases in the process of formulating sound wildlife management policies. Until recently many conventional techniques have been applied for collecting data on natural resources. Relatively large number of ground-based studies have been carried out on habitat and corridor use by the wild animals (Johnsingh and Joshua, 1994; Mishra and Johnsingh et al., 1990; Johnsingh, 1991 & 1992; Bhat and Rawat, 1995; Rodgers, 1990; Hobaugh, 1984 etc.) The role of remote sensing has been emphasised in quick appraisal of habitat attributes, identification of new sites for protected areas and current status of corridors (Panwar, 1986; Kamat, 1986). Ground survey methods such as counting animals, trapping, collection of droppings, investigations of feeding sites as well as ground mapping of habitats (Lamprey, 1963; Panwar, 1972; Giles, 1978; Kotwal and Parihar, 1988 etc.) will always be useful. However, in a number of cases other techniques can supplement or partially replace tedious ground survey methods. Moreover, it is felt that ground methods have limitations as whole area can not be accessed in one go in many of the cases and the information collected may not be as accurate as is possible through remote sensing aided by limited ground survey.

Table 4 Tiger reserves in India
Name State Area (km2)
Bandipur Karnataka 866.00
Corbett Uttaranchal 1316.00
Kanha Madhya Pradesh 1945.00
Manas Assam 2840.00
Melghat Maharashtra 1597.00
Palamau Bihar 1026.00
Ranthambhore Rajasthan 1334.00
Simlipal Orissa 2750.00
Sundarbans West Bengal 2585.00
Periyar Kerala 777.00
Sariska Rajasthan 866.00
Buxa West Bengal 759.00
Indiravati Madhya Pradesh 2799.00
Nagarjunasagar Andhra Pradesh 3568.00
Namdapha Arunachal Pradesh 1985.00
Dudhwa Uttar Pradesh 811.00
Kalakad Mundanthurai Tamil Nadu 800.00
Valmiki Bihar 840.00
Pench Madhya Pradesh 758.00
Dampa Mizoram 500.00
Panna Madhya Pradesh 542.00
Bandhavgarh Madhya Pradesh 1162.00
Taroba Maharashtra 620.00
Total 33046.00


Remote sensing, which provides spatial data, is a less used but is powerful tool to acquire accurate, up-to-date information essential for wildlife management programmes (De Wulf et al., 1988; Kushwaha et al., 2000). At present most wildlife biologists have very limited knowledge of remote sensing. Wildlife managers have been using topographic maps to generate management and other maps of their interest (Leopold, 1933; Mosby, 1971). Although technically complex, the remote sensing techniques have revolutionalised the process of data gathering and map making. Remote sensing can be applied to wildlife habitat inventory, evaluation and wildlife census. Wildlife habitat mapping is similar to any type of land cover mapping (Lindgren, 1985). Both biotic and a biotic surface features including vegetation species composition and/or density and local landforms can be mapped interspersion of habitat components, the extent of habitat types and the distance to other critical habitat components can be measured (Best, 1984). The repetitive NOAA, IKONOS and SPOT add a temporal dimension to habitat mapping and change detection.

Adams and Gentle (1978) used digitised aerial photos to monitor changes in waterfowl habitat over a 10-year period in the Manitoba parklands. Nichol (1975) concluded that variations in photographic density on true colour transparencies correlated well with intra-habitat parameters. Digital analysis of remote sensed data has been used for habitat assessment of elk, Cervus canadensis (Bright, 1984), reindeer, Rangiter tarandus (George et al., 1977) and kangaroo, Macropus giganteus (Hill and Kelly, 1987). By using a nesting habitat model in combination with Landsat digital image classification, Lyon (1983) was able to predict satisfactorily the nesting sites of Americal Kestrel, Falco sparverius. Visual Landsat image interpretation was an effective tool in the re-introduction programme of the white oryx, Oryx leucoryx (Harris, 1983). Wiersema (1983) studied ibex habitat using Landsat data and concluded that remote sensing data contributes in better understanding of environmental patterns and processes.

In India, Roy et al. (1995), Porwal et al. (1996) and Kushwaha et al. (2000 & 2001) have used remote sensing and geospatial modeling to evaluate the habitats for one-horned Indian rhino, Rhinoceros unicornis sambar, Cervus unicolor, and goral Nemorhaedus goral in Kaziranga, Kanha and Rajaji National Parks respectively. Anjana Pant et al. (1999) analysed the habitat of sambar in Corbett National Park using these techniques. By now it is established that use of satellite remote sensing and GIS is a time and cost-effective tool for habitat evaluation (Parihar et al., 1986; Prasad et al., 1994; Kushwaha and Madhavan Unni, 1986). Roy et al. (1986) analysed vegetation types for wildlife habitat evaluation using remote sensing.

Remote sensing in wildlife census
A classical wildlife census is commonly made by means of mark - recapture techniques, track counts, pellet group counts, audible indices, hunter kill data and actual field sightings. Trained observers in low-flying aircrafts are usually able to make very accurate estimates of wildlife population in open areas. With the exception of thermal scanning (Burkhalter and Kientz, 1984), direct animal census with remote sensing techniques is only possible for diurnal species living in open areas. In a number of cases, aerial photography has proved to be an efficient and cheap alternative to field observations. Ericson et al. (1983) discussed the use of aerial photographs for censusing Sandhill cranes, Grus Canadensis.

Ferguson (1981) concluded that aerial photography on scale 1:2000 was cheaper than ground observation methods for determining the sex ratio among mallard (Anas platyrhynchos). Wyatt et al., (1984) reported on the use of an airborne multispectral linear array scanner operating in the visible and near infrared wavelength for detecting deer. Fossorial mammals can be detected by existence of mounds on the earth’s surface. Aerial photography has been used successfully to detect pocket goper, Thomomys sp., and prairie dog colonies, Cynomys sp. (Best, 1984). The use of satellite data is limited by sensor resolution, but Loeffer and Margules (1980) were able to detect warrens of hairy-nosed wombats, Lasiorhinus latifrons on Landsat images.

Low level aerial survey using light aircraft appears to have potential for mapping distribution, estimating population size and in limited cases in determining population composition for Indian species living in habitats with adequate visibility for a least part of the year. Basking gharials on river banks, elephants and gaurs in deciduous forests such as Mudumalai or rhinos in Kaziranga swamps could be seen from air easily. Already these animals are censused from the air in Africa. The conditions for aerial census of short grass, semi-desert species such as Indian wild ass, Nilgai or black buck populations in Gujarat and Rajasthan are distinctly better than those in which a number of similar sized ungulates are censused from low flying aircraft elsewhere (Sale, 1986). Thus animals with distinctive sexual dimorphism can be reliably enumerated from the air (Leuthold, 1976). Radio collaring is being increasingly used as convenient way of locating animals. Tracking of animals, fitted with radio collar, may either be hand-held, fitted to a vehicle, an aircraft or a spacecraft depending upon the design and capabilities of the particular system. Tracking using aerial and space platforms has been found to be better than hand-held receivers (Leuthold and Sale, 1973).

Wildlife habitat modeling
Habitat evaluation based on ecological science has been well researched in U.S.A., especially in connection with environmental impact assessments, where aim has been to ensure that appropriate consideration is given to wildlife in the decision making process. At the same time, there has been considerable pressure for the use of standardised procedures for habitat evaluation, both for cost-effective reasons and for ease of communication of data both between and within organisations and professionals. This pressure for standardisation of inventories and evaluations was one of the reasons why the Habitat Evaluation Procedure (HEP) was developed (initially by the U.S. Fish and Wildlife Service) for use in the evaluation of water and related land resource development projects (U.S. Fish and Wildlife Service, 1980), first developed in 1976 HEP has since been modified after detailed assessments and there are now many descriptions of models for HEP (for example Lancia et al., 1982).

In outline, the aim of any HEP is to evaluate an area on the basis of the sustainability of key habitat factors for certain species. That is, with detailed ecological information about a species, the characteristics of the habitat can be evaluated (using numerical rating schemes) on the basis of key habitat factors. The basic steps for the HEP are as follows:
  • The area being evaluated is divided into stands with relatively homogeneous cover types (e.g. evergreen forest, deciduous forest, secondary forest, shifting cultivation, fallow land etc.) using remote sensing or ground based methods.
  • A species is selected and its sensitivity to habitat types and range requirement is investigated.
  • A Habitat Suitability Index (HSI) is calculated for each species stand-wise in the evaluation area using ecological parameters such as extent of canopy cover, successional stage. The HSI is defined as a value between 0 and 1 with the latter being the best quality of habitat in a defined area. The final aggregate value is an indication of the carrying capacity of the area for that particular species.
An example of HEP based on the ecology of the marten (Martes americana) was used by Schamberger and Krohn (1982). On the basis of ecological information from literature on the marten, suitability index was calculated based on extent of tree canopy, extent of canopy composed of fir and spruce and successional stage of the stand. The final HSI was derived by aggregating the scores:

HSI = (V1 * V2 * V3)1/3

where, V1 - canopy closure, V2 - canopy composition and V3 – successional stage

The power of 1/3 was used because a simple geometric mean of the three values was derived (latter development of HEP have tended to use multivariate regression). In the case of marten, an area with 40% tree canopy closure (V1 = 0.8), 24% canopy composed of fir or spruce (V2 =0.4) and a young successional stage (v3 = 0.7) give the following:

HSI= (0.8 * 0.4 * 0.7)1/3 = 0.6

This example is made very simple by assuming only three variables and one stand type. If there was more than one stand type then the final HSI would be:

HSI = (S(HSIi * ai))/A

where, HSIi is the index for the ith stand, which has area ai and A is the sum of the stand areas (ai).

Obviously, the species selected and the extent of information with respect to ecological habitat parameters is of key importance in HEP. Many models can be calculated for HEP but there has been a tendency to overlook exceptions. That is, although good ecological research can give very precise information about a species, sometimes plants and animals may survive without difficulty in what appears to be less than satisfactory habitat. In other words, the apparent survival at the time of surveying does not imply future success in the area. One should, therefore, always be alert to the exceptions and not be ruled by the expected.

Another index similar to HSI is Pikering’s index (see Marsh, 1978):

IEV = Log10 S(v3 *e * r * s)

where, IEV is the index of ecological evaluation, v is the number of vertical layers in the habitat, e is the extent of habitat in hectares, s is the total number of species and r is species rarity factor calculated as follows:

r = 100C
C = e (extent of habitat in ha)/A (area of land in km2)

In HSI and IEV models, often more than one variable is involved. The decision on weights of these variables is generally a difficult task. Also if interest is how to combine different criteria, which have different weights. One possibility is by using Saaty scale of relative importance and its application in multi-criteria evaluations.

Saaty’s multi-criterion evaluation
Anselin et al. (1989) have pursued the idea of multi-criterion techniques with an analytic Hierarchy Process (AHP), originally developed by Saaty (1977) and compared their results with previous methods. The measurement scale according to Saaty is as follows: 1  =  equal importance
3  =  week importance
5  =  essential or strong importance
7  =  demonstrated importance
9  =  absolute importance
2,4,6,8  =  intermediate values between two adjacent judgements.
If variable I has one the above assigned to it where compared with variable j, then j has the reciprocal value when compared with i. For example:

j/ I Var. A Var. B Var. C Weights
Var. A 1 8 8 0.8
Var.B 1/8 1 1 0.1
Var. C 1/8 1 1 0.1


The absolute weights from these relative pair-wise weights can be obtained by finding the eigen value and associated eigen vector. In practice, however, the weights are unknown and must be derived if done properly then it required complex calculation. However, one rough method is to take the geometric mean of each row (the cube root of the product of the weights):

3Ö1*8*8  =  4/5  =  0.8
3Ö1/8*1*1  =  0.55  =  0.1
3Ö1/8*1*1  =  0.5/5  =  0.1

The weights are scored as fraction of 1 and hence, in this example each result is then divided by 5 to give the weights or priority vectors of 0.8 (A), 0.1 (B) and 0.1 (C ). There is no doubt that in many evaluations and assessments, some criteria are considered more important than the other. That being the case, the multi-criterion approach could have applications but it needs to be tested. There are, however, others who feel that such an approach is essentially flawed because some criteria are so similar and therefore, contribute in a cumulative manner in the evaluation. That being true, only independent criteria should be included.

The U.S Fish and Wildlife Service has evolved nearly 157 HSI models for large number of temperate wild animals in the last 20 years. This makes the most comprehensive database for any habitat evaluation activity. Many studies on habitat suitability analysis have been carried out in India using different landscape (Roy et al., 1995), habitat parameter driven models Porwal et al.; Kushwaha et al., 2000; Pant et al., 2000 etc.) Roy et al. (1995) have used landscape variables such as interspersion (Is) and juxtaposition (jx) along with other habitat variables–roads, settlements, water, slope and forest type in a linear Habitat Quality (HQ) model to calculate overall habitat quality for goral in Rajaji National Park:

HQ = (0.2 * Is/8) + (0.8*Jx/12) + (0.2 * RDF)

where, RDF is relative disturbance factor calculated as a function of restrictive and disturbance factors like slope, settlements, water and roads. ‘Is’ is interspersion and ‘Jx’ is juxtaposition.

Porwal et al., (1996) converted vegetation parameters into food and shelter value along with terrain and water and calculated in Kanha National Park in Madhya Pradesh. This method of index is operationally easy. Kushwaha et al. (2000) and Anjana Pant (2000) have used rules/criteria-based (Table 5) modeling of habitat evaluation for goral in Chilla Sanctuary and sambar in Corbett National Park respectively. This approach does not require a linear model to integrate the information. The typical paradigm of this approach has been shown in Figure 1. A habitat suitability map for goral is shown in Figure 2. Rawat (1993) have used following Habitat Suitability Index model to calculate overall habitat suitability for musk deer (Moschus chrysogaster) in subalpine region of the Himalaya:

HSIt = nS (HSIi) Ai /nS Ai

where, HSIt = HSI for total area
HSIi = HSI for the ith response unit or stand calculated using equation :

HSI = (V1 + V2 + V3 + V4) / 5

Ai = surface area of the ith response unit
n = number of response units

The cost estimate of the one-time habitat evaluation studies carried out in India work out to be approx. US $ 10.00 per square kilometer, which is reasonable.

Table 5: The rules/criteria used in goral habitat modeling
No. Habitat suitability classes Slope Forest Type Forest Density Buffer zone of perennial water Buffer zone of settlements Buffer zone of roads
1. Most suitable >50o Mixed sal Mixed forest Forest blanks 30%-60% Within 1 km from permanent drainage Farther than 1.5 km from settlements Farther than 1.5 km from road
2. Suitable 31o-50o Sal Mixed sal Mixed forest 10%-30%  Within 2 km from permanent drainage Farther than 1 km from settlements Farther than 1 km from road
3. Moderately suitable 11o-30o Sal Mixed sal Mixed forest >60% Within 3 km from permanent drainage Farther than 0.5 km from settlements Farther than 0.5 km from road
4. Unsuitable Irrespective of slope Plantations Non-forest Scrub Settlements Irrespective of density Further than 3 km from permanent drainage Near settlement locations Less than 250 m from road


U.S. Fish and Wildlife Service HSI models series
The U.S. Fish and Wildlife Service has developed 157 HSI Models. These models are available in PDF as part of the National Wetlands Research Centre’s digital library collection. The Habitat Suitability Index Models Series published by U.S. Fish and Wildlife Service provides habitat information for evaluating impacts on fish and wildlife habitat resulting from water or land use changes. The habitat information in this series has been formatted according to standards for the Development of Habitat Suitability Index Models (U.S. Fish and Wildlife Service, 1981). Several efforts to compile species databases have been initiated in recent years. Whereas these and other data bases are descriptive in nature and contain an array of habitat and population information, this series is unique in that it is constrained to habitat information only, with an emphasis on quantitative relationships between key environmental variables and habitat suitability. In addition this series emphasizes habitat information into explicit habitat models useful in quantitative assessments.

The models in this series are an effort to consolidate scientific information on species habitat relationship. As stated earlier also, models are included that provide a numerical index of habitat suitability on a 0.0 to 1.0 scale, based on the assumption that there is a positive relationship between the index and habitat carrying capacity. The models vary in generality and precision, due in part to the amount of available quantitative habitat information and the frequent qualitative nature of existing information. When possible, models are included that are derived from site-specific population and habitat data. The HSI models are usually presented in three basic formats: 1) graphic, 2) word and 3) mathematical. The graphic format is a representation of the structure of the model and displays the sequential aggregation of variables into HSI. Following this, the model relationships are discussed and the assumed relationships between variables, components and HSI are documented. The discussion of model relationships provides a working version of the model and is, in effect, a word model. Finally, the model relationships are described in mathematical language, mini as closely and as simply as possible, the preceding word descriptions.

The models should be viewed as hypotheses of species habitat relationships rather than statement of proven cause and effect relationships. Their value is to serve as a basis for improved habitat relationships. The results of model performance tests, when available, are presented or reference with each model. However, models that have been reliable in specific studies may be less reliable in other situations. For this reason, the feedback is encouraged from model users.


Fig 2: Habitat suitability for goral in Chilla Sanctuary


Discussion and conclusions
As evident from the foregoing account, a large number of studies have been done to evaluate the habitat of different animal species in various parts of the world. A very useful database on species habitat relationships has been put in place, whereas new studies are being carried out to fortify the models. This will help not only, in better understanding of species–habitat relationships but also remove subjectivity from the models. The model building is definitely a long-term effort as it is difficult to understand species-habitat interactions in short span of time. Also the knowledge about the exact number of variables associated with a particular species is vital to model building exercise.

The potential of remote sensing in providing accurate and timely information on essential habitat variables such as shelter, food and water is clearly tremendous. Needless to mention that accurate input information to any habitat suitability model increases the reliability of the model outputs i.e. habitat suitability map/information. The improvement in spectral spatial and temporal resolutions and better digital processing of remote sensed data over past 30 years has kept pace with the information needs of the wildlife habitat suitability analysis in time and space. The geographic information system (GIS) too has developed fast with greater facility of large area, multiple spatial and non-spatial data integration and analysis. GIS software like Arc/Info, archive etc. coupled with user-friendly multiple-module image processing software like ERDAS Imagine etc. have provided many opportunities to look at data and information from different angles. ERDAS Imagine module, Modeler is a noon to those interested in spatial data modeling and integration. As more and more user-friendly software for spatial data processing are developing, there is need for larger capacity hardware devices. The divide between digital image processing and GIS software is narrowing day-by-day. Two examples are ERDAS Imagine and ILWIS software, which combine both image processing and spatial data handling. The future software will further reduce this divide, thus saving the user from import and export of data files from one software to the other, and with all these developments, the role of remote sensing and GIS in wildlife evaluation and management will be even greater

Acknowledgements
The authors are grateful to the Director, NRSA for allowing us to use the IIRS facilities during preparation of this paper. Thanks are also due to Mrs. Meena Jethi in preparation of this paper.

References
  • Adams, G.D. and Gentle, G.C. 1978. Documenting a 10-year change in landuse and waterfowl habitat from digitized aerial photomaps. Proc. 5th Canadian Symposium on Remote Sensing, Victoria, pp 415-426.
  • Ancelin, A., Meire, P.M. and Anselin, L. 1989. Multicriteria techniques in ecological evaluation : an example using the analytical hierarchy process. Biological Conservation. 49, 215-229.
  • Anon. 1999. National Policy and Macrolevel Action. Strategy on Biodiversity. Min. of Enviorn. & Forests, Govt. of India, New Delhi, pp. 43-46.
  • Best, R. 1984. Remote sensing approaches for wildlife management. In Renewable Resources Management, ASP, pp 55-96.
  • Bright, L.R. 1984. Assessment of elk habitat for resource management and planning activities from Landsat mapping products. In Renewable Resources Management, ASP, pp. 101-108.
  • Burkhalter, R. and Kientz, B. 1984. Counting big game by aerial thermography in the Arc-en-Barro is forest. Proc. 18th International Symposium on Remote Sensing of Environment, Paris, pp. 1489-1495.
  • Bhargava, a.K., Kumar, V. and Sharma, A.K. 1988. Wildlife conservation in Nepal. Tigerpaper, January-March 1988, pp.27-30.
  • Bhat, S.D. and Rawat, G.S. 1995. Habitat use by chital (Axis axis) in Dhaulkhand, Rajaji National Park, India. Tropical Ecology, 36(2), 177-189.
  • Chakraborti, K. 1990. Wildlife conservation in India. Tigerpaper, April-June 1990, pp 22-27.
  • De Wulf, R.R., Mac Kinnon, J.R. and Cai, W.S. 1988. Remote sensing for wildlife mangement : giant panda habitat mapping from Landsat MSS images. Geocarto International, 1, 41-50.
  • Ericson, J.E., Mower, R.D. and Wyckoff, J.W. 1983. The use of remote sensing techniques for monitoring sandhill crane populations. Proc. ACSM-ASP Fall Convention, Salt Lake City, pp 493-501.
  • Ferguson, E.L., Jorde, D.G. and Sease, J.J. 1981. Use of 35mm colour aerial photography to acquire mallard sex ratio data. Photogrammetric Engineering and Remote Sensing, 47, 823-828.
  • George, T.H., Stringer, W.J. and Baldridge, J.N. 1977. Reindeer range inventory in Western Alaska from computer-aided digital classification of Landsat data. Proc. 11th International Symposium on Remote Sensing of Environment. Ann Arbor, pp. 671-682.
  • Giles, R.H. 1978. Wildlife Mangement. Freeman & Co., San Francisco.
  • Harris, R. 1983. Remote sensing support for the Omani white Oryx project. Proc. Conference on the application of remote sensing techniques to aid Range Management, Silsoe, pp 17-24.
  • Hobaugh, W.C. 1984. Habitat use by snow geese wintering in southeast Texas. Journal of Wildlife Management 48(4), 1085-1098.
  • Hill, G.J. and Kelly, G.D. 1987. Habitat mapping by Landsat for aerial census of kangaroos. Remote Sensing of Environment, 21, 53-60.
  • Johnsingh, A.J.T. 1991. Rajaji. Sanctuary, 11, 14-25.
  • Johnsingh, A.J.T. 1992. The goral story. Sanctuary, 12 (5), 32-35.
  • Johnsingh, A.J.T. and Joshua, J. 1994. Conserving Rajaji and Corbett National Parks – the elephant as flagship species. Oryx, 28(2), 135-140.
  • Kushwaha, S.P.S. and Madhavan Unni, N.V. 1986. Application of remote sensing techniques in forest cover monitoring and habitat evaluation : A case study in Kaziranga National Park, Assam. Proc. Seminar-cum-Workshop, Wildlife Habitat Evaluation using Remote Sensing Techniques, 22-23 October, 1986, Dehradun, pp. 238-247
  • Kamat, D.S. 1986. An integrated approach to remote sensing studies for wildlife habitat evaluation. . Proc. Seminar-cum-Workshop, Wildlife Habitat Evaluation using Remote Sensing Techniques, 22-23 October, 1986, Dehradun, pp. 165-182.
  • Kotwal, P.C. and Parihar, J.S. 1988. Management plan of Kanha National Park and Project Tiger Kanha for the period 1989-90 to 1998-99, Mandla, M.P.
  • Kushwaha, S.P.S. et al., 2000. Land area change and habitat suitability analysis in Kaziranga. Tigerpaper 27(2), 9-17.
  • Kushwaha, S.P.S., Munkhtuya, S. and Roy, P.S. 2001. Mountain goat habitat evaluation in Rajaji National Park using remote sensing and GIS. Journal Indian Society of Remote Sensing (accepted).
  • Leopold Aldo 1933. Game Management. Charles Scribner’s Sons, NY pp 481.
  • Lamprey, H.F. 1963. Ecological separation of large mammal species in the Tarangire Game Reserve, Tanganyika. East African Wildlife Journal, 5, 151-166.
  • Leuthold, W. and Sale, J.B. 1973. Movements and Patterns of habitat utilization of elephants in Tsavo National Park, Kenya, East African .Wildlife Journal 1, 369-384.
  • Leuthold, W., 1976. Age structure of elephants in Tsavo National Park, Kenya, Journal Appl. Ecol. 13, 435-444.
  • Loeffler, E. and Margules, C. 1980. Wombats detected from space. Remote Sensing of Environment, 9, 47-56.
  • Lancia, R.A., Miller, S.D., Adams, D.A. and Hazel, D.W. 1982. Validating habitat quality assessment : An example. Transactions of North American Wildlife and Natural Resources Conference, 47, 96-110.
  • Lyon, J.G. 1983. Landsat derived land-cover classifications for locating potential kestrel nesting habitat. Photogrammetric Engineering and Remote Sensing, 49, 245-250.
  • Lindgren, D.T. 1985. Landuse Planning and Remote Sensing, Martinus Nyhoff Publishers, Dordrecht, 173 p.
  • Marsh, P. 1978. Formula for the needs of man and nature. New Scientist, 77- 84-86.
  • Mishra, C. and Johnsing, A.J.T. 1996. On habitat selection by the goral Nemorhaedus goral (Bovidal, Artio dactyla). Zool. London, 240, 573-580.
  • Nichol, J.E. 1975. Collection and processing of remote sensing data related to wildlife conservation in natural environments. Proc. 10th International Symposium on Remote Sensing of Environment, Ann Arbor, pp 369-372.
  • Panwar, H.S. 1986. Forest cover mapping for planning tiger corridors between Kanha and Bandhavgarh – a proposed project. Proc. Seminar-cum-Workshop, Wildlife Habitat Evaluation using Remote Sensing Techniques, 22-23 October, 1986, Dehradun, pp. 209-212.
  • Parihar, J.S., Kotwal, P.C., Panigraphi, S., and Chaturvedi, N. 1986. Study of wildlife habitat using high resolution space photographs : A case study of Kanha National Parks. ISRO Special Publication, ISRO-SP-17-86, SAC, Ahmedabad.
  • Prasad, S.N., Goyal, S.P., Roy, P.S. and Singh, S. 1994. Changes in wild ass (Equus heminous khur) habitat conditions in Little Runn of Kutch, Gujarat from a remote sensing perspective. Int. J. Remote Sensing.
  • Porwal, M.C., Roy, P.S. and Chellamuthu, V. 1996. Wildlife habitat analysis for sambar (Cervus unicolor) in Kanha National Park using remote sensing. Int. J. Remote Sensing, 17 (14), 2683-2697.
  • Pant, A., Chavan, S.G., Roy, P.S. and Das. K.K. 1999. Habitat analysis for sambar in Corbett National Park using remote sensing and GIS. Journal Indian Society of Remote Sensing, 27 (3), 133-139.
  • Roy, P.S., Saxena, K.G., Pant, D.N., Kotwal, P.C. 1986. Analysis of vegetation types using remote sensing techniques for wildlife evaluation in Kanha National Park. . Proc. Seminar-cum-Workshop, Wildlife Habitat Evaluation using Remote Sensing Techniques, 22-23 October, 1986, Dehradun, pp. 83-116.
  • Rodgers, W.A. 1990. Grassland production and nutritional implications for wild grazing herbivores in Rajaji National Parks, India. Tropical Ecology, 31(2), 41-49.
  • Rawat, G.S. 1993. A preliminary habitat suitability index model for Himalayan musk deer. In High Altitudes of Himalaya (eds: Pangtey, Y.P.S. and Rawat, R.S.) Gyanodaya Prakashan, Nainital, pp. 209-219
  • Roy, P.S., Ravan, S.A., Rajadnya, N., Das, K.K., Jain, A. and Singh, S. 1995. Habitat suitability analysis of Nemorhaedus goral - A remote sensing and geographic information system approach. Current Science 69(8), 685-691.
  • Saaty, A.A. 1977. A scaling method for priorities in hierarchical structures. Journal of Mathematical Pschychology, 15, 234-281.
  • Schamberger, M. and Krohn, W.B. 1982. Status of the habitat evaluation procedures. Transactions of North American Wildlife and Natural Resources Conference, 47, 154-164.
  • Sale, J.B. 1986. The information requirement of Wildlife mangement in the Indian context. Proc. Seminar-cum-Workshop. Wildlife Habitat Evaluation using Remote Sensing Techniques, October 22-23, 1986, pp 6-12.
  • U.S. Fish and Wildlife Service 1980. Habitat Evaluation Procedures (HEP), USDI Fish and Wildlife Service, Division of Ecological Services, ESM 102, Washington, D.C.
  • U.S. Fish and Wildlife Service 1981. Standards for the development of habitat suitability index models for use in the Habitat Evaluation Procedures, USDI Fish and Wildlife Service, Division of Ecological Sciences ESM 103, Washington, D.C.
  • Wiersema, G. 1983. Ibex habitat analysis using Landsat imagery. ITC Journal 2, 139-147.
  • Wyatt, C.L., Anderson, D.R., Harshbarger, R. and Trivedi, M.M. 1984. Deer census using a multi-spectral linear array instrument. Proc. 18th International Symposium on Remote Sensing of Environment. Paris, pp. 1475-1488.
© GISdevelopment.net. All rights reserved.