Habitat Suitability Modelling for Sambar (Rusa unicolor): A Remote Sensing and GIS Approach

  • Ekwal Imam
Part of the Springer Remote Sensing/Photogrammetry book series (SPRINGERREMO)


The concept of wildlife species conservation starts with the identification of their suitable habitat as it provides essential information for wildlife refuge design and management. In this study, multiple logistic regression is integrated with remote sensing and geographic information system to evaluate the suitable habitats available for sambar (Rusa unicolor) in Chandoli Tiger Reserve, Maharashtra, India (17° 04′ 00″N to 17° 19′ 54″N and 73° 40′ 43″E to 73° 53′ 09″E). Satellite imageries of LISS-III of IRS-P6 of study area were digitally processed. To generate collateral data topographic maps were analysed in a GIS framework. Layers of different variables such as land use land cover, forest density, proximity to disturbances and water resources and a digital terrain model were created from satellite imageries and topographic maps. These layers along with GPS location of sambar presence/absence and multiple logistic regression (MLR) techniques were integrated in a GIS environment to model habitat suitability index of sambar. The results indicate that approximately 69.92 km2 (24 %) of the forest of tiger reserve was least suitable for sambar, whereas 82.60 km2 (28 %) was moderately suitable, 88.25 km2 (30 %) suitable and 54.01 km2 (18 %) was highly suitable. The accuracy level of this model was 80.2 %. The model advocates that forests of this area are most appropriate for declaring it as a reserve for sambar conservation.


Sambar Habitat suitability index Multiple logistic regression Modelling Remote sensing 



The author is thankful to Prof. SPS Kushwaha, former Head, Forestry and Ecology Division and the Dean, Indian Institute of Remote Sensing (IIRS), Dehradun, India for supervision and GIS-laboratory facilities. I am also thankful to Prof. H.S.A. Yahya, Dean Faculty of Life Science & former Chairman, Department of Wildlife Sciences, AMU, Aligarh (India) for encouraging and providing opportunity to work with IIRS, Dehradun. Thanks are also due to Aditya Singh, Dr. Mohammed Irfan (formerly from ATREE) and Director and forest staffs of Chandoli Tiger Reserve, Maharashtra, India for their technical and logistic supports during my field studies.


  1. Andries AM, Gulinck H, Herremans M (1994) Spatial modeling of the barn owl Tyto alba habitat using landscape characteristics derived from SPOT data. Ecography 17:278–287CrossRefGoogle Scholar
  2. Anonymous (2005) Management plan of Chandoli National Park. Kolhapur Forest Division, Maharashtra, IndiaGoogle Scholar
  3. ArcView 3.2 (1999) Environmental Systems Research Institute. Redlands, CA, USAGoogle Scholar
  4. Bio AMF, Becker PD, Bie ED, Huybrechts W, Wassen M (2002) Prediction of plant species distribution in lowland river valleys in Belgium: modelling species response to site conditions. Biodivers Conserv 11:2189–2216CrossRefGoogle Scholar
  5. Braunisch C, Bullmann K, Graf RF, Hirzel AH (2008) Living on the edge—modeling habitat suitability for species at the edge of their fundamental niche. Ecol Model 214(2–4):153–167CrossRefGoogle Scholar
  6. Brian L, West E (1997) GIS modeling of elk calving habitat in a prairie environment with statistics. Photogram Eng Remote Sens 63:161–167Google Scholar
  7. Bright LR (1984) Assessment of elk habitat for resource management and planning activities from Landsat mapping products. Am Soc Photogram Remote Sens Renew Resour Manag. Falls Church, Virginia, USA, pp 101–108Google Scholar
  8. Campbell JB (1996) Introduction to remote sensing, 2nd edn. London, Taylor and FrancisGoogle Scholar
  9. Census of India (1981) Population census of India. Ministry of Home Affairs, Government of India, Delhi, IndiaGoogle Scholar
  10. Champion HG, Seth SK (1968) A revised survey of the forest types of India. Government of India, Delhi, IndiaGoogle Scholar
  11. Clark PJ, Evans FC (1954) Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology 35:445–453CrossRefGoogle Scholar
  12. Davis FW, Goetz S (1990) Modeling vegetation pattern using digital terrain data. Landscape Ecol 4:69–80CrossRefGoogle Scholar
  13. De La Ville N, Cousins S, Bird C (1997) Habitat suitability analysis using logistic regression and gis to outline potential areas for conservation of the Grey Wolf (Canis lupus). In: proceeding of conference, GIS Research, University of Leeds, Leeds, UK. pp 9–11Google Scholar
  14. Dendoncker N, Bogaert P, Rounsevell M (2006) A statistical method to downscale aggregated land use data and scenarios. J Land use Sci 1(2–4):63–82CrossRefGoogle Scholar
  15. ERDAS IMAGINE 8.7 (2004) Leica geosystems GIS and mapping.
  16. Geist V (1998) Deer of the world: their evolution, behaviour, and ecology. Stackpole Books, pp 73–77Google Scholar
  17. Hirzel AH, Helfer V, Metral F (2001) Assessing habitat-suitability models with a virtual species. Ecol Model 145:111–121CrossRefGoogle Scholar
  18. Homer CG, Edwards TC, Ramsey RD, Price KP (1993) Use of remote sensing methods in modelling sage grouse winter habitat. J Wildl Manag 57:78–84CrossRefGoogle Scholar
  19. Imam E (2011) Use of geospatial technology in evaluating landscape cover type changes in Chandoli National Park, India. Comput Ecol Softw 1(2):95–111Google Scholar
  20. Imam E, Kushwaha SPS, Singh A (2009) Evaluation of suitable tiger habitat in Chandoli National Park, India, using multiple logistic regression. Ecol Model 220:3621–3629CrossRefGoogle Scholar
  21. IUCN (2012) Red list of threatened species. Version 2012.1. Available online:
  22. Johnson LB (1990) Analyzing spatial and temporal phenomena using geographical information systems. Landscape Ecol 4:31–43CrossRefGoogle Scholar
  23. Johnson AR, Milne BT, Wiens JA, Crist TO (1992) Animal movements and population dynamics in heterogeneous landscapes. Landscape Ecol 7:63–75CrossRefGoogle Scholar
  24. Karanth KU, Nichols JD, Kumar NS, Link WA, Hines JE (2004) Tigers and their prey: predicting carnivore densities from prey abundance. Proc Nat Acad Sci U.S.A. 101:4854–4858. doi: 10.1073/pnas.0306210101
  25. Karanth K, Nichols JD, Karanth KU, Hines JE, Christensen NL (2010) The shrinking ark: patterns of large mammal extinctions in India. Proc. R. Soc. B 277:1971–1979. doi: 10.1098/rspb.2010.0171
  26. Kushwaha SPS (2002) Geoinformatics for wildlife habitat characterization. Map India.
  27. Kushwaha SPS, Hazarika R (2004) Assessment of habitat loss in Kameng and Sonitpur elephant reserves. Curr Sci 87:1447–1453Google Scholar
  28. Kushwaha SPS, Khan A, Habib B, Quadri A, Singh A (2004) Evaluation of sambar and muntjac habitats using geostatistical modelling. Curr Sci 86(10):1390–1400Google Scholar
  29. Lecis R, Norris K (2003) Habitat correlates of distribution and local population decline of the endemic Sardinian new Euproctus platycephalus. Biol Conserv 115:303–317CrossRefGoogle Scholar
  30. Lillesand TM, Kiefer RW (1994) Remote sensing and image interpretation. Wiley, New YorkGoogle Scholar
  31. Lyon JG (1983) Landsat derived land cover classifications for locating potential kestrel nesting habitat. Photogram Eng Remote Sens 49:245–250Google Scholar
  32. Mc Garigal K, Cushman SA, Neel MC, Ene E (2002) FRAGSTATS: spatial pattern analysis program for categorical maps, computer software program produced by the authors at the University of Massachusetts, Amherst. Available from
  33. Palma L, Beja P, Rodrigues M (1999) The use of sighting data to analyse Iberian lynx habitat and distribution. J Appl Ecol 36:812–824CrossRefGoogle Scholar
  34. Parihar JS, Panigrahy S, Parihar JS (1986) Remote sensing based habitat assessment of Kaziranga National Park. In: Kamat DS, Panwar HS (eds) Wildlife habitat evaluation using remote sensing techniques. Indian Institute of Remote Sensing/Wildlife Institute of India, India, pp 157–164Google Scholar
  35. Porwal MC, Roy PS, Chellamuthu V (1996) Wildlife habitat analysis for sambar (Cervus unicolor) in Kanha National Park using remote sensing. Int J Remote Sens 17:2683–2697CrossRefGoogle Scholar
  36. Prater SH (2005) Book of Indian animals. Oxford University Press, UKGoogle Scholar
  37. Ramesh T, Sankar K, Qureshi Q, Kalle R (2012) Group size, sex and age composition of chital (Axis axis) and sambar (Rusa unicolor) in a deciduous habitat of Western Ghats. Mamm Biol 77:53–59Google Scholar
  38. Roy PS, Ravan SA, Rajadnya N, Das KK, Jain A, Singh S (1995) Habitat suitability analysis of Nemorhaedus goral—a remote sensing and geographic information system approach. Curr Sci 69:685–691Google Scholar
  39. Schadt SE, Wegand T, Knauer F, Kaczensky P, Moser UB, Bufka L, Cerveny J, Koubek P, Huber T, Stanisa C, Trepl L (2002) Assessing the suitability of Central European landscapes for the reintroduction of Eurasian lynx. J Appl Ecol 39:189–203CrossRefGoogle Scholar
  40. Singh A (2004) Wildlife habitat analysis and vulnerability assessment of the Binsar wildlife sanctuary, Uttaranchal. Dissertation, Indian Institute of Remote Sensing, Dehradun, IndiaGoogle Scholar
  41. Singh A, Kushwaha SPS (2010) Refining logistic regression models for wildlife habitat suitability modeling—a case study with muntjak and goral in the Central Himalayas, India. Ecol Model 222(8):1354–1366CrossRefGoogle Scholar
  42. SPSS-10 (1988) SPSS-X user’s guide. 3rd edn. SPSS Inc., Chicago, USAGoogle Scholar
  43. Store R, Jokimaki J (2003) A GIS-based multi-scale approach to habitat suitability modelling. Ecol Model 169:1–15CrossRefGoogle Scholar
  44. Thakur AK, Sing S, Roy PS (2008) Orthorectification of IRS-P6 LISS-IV data using Landsat ETM and SRTM datasets in the Himalayas of Chamoli district, Uttarakhand. Curr Sci 95:1459Google Scholar
  45. Timmins RJ, Steinmetz R, Baral HS, Kumar NS, Duckworth JW, Islam MdA, Giman B, Hedges S, Lynam AJ, Fellowes J, Chan BPL, Evans T (2008) Rusa unicolor. In: IUCN 2012, IUCN red list of threatened species, Version 2012.1.
  46. Unial DP (2005) Habitat suitability analysis of Lion in proposed Palpur Kuno sanctuary using Remote Sensing and GIS. Dissertation, Indian Institute of Remote Sensing, Dehradun, IndiaGoogle Scholar
  47. USFWS (1981) Standards for the development of habitat suitability models for use in the habitat evaluation procedures. USDIFWS, ESM 103, Washington, DCGoogle Scholar
  48. WPA (1991) Wildlife protection act of India. Natraj Publisher, Dehradun, IndiaGoogle Scholar
  49. Zarri AA, Rahmani AR, Singh A, Kushwaha SPS (2008) Habitat suitability assessment for the endangered Nilgiri Laughingthrush: a multiple logistic regression approach. Curr Sci 94:1487–1494Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Department of Wildlife SciencesAligarh Muslim UniversityAligarhIndia

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