Geospatial multiple logistic regression approach for habitat characterization of scarce plant population: A case study o
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J. Indian Soc. Remote Sens. (September 2010 : Special issue on Biodiversity and Landscape Ecology) 38 : 513-521
RESEARCH ARTICLE
Geospatial Multiple Logistic Regression Approach for Habitat Characterization of scarce Plant Population: A case study of Pittosporum eriocarpum Royle (An Endemic Species of Uttarakhand, India) H. Padalia . R.R. Bharti . Y.P.S. Pundir . K.P. Sharma
Received: 23 September 2009 / Accepted: 10 May 2010
Keywords Endemic species . Habitat characterization . GIS . Logistic regression . Pittosporum eriocarpum . Himalaya species of Uttarakhand, yet till now its regional Abstract Increasing concern for biodiversity distribution is poorly known. This study using conservation at species level resulted in the geospatial modelling tools indentified several development of cost effective tools for getting localities of potential occurrence of this species in information at larger scale. Modeling distribution of the Mussoorie hills and Doon valley, and also species using remote sensing and geographic provides information on its habitat specificity. The information has already proved its potentials to get main objective of the study is to predict the suitable such information with less effort. Pittosporum habitats for endangered plant species in Himalayan eriocarpum Royle is an endemic and threatened tree region using logistic regression model where availability of sufficient data on species presenceabsence is a major limitation for larger areas. H. Padalia ( )1 . R.R. Bharti1. Y.P.S. Pundir2 . K.P. Sharma2 1
Regional Remote Sensing Centre-North (NRSC), Dehradun-248001, India
2
Introduction
D.B.S. College, Dehradun-248001, India
email: [email protected]
Many species are adversely affected by human activities at large spatial scales and their conservation requires detailed information on distributions. A few examples of habitat-modelling studies of rare and endangered species exist in the literature, although from conservation perspective predicting their
514 J. Indian Soc. Remote Sens. (September 2010 : Special issue on Biodiversity and Landscape Ecology) 38 : 513-521
distribution would prove particularly useful. Predicting distribution of species may serves the purposes including determination of the response of species to changing environment and land use (Buckland and Elston, 1993) and searching additional populations of threatened species (Pfab and Witowski, 1997). Increasingly, there is a need to identify those areas that might be candidate locations that have a high risk of species extinction (Araujo and Williams, 2000). Habitat distribution models have been used successfully to predict the potential distribution of plant species (Guisan et al., 1998; Guisan et al., 1999; Peterson, 2001; Bakkenes et al., 2002). Most of these models rely on adjusting a quantitative relationship between a taxon and its direct environment. These models result in spatial predictions indicating locations of the most suitable (and unsuitable) habitats for a target species. However, as yet relatively few pr
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