Delhi, India
Delhi, India
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Areendran G.,IGCMC | Raj K.,IGCMC | Mazumdar S.,IGCMC | Munsi M.,IGCMC | And 2 more authors.
Tropical Ecology | Year: 2011

We used remote sensing data and geospatial modeling techniques to assess the elephant habitat suitability and dispersal corridor in northern parts of Chhattisgarh, Central India. This region is frequently visited by elephants from the neighboring states of Orissa and Jharkhand in search of better habitat and often enter human habitations and agricultural fields resulting in conflicts with humans. Satellite images and ground information were used for land use/ land cover mapping and identification of conflict zones. Analytic Hierarchy Process (AHP) was used to assign weights to the three factors, viz., type of vegetation cover, proximity to water body and proximity to human habitation. Based on the analysis a corridor for elephant movement and migration has been identified which could be notified and managed by the state government in order to minimize human - elephant conflicts in the region. © International Society for Tropical Ecology.

Puri K.,Guru Gobind Singh Indraprastha University | Areendran G.,IGCMC | Raj K.,IGCMC | Mazumdar S.,IGCMC | Joshi P.K.,TERI University
Journal of Forestry Research | Year: 2011

Forest fire is a major cause of changes in forest structure and function. Among various floristic regions, the northeast region of India suffers maximum from the fires due to age-old practice of shifting cultivation and spread of fires from jhum fields. For proper mitigation and management, an early warning of forest fires through risk modeling is required. The study results demonstrate the potential use of remote sensing and Geographic Information System (GIS) in identifying forest fire prone areas in Manipur, southeastern part of Northeast India. Land use land cover (LULC), vegetation type, Digital elevation model (DEM), slope, aspect and proximity to roads and settlements, factors that influence the behavior of fire, were used to model the forest fire risk zones. Each class of the layers was given weight according to their fire inducing capability and their sensitivity to fire. Weighted sum modeling and ISODATA clustering was used to classify the fire zones. To validate the results, Along Track Scanning Radiometer (ATSR), the historical fire hotspots data was used to check the occurrence points and modeled forest fire locations. The forest risk zone map has 55. 63% of agreement with ATSR dataset. © 2011 Northeast Forestry University and Springer-Verlag Berlin Heidelberg.

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