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Citation |
Vadrevu KP, Eaturu A, Badarinath KV. Environ. Monit. Assess. 2010; 166(1-4): 223-239. |
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Affiliation |
Agroecosystem Management Program, Ohio Agricultural Research and Development Center (OARDC), The Ohio State University, 201 Thorne Hall, 1680 Madison Avenue, Wooster, OH, 44691, USA, krisvkp@yahoo.com. |
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Copyright |
(Copyright © 2010, Holtzbrinck Springer Nature Publishing Group) |
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DOI |
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PMID |
19472063 |
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Abstract |
Forest fires are one of the major causes of ecological disturbance and environmental concerns in tropical deciduous forests of south India. In this study, we use fuzzy set theory integrated with decision-making algorithm in a Geographic Information Systems (GIS) framework to map forest fire risk. Fuzzy set theory implements classes or groupings of data with boundaries that are not sharply defined (i.e., fuzzy) and consists of a rule base, membership functions, and an inference procedure. We used satellite remote sensing datasets in conjunction with topographic, vegetation, climate, and socioeconomic datasets to infer the causative factors of fires. Spatial-level data on these biophysical and socioeconomic parameters have been aggregated at the district level and have been organized in a GIS framework. A participatory multicriteria decision-making approach involving Analytical Hierarchy Process has been designed to arrive at a decision matrix that identified the important causative factors of fires. These expert judgments were then integrated using spatial fuzzy decision-making algorithm to map the forest fire risk. Results from this study were quite useful in identifying potential "hotspots" of fire risk, where forest fire protection measures can be taken in advance. Further, this study also demonstrates the potential of multicriteria analysis integrated with GIS as an effective tool in assessing "where and when" forest fires will most likely occur. Language: en |