Fire risk evaluation using multicriteria analysis--a case study.

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.

Biophysical and anthropogenic controls of forest fires in the Deccan Plateau, India.

Forest fires constitute one of the most serious environmental problems in several forested regions of India. In the Indian sub-continent, relatively few studies have focused on the assessment of biophysical and anthropogenic controls of forest fires at a landscape scale and the spatial aspects of these relationships. In this study, we used fire count data sets from satellite remote sensing data covering 78 districts over four different states of the Deccan Plateau, India, for assessing the underlying causes of fires. Spatial data for explanatory variables of fires pertaining to topography, vegetation, climate, anthropogenic and accessibility factors have been gathered corresponding with fire presence/absence. A logistic regression model was used to estimate the probability of the presence of fires as a function of the explanatory variables. Results for fire area estimates suggested that, of the total fires covering 47,043km(2) that occurred during the year 2000 for the entire Indian region, 29.0% occurred in the Deccan Plateau, with Andhra Pradesh having 13.5%, Karnataka 14.7%, Kerala 0.1%, and Tamilnadu 1.15%. Results from the logistic regression suggest that the strongest influences on the fire occurrences were the amount of forest area, biomass densities, rural population density (PD), average precipitation of the warmest quarter, elevation (ELE) and mean annual temperature (MAT). Among these variables, biomass density (BD) and average precipitation of the warmest quarter had the highest significance, followed by others. These results on the best predictors of forest fires can be used both as a strategic planning tool to address broad scale fire risk concerns, and also as a tactical guide to help forest managers to design fire mitigation measures at the district level.

Spatial distribution of forest fires and controlling factors in Andhra Pradesh, India using SPOT satellite datasets.

Fires are one of the major causes of forest disturbance and destruction in several dry deciduous forests of southern India. In this study, we use remote sensing data sets in conjunction with topographic, vegetation, climate and socioeconomic factors for determining the potential causes of forest fires in Andhra Pradesh, India. Spatial patterns in fire characteristics were analyzed using SPOT satellite remote sensing datasets. We then used nineteen different metrics in concurrence with fire count datasets in a robust statistical framework to arrive at a predictive model that best explained the variation in fire counts across diverse geographical and climatic gradients. Results suggested that, of all the states in India, fires in Andhra Pradesh constituted nearly 13.53% of total fires. District wise estimates of fire counts for Andhra Pradesh suggested that, Adilabad, Cuddapah, Kurnool, Prakasham and Mehbubnagar had relatively highest number of fires compared to others. Results from statistical analysis suggested that of the nineteen parameters, population density, demand of metabolic energy (DME), compound topographic index, slope, aspect, average temperature of the warmest quarter (ATWQ) along with literacy rate explained 61.1% of total variation in fire datasets. Among these, DME and literacy rate were found to be negative predictors of forest fires. In overall, this study represents the first statewide effort that evaluated the causative factors of fire at district level using biophysical and socioeconomic datasets. Results from this study identify important biophysical and socioeconomic factors for assessing 'forest fire danger' in the study area. Our results also identify potential 'hotspots' of fire risk, where fire protection measures can be taken in advance. Further this study also demonstrate the usefulness of best-subset regression approach integrated with GIS, as an effective method to assess 'where and when' forest fires will most likely occur.