Forest fires have adverse ecological, economic, and social impacts. In this light, the present research aimed, first, to construct a fire risk model using a GIS-based multi-criteria analysis and second, to derive a forest fire risk modeling strategy that alleviates the problem of inconsistency in the assigning of scores and weights to forest fire categories and layers. Third, the local-orientation effects and causes, which are relevant to the subjectivity problem, were investigated by comparing the risk scoring and weighting outcomes from Indian and Korean expert groups (IEG and KEG). Fourth, forest fire factors that can be considered regional and global also were investigated. Kolli Hills, India, was selected as the study area in this research. In the interests of alleviating the inconsistency problem, a weighting and scoring scheme based on the analytic hierarchy process was applied. The experiences from the existence of prevailing westerly winds, the most common forest types (i. e., in Korea: pine trees), and the different anthropogenic pressures between Korea and India resulted in the different scoring and weighting decisions of the two expert groups. Among the five fire risk factors, slope, road, and settlement can be considered to be global factors. On the other hand, forest cover and aspect are regional factors that can be more influenced by local environmental conditions. When considering the producer's accuracy, the approach of the IEG together with the natural breaks thresholding method provided the best fire risk mapping result. On the other hand, the model from the IEG with equal interval provided the best result from the viewpoint of user's accuracy and overall accuracy. Overall, this paper proposes a forest fire risk mapping procedure as basis for developing a global forest fire risk modeling in the future, where a series of standardized modeling steps and variables should be defined.
|Number of pages||18|
|Publication status||Published - 2013 Jan|
Bibliographical noteFunding Information:
Acknowledgments We gratefully acknowledge the financial support provided for this work by the 21C Frontier Project (CDRS:16-2008-04-007-00) of the Ministry of Education, Science, and Technology, and by the Development of Spatial Model for Estimation of Carbon Stocks in Aboveground Biomass Project (20110224779-01) of the Ministry for Food, Agriculture, Forestry, and Fisheries, Republic of Korea. We also thank the Tamil Nadu Forest Department, India, for its valuable support and necessary permission.
All Science Journal Classification (ASJC) codes
- Water Science and Technology
- Atmospheric Science
- Earth and Planetary Sciences (miscellaneous)