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Forest fire risk mapping of Kolli Hills, India, considering subjectivity and inconsistency issues

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  • Jaehoon Jung
  • Changjae Kim
  • Shanmuganathan Jayakumar
  • Seongsam Kim
  • Soohee Han
  • Dong Kim
  • Joon Heo

Abstract

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. Copyright Springer Science+Business Media Dordrecht 2013

Suggested Citation

  • Jaehoon Jung & Changjae Kim & Shanmuganathan Jayakumar & Seongsam Kim & Soohee Han & Dong Kim & Joon Heo, 2013. "Forest fire risk mapping of Kolli Hills, India, considering subjectivity and inconsistency issues," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 65(3), pages 2129-2146, February.
    • Handle: RePEc:spr:nathaz:v:65:y:2013:i:3:p:2129-2146
    DOI: 10.1007/s11069-012-0465-1
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    References listed on IDEAS

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    1. Shon, Cheolho, 2006. "The Current Situation and Policy Direction of Agroforestry in Korea," Journal of Rural Development/Nongchon-Gyeongje, Korea Rural Economic Institute, vol. 29(1), June.
    2. Brigitte Leblon, 2005. "Monitoring Forest Fire Danger with Remote Sensing," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 35(3), pages 343-359, July.
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    Cited by:

    1. Jinghu Pan & Weiguo Wang & Junfeng Li, 2016. "Building probabilistic models of fire occurrence and fire risk zoning using logistic regression in Shanxi Province, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(3), pages 1879-1899, April.
    2. Gianluigi Busico & Elisabetta Giuditta & Nerantzis Kazakis & Nicolò Colombani, 2019. "A Hybrid GIS and AHP Approach for Modelling Actual and Future Forest Fire Risk Under Climate Change Accounting Water Resources Attenuation Role," Sustainability, MDPI, vol. 11(24), pages 1-20, December.
    3. Sarkawt G. Salar & Arsalan Ahmed Othman & Sabri Rasooli & Salahalddin S. Ali & Zaid T. Al-Attar & Veraldo Liesenberg, 2022. "GIS-Based Modeling for Vegetated Land Fire Prediction in Qaradagh Area, Kurdistan Region, Iraq," Sustainability, MDPI, vol. 14(10), pages 1-31, May.

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