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A Comparison of the Qualitative Analytic Hierarchy Process and the Quantitative Frequency Ratio Techniques in Predicting Forest Fire-Prone Areas in Bhutan Using GIS

Author

Listed:
  • Kinley Tshering

    (Forest Fire Management Section, Department of Forests and Park Services, Thimphu 11001, Bhutan)

  • Phuntsho Thinley

    (Ugyen Wangchuck Institute for Conservation and Environment, Lamaigoenpa, Bumthang 32001, Bhutan
    Ecosystem Management, University of New England, Armidale NSW 2351, Australia)

  • Mahyat Shafapour Tehrany

    (Department of Geodesy, Kandilli Observatory and Earthquake Research Institute, Bogazici University, 34680 Cengelkoy-Istanbul, Turkey)

  • Ugyen Thinley

    (College of Natural Resources, Royal University of Bhutan, Lobesa, Punakha 13001, Bhutan)

  • Farzin Shabani

    (ARC Centre of Excellence for Australian Biodiversity and Heritage, Global Ecology, College of Science and Engineering, Flinders University, Adelaide, SA GPO Box 2100, Australia
    Department of Biological Sciences, Macquarie University, Sydney NSW 2109, Australia)

Abstract

Forest fire is an environmental disaster that poses immense threat to public safety, infrastructure, and biodiversity. Therefore, it is essential to have a rapid and robust method to produce reliable forest fire maps, especially in a data-poor country or region. In this study, the knowledge-based qualitative Analytic Hierarchy Process (AHP) and the statistical-based quantitative Frequency Ratio (FR) techniques were utilized to model forest fire-prone areas in the Himalayan Kingdom of Bhutan. Seven forest fire conditioning factors were used: land-use land cover, distance from human settlement, distance from road, distance from international border, aspect, elevation, and slope. The fire-prone maps generated by both models were validated using the Area Under Curve assessment method. The FR-based model yielded a fire-prone map with higher accuracy (87% success rate; 82% prediction rate) than the AHP-based model (71% success rate; 63% prediction rate). However, both the models showed almost similar extent of ‘very high’ prone areas in Bhutan, which corresponded to coniferous-dominated areas, lower elevations, steeper slopes, and areas close to human settlements, roads, and the southern international border. Moderate Resolution Imaging Spectroradiometer (MODIS) fire points were overlaid on the model generated maps to assess their reliability in predicting forest fires. They were found to be not reliable in Bhutan, as most of them overlapped with fire-prone classes, such as ‘moderate’, ‘low’, and ‘very low’. The fire-prone map derived from the FR model will assist Bhutan’s Department of Forests and Park Services to update its current National Forest Fire Management Strategy.

Suggested Citation

  • Kinley Tshering & Phuntsho Thinley & Mahyat Shafapour Tehrany & Ugyen Thinley & Farzin Shabani, 2020. "A Comparison of the Qualitative Analytic Hierarchy Process and the Quantitative Frequency Ratio Techniques in Predicting Forest Fire-Prone Areas in Bhutan Using GIS," Forecasting, MDPI, vol. 2(2), pages 1-23, March.
  • Handle: RePEc:gam:jforec:v:2:y:2020:i:2:p:3-58:d:338921
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    References listed on IDEAS

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    1. Rodeano Roslee & Alvyn Clancey Mickey & Norbert Simon & Mohd. Norazman Norhisham, 2017. "Landslide susceptibility analysis lsa using weighted overlay method wom along the genting sempah to bentong highway pahang," Malaysian Journal of Geosciences (MJG), Zibeline International Publishing, vol. 1(2), pages 13-19, September.
    2. Sturtevant, Brian R. & Scheller, Robert M. & Miranda, Brian R. & Shinneman, Douglas & Syphard, Alexandra, 2009. "Simulating dynamic and mixed-severity fire regimes: A process-based fire extension for LANDIS-II," Ecological Modelling, Elsevier, vol. 220(23), pages 3380-3393.
    3. Hamid Pourghasemi & Biswajeet Pradhan & Candan Gokceoglu, 2012. "Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran," 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. 63(2), pages 965-996, September.
    4. 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.
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    1. Hazem Ghassan Abdo & Hussein Almohamad & Ahmed Abdullah Al Dughairi & Motirh Al-Mutiry, 2022. "GIS-Based Frequency Ratio and Analytic Hierarchy Process for Forest Fire Susceptibility Mapping in the Western Region of Syria," Sustainability, MDPI, vol. 14(8), pages 1-20, April.

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