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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

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Forest Fire Management Section, Department of Forests and Park Services, Thimphu 11001, Bhutan
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Ugyen Wangchuck Institute for Conservation and Environment, Lamaigoenpa, Bumthang 32001, Bhutan
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Ecosystem Management, University of New England, Armidale NSW 2351, Australia
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Department of Geodesy, Kandilli Observatory and Earthquake Research Institute, Bogazici University, 34680 Cengelkoy-Istanbul, Turkey
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College of Natural Resources, Royal University of Bhutan, Lobesa, Punakha 13001, Bhutan
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ARC Centre of Excellence for Australian Biodiversity and Heritage, Global Ecology, College of Science and Engineering, Flinders University, Adelaide, SA GPO Box 2100, Australia
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Department of Biological Sciences, Macquarie University, Sydney NSW 2109, Australia
*
Author to whom correspondence should be addressed.
Forecasting 2020, 2(2), 36-58; https://doi.org/10.3390/forecast2020003
Received: 9 February 2020 / Revised: 24 March 2020 / Accepted: 27 March 2020 / Published: 30 March 2020
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. View Full-Text
Keywords: forest fire-prone areas mapping; forest fire management; Analytic Hierarchy Process (AHP); Frequency Ratio (FR); Geographic Information System (GIS) forest fire-prone areas mapping; forest fire management; Analytic Hierarchy Process (AHP); Frequency Ratio (FR); Geographic Information System (GIS)
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MDPI and ACS Style

Tshering, K.; Thinley, P.; Shafapour Tehrany, M.; Thinley, U.; Shabani, F. 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 2020, 2, 36-58. https://doi.org/10.3390/forecast2020003

AMA Style

Tshering K, Thinley P, Shafapour Tehrany M, Thinley U, Shabani F. 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. 2020; 2(2):36-58. https://doi.org/10.3390/forecast2020003

Chicago/Turabian Style

Tshering, Kinley; Thinley, Phuntsho; Shafapour Tehrany, Mahyat; Thinley, Ugyen; Shabani, Farzin. 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 2, no. 2: 36-58. https://doi.org/10.3390/forecast2020003

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