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Title: A Comparison of the Qualitative Analytic Hierarchy Process and the Quantitative Frequency Ratio Techniques in Predicting Forest Fire-Prone Areas in Bhutan Using GIS
Contributor(s): Tshering, Kinley (author); Thinley, Phuntsho  (author)orcid ; Tehrany, Mahyat Shafapour (author); Thinley, Ugyen (author); Shabani, Farzin  (author)
Publication Date: 2020-06
Early Online Version: 2020-03-30
Open Access: Yes
DOI: 10.3390/forecast2020003
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Fields of Research (FoR) 2020: 300706 Forestry fire management
Socio-Economic Objective (SEO) 2020: 180604 Rehabilitation or conservation of terrestrial environments

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.

Publication Type: Journal Article
Source of Publication: Forecasting, 2(2), p. 36-58
Publisher: MDPI AG
Place of Publication: Switzerland
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Environmental and Rural Science

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