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

Kinley Tshering,Mahyat Shafapour Tehrany,Phuntsho Thinley,Farzin Shabani,Ugyen Thinley

doi:10.3390/forecast2020003

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.

Highlights

Forest fire is an environmental catastrophe that threatens the safety of humans, infrastructure, and biodiversity [1,2]
The weakness of the Analytic Hierarchy Process (AHP) model is due to potential errors in pair-wise comparisons of the conditioning factors
The AHP model is highly reliant on expert judgment that is prone to error in the sense that its accuracy can be greatly altered by divergent views from the fire experts

Summary

Introduction

Forest fire is an environmental catastrophe that threatens the safety of humans, infrastructure, and biodiversity [1,2]. Abatzoglou and Williams [14] stated the increased fire activity in the western US and in the US Northern Rockies has been driven by both rising temperatures and widespread drought, since 2000. These factors have altered the trend and frequency of forest fires at an alarming rate in many regions of the world [15,16,17]. Several methods have been proposed and tested to map forest fire-prone regions These approaches can be categorized into three major groups of physics-based techniques [19], statistical techniques [20], and machine learning techniques [21]

Objectives

Methods

Results

Discussion

Conclusion