Modeling of landslide sensitive areas using GIS in semi-arid forests and evaluation in terms of forest rehabilitation

Abstract

Introduction: In order to increase, protect, and sustain forest assets, it is important to determine the factors that affect forestry activities and minimize their impact. In this study, the landslide factor in forestry applications was tackled. The negative effect of unpredictable factors of forestry activities (road construction, harvesting, afforestation, etc.) can be reduced by calculating and modeling the landslide susceptibility ratios of degraded forests. Objective: To demonstrate the applicability of a landslide susceptibility map for supporting decision makers in the assessment of semi-arid and landslide-sensitive forestlands in forestry activities and rehabilitation works. Materials and method: Six models were introduced by using the fuzzy inference system (FIS) and modified analytical hierarchy process (M-AHP) approaches. A combination of elevation, slope (degree), distance to faults, lithology, aspect, and plan curvature was used in the models. Results and discussion: The most successful models under the FIS and M-AHP approaches were FIS Model 3, and M-AHP Model 1, with areas under the curve (AUC) of 80.2 %, and 78.1 %, respectively. Using precision forestry by making decisions based on the area's landslide susceptibility in the management and planning stage (e.g., construction of forest infrastructure facilities, afforestation, and forest harvesting and rehabilitation), will increase the success of forestry activities. Conclusion: It is very important to determine the landslide areas in advance and reliably for effective execution of forestry practices in landslide sensitive forestlands, in order to increase the success of forestry activities in accordance with the sustainable forest management approach.