Remote-Sensing-Based Prioritization of Post-Fire Restoration Actions in Mediterranean Ecosystems: A Case Study in Cyprus
Journal
Remote Sensing
Date Issued
April 2, 2025
DOI
10.3390/rs17071269
Abstract
Global forest degradation and deforestation present urgent environmental challenges demanding efficient strategies for ecological restoration to maximize the impacts and minimize the costs. This study aims to develop a spatial decision support tool to prioritize post-fire restoration actions in Mediterranean ecosystems, with a focus on Cyprus. At the core of this study is the GRESTO Index (GreenHIT-RESTORATION Index), a novel geospatial tool designed to guide reforestation efforts in fire-affected areas. GRESTO integrates geospatial data and ecological criteria through a multi-criteria decision-making approach based on the Analytic Hierarchy Process (AHP). The model incorporates nine key indicators, including fire severity, tree density, land cover, fire history, slope, elevation, aspect, precipitation, and temperature, and classifies restoration priority zones into low, medium, and high categories. When applied to the Solea fire event in Cyprus, the model identified 24% of the area as high priority, 66% as medium and 10% as low. The validation against previous restoration actions implemented in the study area demonstrated reliable agreement, with an overall accuracy of 80.9%, a recall of 0.70 for high priority areas, and an AUC of 0.79, indicating very good separability. Moreover, sensitivity analysis further confirmed the robustness of the model under varying parameter weights. These findings highlight the GRESTO model’s potential to support data-driven, cost-effective restoration planning aligned with national and international environmental goals.
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