Güngöroğlu, C.; İsmailoğlu, İ.; Kapukaya, B.; Özcan, O.; Yanalak, M.; Musaoğlu, N. Comparison between Post-Fire Analysis and Pre-Fire Risk Assessment According to Various Geospatial Data. Sustainability2024, 16, 1569.
Güngöroğlu, C.; İsmailoğlu, İ.; Kapukaya, B.; Özcan, O.; Yanalak, M.; Musaoğlu, N. Comparison between Post-Fire Analysis and Pre-Fire Risk Assessment According to Various Geospatial Data. Sustainability 2024, 16, 1569.
Güngöroğlu, C.; İsmailoğlu, İ.; Kapukaya, B.; Özcan, O.; Yanalak, M.; Musaoğlu, N. Comparison between Post-Fire Analysis and Pre-Fire Risk Assessment According to Various Geospatial Data. Sustainability2024, 16, 1569.
Güngöroğlu, C.; İsmailoğlu, İ.; Kapukaya, B.; Özcan, O.; Yanalak, M.; Musaoğlu, N. Comparison between Post-Fire Analysis and Pre-Fire Risk Assessment According to Various Geospatial Data. Sustainability 2024, 16, 1569.
Abstract
Abstract: Wildfires in forest ecosystems exert substantial ecological, economic, and social impacts. The effectiveness of fire management hinges on precise pre‐fire risk assessments to inform mitigation efforts. This study aims to investigate the relationship between predictions from pre‐fire risk assessments and outcomes observed through post‐fire burn severity analyses. In this study, forest fire risk was assessed with the Fuzzy Analytical Hierarchy Process, in which fire behavior factors were used as input. The degree of burn was determined using the Random Forest method using 11519 training points and 400 test points on Sentinel‐2 satellite images in three different classes. According to the results obtained from 266 selected test points located within the forest boundaries, all primary factors showed an increased effect in areas with high burn severity. Climate, in particular, emerged as the most influential factor, accounting for 52% of the overall impact. However, in cases of high fire severity, climate proved to be the most effective risk factor, ac‐counting for 67%. It followed by topography with 50% accuracy at high fire intensity. In the risk assessment is based on the FAHP method, climate was assigned the highest weight value among other factors with 32.2%. Topography emerged as the second most effective risk factor with 27 %. To evaluate the results more comprehensively both visually and statistically, two regions with different stand canopy characteristics were selected within the study area. While high burn severity had the highest accuracy in Case 1 area, moderate burn severity had the highest in Case 2 area. During the days when the fire continued, the direction of spread was obtained from MODIS images. In this way, the fire severity depending on the direction of fire progression was also interpreted. Through an analysis of various case studies and existing literature, the research underscores both the strengths and limitations inherent in predicting forest fire behavior‐based pre‐fire risk assessments. Furthermore, it emphasizes the necessity of continuous improvement to increase the success of forest fire management.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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