Izvorni znanstveni članak
https://doi.org/10.15177/seefor.26-014
Assessing Wildfire Susceptibility in Mediterranean Forest Ecosystems: A Spatial Ensemble Machine Learning Approach in Portugal
Mohamed Amine Laghmich
; Ibn Tofail University, Faculty of Humanities and Social Sciences, Department of Geography, Avenue de l’Université, B.P. 401, MA-14000 Kénitra, Morocco
*
Mohammed Ariche
; Ibn Tofail University, Faculty of Humanities and Social Sciences, Department of Geography, Avenue de l’Université, B.P. 401, MA-14000 Kénitra, Morocco
Bouthaina Ahayk
; Ibn Tofail University, Faculty of Humanities and Social Sciences, Department of Geography, Avenue de l’Université, B.P. 401, MA-14000 Kénitra, Morocco
* Dopisni autor.
Sažetak
Wildfires constitute a significant ecological disturbance in Mediterranean ecosystems, exerting profound effects on forest dynamics, biodiversity, and land management practices. The development of precise susceptibility mapping is essential for informing prevention strategies, optimizing resource allocation, and promoting sustainable forest management under increasing fire pressure. This study employed and compared four machine learning classifiers—Random Forest, Classification and Regression Trees (CART), Gradient Boosting, and Extreme Gradient Boosting (XGBoost)—to model wildfire susceptibility across Portugal. Six environmental and anthropogenic predictors were utilized: vegetation indices (NDVI), land use/land cover, slope, land surface temperature (LST), wind speed, and distance to human settlements. The results indicated that vegetation-related variables, particularly NDVI and land cover, were the most significant determinants of fire occurrence, followed by slope and wind speed, thus underscoring the role of biophysical conditions in shaping the fire regimes. A spatial block cross-validation strategy was implemented to rigorously account for spatial autocorrelation. Under this evaluation, XGBoost demonstrated the highest predictive performance (overall accuracy = 90.03%, AUC = 0.951), surpassing or equaling that of the other ensemble methods. The resulting susceptibility maps, generated utilizing simple Kriging interpolation to translate discrete model predictions into continuous surfaces, identified the northern and central interior regions as the most fire-prone, consistent with historical fire records. Quantitatively, the optimal model classified 19.08% of the national territory as having a very high fire susceptibility. Our findings underscore the efficacy of ensemble machine learning techniques in capturing complex fire–environment interactions and provide spatially explicit information that can enhance fire prevention planning, support conservation priorities, and guide adaptive forest management in Mediterranean regions.
Ključne riječi
wildfire susceptibility; forest fire risk assessment; Mediterranean forest ecosystems; forest management; spatial block cross-validation; biophysical drivers; fire ecology
Hrčak ID:
349127
URI
Datum izdavanja:
30.6.2026.
Posjeta: 0 *