Burned area detection using Sentinel-1 and Sentinel-2 features and analysis of ensemble models with explainable AI
ENVIRONMENTAL AND ECOLOGICAL STATISTICS, cilt.33, sa.1, ss.1-34, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 33 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s10651-025-00693-3
- Dergi Adı: ENVIRONMENTAL AND ECOLOGICAL STATISTICS
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, IBZ Online, ABI/INFORM, Aerospace Database, BIOSIS, Environment Index, Geobase, Greenfile, Zoological Record, Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Earth, Atmospheric, & Aquatic Science Collection (ProQuest), Sociology Source Ultimate (EBSCO), Technology Collection (ProQuest)
- Sayfa Sayıları: ss.1-34
- Erciyes Üniversitesi Adresli: Evet
Özet
Wildfires are serious environmental disasters that threaten ecosystem sustainability, reduce biodiversity, and disrupt the global carbon cycle. This study aimed to improve the accuracy and interpretability of burned area mapping in the Bodrum, K & ouml;yce & gbreve;iz, and Marmaris districts of Mu & gbreve;la province, T & uuml;rkiye, by integrating multi-sensor satellite data with ensemble machine learning and explainable artificial intelligence (XAI). Sentinel-1 and Sentinel-2 imagery were processed to derive 50 optical and radar-based features, which were classified using four ensemble methods: AdaBoost.M1, Random Forest, GentleBoost, and RUSBoost. The SHapley Additive exPlanations framework was applied to quantify the contribution of each feature and guide feature selection. Results indicated that Sentinel-2 spectral indices, particularly NBR, NBRSWIR, and bands B12 and B8, were the most decisive variables for burned area classification, while Sentinel-1 features contributed less strongly overall but the IVH_ratio variable emerged as the most informative radar-based indicator. Among the classifiers, AdaBoost.M1 achieved the most stable and accurate performance, Random Forest showed strong sensitivity to spectral variability, GentleBoost performed effectively with reduced feature sets, and RUSBoost addressed class imbalance but showed weaker spatial continuity. Importantly, classification accuracy saturated after approximately 15 features, demonstrating that high accuracy can be achieved with fewer inputs and lower computational demand. Moreover, McNemar's statistical test confirmed that the observed differences among classifiers were significant, highlighting GentleBoost's clear superiority over other methods. Overall, the integration of optical and radar data with XAI provides a transparent, efficient, and replicable framework for operational burned area monitoring in Mediterranean ecosystems.