Site-Dependent Performance of Spectral Indices and Machine Learning for Flood Detection in Mediterranean Karst Poljes
2026 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium, M2GARSS 2026, Hybrid, Marrakech, Fas, 22 - 24 Nisan 2026, ss.36-40, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/m2garss67833.2026.11582324
- Basıldığı Şehir: Hybrid, Marrakech
- Basıldığı Ülke: Fas
- Sayfa Sayıları: ss.36-40
- Anahtar Kelimeler: flood detection, karst polje, machine learning, Sentinel-2, spectral indices, transferability
- İstanbul Üniversitesi Adresli: Evet
Özet
Karst poljes experience recurrent flooding, yet optimal remote sensing detection methods remain poorly documented across diverse environments. This study evaluates six approaches-three spectral indices (Modified Normalized Difference Water Index [MNDWI], Sentinel Water Index [SWI], and Automated Water Extraction Index-shadow [AWEI-sh]) and three machine learning (ML) classifiers (Random Forest [RF], XGBoost, and Support Vector Machine [SVM])-across five morphometrically diverse poljes in southwestern Turkey using Sentinel-2 imagery (2016-2024). Results reveal pronounced site-dependent performance, with ML advantages ranging from negligible (0-1%, simple agriculture) to substantial (18.6%, greenhouse sites). MNDWI achieved 98% accuracy in urban areas but degraded to 56.9% where greenhouse plastic created spectral confusion, while ML methods maintained 95-96% accuracy through multi-feature integration. No single method universally outperforms others-site-specific land use complexity determines method selection, contradicting the common assumption that machine learning always performs best.