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Large-scale mangrove mapping in Thailand using multi-sensor ensemble machine learning with sentinel-1/2 and SRTM data
Pinkaew, S.; Koedsin, W.; Chan, J.C.; Huete, A. (2025). Large-scale mangrove mapping in Thailand using multi-sensor ensemble machine learning with sentinel-1/2 and SRTM data. Remote Sensing Applications 40: 101744. https://dx.doi.org/10.1016/j.rsase.2025.101744
In: Remote Sensing Applications. Elsevier: Amsterdam. ISSN 2352-9385; e-ISSN 2352-9385, meer
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| Author keywords |
Mangrove mapping;Ensemble machine learning;Multi-sensor fusion;Random forest;XGBoost;discrimination;ecosystems;forests;carbon |
| Auteurs | | Top |
- Pinkaew, S.
- Koedsin, W.
- Chan, J.C., meer
- Huete, A.
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| Abstract |
Mangrove ecosystems provide critical ecological services but face increasing pressure from anthropogenic activities and climate change. Accurate large-scale mapping is essential for effective conservation strategies. We produced a 2024 national mangrove map by merging Sentinel-2 multispectral imagery, Sentinel-1 synthetic-aperture radar (SAR) backscatter and a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM). The analysis domain comprised all Global Mangrove Watch (2023) polygons with a 2 km buffer. From these layers we derived 23 predictors, including six spectral bands, six vegetation indices (e.g., Normalized Difference Vegetation Index, NDVI; Enhanced Vegetation Index, EVI; Mangrove Vegetation Index, MVI), four radar texture metrics (VV, VH, VV/VH ratio, contrast) and terrain variables(elevation, slope, aspect). Five widely used machine-learning classifiers-Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Tree (CART), Gradient Tree Boosting (GTB) and XGBoost-were combined through soft voting after grid-based hyper-parameter tuning. The ensemble achieved an overall accuracy of 97.0 %, outperforming individual models (95.8-96.9 %). Feature-importance analysis identified MVI as the strongest discriminator (0.209-0.720), followed by VV contrast (0.052-0.097) and elevation (0.044-0.089). The final map shows 2557 km2 of mangroves distributed across 24 provinces, with 75 % located along the Andaman Sea coast. By blending complementary optical, radar and topographic information in a fully scriptbased Google Earth Engine (GEE) workflow, the study delivers an operationally scalable tool for national monitoring that supports conservation planning, carbon accounting and climateadaptation policies. |
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