Estimating mangrove above-ground biomass using GEDI LiDAR, Sentinel data, and machine learning in Rawa Aopa National Park, Indonesia
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Abstract. Zulkarnain, Saleh MB, Purnomo H, Kuncahyo B, Tiryana T, Puspaningsih N. 2025. Estimating mangrove above-ground biomass using GEDI LiDAR, Sentinel data, and machine learning in Rawa Aopa National Park, Indonesia. Asian J For 9: 166-181. Accurate above-ground biomass estimation is crucial for climate change mitigation and sustainable mangrove management. Therefore, this study aimed to integrate Global Ecosystem Dynamics Investigation Light Detection and Ranging (GEDI LiDAR), Sentinel-1, and Sentinel-2, with field measurement data to estimate above-ground biomass in the mangrove forest of Rawa Aopa Watumohai National Park (RAWNP), Indonesia. Data processing was performed using Google Earth Engine, model training was conducted in Google Colab, and spatial analysis was performed with QGIS. Mangrove cover was mapped using Normalized Difference Moisture Index (NDMI) and Modified Normalized Difference Water Index (MNDWI) to estimate mangrove area in RAWNP. Furthermore, machine learning models, including Random Forest (RF), Gradient Boosting, Extreme Gradient Boosting, and Support Vector Regression, were developed and validated based on 30 field plots and 10-fold cross-validation. A total of 2,176 GEDI footprint points were used as reference data for model training. The results showed that RF outperformed the other model, with R²=0.46, Root Mean Square Error (RMSE)=72.52 during the evaluation stage and R²=0.69, RMSE=29.47 during validation. Multitemporal analysis (2019-2023) showed peak above-ground biomass density in 2021 at 148.94 tons ha-¹, followed by a decline. This study presents an effective approach for estimating and mapping above-ground biomass in the mangrove forest of RAWNP, and the resulting biomass maps can be used to support mangrove conservation strategies, REDD+ verification, and coastal ecosystem management in tropical protected areas.
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