Estimation of tree attributes in mixed tropical hill forests using Landsat-8 and Sentinel-1 data

Estimating forest attributes is crucial for understanding forest performance. While forest protection and tree plantations can serve as cost-effective mitigation strategies to address climate change challenges, monitoring natural forests and plantations remains expensive and challenging for a developing nation like Bangladesh, which is highly donor-dependent and lacks advanced remote sensing research facilities such as LiDAR or drone technology. In this context, open-source remote sensing data can serve as an effective tool for monitoring forest structure. In this study, we evaluated the ability of Landsat-8 and Sentinel-1 data to predict forest attributes using ground-measured tree data from 110 plots (each 400 m2 in size). We applied the random forest algorithm to predict tree height, density, basal area, and volume in two forest-protected areas of Bangladesh. For tree height and tree density, Sentinel-1 showed slightly higher prediction accuracy (RMSE = 7% and 46%, respectively) compared to Landsat-8 and combined data (Landsat-8 and Sentinel-1). Landsat-8 data had a higher prediction accuracy (RMSE = 23%) for basal area compared to Sentinel-1 and combined data. For volume, the combined dataset outperformed Sentinel-1 and Landsat-8; however, prediction accuracy was low. Our results indicate that height and basal area can be well predicted by combining Sentinel and Landsat data. The results underscore the value of open-source remote sensing tools as cost-effective alternatives for forest monitoring, offering critical insights for forest management and climate change mitigation strategies in developing nations.