Estimating Above-Ground Biomass of the Regional Forest Landscape of Northern Western Ghats Using Machine Learning Algorithms and Multi-sensor Remote Sensing Data

Forest Biomass Assessment Using Multisource Earth Observation Data: Techniques, Data Sets and Applications

“…This issue comprises 17 articles, all focusing on Indian sites as study regions and encompassing various forest types, including two studies on mangroves. These articles can be broadly categorized as: (a) Forest biomass estimation using optical sensors, with attention to very high resolution and hyperspectral data (Pardeshi et al, 2024;Pasha & Dadhwal, 2024;Singh et al, 2024;Verma et al, 2024), (b) Utilization of SAR sensors, including polarimetric data, for biomass estimation (Ali & Khati, 2024;Bhavsar et al, 2024;Hati et al, 2024;Singhal et al, 2024), (c) Application of LiDAR sensors, both terrestrial and spaceborne, for biomass estimation Rodda et al, 2024aRodda et al, , 2024b, (d) Integration of multi-sensor EO data for biomass estimation (Behera et al, 2024;Prakash et al, 2024;Sainuddin et al, 2024;Sanam et al, 2024), and (e) Biomass product validation, regional studies, and application-focused research (Bhat et al, 2024).…”

“…This study underscored the importance of multi-sensor data integration and ML models in AGB estimation, highlighting their potential applications in forest management and climate change mitigation efforts in the Himalayan mountainous region. Sainuddin et al (2024) integrated SAR and multispectral imagery with in-field observations to estimate AGB in the Purna regional landscape of northern Western Ghats, utilizing satellite data/products such as Sentinel-1, Sentinel-2, SRTM DEM, and global canopy height products. They applied machine learning algorithms including RF, Extreme Gradient Boosting (XGB), and Boosted Regression Trees (BRT) to effectively predict AGB.…”