Conference Agenda

Understanding above-ground biomass (AGB) in forests is essential for unravelling the complexities of biogeochemical cycles and climate change. Remote sensing, particularly through NASA's GEDI (Global Ecosystem Dynamics Investigation) mission, significantly boosts our ability to monitor AGB on a large scale, thanks to advanced technologies like LiDAR (Light Detection and Ranging). As for the PolInSAR (Polarimetric Interferometric Synthetic Aperture Radar) technique, it is particularly effective in measuring forest height, which in turn enhances AGB estimation across different forest types by utilising radar data and models such as the Random Volume over Ground model for better accuracy because biomass is related to forest height and her volume. Recent studies emphasise the significance of diverse data sources and sophisticated machine learning (ML) techniques in achieving precise AGB estimates, showcasing a variety of successful models and data integrations from global research. Additionally, recent research findings indicate that ML algorithms significantly enhance AGB estimation by utilising multisensor data. Our study specifically focuses on estimating AGB density in Gabon's Pongara National Park, where we fused PolInSAR, Sentinel-2, Dynamic World V1, and GEDI (specifically GEDI Level 4B Gridded AGB density) data with ML regression algorithms such as Gradient Tree Boosting (GTB), Random Forest (RF), and Classification and Regression Trees (CART). Our findings achieved the best estimation for biomass mapping using the GTB model, and alongside all models, they demonstrated a high correlation (up to 0.99) with GEDI, emphasising the effectiveness and influence of PolInSAR features specifically, topographic and structural metrics (e.g., DEM heights and incidence angle), followed by forest height derived from the RVoG inversion model and percent canopy cover, while Sentinel-2 indices primarily played a supporting role.