2025/12/16 by Eustache Diemert, Anaëlle Dambreville · 1 voice
Environmental Science · #Forest ecology and management #Plant Water Relations and Carbon Dynamics #Remote Sensing and LiDAR Applications
paper · doi:10.64898/2025.12.16.694295
openalex publication_date 2025/12/16 · openalex created_date 2025/12/16 · openalex updated_date 2026/07/15
Abstract Allometric Models (AMs) play a central role in monitoring and mitigating climate change as they provide accurate estimation of biomass and carbon sequestered by trees from non-destructive, easy to obtain physical measurements. Unfortunately, practitioners spend considerable effort in researching, qualifying and choosing AMs for specific growth conditions. To overcome this situation Chave et al. (2014) developed a pan-tropical AM with equivalent accuracy to local, site-specific AMs. We ameliorate this result by incorporating contextual information pertaining to growth conditions in a Machine Learning (ML) model, eventually achieving a reduction in Mean Average Error (MAE) of -17% as measured on hold-out data. This breakthrough shall have important impact in applications such as national forest inventories, carbon certifications and calibration of satellite based biomass maps to field data. To complete, we propose a principled method to estimate how much additional error one can expect when applying a given AM to shifting conditions and provide a data-driven safety check to practitioners.