Spatially explicit prediction of Nepal's forest biomass stocks, a data-driven bioregionalisation and machine learning approach.

ABSTRACT:

Background

Estimation of forest biomass stocks in vast and heterogeneous mountain ranges is critical in the context of climate change mitigation and remains challenging because of limited field observations and unknown relationships between variation in forest biomass and environmental heterogeneity. We addressed this challenge by using forest inventory plot observations and a novel spatial modelling approach. In the first step of our approach, we employ a rigorous clustering process to identify a homogeneous group of locations based on tree species and topoclimatic variables and predict potential forest aboveground biomass (AGB). Subsequently, in the second step, we incorporate finer-scale variables, including proxies of forest structure, disturbance likelihood, and elevation zone

SUBMITTER: Khanal S 

PROVIDER: S-EPMC12838514 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature