ArticleCarbon balance and management2025
Spatially explicit prediction of Nepal's forest biomass stocks, a data-driven bioregionalisation and machine learning approach.
Article in Carbon balance and management, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundEstimation 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 zones, to model deviations from the predicted potential AGB.
resultsOur method significantly improves forest AGB estimation in heterogeneous mountain landscapes, achieving a 25% reduction in prediction error compared to the best-performing existing model. The final forest AGB map, generated at 30 m resolution, reveals distinct spatial patterns, with the Central Himalayas emerging as a key carbon reservoir, harbouring forest patches exceeding 1000 t ha
conclusionThe spatially explicit estimates of actual and potential forest biomass presented is important step towards elucidation of spatial distribution patterns of forest carbon pools and environmental controls. It also provides support for critical initiatives, including climate change mitigation strategies, monitoring forest landscape restoration, and combatting forest degradation challenges. The proposed approach, integrating both broad-scale environmental controls and fine-scale deviations, offers a robust method that is potentially applicable other mountainous regions and contributes for tracking changes in forest carbon over time, essential for REDD+ initiatives.
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