Seventeen-Year Reconstruction of Tropical Forest Aboveground Biomass Dynamics in Borneo Using GEDI L4B and Multi-Sensor Data Fusion

Abstract

Forest aboveground biomass (AGB) is a key component of terrestrial carbon storage, essential for understanding the carbon cycle and evaluating carbon sink potential. However, estimating long-term AGB in tropical forests and detecting its spatial and temporal trends remain challenging due to observational gaps and methodological constraints. Here, we integrate GEDI L4B gridded biomass data with features from MODIS, PALSAR/PALSAR-2, SRTM, and climate datasets, and apply the AutoGluon ensemble learning framework to develop AGB retrieval models. We generated annual AGB maps at 1 km resolution for Borneo’s forests from 2007 to 2023, achieving high predictive accuracy (R2 = 0.92, RMSE = 32.84 Mg/ha, rRMSE = 21.06%). Residuals were generally balanced and close to a symmetric distribution, indicating no strong bias within the moderate biomass range (50–350 Mg/ha). However, in very high-biomass stands, the model tended to underestimate AGB, reflecting saturation effects that persist despite clear improvements over existing products. Estimated mean AGB values ranged from 180.52 to 214.09 Mg/ha, with total AGB varying between 13.05 and 14.10 Pg. Trend analysis using Sen’s slope and the Mann–Kendall test revealed significant AGB trends in 31.31% of forested areas, with 68.76% showing increases. This study offers a robust and scalable framework for continuous tropical forest carbon monitoring, providing critical support for carbon accounting, forest management, and policy-making.

Plant phenotyping relevance

衛星・LiDAR等のマルチセンサーデータと機械学習により、森林の地上部バイオマスという植物群落形質を推定する手法を開発・検証しており、形質推定法が中心である。

Code and data availability

The paper's Data Availability Statement only points to generic public data providers (GEE datasets, NASA Earthdata GEDI L4B). No author-generated AGB maps, analysis code, trained models, or paper-specific data deposit with a public URL is mentioned; the cited datasets are standard public inputs, not paper-specific phen

No evidence-backed public reproduction asset is currently recorded.