Aboveground Biomass Retrieval and Time Series Analysis Across Different Forest Types Using Multi-Source Data Fusion

Accurate monitoring of aboveground biomass (AGB) is essential for forest carbon accounting and climate change mitigation, yet signal saturation and the treatment of forest landscapes as biophysically homogeneous entities remain significant barriers to high-fidelity mapping. This study implements an ecologically integrated model that leverages forest-type specific (coniferous vs. broadleaf) to enhance regional AGB retrieval. By refining established data fusion techniques with structural and compositional parameters, this approach seeks to mitigate systematic biases often found in generic regional assessments. Compared with 360 geo-referenced subplots, our stratified Support Vector Regression (SVR) model significantly outperformed non-classified counterparts, achieving an R2 of 0.76 and a reduced RMSE of 18.48 Mg/ha. This refined precision enabled a nuanced time-series analysis (2013–2020), revealing that while regional AGB increased from 157.13 to 192.23 Mg/ha, this trajectory was punctuated by a distinct sub-regional growth plateau between 2016 and 2018. By correlating these trends with disturbance data, we identified a 11.27% biomass decline in southwestern sectors linked to a tripling of burned area, pinpointing intensified fire regimes as the primary driver overriding recovery-driven carbon gains. These findings demonstrate that harmonizing multi-sensor signals with functional forest differentiation provides the necessary sensitivity to track carbon resilience, offering a scalable and robust tool for operational forest management and global carbon cycle research.