Rong Wei

Aboveground biomass (AGB) in grasslands is a key biophysical indicator for evaluating grassland productivity, ecosystem functioning, and carbon storage. However, accurate regional-scale AGB estimation in complex mountainous terrain remains challenging because of fragmented topography, strong environmental gradients, and heterogeneous grassland patches. This study evaluated the applicability of satellite embedding features for grassland AGB estimation in the Three Parallel Rivers region of Yunnan Province, China. Based on 135 field plots surveyed during the 2022 growing season, 64-dimensional annual satellite embedding features were extracted, and a conventional feature system derived from Sentinel-1, Sentinel-2, and DEM data was constructed for comparison. Four feature systems, namely Traditional-9, Traditional-40, Emb-9-PCA, and Emb-64, were evaluated using six regression models, including RF, SVR, GPR, XGBoost, LightGBM, and Elastic Net. Random five-fold cross-validation was used to compare feature systems and model combinations, while spatial cross-validation was further applied to assess model robustness under spatially independent conditions. The results showed that satellite embedding features outperformed conventional remote sensing features. Under random five-fold cross-validation, the Emb-64-based XGBoost model achieved the best performance, with an R² of 0.7949 ± 0.0405, an RMSE of 0.1388 ± 0.0191 t/ha, and an MAE of 0.1141 ± 0.0203 t/ha. Under spatial cross-validation, XGBoost retained the highest mean performance, with an R² of 0.7660 ± 0.1003, an RMSE of 0.1417 ± 0.0111 t/ha, and an MAE of 0.1165 ± 0.0124 t/ha. SHAP and Spearman correlation analyses further indicated that important embedding dimensions were associated with AGB and selected conventional environmental variables. Regional mapping showed that predicted grassland AGB ranged from 0.17 to 1.26 t/ha, with a mean value of 0.79 t/ha, and exhibited significant positive spatial autocorrelation. Bootstrap-based uncertainty analysis indicated that higher uncertainty mainly occurred in fragmented mountainous areas, grassland edges, and transition zones. These findings suggest that satellite embedding features provide a promising high-dimensional representation for grassland AGB estimation in complex mountainous landscapes, while their ecological interpretability and transferability still require further investigation.

Accurate estimation of forest aboveground carbon (AGC) is crucial for understanding the carbon cycle and formulating climate policies, yet it remains challenging in complex mountainous regions. This study used machine learning framework to estimate the spatiotemporal dynamics of AGC in the Three Parallel Rivers region of China from 2003 to 2024. By integrating China’s National Forest Continuous Inventory (NFCI) data with multispectral satellite imagery, we employed a two-stage feature selection strategy to identify key predictor variables. Among three ensemble algorithms tested, the Random Forest model achieved the optimal performance (R² = 0.74). The results indicated a net increase of 67.05 Tg in total AGC over the two decades, with a spatial pattern characterized by higher densities in the west and north. Geographical Detector analysis revealed that the driving forces were synergistic, with the interaction between temperature and population density exhibiting the most prominent explanatory capacity. This study provides a high-resolution (30 m) benchmark for AGC in a global biodiversity hotspot and underscores the critical role of ecological protection policies in enhancing carbon sequestration, offering valuable insights for managing similar mountain ecosystems worldwide.

Aboveground biomass (AGB) in grasslands is a vital metric for assessing ecosystem functioning and health. Accurate and efficient AGB estimation is essential for the scientific management and sustainable use of grassland resources. However, achieving low-cost, high-efficiency AGB estimation via remote sensing remains a key challenge. This study integrates Sentinel-1 and Sentinel-2 imagery to derive 38 multi-source feature variables, including backscatter coefficients, texture, spectral reflectance, vegetation indices, and topographic factors. These features are combined with AGB data from 112 field plots in the Three Parallel Rivers area. Feature selection was performed using Pearson correlation, Random Forest (RF), and SHAP values to identify optimal variable sets. Genetic Programming (GP) was then applied for nonlinear optimization of the selected features. Three machine learning models—RF, GBRT, and KNN—were used to estimate AGB and generate spatial distribution maps. The results revealed notable differences in model accuracy, with RF performing best overall, outperforming GBRT and KNN. After GP optimization, all models showed improved performance, with the RF model based on RF-selected features achieving the highest accuracy (R² = 0.90, RMSE = 0.31 t/ha, MAE = 0.23 t/ha), improving R² by 0.03 and reducing RMSE and MAE by 0.05 and 0.03 t/ha, respectively. Spatial mapping showed the AGB ranged from 0.41 to 3.59 t/ha, with a mean of 1.39 t/ha, closely aligned with the actual distribution characteristics. This study demonstrates that the RF model, combined with multi-source features and GP optimization, provides an effective approach to grassland AGB estimation and supports ecological monitoring in complex areas.

Accurate above-ground biomass (AGB) quantification is confounded by signal saturation and data fusion challenges, particularly in structurally complex ecosystems like bamboo forests. To address these gaps, this study developed a two-stage framework to map the AGB of Dendrocalamus giganteus in a subtropical mountain environment. This study first employed Empirical Bayesian Kriging Regression Prediction (EBKRP) to spatialize sparse GEDI and ICESat-2 LiDAR metrics using Sentinel-2 and topographic covariates. Subsequently, a stacked ensemble model, integrating four machine learning algorithms, predicted AGB from the full suite of continuous variables. The stacking model achieved high predictive accuracy (R² = 0.84, RMSE = 11.07 Mg ha⁻¹) and substantially mitigated the common bias of underestimating high AGB, improving the predicted observed regression slope from a base model average of 0.63 to 0.81. Furthermore, SHAP analysis provided mechanistic insights, identifying the canopy photon rate as the dominant predictor and quantifying the ecological thresholds governing AGB distribution. The mean AGB density was 71.8 ± 21.9 Mg ha⁻¹, with its spatial pattern influenced by elevation and human settlements. This research provides a robust framework for synergizing multi-source remote sensing data to improve AGB estimation, offering a refined methodological pathway for large-scale carbon stock assessments.

Aboveground biomass (AGB) in grasslands is a vital metric for assessing ecosystem functioning and health. Accurate and efficient AGB estimation is essential for the scientific management and sustainable use of grassland resources. However, achieving low-cost, high-efficiency AGB estimation via remote sensing remains a key challenge. This study integrates Sentinel-1 and Sentinel-2 imagery to derive 38 multi-source feature variables, including backscatter coefficients, texture, spectral reflectance, vegetation indices, and topographic factors. These features are combined with AGB data from 112 field plots in the Three Parallel Rivers Area. Feature selection was performed using Pearson correlation, Random Forest (RF), and SHAP values to identify optimal variable sets. Genetic Programming (GP) was then applied for nonlinear optimization of the selected features. Three machine learning models—RF, GBRT, and KNN—were used to estimate AGB and generate spatial distribution maps. Results revealed notable differences in model accuracy, with RF performing best overall, outperforming GBRT and KNN. After GP optimization, all models showed improved performance, with the RF model based on RF-selected features achieving the highest accuracy (R² = 0.90, RMSE = 0.31 t/hm², MAE = 0.23 t/hm²), improving R² by 0.03 and reducing RMSE and MAE by 0.05 and 0.03 t/hm², respectively. Spatial mapping showed AGB ranged from 0.41 to 3.59 t/hm², with a mean of 1.39 t/hm², closely aligned with the actual distribution characteristics. This study demonstrates that the RF model, combined with multi-source features and GP optimization, provides an effective approach to grassland AGB estimation and supports ecological monitoring in complex areas.

Accurate above-ground biomass (AGB) quantification is confounded by signal saturation and data fusion challenges, particularly in structurally complex ecosystems like bamboo forests. To address these gaps, this study developed a two-stage framework to map the AGB of Dendrocalamus giganteus in a subtropical mountain environment. This study first employed Empirical Bayesian Kriging Regression Prediction (EBKRP) to spatialize sparse GEDI and ICESat-2 LiDAR metrics using Sentinel-2 and topographic covariates. Subsequently, a stacked ensemble model, integrating four machine learning algorithms, predicted AGB from the full suite of continuous variables. The stacking model achieved high predictive accuracy (R² = 0.84, RMSE = 11.07 Mg ha⁻¹) and substantially mitigated the common bias of underestimating high AGB, improving the predicted-observed regression slope from a base-model average of 0.63 to 0.81. Furthermore, SHAP analysis provided mechanistic insights, identifying canopy photon rate as the dominant predictor and quantifying the ecological thresholds governing AGB distribution. The mean AGB density was 71.8 ± 21.9 Mg ha⁻¹, with its spatial pattern influenced by elevation and human settlements. This research provides a robust framework for synergizing multi-source remote sensing data to improve AGB estimation, offering a refined methodological pathway for large-scale carbon stock assessments.