Forest aboveground biomass (AGB) was mapped throughout China using large footprint LiDAR waveform data from the Geoscience Laser Altimeter System (GLAS) onboard NASA’s Ice, Cloud, and land Elevation Satellite (ICESat), Moderate Resolution Imaging Spectro-radiometer (MODIS) imagery and forest inventory data. The entire land of China was divided into seven zones according to the geographic characteristics of the forests. The forest AGB prediction models were separately developed for different forest types in each of the seven forest zones at GLAS footprint level from GLAS waveform parameters and biomass derived from height and diameter at breast height (DBH) field observation. Some waveform parameters used in the prediction models were able to reduce the effects of slope on biomass estimation. The models of GLAS-based biomass estimates were developed by using GLAS footprints with slopes less than 20° and slopes ≥ 20°, respectively. Then, all GLAS footprint biomass and MODIS data were used to establish Random Forest regression models for extrapolating footprint AGB to a nationwide scale. The total amount of estimated AGB in Chinese forests around 2006 was about 12,622 Mt vs. 12,617 Mt derived from the seventh national forest resource inventory data. Nearly half of all provinces showed a relative error (%) of less than 20%, and 80% of total provinces had relative errors less than 50%.

Mapping the magnitude and spatial distribution of forest aboveground biomass (AGB, in Mg·ha−1) is crucial to improve our understanding of the terrestrial carbon cycle. Landsat/TM (Thematic Mapper) and ICESat/GLAS (Ice, Cloud, and land Elevation Satellite, Geoscience Laser Altimeter System) data were integrated to estimate the AGB in the Changbai Mountain area. Firstly, four forest types were delineated according to TM data classification. Secondly, different models for prediction of the AGB at the GLAS footprint level were developed from GLAS waveform metrics and the AGB was derived from field observations using multiple stepwise regression. Lastly, GLAS-derived AGB, in combination with vegetation indices, leaf area index (LAI), canopy closure, and digital elevation model (DEM), were used to drive a data fusion model based on the random forest approach for extrapolating the GLAS footprint AGB to a continuous AGB map. The classification result showed that the Changbai Mountain region was characterized as forest-rich in altitudinal vegetation zones. The contribution of remote sensing variables in modeling the AGB was evaluated. Vegetation index metrics account for large amount of contribution in AGB ranges <150 Mg·ha−1, while canopy closure has the largest contribution in AGB ranges ≥150 Mg·ha−1. Our study revealed that spatial information from two sensors and DEM could be combined to estimate the AGB with an R2 of 0.72 and an RMSE of 25.24 Mg·ha−1 in validation at stand level (size varied from ~0.3 ha to ~3 ha).

Aboveground biomass (AGB) serves as an important indicator for assessing the productivity of forest ecosystems and exploring the global carbon cycle. However, accurate estimation of forest AGB remains a significant challenge, especially when integrating multi-source remote sensing data, and the effects of different feature combinations for AGB estimation results are unclear. In this study, we proposed a method for estimating forest AGB by combining Gao Fen 7 (GF-7) stereo imagery with data from Sentinel-1 (S1), Sentinel-2 (S2), and the Advanced Land Observing Satellite digital elevation model (ALOS DEM), and field survey data. The continuous tree height (TH) feature was derived using GF-7 stereo imagery and the ALOS DEM. Spectral features were extracted from S1 and S2, and topographic features were extracted from the ALOS DEM. Using these features, 15 feature combinations were constructed. The recursive feature elimination (RFE) method was used to optimize each feature combination, which was then input into the extreme gradient boosting (XGBoost) model for AGB estimation. Different combinations of features used to estimate forest AGB were compared. The best model was selected for mapping AGB distribution at 30 m resolution. The outcomes showed that the forest AGB model was composed of 13 features, including TH, topographic, and spectral features extracted from S1 and S2 data. This model achieved the best prediction performance, with a determination coefficient (R²) of 0.71 and a root mean square error (RMSE) of 18.11 Mg/ha. TH was found to be the most important predictive feature, followed by S2 optical features, topographic features, and S1 radar features.

Forest ecosystems serve as pivotal components of the global carbon cycle, with canopy height representing a critical biophysical parameter for quantifying ecosystem functionality, thereby holding substantial implications for forest resource management and carbon sequestration assessments. The precise extraction of ground elevation and vegetation canopy height is essential for advancing topographic and ecological research. The Terrestrial Ecosystem Carbon Inventory Satellite (referred to as TECIS hereafter) offers unprecedented capabilities for the large-scale, high-precision extraction of ground elevation and vegetation canopy height. Using the Northeast China Tiger and Leopard National Park as our study area, we first processed TECIS data to derive topographic and canopy height profiles. Subsequently, the accuracy of TECIS-derived ground and canopy height estimates was validated using onboard light detection and ranging (LiDAR) measurements. Finally, we systematically evaluated the influence of multiple factors on estimation accuracy. Our analysis revealed that TECIS-derived ground and canopy height estimates exhibited mean errors of 0.7 m and −0.35 m, respectively, with corresponding root mean square error (RMSE) values of 3.83 m and 2.70 m. Furthermore, slope gradient, vegetation coverage, and forest composition emerged as the dominant factors influencing canopy height estimation accuracy. These findings provide a scientific basis for optimizing the screening and application of TECIS data in global forest carbon monitoring.