Forest Aboveground Biomass Dataset Derived from Gaofen-7 Satellite Imagery in Daxing District, Beijing

This dataset is a dedicated inversion result dataset for forest aboveground biomass (AGB) in Daxing District, Beijing. Its core data source is high-resolution satellite imagery from Gaofen-7, aiming to provide accurate spatial distribution data of forest AGB for regional forest carbon stock monitoring, carbon dynamics research, and carbon storage capacity assessment. Meanwhile, it can serve as basic data support for forest ecosystem management, remote sensing inversion model validation, and related work.The dataset covers the entire forest ecosystem of Daxing District, Beijing. The AGB inversion results are presented as raster data in TIFF format, with a spatial resolution consistent with the precision of Gaofen-7 satellite imagery, which can clearly reflect the spatial heterogeneity of forest AGB in the region. These inversion results are generated based on multi-dimensional remote sensing features modeling. In the early stage, key features were extracted from the preprocessed Gaofen-7 satellite data, including texture features (calculated based on the Gray-Level Co-occurrence Matrix) that reflect forest structure, visible spectral vegetation indices that characterize vegetation growth status, and original three-band RGB spectral information, laying a solid feature foundation for accurate inversion.During the modeling process, three single machine learning algorithms—Random Forest (RF), Gradient Boosting Tree (GBT), and XGBoost—were compared, and the Stacking ensemble learning method was adopted to optimize model performance. Finally, the inversion results of the Stacking ensemble model were selected as the core content of the dataset. Verified by five-fold cross-validation, the coefficient of determination (R²) of this core result reaches 0.6229, the root mean square error (RMSE) is 57.34 Mg/ha, and the mean absolute error (MAE) is 39.99 Mg/ha. Compared with the best-performing single algorithm (XGBoost, R²=0.5852), its accuracy is improved by 6.44%, and it effectively solves the common overestimation or underestimation problems of AGB in traditional modeling. The reliability and accuracy of the data have been strictly verified, which can meet the needs of regional-scale forest AGB-related research and applications.