## 5 Discussion  
The GGP and other large-scale afforestation programs play a pivotal role in mitigating climate change, preserving biodiversity, and preventing land degradation. However, in the early stages of these projects, planted forests often feature low canopy densities due to immature tree growth, failing to form dense forest stands. This makes it challenging for satellite remote sensing to effectively monitor these areas [29]. The advancement of UAV LiDAR technology offers new opportunities for large-scale AGB estimation [30]. A few studies have demonstrated the feasibility of using UAV LiDAR data to map AGB distributions in sparse woodlands in arid and semi-arid regions [31; 32]. This study focuses on AGB estimation in the GGP stands, leveraging LiDAR data, remote sensing imagery, and machine learning models. From AGB sample set construction to model validation, the proposed approach achieved satisfactory accuracies. The findings highlight the capability of UAVs combined with remote sensing imagery to effectively monitor AGB in sparse woodlands with low tree heights and low canopy densities. Additionally, this method addresses the challenge of limited ground-based sample data across large areas, laying a solid foundation for the long-term dynamic monitoring of GGP stands. However, the GGP stands are generally fragmented, and the spatial resolution of Sentinel-2 is still limited in capturing the subtle variability of AGB in sparsely vegetated areas of GGP; the introduction of higher-resolution remote sensing data in the future can further enhance the ability to estimate aboveground biomass, such as the combination of multispectral and UAV-LiDAR [33] or the combination of GF-1 imagery data and UAV-LiDAR to estimate AGB [34].  
Spectral information from Sentinel-2, vegetation indices, and tasseled cap transformation indices was selected due to its ability to capture vegetation health and biomass variability. In the variable importance ranking of the AGB estimation model, \"TCG\", \"RENDVI\", and \"VDVI\" exhibited relatively high contributions. These findings were consistent with previous studies for sparse vegetation in arid and semiarid areas. [PERSON] et al. [35] utilized tasseled cap transformation indices to estimate AGB, further confirming the relevance of greenness to biomass estimation. In sparse vegetation areas, spectral signals of vegetation greenness are typically weak, making them difficult to capture using traditional vegetation indices such as NDVI [36; 37; 38]. In arid and semi-arid regions, TCG effectively extracts the localized greenness features of sparse vegetation, distinguishing  
Figure 8: AGB distribution map of the main tree species in GGP stands.  
subtle differences between low-density vegetation and bare soil and thereby enhancing the recognition of sparse vegetation distributions [39]. Currently, the red-edge normalized difference vegetation index (RENDVI) is extensively utilized for vegetation detection and ecological monitoring in arid regions [40, 41]. The visible-band difference vegetation index (VDVI) is highly sensitive to vegetation information and has been widely applied in arid desert regions with sparse vegetation, significantly improving vegetation detection and monitoring [42, 43, 44]. [PERSON] et al. [45] demonstrated the importance of Sentinel-2's red-edge bands for vegetation estimation.