## 1 Introduction  
Forest ecosystems constitute a crucial component of global natural resources. As the largest terrestrial carbon pool, they contribute over 60% of terrestrial carbon sinks and play an irreplaceable role in regulating the climate and stabilizing the biosphere [1]. Tree height serves as a key factor that characterizes the vertical structure of forests. It is also an important indicator for assessing carbon stock, productivity, and ecosystem service functions [2, 3, 4], as well as for estimating biomass and evaluating forest health [5, 6]. Therefore, it holds significant importance to acquire tree height information within forest systems in a rapid, real-time, and high-precision manner. Tree height information is predominantly obtained via two approaches, namely manual ground-based surveys and remote sensing inversion [7]. Manual survey methodsare capable of providing more precise forest data in low-density and sparse forests [8,9]. However, it proves challenging to acquire accurate information in forests featuring complex topography, intricate stand structures, and high vegetation coverage. In this study, topography refers to the physical characteristics of the land surface, including elevation, slope, and terrain features, which can significantly influence the accuracy of tree height measurements. Moreover, such surveys typically demand a substantial amount of time and labor [10]. To address the limitations of manual surveys, remote sensing technology, which boasts the advantages of extensive coverage and high precision, has been extensively utilized in recent years for the collection and monitoring of forest- related data, including that of various tree species [11,12]. Light detection and ranging (LiDAR) can be classified into ground-based LiDAR (TLS), airborne laser scanning (ALS), and satellite-borne LiDAR based on different platforms. ALS is capable of acquiring high-precision three-dimensional forest structural information and extracting forest features such as tree heights. Nevertheless, it is constrained by the flight altitude of the aircraft, route planning, and the high cost [13, 14, 15, 16, 17]. Satellite-borne LiDAR, on the contrary, is characterized by large-scale coverage and all-weather operability. This feature can guarantee the continuity and stability of the data. It also has robust anti-interference capabilities and can penetrate forest vegetation to provide information on the canopy stratification structure, understory vegetation, and the ground surface [15,18,19]. The Ice, Cloud, and Land Elevation Satellite (ICESat-1; 2003-2009) pioneered space-borne full-waveform altimetry through its Geoscience Laser Altimeter System (GLAS), enabling unprecedented monitoring of polar elevation dynamics across glaciers, ice sheets, and sea ice [20]. As ICESat-1's successor, ICESat-2 (launched September 2018) [21] features the Advanced Terrain Laser Altimeter System (ATLAS), employing photon-counting LiDAR technology with multi-beam micropulses at high repetition rates for three-dimensional along-track feature characterization [22,23]. [PERSON] et al. [24] conducted accuracy assessments of extracted ground and canopy heights in Finnish forests, while [PERSON] et al. [25] systematically analyzed influencing factors on ICESat-2's height estimation accuracy. Their respective studies reported Bias of 0.28 m (ground) and \(-\)0.21 m (canopy), with corresponding RMSE values of 0.96 m and 2.50 m. Launched in December 2018, the Global Ecosystem Dynamics Investigation (GEDI) [26] represents the first dedicated satellite LiDAR system for vegetation vertical structure monitoring, featuring superior technical specifications including reduced footprint diameter, enhanced sampling density, and improved resolution/sensitivity compared to conventional spaceborne LiDAR systems. However, its performance remains susceptible to slope, vegetation height, and beam sensitivity factors.