## 4 Discussion
### Limitations and Prospects  
GEDI footprints were not continuous, and in order to solve this problem and obtain the GEDI parameter information in entire Shangri-La, the Kriging method was used to interpolate footprints information into the polygon. The footprint of the spaceborne LiDAR is evenly distributed along the track. Usage of single spaceborne LiDAR interpolation mainly displays an intensive strip effect across ground tracks. Therefore, before interpolation, we carried out a screening of all footprints in the study area according to the filtering principle. The poor-quality footprints on one strip or adjacent strips were deleted to enhance the spatial randomness. However, the remaining footprints still cannot fully meet the spatial random distribution. Previous research demonstrated that increasing the footprints from other spaceborne LiDAR may solve the problem that the stripe effect of spatial interpolation creates [53]. Therefore, future research can consider combining the footprints of another LiDAR sensor, such as ICESat-2; combining two sensor data will allow to disrupt the phenomenon wherein homogeneous distribution of footprints from a single sensor weaken the strip effect during spatial interpolation.  
At the same time, due to a lack of verification data from airborne LiDAR, we cannot verify the accuracy of the GEDI L2B parameters under different algorithm setting groups. Therefore, this study relied on the default algorithm settings to obtain the GEDI parameters. Future research can develop some custom algorithms to decompose the original GEDI L1B waveform and acquire high-precision parameters of the forest vertical structure. Rather than using the default values provided in the standard GEDI L2B data product, a special algorithm can be tailored to different terrain conditions, regions, forest types, and vegetation coverage [54]. Combined with spaceborne LiDAR and optical remote sensing data, the synthesis of multiple variables provided rich information and it also offered more possibil  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm Setting** & **Smoothing** & **Smoothing** & **Waveform Signal** & **Waveform Signal** \\
**Group** & **Width (Noise)** & **Width (Signal)** & **Start Threshold** & **end Threshold** \\ \hline
1 & 6.5\(\sigma\) & 6.5\(\sigma\) & 3\(\sigma\) & 6\(\sigma\) \\
2 & 6.5\(\sigma\) & 3.5\(\sigma\) & 3\(\sigma\) & 3\(\sigma\) \\
3 & 6.5\(\sigma\) & 3.5\(\sigma\) & 3\(\sigma\) & 6\(\sigma\) \\
4 & 6.5\(\sigma\) & 6.5\(\sigma\) & 6\(\sigma\) & 6\(\sigma\) \\
5 & 6.5\(\sigma\) & 3.5\(\sigma\) & 3\(\sigma\) & 2\(\sigma\) \\
6 & 6.5\(\sigma\) & 3.5\(\sigma\) & 3\(\sigma\) & 4\(\sigma\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: GEDI algorithm setting groups.  
ities for finding the correlation between remote sensing images and _Quercus aquifolioides_ sample data. However, the combination of different remote sensing data sources, with variations in sensors, time resolutions, spectral resolutions, and radiation resolutions, will cause some effect on the precision of the models [55]. The diameter of the GEDI footprint level was 25 meters, but the resolution of the Landsat 9 optical remote sensing image was 30 meters. Therefore, the integration of remote sensing images with different resolutions was a major problem to be solved in this study. To overcome this challenge, based on the surface information obtained by the interpolation of GEDI footprints variable data, the spatial resolution of the surface GEDI parameters was resampled to 30 m when the raster data were exported, which was unified with the resolution of Landsat 9. In future research, GEDI data can be integrated and estimated with other data sources, such as MODIS and Sentinel, at a unified resolution.