## 4 Discussion
### Canopy Height  
Among CASAL-derived relative height (RH) metrics, RH90 exhibited the strongest correlation with ALS-Hmax. This finding contrasts with [PERSON] and [PERSON] et al. [16; 18], who identified GEDI's RH95 as the optimal indicator of true forest height. This discrepancy likely arises from differences in pulse width between CASAL and GEDI systems. Furthermore, our results diverge from [PERSON] et al.'s [39] findings. Their research demonstrated that ICESat-2's RH98 most accurately represented true forest height. This inconsistency may result from temporal discrepancies between CASAL and ALS data acquisitions, which were not considered in our analysis. Additionally, their study incorporated diverse land cover types, potentially influencing results. These findings suggest that optimal height percentiles may vary across satellite systems depending on land cover characteristics.  
Steep terrain significantly contributes to CASAL waveform broadening and increased overlap between canopy and understory echoes. This broadening effect exacerbates waveform overlap, making CASAL-based canopy height estimation in high-slope areas particularly sensitive to terrain influence, thereby reducing estimation accuracy. Canopy height estimation accuracy (\(R^{2}\), RMSE) declines with increasing slope. This occurs because waveform decomposition's last peak reliably references canopy height in flat terrain, whereas slope-induced waveform broadening in non-flat areas reduces accuracy when using the last peak as a reference. As terrain slope increases, last-peak-based canopy height estimation becomes less accurate, explaining the observed inverse relationship between slope and estimation precision.  
The forest canopy height estimation accuracy (\(R^{2}\) and RMSE) in this study surpasses that reported by [PERSON] et al. [43] for southwestern Quebec using CASAL parameters. This improvement is attributed to winter data collection, when reduced vegetation coverage enables CASAL laser pulses to penetrate forest stands more effectively, yielding precise understory topographic information. Enhanced penetration of CASAL laser pulses through winter canopies facilitates accurate differentiation between canopy and ground peaks, thereby improving canopy height estimation precision.  
CASAL exhibits maximum canopy height inversion error at vegetation cover \(>0.9\), likely due to limited laser pulse penetration through dense, structurally complex canopies, resulting in a misclassification of canopy echoes as ground returns. Conversely, in low-vegetation-cover areas, the highest waveform echo elevation may not correspond to the tallest canopy tree, potentially due to tree dispersion within the 25 m footprint and geolocation errors. The observed discrepancy between highest echo elevation and actual canopy height may result from tree dispersion within the 25 m footprint and geolocation inaccuracies.  
CASAL demonstrated optimal accuracy in mixed forests, evidenced by maximum \(R^{2}\) values and minimum RMSE and MAD metrics. Broadleaf forests showed moderate accuracy, whereas coniferous forests exhibited the poorest performance among the three forest types. These performance variations likely stem from structural and canopy complexity differences among forest types, influencing system inversion efficacy.The study area contains extensive mixed and broadleaf forests. During winter leaf-off conditions, CASAL's laser pulses effectively penetrate the canopy, enabling precise retrieval of sub-canopy terrain information. Our findings demonstrate that winter leaf-off conditions in deciduous broadleaf forests significantly enhance the performance of spaceborne LiDAR canopy height retrievals. The improved signal penetration during winter is evidenced by the distinct separation of canopy and ground peaks in waveform data (Figure 4), enabling a more accurate identification of these features and consequently higher precision in forest canopy height estimation.