## 2 Materials and Methods
### Data Processing
#### 2.3.2 Airborne Laser Scanning Data Processing  
Given that all ALS-derived products employ the Universal Transverse Mercator (UTM) WGS84 datum, while the ALS elevation reference relies on the Geoid 12A datum--inconsistent with the WGS84 ellipsoidal elevation reference of CASAL data--a datum conversion is required to establish elevation consistency prior to assessing CASAL's vertical accuracy using ALS as the reference dataset [38]. Following point cloud preprocessing (including noise removal and classification via LiDAR 360 software), vegetation point clouds undergo normalization to mitigate terrain-induced biases in tree height estimation. A 1 m resolution digital terrain model (DTM) derived from ground points was applied to vegetation point heights through differential subtraction to generate normalized point clouds.  
To enhance CASAL height estimation accuracy and mitigate ground elevation effects, we normalized vegetation point cloud data and implemented a 25 m buffer zone around footprint centroids derived from CASAL data [39]. Using waveform geolocation data, we aligned 25 m buffered footprints with onboard point clouds, extracting laser points within each footprint. The vertical structure and spatial distribution of vegetation point clouds are illustrated in Figure 5. Unique identifiers were assigned based on acquisition date, laser channel (Laser[x]), and footprint index.  
Vegetation cover and corresponding height parameters were calculated for each laser footprint, with detailed metrics presented in Table 4. The CASAL-derived canopy relative height was calculated from the ground position relative to the waveform start (assuming waveform initiation at the vegetation maximum within the footprint) [38; 40]. Consequently, elev_max was designated as the reference canopy height (ALS-Hmax) for each footprint. The digital terrain model (DTM) generated from the point cloud was resampled to a 5 m resolution, and the slope was extracted (resampling was performed to minimize the effect of cells with extreme slope values, a methodology found in other similar studies [41]. For each CASAL buffer zone, DTM and slope values were extracted and integrated into the CASAL dataset attribute table. Median values were calculated for both DTM and slope rasters to reduce outlier influence in ALS-derived data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Quantity** & **Description** \\ \hline Vegetation cover & 1 & Ratio of vegetation points in the first return to the total number of first return points \\ elev\_max & 1 & Maximum height value of all points within a statistical unit \\ elev\_min & 1 & Minimum height value of all points within a statistical unit \\ elev\_mean & 1 & Average height value of all points within a statistical unit \\ elev\_median\_z & 1 & Median height value of all points within a statistical unit \\ elev\_percentile\_1 st-100 th & 15 & Normalized point cloud heights within a statistical unit, sorted and divided into percentiles (1 st to 100 th) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Feature parameters derived from airborne LiDAR (ALS) data products.  
Figure 4: Characteristic full-waveform LiDAR return from the CASAL system. Typical waveforms and RH metrics for CASAL on relatively flat terrain. On flat terrain, the first Gaussian corresponds to reflections from the top of the canopy, while the last Gaussian refers mainly to the lowest point in the footprint, i.e., the ground.