## 2 Materials and Methods
### Data Acquisition and Pre-Processing
#### 2.2.3 Data Pre-Processing and Data Alignment  
The preprocessing of multispectral data includes two parts: image stitching and image registration. Image stitching was completed using INPHO5.7 software, while image registration was carried out using ArcMap10.5. The multispectral images were processed using INPHO software to generate an orthoimage of the study area. The INPHO system for processing airborne multispectral data includes the following steps [30]: (a) The preparation of raw data; (b) data preprocessing; (c) digital aerial triangulation processing. The average error of each parameter was X: 0.084 m, Y: 0.081 m, Z: 0.068 m, omega: 3.0 mdeg, phi: 3.2 mdeg, kappa: 3.7 mdeg. (d) The construction of a digital elevation model (DEM), and (e) the construction of a digital orthophoto map (DOM). The final output was a multispectral orthoimage of the study area with an average resolution of 0.2 m.  
The LiDAR data acquisition instruments consisted of a scanner, which was mainly used to record the distance between the sensor and the ground, a kinematic GPS receiver, which was used to record the spatial position of the aircraft center, and an IMU, which was used to record the flight attitude data. The data from these instruments were jointly processed with differential calculation results to obtain the spatial position and attitude of each observation time, as well as the imaging device. The preprocessing of the airborne LiDAR data in this study included ground point classification, point cloud normalization, and the generation of a digital elevation model (DEM) and canopy height model (CHM). R software was used for the preprocessing of the airborne LiDAR data. The progressive morphological filter (PMF) proposed by [PERSON] et al. [31] was utilized to classify ground points from the point cloud and output them. The ground points were processed using the irregular triangular network (TIN) algorithm to obtain a smooth DEM of the study area. The point cloud was normalized with respect to the DEM (i.e., by subtracting the elevation value of the point cloud from the elevation value of the same location). The normalized point cloud was then processed using point-to-raster methods and smoothing to generate the CHM of the study area. As the airborne multispectral data and LiDAR data were acquired through different sensors, georeferencing was required. In this study, the nearest neighbor resampling method was used in ArcMap 10.5 to generate multispectral data and the CHM data of LiDAR with a spatial resolution of 1 m. Then, the geographic registration method was used to select 10 pairs of control points with uniform spatial distribution on two images of the study areas of different altitudes. The feature elements that were easy to distinguish were mainly \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Airborne LiDAR: Leica ALS80-HP** & \\ \hline Max. flight height & 3500 m & Max. pulse frequency & 1000 KHz \\ Min. flight height & 100 m & Scan method & Sine, triangle, parallel \\ View field angle & 0-72\({}^{\circ}\) & Max. scan frequency & 200 Hz, 158 Hz, 120 Hz \\ Laser pulse width & 400 ps & Density echo count & 3 \\ \hline \multicolumn{4}{c}{Airborne Multi-Spectrum} \\ \hline Along field of view angle & 48.2\({}^{\circ}\) & Along Pixels & 6708 \\ Across field of view angle & 61.7\({}^{\circ}\) & Across Pixels & 8956 \\ Spectral band & R, G, B, NIR & Color depth & 14 bit \\ Focal length & 45.0 mm & Pixel size & 5.2 \\ Frame rate & 1.9 s & Storage & 6.4 TByte \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Airborne integrated sensor system detailed parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Altitude** & **Species Number** & **Tree Number** & **Average DBH** & **Average Height** \\ \hline
1600 & 32 & 400 & 16.18 cm & 14.62 m \\
1800 & 23 & 502 & 17.88 cm & 13.82 m \\
2000 & 10 & 439 & 18.92 cm & 13.68 m \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Survey information on tree species in sample plots at different altitudes. selected.