## 2 Materials and Methods
### Classification Method  
#### 2.6.1 Random Forest  
The random forest (RF) algorithm is a highly effective machine learning algorithm. It is composed of a series of decision trees, and the classification result is determined by the vote of all decision trees, which has a stronger generalization ability than a single decision  
\begin{table}
\begin{tabular}{c c c} \hline
**Texture Feature** & **Abbreviation** & **Formula** & **Description** \\ \hline Mean & ME & \(ME=\sum_{i}\sum_{j}i\cdot P(i,j)\) & Indicates the average degree of image gray. \\ Variance & VA & \(VA=\sum_{i}\sum_{j}(i-\mu)^{2}P(i,j)\) & Reflects the gray level change degree of remote \\ Homogeneity & HO & \(HO=\sum_{i}\sum_{j}P(i,j)\Big{[}1+(i-j)^{2}\Big{]}\) & sensing image. \\ Contrast & CO & CO \(=\sum_{i}\sum_{j}P(i,j)(i-j)^{2}\) & Reflects the clarity of the image and the depth of texture grooves. \\ Dissimilarity & DI & \(DI=\sum_{i}\sum_{j}|i-j|P(i,j)\) & Represents local regional texture features in remote sensing images. \\ Entropy & EN & \(EN=-\sum_{i}\sum_{j}P(i,j)logP(i,j)\) & A randomness measure representing the amount of information contained in an image. \\ Second moment & SM & \(SM=\sum_{i}\sum_{j}P(i,j)^{2}\) & Uniformity of gray distribution and texture measurement of remote sensing image. \\ Correlation & CR & \(CR=\sum_{i}\sum_{j}(i-\mu_{x})(j-\mu_{y})P(i,j)/\sigma_{x}\sigma_{y}\) & Indicates the similarity of image gray level. \\ \hline \end{tabular} Note: \(P(i,j)\) denotes the probability of grayscale \(P(g_{1},g_{2})\) in the grayscale co-occurrence matrix; \(\mu_{x},\mu_{y},\sigma_{x},\sigma_{y}\) are the mean and standard deviation of the rows and columns, respectively.  
\end{table}
Table 4: Descriptions of texture feature statistics.  
\begin{table}
\begin{tabular}{c c c} \hline
**Characteristic Variable** & **Abbreviation** & **Describe** \\ \hline Canopy height model & CHM & Maximum height of point cloud above ground. \\ Mean canopy height & \(Z_{\text{mean}}\) & Average point cloud height above ground. \\ Percentage of mean canopy height & \(Z_{\text{above\_zm}}\) & Percentage of total points for all canopy point clouds above. \\ Percentage of crown base height & \(Z_{\text{above}}\) & Proportion of the number of point clouds above \\  & & the basic height of the tree canopy. \\ Percentile of point cloud height & \(H_{5}\), \(H_{10}\), \(H_{15}\)\(\ldots\)\(H_{90}\), \(H_{95}\) & The 5 th, 10 th, 15 th, 20 th \(\ldots\) The points in the 90 th and 95 th height cells, the value of each raster cell is calculated between all points in the cell. \\  & & The point cloud density of each raster cell is divided into 9 parts according to the height of the points larger than 0.5 m in the cell, and then the points in different layers are divided by the total number of points in the cell. \\ Point cloud height distribution statistics & \(Z_{\text{max}},Z_{\text{sd}},Z_{\text{skew}},Z_{\text{kurt}},Z_{\text{en}}\) & Maximum height value, standard deviation, \\  & & deviation, kurtosis, information entropy of all \\  & & canopy point clouds. \\ \hline \end{tabular}
\end{table}
Table 5: LiDAR characteristics and variable descriptions.  
tree [38]. RF trains the samples with the training data to create a forest decision model with multiple decision trees.