## 3 Results
### Results of the Importance of Tree Species Characteristics at Different Altitudes
\begin{tabular}{c c c} \hline \hline
**Evaluation** & **Formula** & **Describe** \\
**Indicators** & & \\ \hline Producer’s & \(PA=\frac{X_{i}}{X_{i+i}}\) & Denotes the number of correct classifications of a category X to the total number of true reference samples of that category X, which reflects the ratio of the probability of being correctly classified. \\  
variables for tree species classification in each elevation study area. The optimal feature variables selected for each elevation are shown in Figure 3.  
Analyzing the importance of the selected features, it was found that for the study area at an elevation of 1600 m (Figure 3a), IPVI, SAVI, NDVI, RVI, and Blue had higher importance scores of 0.059169, 0.057463, 0.055815, 0.055256, and 0.051124, respectively. In the study area at an elevation of 1800 m (Figure 3b), SAVI, B3_Mean, RVI, and Blue had higher importance scores of 0.050658, 0.050043, 0.046915, and 0.04652, respectively. In the study area at an elevation of 2000 m (Figure 3c), Red, NDVI, B3_Mean, and SAVI had higher importance scores of 0.049911, 0.045405, 0.04376, and 0.042695, respectively. It can be observed that SAVI played a crucial role in the classification of tree species in all three elevation study areas. Furthermore, the feature variables extracted from the LiDAR data  
Figure 3: Feature importance rankings at different altitudes: (**a**) 1600 m; (**b**) 1800 m; (**c**) 2000 m.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Altitude 1600 m**} & \multicolumn{2}{c}{**Altitude 1800 m**} & \multicolumn{2}{c}{**Altitude 2000 m**} \\ \hline \multirow{2}{*}{**Species**} & **Important** & \multirow{2}{*}{**Species**} & **Important** & \multirow{2}{*}{**Species**} & **Important** \\  & **Values** & & **Values** & & **Values** \\ \hline Parm & 0.391 & Parm & 0.416 & Parm & 0.502 \\ C.cor & 0.083 & B.pla & 0.154 & L.gme & 0.151 \\ B.alb & 0.074 & C.cor & 0.049 & Q.ali & 0.068 \\ B.pla & 0.044 & L.gme & 0.038 & C.gla & 0.054 \\ L.gme & 0.029 & A.far & 0.037 & F.spa & 0.054 \\ C.lan & 0.028 & Q. & 0.029 & B.pla & 0.046 \\ A.misy & 0.024 & M.gly & 0.028 & C.mol & 0.042 \\ C.tur & 0.024 & L.bolt & 0.027 & A.fab & 0.037 \\ L.bolt & 0.022 & C.con & 0.026 & C.con & 0.023 \\ D.kak & 0.019 & A.bat & 0.022 & C.seg & 0.023 \\ All & 0.738 & All & 0.826 & All & 1.000 \\ \hline \multicolumn{2}{c}{Note: Parm _(Pims armandis)_; L.gme _(Laris gmelini)_; B.pla _(Betula gmelini)_; C.cor _(Carpinus cor-data)_; C.(Cumisbarnia)_; B.alb _(Betula gmeliniensis)_; A.far _(Abres jargessi)_; Q.(Quercus)_; Q.ali _(Quercus alimen_; var.acuisterra)_; C.gla _(Cyclcholomiss glaucuci)_; F.spa _(Fargesia gmelmat)_; C.lan _(Cumisbarnia lanccolata)_; A.miy _(Accr mijabet)_; C.tur _(Carpinus glaucuci)_; L.bolt _(Lindera otsibula)_; D.kak _(Diospyros hzik)_; M.gly _(Metaspoa gylbytostrotokoides)_; C.con _(Cornus controvers)_; A.bat _(Amygdalahesygenica batch)_; C.mol _(Castanan nollissima Blume)_; A.fab _(Abies fjtr)_; C.seg _(Castanan seguinii_ Dode)_; Other (other tree species). The following abbreviations are the same.} \\ \hline \hline \end{tabular} Note: Parm _(Pims armandis)_; L.gme _(Laris gmelini)_; B.pla _(Betula gmelini)_; C.cor _(Carpinus cor-data)_; C.(Cumisbarnia)_; B.alb _(Betula gmeliniensis)_; A.far _(Abres jargessi)_; Q.(Quercus)_; Q.ali _(Quercus alimen_; var.acuisterra)_; C.gla _(Cyclcholomiss glaucuci)_; F.spa _(Fargesia gmelmat)_; C.lan _(Cumisbarnia lanccolata)_; A.miy _(Accr mijabet)_; C.tur _(Carpinus glaucuci)_; L.bolt _(Lindera otsibula)_; D.kak _(Diospyros hzik)_; M.gly _(Metaspoa gylbytostrotokoides)_; C.con _(Cornus controvers)_; A.bat _(Amygdalahesygenica batch)_; C.mol _(Castanan nollissima Blume)_; A.fab _(Abies fjtr)_; C.seg _(Castanan seguinii_ Dode)_; Other (other tree species). The following abbreviations are the same.} \\ \hline \hline \end{tabular} 
\end{table}
Table 8: Ranking of the top ten important values of tree species compositions at different elevations.