## 4 Conclusions  
The use of GIS allowed the analysis of GE cultivation suitability in mountainous areas, clearly identifying the characteristics and limitations of various classification methods. Among the factors crucial to crop growth conditions, temperature conditions, especially the application of extreme temperatures, can provide significant scientific benefits, such as the accurate identification of suitable cultivation areas for GE, the reduction of cultivation failure risks, sustainable cultivation management, the efficient use of cultivation areas, environmental monitoring, and ecosystem maintenance. Therefore, the application of extreme temperatures is essential for analyzing GE cultivation suitability, acting as a crucial methodological result for the successful cultivation of GE and the practice of sustainable agriculture and forestry. Additionally, this study evaluated the characteristics and similarities of various classification methods in the exploration of GE cultivation suitability, confirming the applicability of GIS-based GE cultivation suitability exploration techniques and providing essential foundational data for selecting appropriate classification methods. However, empirical research on whether farmers have identified and cultivated GE in suitable areas in mountainous regions or whether they are producing GE stably remains a task that needs to be addressed. Therefore, further research is needed to investigate GE cultivation areas in mountainous regions through field surveys and to compare the accuracy of these areas with the results of various classification methods through overlay analysis. Additionally, by investigating the native habitats of GE in mountainous regions and applying correlative algorithms such as MaxEnt, Random Forest, and ENFA, and comparing these results, the completeness of the GE cultivation suitability analysis can be enhanced, providing a means \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Classification**} & \multirow{2}{*}{**Grade**} & \multicolumn{4}{c}{**GI**} & \multicolumn{2}{c}{**Overall**} & \multirow{2}{*}{**Kappa**} \\ \cline{3-3} \cline{5-8}  & & & **SS y** & & **PSS** & & **US** & **Total** & **Accuracy (\%)** & **Coefficient** \\ \hline \multirow{8}{*}{NB z} & SS & 107,169 & 59,549 & 0 & 0 & 166,718 & \multirow{8}{*}{63.42} & \multirow{8}{*}{0.47} \\  & PSS & 0 & 286,376 & 127,084 & 0 & 413,460 & & \\  & PUS & 0 & 0 & 489,885 & 366,241 & 856,126 & & \\  & US & 0 & 0 & 0 & 75,110 & 75,110 & & \\  & Total & 107,169 & 345,925 & 616,969 & 441,351 & 1,511,414 & & \\  & Total & (100.0) x & (82.8) & (79.4) & (17.0) & (69.8) w & & \\ \hline \multirow{8}{*}{Qu} & SS & 107,169 & 345,925 & 0 & 0 & 453,094 & \multirow{8}{*}{39.59} \\  & PSS & 0 & 0 & 413,902 & 0 & 413,902 & & \\  & PUS & 0 & 0 & 203,067 & 153,145 & 356,212 & & \\  & US & 0 & 0 & 0 & 288,206 & 288,206 & & \\  & Total & 107,169 & 345,925 & 616,969 & 441,351 & 1,511,414 & & \\  & Total & (100.0) & (0.0) & (32.9) & (65.3) & (49.6) & & \\ \hline \multirow{8}{*}{EI} & SS & 5872 & 0 & 0 & 0 & 5872 & \multirow{8}{*}{65.52} \\  & PSS & 101,297 & 345,925 & 0 & 0 & 447,222 & \\ \cline{1-1}  & PUS & 0 & 0 & 616,969 & 419,836 & 1,036,805 & \\ \cline{1-1}  & US & 0 & 0 & 0 & 21,515 & 21,515 & 65.52 & 0.47 \\ \cline{1-1}  & Total & (107,169 & 345,925 & 616,969 & 441,351 & 1,511,414 & \\ \cline{1-1}  & Total & (5.5) & (100.0) & (100.0) & (4.9) & (52.6) & \\ \hline \hline \end{tabular}  
* NB = Natural breaks (panks), Qu = Quantile, EI = Equal interval, and GI = Geometric interval. y SS = Suitable site, PSS = Possibly suitable site, PUS = Probably unsuitable site, and US = Unsuitable site. x Parentheses indicate the percentage (%) of the number of pixels in the grade of the comparative classification method relative to the number of pixels in the grade of the reference classification method. w Parentheses indicate the arithmetic mean value (%) of reclassification accuracy for the same grade between the different classification methods.  
\end{table}