## 2 Materials and Methods
### Generation and Analysis of Composite Suitability Map and Final Suitability Map  
The composite suitability map is a thematic map reclassified into four grades from the initial weighted sum map, which was created by applying a weighted sum to the thematic maps of environmental factors to explore suitable cultivation sites for GE. To generate a composite suitability map, thematic maps for each factor were created as raster data with a spatial resolution of 10 m \(\times\) 10 m. The attribute values of the raster data were reclassified into ranks corresponding to the criteria for each factor. Using the weighted sum function, an initial weighted sum map was created by assigning weights to the ranks of 11 thematic maps, excluding the extreme-temperature maps (summer and winter temperatures). The initial weighted sum map was reclassified using various classification methods to generate a composite suitability map. The suitability grades for GE cultivation were reclassified into four grades: suitable site (SS), possibly suitable site (PSS), probably unsuitable site (PUS), and unsuitable site (US). The classification methods used for reclassification were \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Temperature** \\ **Factor** \\ \end{tabular} } & \multicolumn{6}{c}{**Average Temperatures by AWS Locations (\({}^{\circ}\)C)**} \\ \cline{2-7}  & **Muju** & **Deogyusan** & **Seolcheonbons** & **Gagok** & **Daedeok** & **Buksang** & **Donghyang** & **Jinan-Jucheon** \\  & **(212 \({}^{\circ}\)7)** & **(660)** & **(1515)** & **(118)** & **(205)** & **(324)** & **(320)** & **(269)** \\ \hline GST \({}^{\text{z}}\) & 17.6 & 15.5 & 11.1 & 17.9 & 17.4 & 17.2 & 16.6 & 16.6 \\ \hline AGPT & 20.8 & 18.6 & 14.1 & 21.2 & 20.5 & 20.3 & 19.9 & 20.0 \\ \hline ST & 29.9 & 26.5 & 21.6 & 30.3 & 29.3 & 29.0 & 29.2 & 29.9 \\ \hline WT & \(-\)13.9 & \(-\)14.8 & \(-\)20.6 & \(-\)13.7 & \(-\)12.3 & \(-\)12.5 & \(-\)15.6 & \(-\)15.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average temperatures for temperature factors over the last 10 years (2014–2023) obtained from AWS. the Natural Breaks (Jenks) (NB), Quantile (Qu), Equal Interval (EI), and Geometric Interval (GI) methods [48]. The NB groups had similar values and maximized the differences between grades. This method is useful when there are no meaningful intervals in the data, and an optimal grouping is sought [49; 50; 51]. Qu is a useful method for understanding the ranking of each value within a dataset. EI is recommended when intervals are meaningful and a specific number of grades is expected. Finally, the GI is useful for finding natural groupings in the data while keeping the range of grades approximately equal [49; 51]. The reclassification results of the various methods were compared to analyze their differences. A final suitability map was generated by removing areas corresponding to extreme temperatures from the composite suitability map. Using this map, the impact of applying extreme temperatures on the area proportions of each grade according to different classification methods was analyzed. Finally, to evaluate the similarity between the classification methods, a confusion matrix based on pixel counts was generated for each classification method as a reference. The overall accuracy (Equation (3)), which represents the proportion of agreement between the classification methods, and kappa (\(\hat{K}\)) analysis (Equation (4)), which measures classification accuracy while considering the probability of agreement by chance, were performed. A \(\hat{K}\) value of 0.8 or above indicates a high level of agreement, 0.4 to 0.8 indicates a moderate level of agreement, and below 0.4 indicates a low level of agreement [52]. Additionally, the reclassification accuracy for the same grade among the different classification methods was evaluated.