## 3 Results and Discussion
[PERSON] et al. (2011) [20] reported that the  
Figure 5: Vegetation classification image on Geomunoreum forest from 2017 to 2019. (**a**) Evergreen (NDVI values > 0.6); (**b**) deciduous (0.6 \(\geq\) NDVI values). region comprises deciduous forest, evergreen broad-leaved forest, shrubbery, grassland, and artificial aforested regions through an actual vegetation survey. Especially in the artificial aforested region, the major tree species were _Cryptomeria japonica_, _Pinus Hunbergii_, and _Chamacyparis obtusa_. Additionally, the north and west sides of the oreum showed monocultural species by _C. japonica_. Compared with the current study, the region division by actual vegetation survey was very similar to the results of this study. In addition, comparing the results with the large-scale vegetation map by the Korea Forest Service [27], the compartment of each tree region is very similar to Figures 3-6. Assessing a region's vegetation is essential to constructing an efficiently mixed forest. It helps to determine the composition of a mixed forest. For example, in this Geomunoreum forest, the vast northern region was occupied by evergreen trees, and the deciduous tree cover was minor (Figures 3-6). \begin{table}
\begin{tabular}{c c c} \hline  & **Evergreen (\%)** & **Deciduous (\%)** \\ \hline
12 April 2017 & 246,313 (61.54\%) & 153,912 (38.46\%) \\
18 April 2018 & 279,296 (69.78\%) & 120,929 (30.22\%) \\
24 March 2019 & 249,097 (62.24\%) & 151,128 (37.76\%) \\
23 March 2020 & 249,152 (62.25\%) & 151,073 (37.75\%) \\
24 March 2021 & 239,550 (59.85\%) & 160,675 (40.15\%) \\ \hline \end{tabular}
\end{table} 
Table 1: The number of pixels and ratio of the evergreen and deciduous area on Geomunoreum for 2017 to 2021. Figure 6: Vegetation classification image on Geomunoreum forest from 2020 to 2021. (**a**) Evergreen (NDVI values \(>\) 0.6); (**b**) deciduous (0.6 \(\geq\) NDVI values). Therefore, deciduous trees might be needed in this region; some evergreen trees might be eliminated, and some tall deciduous trees would be implanted in the mixed forest. The southern region of the forest had the opposite situation (Figures 3-6). Deciduous trees occupy this region; therefore, this region needs some evergreen trees. This region's vegetation is complex. _Styrax japonica_, _Cormus kousa_, _Carpinus laxiflora_, _Mallotus japonicus_, _Melisonsal ddhunii_, and _Acer pulatum_ compose the deciduous forest [20]. Additionally, the grassland region, which was included in the deciduous area, needs direct investigation to confirm why this region has no tree vegetation. The reason the region remains grassland could come from the soil construction, environment, or other things. Most traditional approaches for classifying types of vegetation regions are performed by manual human efforts, such as direct vegetation surveys; human experts visit a study area and then explore the area on foot with equipment such as a camera. It would not be easy to explore an area that cannot be reached by car or walking. Flying a drone could help monitor the forest that cannot be reached by human experts. However, it also requires human efforts to operate drones. There may be some areas that human experts miss if they explore by walking. Moreover, it would not be easy for them to visit the study area frequently to monitor environmental changes over time. The data-driven approach proposed in this paper can be a more efficient method than the traditional direct vegetation  
Figure 8: The temperature data of the study area were obtained from Seonheul Automatic Weather Station (AWS), which is 3 km away from the study area. Figure 7: The temperature in Celsius in the study area from 2017 to 2022. The vertical line in black represents the maximum and minimum temperature. We highlight the case of January and February, because the vegetation needs time to change. surveys. The novelty of the proposed method comes from the simplicity to see the trends without screening the given area every time, which can be time- consuming, labor-intensive, and expensive.