## 3 Results and Discussion  
Three types of information of Geomunoreum from 2017 to 2021 are depicted in Figures 3-6; RGB images, corresponding NDVI images calculated from each RGB image, and NDVI histogram where the X-axis represents the NDVI values and the Y-axis represents the frequency of the values. It was hard to find significant differences over the years by observing these images. Additionally, in the NDVI value histogram, the distributions and means of NDVI values were similar for five years. However, the ratio of color pixels of the evergreen and deciduous areas of the region shows some difference. We counted these pixels based on the NDVI value threshold, as mentioned in the previous section. Table 1 indicates that the evergreen area slightly decreased from 61.54% to 59.85%, and the deciduous area increased from 38.46% to 40.15%. We did not include a map for 2022 because when we downloaded images from Sentinel hub, we found a mismatch between a downloaded image and a map displayed in Sentinel hub EO browser. In Table S1, each row corresponds to a date with the left column for an NDVI histogram calculated from the downloaded image and the right column for a screenshot of an NDVI histogram displayed in a web browser. As can be seen, the two NDVI histograms in the left and right columns are almost the same for the first and second row (23 December 2021 and 7 January 2022). However, from the third row (11 February 2022), we can observe mismatches between the two histograms. We are unable to ensure the correctness of the data for 2022. Due this issue, it is not meaningful to apply the proposed method to 2022 data. Figure 3: _Cont._Figure 4: RGB and NDVI image of Geomunoreum forest from 2020 to 2021. (**a**) RGB image; (**b**) NDVI image; (**c**) histogram of NDVI frequency distribution. Figure 3: RGB and NDVI image of Geomunoreum forest from 2017 to 2019. (**a**) RGB image; (**b**) NDVI image; (**c**) histogram of NDVI frequency distribution. Some internal problems might exist in Sentinel hub for recent data. We tried to examine the reason from another perspective, assuming that there was no problem in Sentinel hub. The reason can be understood by the fact that the temperature affects the NDVI value [26]. Figure 7 shows the average temperature of the study area. The temperate data were collected from Seonheul Automatic Weather Station (AWS) which is 3 km away from the study area (Figure 8). As shown in the highlighted months (January and February), 2022 was different from the other years in terms of temperature changes. From 2017 to 2021, February was warmer than January. On the other hand, in 2022, February was colder than January, which affected the NDVI values. The temperature was highly correlated with the NDVI value; therefore, the criteria should have reflected the temperature. Thus, the vegetation composition can be classified based not only on the NDVI value, but also on the lagged response of the temperature change. At this moment, no further investigation is possible since not enough data are available. This could be accomplished in future work when more data are accumulated for these exceptional cases. Finding these differences from 2017 to 2021, the thing to importantly consider is the image acquisition date. If the image acquisition date is the summer, the evergreen and deciduous trees cannot be differentiated by the NDVI value. It is the same in winter. Additionally, the leaf color change by deciduous trees in fall could impact the goal of this study which differentiated evergreen and deciduous trees in the forest. Therefore, early spring, before a deciduous tree forms a bud or new leaf, is the appropriate date for image acquisition. Figures 3-6 shows NDVI images representing the Geomunoreum's evergreen and deciduous area. The evergreen tree occupied the north and west side of the oreum, in comparative highland, and the deciduous tree occupied the south and east side, the comparative lowland. Except for some mixed forests in the east region, the mixed forest had barely been found in the study area.