## 5 Conclusions  
The purpose of this study is to analyze forest vertical structure in Gongju-si to enhance the function of forest related to water resource management. MLP-ANN model machine learning technology was applied to Sentinel-2 optical satellite images and previously constructed DTM and DSM data. Machine learning techniques can be applied based on large amounts of remote sensing data to produce information about continuous internal structures in areas with high uncertainty. Therefore, input data NDVI, NDWI1, NDWI2, NDre1, NDre2, and PCA texture maps were generated from Sentinel-2 optical satellite images. In addition, an average canopy height map and a standard deviation canopy height map were generated from DTM and DSM. The classification result from ANN shows that the triple-layer forest with the highest water reserves has shown a relatively accurate 73.92% classification in producer accuracy. Understanding the vertical structure of forests is essential for estimating forest water storage capacity for integrated water management. The results of this study show that the satellite image data, including optical satellite images and DEM data, could be used for more accurate estimation of the forest water storage by constructing data on forest vertical structures. Therefore, more accurate water storage on forest could be estimated using the vertical structure constructed using satellite images along with the forest type data provided from the forest map. The diversity of vertical structures with the various age of forests could improve the soil penetrating structure of rainwater and increase the amount of water the forest can store. However, estimation of the forest structure in a large area is difficult due to its difficult accessibility and various forms; it is necessary to use periodic satellite images for estimating vertical structures in national-scale forests which could support forest management for water resource management. Also, the combination of remote sensing, which is essential for exploring large areas, and machine learning, which is effective for classifying and analyzing large amounts of data, could be very useful as in this study. Especially, as there is a limit in obtaining information on the internal structure of forests, it is possible to estimate the  
Figure 13: The forest vertical classification map with the original Sentinel-2 RGB image. (**a**) single-layer, (**b**) double-layer and (**c**) triple-layer structure forest. vertical structure of the forest through the methodology applied in this study using remote sensing satellite imagery and machine learning technology. It is also expected to reduce research costs, such as time and budget for field surveys. In this study, the degradation of the learning and classification results was derived using the data with the difference between the acquisition time of the image and the investigation time of the reference data. In the future study, images of similar time should be collected to reflect the difference in survey time and improved results could be expected by obtaining reference data from multiple regions with different forest types. Additionally, various deep learning technologies which are being developed recently could reflect the detailed characteristics of the images. Based on the results of this study, the forest vertical structure data through remote sensing and machine learning could be used to estimate the forest water storage and establish forest administration measures for integrated water management. Conceptualization, H.-S.J. ; methodology, Y.-S.L., S.L., and H.-S.J. ; software, Y.-S.L. and S.L. ; validation, Y.-S.L. and S.L. ; formal analysis, Y.-S.L., S.L., and H.-S.J. ; investigation, Y.-S.L., S.L., and H.-S.J. ; writing--original draft preparation, Y.-S.L. and S.L. ; writing--review and editing, S.L. and H.-S.J. ; supervision, H.-S.J. All authors have read and agreed to the published version of the manuscript.