## 1 Introduction
On the other hand, rather than using many bands for plant or species classification, a method of selecting specific bands suitable for classification and classifying them into several bands by using such as principal component analysis (PCA) [12]. In addition, forest density classifications were conducted by using aerial images and forest textures [18]. The texture information of the forest can be extracted by considering the difference in canopy arrangement between the single-layer structure and the multi-layer structure. The arrangement of crowns is considered uniform in single-layer forests and uneven in multi-layer forests [18]. In this study, Sentinel-2 optic satellite images were used to obtain optical information based on differences in reflectance caused by forests, height, and arrangement of canopy. As the height of trees is closely related to the vertical structure of forests, Light Detection And Ranging (LiDAR) data are mainly used to measure tree height [19; 20; 21]. The height of trees was estimated using digital surface model (DSM) and digital elevation model (DEM) extracted from LiDAR data, and the texture data was derived from the estimated height [22]. However, LiDAR data is time- and cost-consuming such as field surveys, which could lead to a long update cycle. Among the remote sensing satellite images, the optical image only observes the surface information of the forest, and hence there is a limitation in the internal structure observation. In this regard, digital terrain models (DTM) and DSM data, which are already constructed around the world, could be used to reflect forest height components [23]. Research has been conducted to estimate and validate the vertical distribution of forests from DEM data [24; 25]. Additionally, machine learning has recently been increasingly utilized in terms of the abundance of data available for learning, including satellite images. Machine learning techniques can be applied based on large amounts of remote sensing data [26] to produce information about continuous internal structures in forest areas with high uncertainty [27; 28; 29]. When conducting forest investigations over large areas of high uncertainty, the application of artificial neural networks (ANN) to satellite remote sensing data could be more effective than time- and cost-consuming aerial photography or human surveying. In addition, support vector machines (SVM) [30; 31] and decision tree-based models such as random forests [32] and boosted trees [33; 34] are being applied to various fields. In this study, the analysis of forest vertical structure using optical satellite images and machine learning of ANN was conducted. In this study, the analysis of forest vertical structure to estimate forest water capacity from satellite images by using machine learning method. For this purpose, to extract input layers, index map and texture map were prepared from two seasonal Sentinel-2 optical satellite images after preprocessing. In addition, two types of canopy height maps were produced using the difference between two topographic data of DTM and DSM. The 14 input layers were produced considering the vitality of the canopy, the image texture, and the difference in tree height. Finally, the input layers were applied to ANN algorithms with training/validation and test dataset of forest vertical structure data constructed by field survey; the classification map of forest vertical structure was generated. The accuracy of the result map was evaluated through error matrix. The proposed methods for forest vertical structure could be used for establishing a plan for forest water management by enabling more accurate forest water capacity estimation than previous methods.