## 4 Results
### Results from Artificial Neural Network  
As a result from ANN approaches, probability maps for each vertical structure were obtained as shown in Figure 11: (a) single-layer structure, (b) double-layer structure, and (c) triple-layer structure. As aforementioned, the single-, double-, and triple-layer structures in this study are defined by how many layers it contains among the canopy layer, the understory layer, and the shrub layer. In the probability maps, most pixels of a single-layer structure probability map have relatively low values of less than 10%. Some pixels of the double layer probability maps have low values of less than 20%, whereas others have values between 40% and 60%. In the case of a triple-layer structure probability map, some pixels have a probability value higher than 70%, and the outer pixels of the forest appear to have a probability value lower than 20%. Figure 10: Canopy height maps estimated from DSM and DTM: (**a**) average canopy height map and (**b**) standard deviation canopy height map. A high percentage of low values in the single- and double-layer structure probability maps indicates that there is a relatively small percentage of single- and double-layer forests in the study area. In a triple-layer structure probability map, probability values greater than 70% indicate a high probability for a triple-layer structure forest, whereas probability values lower than 20% indicate little or no triple-layer structure, which means that the triple structure could be clearly distinguished. In addition, the probability of a triple-layer forest occupies most of the study area since the probability density function of triple-layer structure is closer to the Gaussian distribution than the single- and double-layer structure forests. The final classification map was created using the maximum operation that considers the highest value in the same pixel of each layer's probability map (Figure 12). The probability of single- and double-layer structure maps was relatively low, so that the percentage of single- (20%) and double-layer (15%) structure area was low; the triple-layer structure forests dominate with 65% in the study area as represented in the classification map. As the study area is mostly composed of multi-layered natural forests, the classification results are considered to be in line with expectations. Figure 11: Probability maps of forest vertical structure using ANN approach: (**a**) single-layer, (**b**) double-layer, and and (**c**) triple-layer. Figure 12: Forest vertical structure map classified from forest vertical probability maps using the maximum operation. The classification map was evaluated for accuracy, as shown in Table 3, through the error matrix based on the forest vertical structure data based on the field survey of Figure 2a. The total number of test data pixels excluding non-forest areas is 689,534. The overall test accuracy for the total pixels estimated from the error matrix was \(\sim\)65.06%, which was not very high. The application of ANN approach with full-waveform Lidar data could expect higher accuracy. However, full-waveform Lidar data is difficult to build for large areas at a single time, and is difficult to use for most areas because of the high cost. Optical satellite images and topographic data including DSM and DTM are available for most parts of the world, which could be used for cost- and time-efficient production of forest vertical maps. As a result, it is possible to observe the forest vertical structure over a large area using optical images and topographic data with \(\sim\)65% accuracy. The user and producer accuracy in single-layer structure was approximately 51.04% and 56.46%, respectively, with more than half the accuracy. Double-layer forests have 19.73% and 25.23% user and producer accuracy, respectively, which could be represented to have been identified with very low probability.