## 4 Results and Discussion  
Figure 7 shows probability maps of the single-, double-, and triple-layered forests generated using the ANN and the five input layers. The ANN is a pixel-based analysis that calculates a probability of an event happening, and the brightness values represent probabilities. Training samples of ten thousand pixels were selected for the single-, double-, and triple-layered forests from field survey measurements. The non-vegetation areas and the road (red-dotted box in Figure 8) were masked out. The ANN determined the weighting factors from the five input layers. Table 1 summarizes the weighting factor for each input layer. From the single-, double-, and triple-layered forests shown in Figure 7a-c, a final forest structure classification map was produced by selecting the highest value from the probability maps of the single-, double-, and triple-forests. Figure 8 shows the ANN-classified forest structure map and the field forest structure map for comparison. The red-dotted box in Figure 8b was classified as single-layered forest using the ANN approach, but the field measurements recorded it as triple-layered forest (e.g., compare Figure 8a,b). The field measurements have low spatial sampling because most forests in Korea occur in high mountainous regions. This discrepancy was caused by the spatial resolution differences. The forest vertical structure classes between the ANN approach and field measurement were compared and the results are summarized in Table 2. In Table 2, the number denotes the number of pixels. The classification accuracies of the single-, double-, and triple-layered forests were 82.84%, 56.56%, and 69.03%, respectively. The overall accuracy was 71.10%. \begin{table}
\begin{tabular}{c c|c|c} \hline \hline \multirow{2}{*}{**Input Layers**} & \multicolumn{3}{c}{**Weighting Factor**} \\ \cline{2-4}  & **Single-Layered** & **Double-Layered** & **Triple-Layered** \\ \hline
**Median-filtered Index Map** & 0.13 & 0.09 & 0.22 \\
**NL-means Filtered Index Map** & 0.17 & 0.47 & 0.23 \\
**Reflectance Texture Map** & 0.10 & 0.07 & 0.14 \\
**Height Difference Map** & 0.46 & 0.16 & 0.24 \\
**Height Texture Map** & 0.15 & 0.21 & 0.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Estimated weighting factor of the input layers. Figure 6: Characteristics of the five input layers used for the ANN approach in boxes A, B, and C of Figure 3: (**a**) median-filtered index maps; (**b**) NL-means filtered index maps; (**c**) reflectance texture maps; (**d**) height difference maps; and (**e**) height texture maps. The areas in boxes A, B, and C are the single-, double-, and triple-layered forests, respectively. Figure 8: (**a**) Field survey classification map of forest vertical structure (same as Figure 2a); (**b**) final classification map of forest vertical structure using the ANN with red box -road way-. Figure 7: Probability maps of the (**a**) single-; (**b**) double-; and (**c**) triple-layered forests. For the sections with low classification accuracy, the reasons for the misclassification need to be investigated. However, the overall accuracy of the classification is relatively high, considering that it is difficult to classify the vertical structure of a mixed-species forest using remotely-sensed data [17]. Our results show that it is possible to classify the forest vertical structure using remotely-sensed data. Several discrepancies were identified and the causes determined. The triple-layered coniferous colonies on the ridge of the mountain were mostly classified as double-layered coniferous forest. This was due to the ANN classification being strongly influenced by the dominant species. The red-dotted box in Figure 8 is a roadway, which was classified as triple-layered forest in the field measurement data collected in 2009. However, a single-layered forest exists in the roadway when we checked it in the field in 2015 RGB imagery. The road was constructed before 2009.