## 3 Results
Performance of the ResU-Net Model with UAV Datasets for Mapping Japanese Oak Crowns Distribution in an Uneven-Aged Mixed Forest  
As described in Sections 3.1 and 3.2, the performance of the ResU-Net model using the four datasets in predicting Japanese oak crowns was consistently high, as evidenced by OA values of 0.87-0.90 and F1 scores of 0.71-0.81, with higher values from the non-augmented UAV datasets. When we performed overlay analyses using the segmentation results on each entire dataset and the annotated ground truth data over the study area, the OA and F1 scores ranged from 0.91 to 0.98 and 0.73 to 0.95, respectively. Specifically, based on all of the evaluation metric scores, the best performance and best segmentation results were obtained with the model using the non-augmented October UAV dataset, followed by the model with the non-augmented September UAV dataset (Table 3 and Figure 14). Furthermore, according to the confusion matrices (Figure 15), the non-augmented October UAV dataset resulted in the highest rate of Japanese oak detection (95.95%), followed by the non-augmented September UAV dataset (95.70%), the augmented September UAV dataset (81.43%) and the augmented October UAV dataset (68.94%). Consistent with this finding, the misclassification rates of Japanese oak as border and background as Japanese oak were lowest with the model using the non-augmented October UAV dataset (0.99% and 1.06%). However, the misclassification rate of Japanese oak as background (3.06%) was slightly higher than that of the non-augmented September UAV dataset (2.57%), although  
Figure 14: Summarized accuracy scores (OA, precision, recall, F1-score and IoU values) of mapping Japanese oak crowns distribution in an uneven-aged mixed forest by using all UAV datasets and ResU-Net algorithm (through overlay analyses using the segmentation results on each entire dataset). The highest values are in bold. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**UAV** & **OA** & **Precision** & **Recall** & **F1-Score** & **IoU** \\ \hline September (non-augmented) & 0.97 & 0.89 & 0.96 & 0.92 & 0.86 \\ September (augmented) & 0.94 & 0.85 & 0.81 & 0.83 & 0.83 \\ October (non-augmented) & 0.98 & 0.94 & 0.96 & 0.95 & 0.91 \\ October (augmented) & 0.91 & 0.78 & 0.69 & 0.73 & 0.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: OA, precision, recall, F1-score and IoU values of mapping Japanese oak crown distribution in an uneven-aged mixed forest by using UAV datasets and ResU-Net algorithm (through overlay analyses using the segmentation results on each entire dataset). it was lower than the rates obtained with the augmented September and October UAV datasets. Moreover, the incorrect prediction of the border as Japanese oak was highest (30.13%) using the non-augmented October UAV dataset. A comparison of the augmented September and October UAV datasets showed the better performance of the former in predicting Japanese oak, as seen in Table 3, Figure 14 and confusion matrices (Figure 15b,d). Figure 16 shows the predictions of Japanese oak over the entire study area using the trained ResU-Net model and each of the UAV datasets. It was observed that the misclassification of other broadleaf trees (i.e., background) as Japanese oaks was lower with the non-augmented October UAV datasets than with the non-augmented September UAV datasets. However, the latter was better at detecting Japanese oak crowns whereas the non-augmented October UAV datasets sometimes failed, especially at the edges of the crowns. Meanwhile, the models with both augmented September and October UAV datasets often failed to detect Japanese oak crowns. Figure 15: Confusion matrix of Japanese oak segmentation through an overlay analysis using the segmentation results and the entire annotated ground truth data over the study area through the trained ResU-Net model: (**a**) non-augmented September UAV dataset; (**b**) augmented September UAV dataset; (**c**) non-augmented October UAV dataset; and (**d**) augmented October UAV dataset.