## 4 Discussion
### Importance of Preparing a Representative Validation Dataset for the Diversity of the Entire Dataset  
In the present study, the F1 scores of both the non-augmented September and October UAV validation datasets were not very different in detecting Japanese oak crowns, with the values of 0.81 and 0.80. In the overlay analysis using the segmented images and all ground truth masks, as described in Section 3.3, however, higher F1 scores of 0.92 and 0.95 were obtained, with the superior score that of the model using the non-augmented October UAV dataset. This difference probably reflected the nature of the validation datasets, which may not have fully represented the diversity of the entire datasets. For example, in the case of the October UAV dataset, there were a few trees whose leaf color had not completely changed, but some of these trees were included when the validation dataset was created. Thus, it is likely that the model misidentified these areas of Japanese oak as other deciduous trees. This demonstrates the importance of carefully preparing the validation dataset to ensure that it represents the entire dataset.  
Response of the ResU-Net Model to Two Different Seasonal UAV Datasets for Japanese Oak Crown Detection  
In this study, the performance of the ResU-Net model with both the non-augmented September and October datasets was good, with reliable OA values of 0.90 and F1 scores > 0.80 obtained from validation datasets. Of note, the model appears to be sensitive to different features. For example, in the September UAV dataset, taken before leaf color change, the model likely focused on structural features such as crown shape, texture, and other spatial features because the color profile of Japanese oak was almost the same as that of other broadleaf species prior to leaf color change. Therefore, the similar structural characteristics of Japanese oak and other broadleaf species sometimes resulted in misclassification. Meanwhile, the model using the October UAV images, taken after the leaf color change, likely prioritized the color features. As a result, the model incorrectly predicted some Japanese oaks, whose crown color profile is similar to that of other deciduous trees, as others. Similarly, the October dataset sometimes incorrectly identified the edges of the crowns because the color of these crowns had not completely changed.  
According to the accuracy scores obtained from the overlay analyses using all the segmentation results and the annotated ground truth data (Table 3 and Figure 14), the performance of the ResU-Net model was better on the non-augmented October UAV dataset than on the non-augmented September UAV dataset. This finding suggested that the ResU-Net model seems to be better at learning the color characteristics than structural characteristics in the UAV images. The lower accuracy of the September dataset compared to the October dataset can thus be attributed to the similar color of Japanese oak and other broadleaf species before the change in leaf color.  
Regarding tree species classification using the multiple seasonal aerial images, previous studies have demonstrated that merging images from multiple seasons improves average tree species classification accuracy [47; 48].