## 2 Materials and Methods
### Data Collection
#### 2.2.2 UAV Image Collection and Processing  
Since deciduous trees, and especially Japanese oak, have distinct color patterns when their leaves change during the fall season, UAV images were collected on 14 September 2022 (September UAV dataset) and on 7 October 2022 (October UAV dataset), corresponding to before and after the leaf color change of Japanese oak, to determine the optimal timing of UAV image collection to achieve the best performance of the ResU-Net model. A DJI Matrice 300 RTK UAV platform mounted with a DJI Zenmuse P1 camera (with a lens featuring a 35 mm focal length) was used to acquire the UAV images (Figure 2). The image sensor in the Zenmuse P1 delivers 45 megapixels, with an image resolution of 8192 \(\times\) 5490 pixels and a ground resolution of 0.8 cm per pixel. Regarding the camera setting, we set up aperture (f/8), shutter speed (1/1000 s), and ISO speed (ISO-3200) to minimize motion blur and exposure concerns in the collected UAV images. The weight of the UAV platform, including two TB60 batteries, is 6.3 kg. Both UAV flights were scheduled as a square parallel flight plan with a fixed altitude of ~80 m above ground and a flight speed of 8-8.5 m/s. The flights were operated along the sub-compartment and images were obtained at 3-s intervals to achieve 90% longitudinal overlap and 85% side lap overlap, covering a total area of 31 ha. Normally, the external orientation and overall high level of precision of the photogrammetric model can be improved by the proper planning of ground control points (GCPs) prior to the aerial survey [29]. But Matrice 300 enables direct georeferencing of UAV imagery without the need for GCPs by using dual-frequency RTK GNSS [30]; thus, GCPs were not established in this study. Figure 1: (a) Location map of the study area in Japan; (b) location map of Sub-compartment 68E in the University of Tokyo Hokkaido Forest; aerial orthomosaics of Sub-compartment 68E taken (c) before the leaf color change and (d) after the leaf color change; and a sample of Japanese oak taken (e) before the leaf color change and (f) after the leaf color change. The collected UAV images were processed using Pix4 Dmapper Professional software version 4.8.0 (Lausanne, Switzerland), employing the'structure from motion' technique. The workflow starts with an image alignment step, in which camera positions and orientations are computed by analyzing feature points across various images and intrinsic camera calibration parameters such as focal length, principal point, and three radial and two tangential distortion coefficients are refined. Then, a three-dimensional point cloud is reconstructed, ultimately generating an orthomosaic of the study area. In this study, the orthophotos generated from the September and October UAV datasets were used after being resized from an original resolution of 0.8 cm per pixel to 5 cm/pixel to reduce the computational load and to allow them to be used in combination with other data sources in future studies. The corresponding binary mask was generated by manually labeling the orthophotos of the study area using the Fiji plugin \"Labkit\" [31]. In total, 319 Japanese oaks were manually annotated over the entire study area by referencing the field data and visually interpreting the high-resolution UAV imagery. The labels were Japanese oak and background, with the latter referring to all pixels in an image that were not Japanese oak. Then, the binary mask was processed using the OpenCV library to create a new mask containing the border class, to increase the robustness of the model in delineating occluded tree crowns. Dilation, one of the fundamental morphological operations used in image processing, was applied to the binary mask to add a border class using the elliptical kernel size of (3, 3), which allowed the borders to expand by one pixel in all directions. However, because the structuring element was an ellipse, the net effect was zero thickness when considering the original and dilated regions.