## 4 Discussion
### Advantages of Spatiotemporal Change Detection Method  
The widely used single-date classification mainly relies on the spectral information of an image and could yield a good performance for PWD detection in the pure pine forest [17; 22; 23]. The false detections for this method mainly include discolored deciduous trees, brown herbaceous, and sparsely vegetated area (including mixed pixels at the junctions of forest and bare soil). Their characteristics on optical remote sensing images are very similar to the wilted pine trees caused by PWD, and the corresponding values of the spectral indices are close (Figures 6 and 7). However, high-resolution imagery usually contains only four bands of blue, green, red, and near-infrared, which inevitably leads to a large number of commission errors in the detection results. Even if the producer accuracy is high, an excessive number of commission errors still reduces the applicability of the single-date classification. [PERSON] et al. [19] insisted that multi-temporal images can be used to achieve a higher detection accuracy. In this study, for two images taken on similar dates in different years, if the land (or surface) cover does not change, the NGRDI observations of the cover types that are easy to be confused with wilted pine trees will remain unchanged, while the those of pine trees after wilting will significantly decrease (Figure 6). Therefore, the \(\Delta NGRDI\) with the temporal feature can effectively distinguish the wilted pine trees from commonly confused objects.  
In addition to the abovementioned cover types, there were pixels that are similar to the wilted pine trees in the \(\Delta NGRDI\) image (Figure 8a), which can be divided into two categories: (1) human or natural behaviors, such as felling, fallowing, and disasters that lead to the reduction in green vegetation coverage, in which are characterized by large changes in NGRDI and a spatial scale broader than the wilting process of a single pine tree, and (2) the noise caused by differences in atmospheric conditions, radiation, or phenology between the two images [43], in which the distinctive feature of this type of interference is that the magnitude of the change in NGRDI is relatively small while the spatial scale is similar to that of the discoloration of pine trees. Therefore, we constructed a kernel based on the spatial pattern of wilted pine trees to perform a convolution operation on \(\Delta NGRDI\), so that the pixels conforming to the spatial characteristics of wilted pine trees were enhanced and the noise was weakened (Figure 9b). The remaining large-scale changes of the cover type could be filtered by restricting the pixel counts of each \(BB\).  
Although the reported producer accuracies based on the spatiotemporal change detection method and the single-date classification were similar, the commission error results for spatiotemporal change detection were significantly lower than those for the single-date classification (Tables 3 and 4). For example, in the results for Site A, the commission errors for the single-date classification accounted for 40% (8 of 20) of the total \(BB\)s, and the discolored deciduous trees and pixels with low vegetation cover were not effectively eliminated (Figure 9b). Although the single-date classification successfully detected the No. 15 wilted tree that spatiotemporal change detection had missed, the possible reason is that the No. 15 tree and the adjacent No. vi discolored deciduous tree were detected at the same time; however, this cannot prove that the frequency of omissions of the single-date classification might be lower than that of the spatiotemporal change detection.