## 4 Discussion  
### General Framework of Tree-Scale PWD Monitoring  
For satellite remote sensing, whether a wilted pine tree could be detected or on what canopy width could a wilted pine tree be detected largely depend on the spatial resolution of remote sensing images [17; 18; 29; 35]. Generally, the ideal spatial resolution of the satellite image need to reach the canopy width of the pine tree to be detected [34]. Thus, the main reason why the wilted pine trees Nos. 2, 9, 14, and 15 at Site A were not identified were due to the limitations of the resolution of the PlanetScope image. In terms of temporal feature, this study has demonstrated that it is feasible to use two images with similar dates in adjacent years to distinguish the wilted pine trees from other confusing targets. In addition, we believe that attempts to analyze two or more images from the same year when PWD occurs are worthwhile. Despite the relatively limited channels for current high-resolution satellite sensors, it is undeniable that richer spectral information inevitably lead to further improvements in accuracy. The advantage of STM in the PWD detection is that it effectively reduces false detections caused by easily confused targets, which are particularly abundant in mixed or fragmented forests. Therefore, the spatiotemporal change detection is more advantageous in such complex landscapes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Site**} & **Total Wilted Trees** & **True Positive** & **Omission Errors** & **Commission Errors** & **Producer’s** & **User’s** \\  & **for Validation** & **(Tree Count)** & **(Tree Count)** & **(BB Count)** & **Accuracy** & **Accuracy** \\ \hline A & 12 & 12 & 0 & 8 & 100.0\% & 60.0\% \\ B & 6 & 5 & 1 & 4 & 83.3\% & 55.6\% \\ C & 36 * & 30 & 6 & 16 & 83.3\% & 63.6\% \\ D & 11 * & 9 & 2 & 6 & 81.8\% & 60.0\% \\ E & 67 & 53 & 14 & 16 & 79.1\% & 76.1\% \\ \hline Total & 132 & 109 & 23 & 50 & 82.6\% & 67.7\% \\ \hline \hline \end{tabular}  
* A part of wilted pine trees at Sites C and D that used for classifier training was not involved.  
\end{table}
Table 4: Validation results for PWD detection based on the single-date image classification method.  
Figure 9: Comparison of the detection result through spatiotemporal change detection method (**a**) and single-date image classification method (**b**) at Site A.  
In this study, the NGRDI is based on the red and green bands, mainly reflecting the color of pixels. Similar to NDVI [37], RGI [18], and other indices, NGRDI also could indicate the growth states and the canopy traits of vegetation [38; 39; 40; 41]. We chose the index with the normalized difference form to further eliminate errors caused by the inconsistencies in radiation between different images [36]. Another reason for choosing NGRDI is that it has a natural threshold of 0. The plus or minus sign for NGRDI indicates whether a pixel is \"greenish\" or \"reddish\", which is friendly to some threshold settings. In the spatial filtering, the setting of the convolution kernel should match the spatial pattern of the color-changing pine tree on the remote sensing image. Alternatively, the commonly used Sobel, Difference of Gaussian, and Laplace of Gaussian operators [42] can also be used as convolution kernels to enhance the spatial feature of the wilted pine trees in the image.