## Results
### Landslide Detection  
The largest number of landslides was identified in the west-central portion of the _Setana South_ study area (Figure 7). The NDVI filtering method identified 22.0 ha of landslides, while the SAM method and SVM method identified 17.6 ha and 4.7 ha, respectively (Table 3). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Reference**} & \multicolumn{3}{c}{**Accuracy**} & **Error** \\ \cline{3-8}  & & Damaged & Nondamaged & Total & UA & Area (ha) &
\begin{tabular}{c} OA \\ Kappa \\ \end{tabular} & Std Error \\ \hline \multirow{3}{*}{**a) Classified by NDVI Filtering**} & Damaged & 65 & 29 & 94 & 69.1\% & 234.0 & 84.0\% & 0.0159 \\  & Nondamaged & 3 & 103 & 106 & 97.2\% & 13625.1 & 0.67 & \\  & Total & 68 & 132 & 200 & & & \\  & PA & 95.6\% & 78.0\% & & & & \\ \hline \multirow{3}{*}{**b) Classified by SAM**} & Damaged & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\  & Damaged & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\  & Damaged & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\  & Damaged & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\  & Damaged & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & Kappa & \\ \hline \multirow{3}{*}{**c) Classified by SVM**} & Damaged & 67 & 33 & 100 & 67.0\% & 312.8 & 83.0\% & 0.0098 \\  & Nondamaged & 1 & 99 & 100 & 99.0\% & 13546.2 & 0.66 & \\ \cline{1-1}  & Total & 68 & 132 & 200 & & & \\ \cline{1-1}  & PA & 98.5\% & 75.0\% & & & & \\ \hline \hline \end{tabular}  
* PA: producer accuracy, UA: user accuracy, OA: overall accuracy  
\end{table} 
Table 2: False-positive misclassified points of windthrow detection in _Setana North_. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Bare** & **Forest** & **Grassland** & **Total** \\ \hline NDVI filtering method & 13 & 4 & 12 & 29 \\ SAM method & 0 & 4 & 0 & 4 \\ SVM method & 13 & 5 & 15 & 33 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The error matrices of windthrow detection in _Setana North_. Figure 6: Spectral signature for windthrow taken from the entire windthrow area (upper left: N = 5723) and all different misclassified points of bare land (upper right: N = 14), grassland (lower left: N = 15), and forest (lower right: N = 6). The red line shows the spectral signature from the median values of the data distribution of each landcover type. Figure 7: Landslide classification results in _Setana South_ zoomed into the west-central area: (**a**) PlanetScope RGB image, (**b**) NDVI filtering method results, (**c**) SAM method results, and (**d**) SVM method results. The landslide-damaged areas are shown in red. The NDVI filtering method was significantly superior, with an overall accuracy of 96.0% and a standard error of 0.008. The producer accuracy and the user accuracy of the damaged areas also had values over 90% (Table 3). The SAM method had the second best results (93.0% overall accuracy with a standard error of 0.0198), while the SVM method had the lowest overall accuracy of 84.0% with a standard error of 0.0099. The SAM method presented a lower producer accuracy percentage in damaged areas (90.0% : 63/70), while the SVM and NDVI filtering methods had a value of 98.6% (69/70). The NDVI filtering method had the best results in all aspects, with an overall accuracy of 96.0%, a producer accuracy of 98.6%, and a user accuracy of 90.8%. The lowest producer accuracy for the SAM method reflected the largest number of false-negative points. In contrast, compared to the SVM method, the SAM method had a higher user accuracy (90.0% : 63/70), and the SVM method had the lowest accuracy (69.0% : 69/100).