## 4 Discussion
### Windthrow Detection  
For windthrow detection, the SVM method classified 312.0 ha of windthrow, 78.0 ha more than the NDVI filtering method and 109.0 ha more than the SAM method. According to visual interpretation of each classified map, the SVM method and the NDVI filtering method classified windthrow in less detail than the SAM method. The SAM method could differentiate some parts of the road crossing the windthrow area, while the NDVI filtering method and the SVM method classified the whole area as windthrow, increasing the total area classified as being damaged (Figure 9). All three methods identified the main area of damage on the western side of the _Setana North_ study area. The differences in damage size occurred in areas on the northern and eastern sides of the study area and were also scattered in small areas throughout the study area, and these differences were generated by misclassification (Figure 5).  
Figure 8: Spectral signature for the entire landslide area (left: N = 156) and all different misclassified bare points (right: N = 31). The red line shows the spectral signature from the median values of the data distribution of each landcover type.  
Figure 9: Comparison of windthrow delineations by three methods in _Setana North_. The SAM method could distinguish some parts of the road crossing the area (the light-colored line in the RGB image) from windthrow.  
Misclassification occurred with all three methods but occurred at a larger proportion with the NDVI filtering method and the SVM method. Even though the placement of the misclassified points was almost the same for the NDVI filtering method and the SVM method, the hypotheses for misclassification by the two methods were different. Windthrow areas usually consist of tree foliage, trunks, roots, and soil; the energy reflected from those areas has a similar spectral signature to forest, grassland, and bare area classes (Figure 6). The broad reflectance variation in each band from the misclassified points increased the complexity of windthrow detection for the SVM method, which in this study used the linear kernel type, limiting the performance of the algorithm even with different cost values. SVMs are not optimized for noisy data, which are commonly found in remotely sensed datasets [40]. On the other hand, the NDVI filtering method is based only on NDVI values; as vegetation remained on the site, the values obtained by the NDVI filtering method could be similar to the values of grasslands and bare areas that had some amount of vegetation. The difference between the pre- and posttyphoon NDVI values (Equation (1)) did not contribute to the detection of windthrow, and the final classification was based only on the value of the threshold \(T_{2}\). For the detection of windthrow in _Setana North_, the SAM method had the highest overall accuracy (92%) and kappa value (0.82) when using two training samples and a threshold value set at 7 due to its ability to compare spectral similarity between the image and the reference training sample [41]. The threshold value also helped improve the classification results by not classifying pixels if the spectral angle distance was greater than the \(T\) value [30] and eliminating classification noise throughout the area by limiting the angle distance.