## 4 Discussion
### Landslide Detection  
For landslide detection in _Setana South_, the visual interpretation showed that the NDVI filtering method classified the whole landslide area, while the other two methods did not (Figure 10). In contrast to windthrow, landslides usually remove all vegetation, facilitating detection based on NDVI values. Even though the majority of vegetation was removed, the spectral signature of landslides of the reference training samples showed small amounts of remnant vegetation in those areas. Therefore, the SAM method could not correctly classify the areas where the spectral signature showed no vegetation, in turn classifying areas of actual landslides as nonlandslide areas. Although the SVM method could classify the areas with a no-vegetation spectral signature, it struggled to identify the borders of landslides, leading to a higher probability of classifying areas with a higher level of vegetation as nonlandslide areas. The SVM method identified the smallest landslide area and resulted in the largest number of misclassifications, 31 points, which may have been caused by the same problems presented for  
Figure 10: Comparison of landslide delineations by three methods in _Setana South_. While the normalized difference vegetation index (NDVI) filtering method (blue) classified all areas as landslides, the SAM method (yellow) could not detect some areas (top area of the center landslide), and the SVM method (red) did not classify the borders of the landslide area. windthrow detection due to the similarity of spectral signatures between landslide and bare areas and overfitting. As in windthrow detection, we used the default settings for the SVM method of the Google Earth Engine, with the linear kernel, using visually interpreted training samples. We tried to increase the number of training samples, but this did not improve the final result, and cross-validation is recommended to improve the final classification [42]. The SAM method misclassified 7 points, the same ones misclassified by the NDVI filtering method. As mentioned before, although the SAM method classified the smallest landslide area due to the difference in spectral signatures, the algorithm had adequate performance for landslide detection. Compared to the SVM method, this method delivered a higher user accuracy, which represents the probability that the landslide pixel is actually represented on the ground. On the other hand, the SVM method performed better for the no-damage class, elevating its overall accuracy. The landslide areas were visually completely washed out and did not contain remnant vegetation, but the spectral signature showed that vegetation was present. Although bare areas have a different spectral signature, the reflectance variation in the bands of the posttyphoon landslide spectral signature contributed to the misclassification. One simple way to improve the final classification would be masking the bare areas that were present before the typhoon event. The NDVI filtering method had the best overall accuracy and kappa values for landslide detection, which was expected due to the characteristics of the NDVI used to distinguish different amounts of vegetation. The histograms of the landslide areas conveyed clear information to define the threshold values (Figure 3). This result confirms [PERSON] [43] statement about landslide classification based on the NDVI producing the best results. The results obtained from both study sites, combined with the high spatial resolution and horizontal accuracy of the PlanetScope data [44], showed that even with some caveats, all methods were suitable for overall windthrow and landslide detection. The PlanetScope dataset played an important role in this research, and a resolution of 3 meters provided enough information for the methods to identify the damage without being too detailed (where more processing time and tuning of parameters would be necessary) or too rough (where the spatial resolution mixes different landcover classes together).