# A New Method for Large-Scale Landslide Classification from Satellite Radar
## 4 Discussion
### Combining Classifiers  
Predictive landslide models, e.g., (12; 13; 14), rely on a combination of predictors of landslide distribution that together provide a more accurate model than any one individual predictor. One possible way of improving the classification ability of SAR-based methods would be to combine them with one or more such empirical predictors. Various possible landslide predictors such as slope, lithology and distance to rivers and faults; and means of combining them have been tested and developed (12; 13; 14; 49). Using SAR data as an additional input for one of these models would allow incorporation of observed data into models that are traditionally static in time based on pre-earthquake conditions and prior knowledge of landslide likelihood.  
Alternatively, landslides mapped by SAR could be used to calibrate predictive models. [PERSON] et al. (14) used small areas of mapped landslides as training data to predict landslide occurrence across Nepal following the Gorkha earthquake. They found that while the input of observed landslide data improved model performance, the improvement was considerably reduced if these observations were clustered. This clustering is common in optically-derived datasets if mapping is only possible through small gaps in cloud cover. SAR methods may provide a more uniformly distributed set of observations that could be used in the calibration of predictive models. Areas where the SAR coherence was likely to be most reliable could be identified for this purpose, either by masking areas with high normalised backscatter as we have done here, by identifying areas where coherence was consistent and non-zero in pre-event imagery or by using a method such as that of [PERSON] (50) to predict areas where topographic effects (e.g., shadowing) might be problematic based on DEM data and satellite image acquisition parameters. For our case study, we were still able to make landslide observations across a wide area, lessening the clustering effect, even after masking unreliable SAR data.  
SAR products other than coherence may also be used in landslide identification. For example, SAR amplitude has shown some capacity for mapping landslides in other studies, although not enough to be used alone in large-scale landslide identification (51). SAR amplitude is dependent on the proportion of microwave energy that is scattered at the Earth's surface. Vegetation tends to scatter a relatively high proportion of this energy when compared to exposed rock or bare ground so that the amplitude of the signal when it is returned to the satellite is lower for vegetation than for rock. Since landslides remove vegetation, they are expected to correspond to bright areas in an amplitude map and to increased amplitude in time. We tested SAR amplitude in our initial analysis but found it to be outperformed by coherence-based classifiers, so do not report it here. However, SAR amplitude methods are likely to perform better in highly vegetated regions, and therefore may be complementary to coherence-based methods, that are adversely affected by low background temporal coherence in these areas.