# A New Method for Large-Scale Landslide Classification from Satellite Radar
## 4 Discussion
### Application  
The data required for the production of the classification surfaces described in this study are available globally, albeit with varying temporal coverage. Two datasets are required: the SAR imagery and the DEM used to process it. The SRTM 1-arcsecond DEM used here is a global product available between 60\({}^{\circ}\) North and 56\({}^{\circ}\) South and is openly distributed [45]. SAR imagery is available from a variety of platforms but here we use Sentinel-1 imagery which is acquired at regular intervals globally and is also openly distributed. The classification surfaces produced in this study could therefore be produced in most locations across the world following a landslide trigger event. It has been stressed in multiple studies that the speed at which information can be produced and disseminated is vital to its application [8; 9]. The applicability of SAR-derived landslide products in emergency response is therefore largely determined by the time taken to produce them. The ARIA team have demonstrated that damage proxy maps can be generated within 1 day of post-event imagery being acquired. Although the new method we present here requires more SAR data to be processed, the majority of the processing steps can be performed while waiting for this post-event image to be acquired. Preparation of the DEM, downloading pre-event SAR imagery, processing this to obtain interferograms and calculating sibling locations can all be performed without the post-event image. Once this image has been acquired, the final steps could be carried out within 1 day: downloading and processing the post-event image, calculating boxcar and sibling-based co-event coherences and differencing these. Less time may be required for landslide events that affect a smaller region than the 2015 Gorkha earthquake. Exactly when these classifiers could be produced is therefore dependent on the acquisition of SAR imagery. The post-event image used in this study was acquired on day 4 following the mainshock, meaning that a classification surface could have been produced by day 5. However, as is shown on Figure 1, imagery further east of Kathmandu was not acquired until day 10, meaning that it would take 11 days to form a complete classification surface for the affected area. Ascending data on track 85 were acquired 7 days following the mainshock and cover the entire affected area so that classification surfaces from these data could have been produced within 8 days. The frequency at which areas are imaged in SAR varies depending on location globally. The launch of Sentinel-1b in 2016 means that Sentinel imagery is now acquired with a 6- rather than 12-day revisit time across Europe [17]. These data are freely accessible soon after acquisition. Other satellite constellations, such as ALOS-2 or Cosmo-Skymed may also provide imagery of affected areas. Possible uses for a SAR based map of relative landslide density can be illustrated using the case of the Nepal earthquake. [PERSON] et al. [9] divide the emergency response into several phases each with different information requirements. In the first 3 days the'situational analysis' phase aimed to identify, at a broad scale, the spatial extent and severity of the damage. It is conceivable for SAR products to be produced within this time window, although this would depend on the wait time for post-event imagery. After this 3-day period, the requirements of disaster managers begin to transition to more detailed information on specific areas of concern [9]. Based on our findings here, SAR-coherence methods alone would not be capable of mapping individual landslides but could still direct managers toward the areas that were most badly affected--especially if aggregated landslide density maps could be combined with pre-existing population data. The Gorkha earthquake occurred at the end of April, meaning that the onset of the monsoon was expected around 2 months later.