# A New Method for Large-Scale Landslide Classification from Satellite Radar
## 1 Introduction
SAR products are routinely used in other rapid response situations, for example in flood mapping or in the production of interferograms to map ground deformation after an earthquake or during an episode of volcanic unrest [18; 19; 20]. NASA's Advanced Rapid Imaging and Analysis (ARIA) project uses SAR to produce damage proxy maps in urban areas following earthquakes, cyclones or wildfires [20; 21]. SAR methods such as offset tracking, e.g., [22], persistant scatterer interferometry, e.g., [23; 24] and traditional differential InSAR, e.g., ref. [25] are also used in monitoring the movements of slow-moving landslides. Persistent scatterer interferometry and traditional differential InSAR have been used in several cases to supplement pre-existing inventories with ground surface deformation information, which can be used to evaluate the state of activity of the landslides [26; 27; 28]. However, the potential use of SAR in rapid production of landslide maps for emergency response has only been demonstrated on individual landslides or catchments, and with limited success [21; 29]. A clear example of the limitations of landslide mapping using optical imagery, and the potential that SAR has to overcome these limitations was the Gorkha earthquake on 25 April 2015, which triggered over 25,000 landslides in the surrounding mountains (Figure 1) [2]. Figure 2 shows a timeline of mapping efforts carried out by an international team of researchers using optical satellite imagery and intended for use by emergency response coordinators [9]. Although the earthquake occurred during Nepal's dry season, cloud cover caused severe delays to landslide mapping, with almost no cloud-free imagery available in the first week following the earthquake and some areasremaining unmapped up until the onset of the monsoon on 9 June 2015, roughly one-and-half months later. The emergency response process evolves quickly in comparison. For example, the United Nations response framework following a disaster mandates an initial assessment after 72 h and a second after 2 weeks [9; 30]. In the case of the 2015 Gorkha earthquake, the impending monsoon season applied additional time pressure, because the arrival of the monsoon would make cloud-free optical image acquisition unlikely and because it was anticipated that the earthquake would increase the severity of rainfall triggered landsl [7]. The acquisition of useable SAR imagery and generation of associated products occurred comparatively quickly (Figure 2). Five days following the earthquake, NASA's ARIA team released an initial damage proxy map for building damage in Kathmandu based on SAR data acquired by the Italian Space Agency's COSMO-SkyMed satellite system. The first post-event imagery acquired on each satellite acquisition track by ESA's Sentinel-1a satellite is shown on Figure 1, with the first of these being acquired 4 days after the Gorkha mainshock. Sentinel-1 coverage has since improved with the launch of a second satellite, Sentinel-1b, in 2016. Had it been possible to use SAR products in mapping landslides following the Gorkha earthquake, critical information on landslide distribution could have been delivered to first responders and government agencies with greater areal coverage and better timeliness than was possible from optical satellite data. Figure 1: Location map of the 2015 Gorkha earthquake. A white star marks the epicenter of the mainshock on 25 April 2015. The density of earthquake-triggered landsl was calculated based on the inventory of [PERSON] et al. [2]. The first post-event Sentinel-1a Synthetic Aperture Radar (SAR) acquisitions are shown with dashed lines: ascending track 085 (blue); descending track 19 (red) and descending track 121 (orange). The area of SAR imagery used in this study is from track 19 and is outlined in solid red. In this paper, we investigate automatic methods to detect landslides using SAR and present a new method based on SAR coherence.