Article A New Method for Large-Scale Landslide Classification from Satellite Radar Katy Burrows 1,*, Richard J. Walters 1, David Milledge 2, Karsten Spaans 3 and Alexander L. Densmore 4 1 The Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics, Department of Earth Sciences, Durham University, DH1 3LE, UK;
[email protected] 2 School of Engineering, Newcastle University, NE1 7RU, UK;
[email protected] 3 The Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics, Satsense, LS2 9DF, UK;
[email protected] 4 Department of Geography, Durham University, DH1 3LE, UK;
[email protected] * Correspondence:
[email protected]; Tel.: +44‐0191‐3342300 Received: 10 January 2019; Accepted: 17 January 2019; Published: 23 January 2019 Abstract: Following a large continental earthquake, information on the spatial distribution of triggered landslides is required as quickly as possible for use in emergency response coordination. Synthetic Aperture Radar (SAR) methods have the potential to overcome variability in weather conditions, which often causes delays of days or weeks when mapping landslides using optical satellite imagery. Here we test landslide classifiers based on SAR coherence, which is estimated from the similarity in phase change in time between small ensembles of pixels. We test two existing SAR‐coherence‐based landslide classifiers against an independent inventory of landslides triggered following the Mw 7.8 Gorkha, Nepal earthquake, and present and test a new method, which uses a classifier based on coherence calculated from ensembles of neighbouring pixels and coherence calculated from a more dispersed ensemble of ‘sibling’ pixels.