One challenge of course is to use this knowledge to identify and monitor slopes that might be undergoing failure.\u00a0 The holy grail is to have a remote monitoring system that collects data at a regional or national scale and then identifies slopes that are actively deforming.\u00a0 InSAR<\/a> provides one potential basis for this, and national scale deformation maps are now available<\/a>, but identifying correctly slopes that might be dangerous requires more work and a better understanding.<\/p>\n Another approach, applicable for individual landslides at the moment, is to use imaged correlation approaches from optical satellite imagery.\u00a0 In this approach, pairs of images are compared. Perhaps surprisingly, image processing can allows deformations on the scale of 3 to 10% of a pixel to be detected.\u00a0 The Sentinel-2 satellites have an image resolution of about 10 metres, so deformation of less than a metre can be measured.<\/p>\n In an open access paper in the journal Natural Hazards and Earth System Sciences<\/em><\/a>, Yang et al.<\/em> (2020)<\/a> have used image processing of Sentinel-2 imagery to examine the movement through time of a developing failure near to the town of Mindu in Tibet.\u00a0 This is a large area of slope deformation – this is the Google earth image collected in 2011 for example:-<\/p>\n A Google Earth image of the landslide near to Mindu in Tibet.<\/p><\/div>\n .<\/p>\n The location of this landslide is 30.582, 98.925.\u00a0 Whilst there is little in the way of the landslide, clearly a major rock slope failure at this site near to Mindu would potentially\u00a0 block the river, creating a substantial hazard downstream.<\/p>\n![\"The](\"https:\/\/blogs.agu.org\/landslideblog\/files\/2020\/12\/20_12-Mindu-1.jpg\")