Image Thresholding for Landslide Detection by Genetic Programming

Detecting landslides and monitoring their activity is of great relevance for natural hazard assessment and disaster prevention in hilly areas. Very high resolution optical satellite data is now becoming available, and to evaluate the potential application of these new images to detect ground surface changes as a result of landsliding, in previous work we developed and applied change detection and thresholding methods on digital aerial photographs over the Tessina landslide in Veneto, Italy . This is a nearly 3-km long complex landslide consisting of rotational slides in its head and a mudflow in the remaining, largest part of the body. The landslide has developed in Eocene flysch materials partly covered by colluvial and glacial till sediments. The landslide was first triggered in 1960 and has since undergone a number of reactivations. The physical setting of the area and landslide dynamics are further described in . The landslide currently affects an area including a number of small villages and mixed woodland-grassland landcover. It generally appears as a distinct bright feature on visible-wavelength panchromatic images because of outcropping soil under disrupted vegetation (figure 1a&b). This paper focuses on applying genetic programming techniques to detect and monitor landslides from optical remote sensing data. To this end, we have used radiometrically mormalised and orthorectified multitemporal aerial photographs at 1m ground resolution over the Tessina landslide area .