High resolution satellite imagery analysis for inferring surface-subsurface water relationships in unstable slopes

Wasowski J., Lamanna C., Gigante G., Casarano D., 2012, High resolution satellite imagery analysis for inferring surface-subsurface water relationships in unstable slopes, Remote sensing of environment 124 (2012). doi_10.1016/j.rse.2012.05.007,
URL: http://www.cnr.it/prodotto/i/192201

We investigate the instability of slopes in a 15.6 km2 catchment area in the Southern Apennine mountains (Italy) traversed by a mid-slope road and characterized by predominance of clay-rich flysch units. High resolution multispectral satellite imagery is used to identify active landslides, to investigate their close association with seasonally wet zones (areas covered by free surface-water including ponds, migrating surfacewater, seeps), as well as to guide subsurface hydrogeological investigations. By combining the remotely sensed surface information and the extensive subsurface dataset from over 40 monitoring piezometer boreholes we demonstrate that many wet zones initially mapped from the IKONOS imagery are indicative of sites with seasonally persistent very high groundwater levels within landslide-prone slopes and on intermittently active landslides. Where such surface-subsurface water linkage can be established, the appearance of the wet zones (fully saturated ground/soil) resulting from groundwater discharge or seepage can be used as a forewarning signal of the increased susceptibility to landsliding, since the hillslopes with shallow groundwater tables are generally more prone to failure. The information about changing surface-water conditions retrieved from high resolution satellite data timely acquired during rainy seasons can thus provide crucial input for temporal and spatial landslide hazard assessments. We anticipate that in the near future high resolution optical space-borne remote sensing will become a commonly used tool for monitoring landslide activity and for providing temporal series of spatial data necessary to improve our understanding of causative and triggering processes leading to slope failures.

Dati tratti da https://intranet.cnr.it/people/