Abstract
In the framework of the WISELAND project, funded by MIUR, we tested the integration between LiDAR and hyperspectral methodologies in the Valoria landslide (Modena province, Italy), a high risk area with vulnerable elements, subjected to periodic and abrupt reactivations. Multitemporal LiDAR Digital Terrain Models (DTMs) allowed the calculation of a differential surface, highlighting absolute height variations, recognizing the main landslide components and identifying depletion and accumulation zones. Hyperspectral data helped in the landslide terrain roughness characterization, performing the Principal Component Analysis (PCA) and correlating the results with Flatness and Organization geomorphometric parameters derived from LiDAR DTM.
Highlights
Landslide occurrence is related to a variety of factors such as underlying geology, mechanical properties of soil and rocks, degree of weathering, groundwater conditions, and the presence of geological structures such as joints, faults, and shear zones [Fell et al, 2000]
The differential analysis of a DEM of 1973 and of a Digital Terrain Models (DTMs) of 2003 resulted in a clear enough identification of major depletion and accumulation zones occurred after the 2001 reactivation event [Corsini et al 2007]
The study demonstrates the capabilities of remote sensing techniques to recognise the essential features of an active, rapid earthflow
Summary
Landslide occurrence is related to a variety of factors such as underlying geology, mechanical properties of soil and rocks, degree of weathering, groundwater conditions, and the presence (or absence) of geological structures such as joints, faults, and shear zones [Fell et al, 2000]. LiDAR and Hyperspectral data integration for landslide monitoring Other works using these methodologies to study landslides can be found on the web, but there are no examples of the integration between differential LiDAR and hyperspectral data in literature.
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