Contribution of the Landsat ETM+ Spectral Mixing Space and SRTM Analysis to Characterize DSGSD in Italy.

Deep Seated Gravitational Slope Deformations (DSGSD) in rock slopes, Sackung or Deep Seated Block Slides, are a particular category of mass movement conditioned by a 'scale factor', in which long-lasting small-scale movements prevail. Generally a DSGSD has a depth comparable with the entire slope meanwhile the slide surface is not always recognizable. As a consequence, the dimensions and the typical surface evidences characterize this kind of landforms, allowing a distinction between DSGSD and landslides. DSGSD are strictly related to landslide hazard. Taking into account a short time scale DSGSD are associated with an higher frequency of superficial landsliding. In a longer time scale the possible reactivation of these phenomena can generate the mobilization of large rock volumes. The study starts from the collection, implemented as GeoData Base, of a wide case histories published in literature regarding the spread of the phenomenon all over the Italian Alps and Apennines. Preliminary results shows several lacking areas in physiographic regions with the same morphologic and geologic conditions. Moreover the statistical analysis of the morphometric characteristics collected in the DB, underlines a bias due to several cases misinterpreted. The DSGSD reconnaissance is based, above all, on geomorphologic features individuation with a high degree of uncertainty. Considerable enhancement for morphometric interpretation can be obtained through the integration of spectral data with Digital Elevation Model. Our methodology is direct towards the automatic analysis of the geomorphologic parameters which characterize the arrangement of the DSGSD (such as the slope, the curvature and the relief), starting from a DEM. The method classifies the landscape into geographic areas as function of complex interdependent parameters rather than a single parameters. The SRTM data set, in synergy with Spectral Mixture Analysis (SMA) of Landsat ETM+, classifies individual mixed pixels according to the distribution of spectrally pure end member fractions and provides a tool for discrimination and classification. In this research Landsat ETM+ provides the basis for passive optical mapping of DSGSD morphologic features. The coverage and moderate spatial resolution (30m) offered by Landsat ETM+ are a necessary complement to the SRTM imagery and the combined use of both systems allowed for greater accuracy than either could provide independently.