Italy is characterized by a high hydrogeological risk; in case of landslides event, occurred as a result of heavy rainfall, for the institutions responsible of the emergency management as the Civil Protection, it is very important to have a rapid mapping in order to operate in the best way.
The classical methods suitable to detect and map the landslides using remote sensing data are not always quickly applicable and sometimes require operators' experience. In order to find a method that can detect the most landslide areas in the shortest possible time, it have been carried out, using the software of digital's image analysis ENVI, some data processing on an image taken by the QuickBird satellite: the study area is part of the Province of Messina, near Giampilieri and Briga. In this area, in October 2009, as a result of intense precipitation, there were hundreds of landslides, debris flows mainly; this type of landslides leave distinctive marks on the ground and they are easily detectable by remote sensing data, especially in areas mainly vegetated. At the beginning, it were performed some elaborations in order to improve the contrast between vegetated areas and soils, such as the calculation of the normalized vegetation index (NDVI), the ratio between R/IR bands (Ratio Vegetation Index), and the principal component analysis (PCA). Subsequently, it were applied textural filters based on the co-occurrence matrix (Gray Level Co-Occurrence Matrix). The application of filter "Mean", represented by the values of the local media in the editing window, on the image obtained by the ratio R / IR, result in a second image characterized by very few shades of gray, in which are recognizable possible landslides areas, even if the same gray tones are applied to population centers; the contour instead is completely black. This image can be easily transformed into a vector image. The application of other filters on the images did not give satisfactory results. Only the filter "correlation", applied to the PC4, is useful, because, containing minor information but keeping visible the major population centres, it was helpful in the distinction between the latter of these and the landslide areas. By transforming these two images in two vector files and overlaid its on a GIS software (ArcGIS), it was possible to manually remove the vectors corresponding to towns from the image obtained by the application of the filter "mean." It was also decided to carry out a manual digitization of the image relative to NDVI and PC2, in order to compare the results.
Using an inventory map of the same area, created by stereoscopic analysis and field surveys
(Ardizzone, et al., 2012) it was carried out a quantitative analysis, based on the index of
positioning error E proposed by Carrara, et al. (1992), and also it was calculated the percentage of the area of the landslide correctly recognized. The values of E, refers to the automatically digitized image, do not differ from those found in the literature, although the percentage of the recognized landslide area is far away from the values reached by classical methods. The images created by manual vectorization has a lower value of E, so they are more accurate in terms of positioning, but the percentage of recognized landslide areas is less.
In conclusion, the automatically method tested could be useful in the early hours following a
fast landslide event, as debris flows, to evaluate in a short time the possible residual risks for the population and emergency management; the resulting map can then be improved by
performing manual scans, waiting a more precise mapping derived from methods already well experienced in bibliography.