Landslide Mapping and inventory
Mapping of landslide hazard is a direct, qualitative method that relies on the ability of the investigator to estimate actual and potential slope failures. Spatial detection and mapping of mass movements are the basis for all assessments and activities related to geological risk management and mitigation activities, at both regional and local scale. Preparing landslide maps is important to document the extent of landslide phenomena in a region, to investigate the location, types, pattern, recurrence and statistics of slope failures, to determine landslide susceptibility, hazard, vulnerability and risk, and to study the evolution of landscapes dominated by mass wasting processes (Guzzetti et al., 2012). The Landslides Inventory Maps (LIM) date back to the 1970’s (Carrara and Merenda, 1976) and can be defined as representations of the distribution, typology, and, if possible, the date of occurrence of those mass movements that have left detectable evidence on the affected areas.
Landslide inventory maps show the spatial distribution of both past and current landslides, represented as polygons or points and often include also information about their state of activity.
Mapping for landslide inventory is scale dependant, with generally three scales of spatial analysis being defined (Mantovani et al., 1996; Fell et al., 2008): a regional scale (<100,000), a medium scale (1:50,000 - 1:25,000) and a large scale (>1:10,000). Thus, landslide inventory map can range from regional to local scale.
Landslide mapping activities can be performed by using a purely convention approach (i.e. stereoscopic interpretation of aerial photography) or exploiting modern and emerging technologies, mainly relied on remote sensing. Recently, Guzzetti et al. (2012) provided a review about the various approaches for landslide identification and mapping, both the conventional and the new recent techniques.
The traditional methods most commonly used for preparing inventory maps are based on historical archives, local databases, photo-interpretation of stereo aerial photos and geomorphologic surveys (Soeters and Van Westen, 1996). Thus, these conventional methods rely chiefly on the visual interpretation of stereoscopic aerial photography, aided by field checks. These methods are well-established, but are time consuming and resource-intensive (e.g. Brabb, 1991; Galli et al., 2008). Moreover, the inventory maps created with only these traditional techniques are usually subject to uncertainties and limitations related mainly to difficulties in the recognition and the subsequent mapping of morphological features and evidences related to ground instability, especially in highly vegetated and urbanized/built-up areas. Even more problematic is the assessment of the state of activity, for which various but not resolving approaches have been proposed, such as stereoscopic multi-temporal analysis of aerial photos.
New and emerging techniques are mainly based on EO satellite, airborne, and terrestrial remote sensing technologies. These techniques have demonstrated to be valuable for the recognition of landslides and other geomorphological features, and so for facilitating the production of landslide maps, reducing the time and resources required for their compilation and systematic update (Guzzetti et al., 2012).
These modern and promising techniques for the analysis of surface morphology include the exploitation of (i) very-high resolution digital elevation models (DEMs) (i.e. VHR digital representations of surface topography obtained by LiDAR sensors), (ii) SAR images processed through DInSAR and PSInSAR techniques, and (iii) different types of satellite optical images (monoscopic and/or stereoscopic analysis of panchromatic multispectral and hyperspectral satellite imagery). A visual and semi-automated classification and interpretation method for creating and updating landslides maps through optical images analysis (i.e. “Multiple Change Detection”) has been recently carried out by Mondini et al. (2011). From the late 90s, airborne and satellite SAR data have been successfully used to detect, characterize, and map single or multiple landslides (Singhroy et al., 1998). Space-borne SAR Interferometry offer great support in the implementation of landslide inventory maps, because it provides rapid and easily updatable ground displacement measurement over large areas with very high accuracy (millimetre) and spatial resolution (up to 1 m), good
temporal coverage (starting from 1992 up to present) and sampling (monthly acquisitions), reducing field work and costs.
EO-based landslide mapping and inventory applications provide important information on the spatial distribution of mass movements and generally operate at regional scale. They integrate satellite-based ground deformation measurements into pre-existing landslide inventories produced with field surveys, conventional geomorphologic tools, stereoscopic photo-interpretation of multi-temporal aerial and/or satellite optical imagery, thematic, geological and topographic data. Satellite EO offers a cost-effective means to identify indicators of slope instability, in the form of terrain features and landforms identified through interpretation of optical imagery, as well as ground displacement estimates provided by InSAR and PSI technologies.
Overall, the final goal of these applications is the creation or the improvement of landslide inventory maps, through the delivery of qualitative (e.g. state of activity) and quantitative (e.g. intensity) information of each mapped phenomenon and the detection and mapping of those phenomena not previously identified through conventional means. Thus, useful products of this approach include landslide identification and mapping by using traditional in situ surveys techniques and EO data, through the rapid detection of unstable areas and the identification of their spatial extension and temporal evolution to support the emergency management process, especially in deferred time.