Remote sensing as tool for development of landslide databases: The case of the Messina Province (Italy) geodatabase

Abstract

Landslide geodatabases, including inventories and thematic data, today are fundamental tools for national and/or local authorities in susceptibility, hazard and risk management. A well organized landslide geo-database contains different kinds of data such as past information (landslide inventory maps), ancillary data and updated remote sensing (space-borne and ground based) data, which can be integrated in order to produce landslide susceptibility maps, updated landslide inventory maps and hazard and risk assessment maps. Italy is strongly affected by landslide phenomena which cause victims and significant economic damage to buildings and infrastructure, loss of productive soils and pasture lands. In particular, the Messina Province (southern Italy) represents an area where landslides are recurrent and characterized by high magnitude, due to several predisposing factors (e.g. morphology, land use, lithologies) and different triggering mechanisms (meteorological conditions, seismicity, active tectonics and volcanic activity). For this area, a geodatabase was created by using different monitoring techniques, including remote sensing (e.g. SAR satellite ERS1/2, ENVISAT, RADARSAT-1, TerraSAR-X, COSMO-SkyMed) data, and in situ measurements (e.g. GBInSAR, damage assessment). In this paper a complete landslide geodatabase of the Messina Province, designed following the requirements of the local and national Civil Protection authorities, is presented. This geo-database was used to produce maps (e.g. susceptibility, ground deformation velocities, damage assessment, risk zonation) which today are constantly used by the Civil Protection authorities to manage the landslide hazard of the Messina Province

A method for assessing and managing landslide residual hazard in urban areas

“…Detection and mapping need a combination of the Bclassical^approach providing information on the nature, extent, and frequency of past landslide events (i.e. field-based studies, standard geomorphological mapping) and advanced techniques, such as remote-sensing data analysis and geophysical investigations Ciampalini et al 2015). In particular, the extent and the magnitude of the landslide residual hazard could be successfully assessed using ground-based monitoring techniques, and of these, the one giving best results appears to be GB-InSAR (Di Traglia et al, 2014a, b;Carlà et al 2016a, b).…”

Susceptibility of intrusion-related landslides at volcanic islands: the Stromboli case study

“…The geo-database of the Stromboli Island was developed in three different phases: (i) ancillary data collection, (ii) database design and (iii) product generation (Ciampalini et al 2015). All the ancillary data were included in a dedicated database (Fig.…”

“…The effects of other triggers (rainfall, sea waves, seismic shaking) were not considered here. The landslides predisposing factors were estimated by random forest (RF) technique (Catani et al 2013;Ciampalini et al 2015), while the probabilistic volcanic vent opening distribution, as proxy for magma injection, was evaluated by QVAST tool (Bartolini et al 2013). The reasons to apply this methodology to Stromboli volcano are based on the following facts: (i) it has experienced moderate to huge landslides (Tibaldi 2001), (ii) it is geomorphologically prone to instability events (Nolesini et al 2013), (iii) it is affected by active intense volcanic activity that can significantly affect the stability of slopes (Di Traglia et al 2014a) and (iv) its flank instability could affect areas inhabited and intensely frequented by people for touristic activities (Nave et al 2010).…”

Predicting storm-triggered debris flow events: application to the 2009 Ionian Peloritan disaster (Sicily, Italy)

“…The first is possible when multi-temporal landslides inventories are available, the second is based on randomly partitioning single-epoch data sets and the third on sub-dividing the study area into two similar sub-sectors. Random time partition procedures can be applied either on the landslide inventory (Conoscenti et al, 2008a) or on the mapping units database (Conoscenti et al, 2008b), whilst spatial partition can also be performed also on not nested or adjacent areas such as in the study aimed at susceptibility model exportations (von Ruette et al, 2011;Costanzo et al, 2012a;Lombardo et al, 2014).…”

“…1) and the debris flow event of 2009 have been the focus of study in several scientific articles centred on different topics. Several studies have been devoted to the implementation of remote and semi-automatic techniques for landslide recognition and mapping of such a significant multiple occurring regional landslide event (Ardizzone et al, 2012;Mondini et al, 2011;Ciampalini et al, 2015). Del focused their research on the Giampilieri village area, analysing the triggering mechanism and estimating the volumes involved in the debris flow.…”