Landslides occur every year in many areas of the world, causing casualties, economic and environmental losses.
Landslide inventory maps are important to document the extent of the landslide phenomena in a region, for risk
estimation and management, and to study landscape evolution. We present a method to facilitate the semi-automatic
recognition and mapping of event induced shallow landslides. The method is based on the combination in a Bayesian
framework of information extracted from High Resolution optical multispectral satellite images and Digital Elevation
Models (DEM). The landslide membership probability is estimated from post-event satellite images using a supervised
image classification method. The likelihood of landslide occurrence is obtained adopting a “data-driven” approach,
intersecting existing landslide inventories with maps of morphometric parameters (slope and curvature) calculated from
the DEM. We tested the method in the Huaguoshan basin, Taiwan, where it proved capable of detecting and mapping
landslides triggered by Typhoon Morakot in August 2009. Compared to other pixel-based approaches, the method
reduces significantly the typical “salt-and-pepper” effect of landslide classifications, and allows the internal classification
of landslide areas in landslide source areas and landslide travel and depositional (“run out”) areas.
Early warning systems can predict rainfall-induced landslides by comparing rainfall data with landslide rainfall
thresholds. These systems are based on empirical rainfall thresholds defined using rain gauges data. Despite quantitative
satellite rainfall estimates are currently available, limited research has compared satellite estimates and rain gauge
measurements for the forecasting of possible landslide occurrence. In this work, we validate satellite estimates obtained
for Italy by the NASA Tropical Rainfall Measuring Mission (TRMM) against rainfall measurements from the Italian rain
gauge network (< 1950 rain gauges), in the period from 1 September 2009 to 31 August 2010. Using cumulative rainfall
measurements/estimates, we: (i) evaluate the correlation between the rain gauge measurements and the satellite estimates
in different morpho-climatological domains, (ii) analyse the distributions of the ground-based measurements and the
satellite estimates using different statistical approaches, and (iii) compare rainfall events derived automatically from
satellite and rain gauge rainfall series. We observe differences between satellite estimates and rain gauge measurements
in different morpho-climatological domains. The differences are larger in mountain areas, and collectively reveal a
complex relationship between the ground-based measurements and the satellite estimates. We find that a power law
correlation model is appropriate to describe the relation between the two rainfall data series. We conclude that specific
rainfall thresholds must be defined to exploit satellite rainfall estimates in existing landslide early warning systems.
We exploited Differential Synthetic Aperture Radar Interferometry (DInSAR) to investigate the geographical and the
temporal pattern of ground deformations in the Ivancich landslide area, Assisi, Italy, in the 18.4-year period April 1992 -
September 2010. We used SAR data obtained by the European Remote Sensing (ERS-1/2) satellites in the period April
1992 - July 2007, and SAR data captured by the ASAR sensor on board the Envisat satellite in the period October 2003
- September 2010. We used the Small Baseline Subset (SBAS) technique to process the SAR data, obtaining full
resolution measurements for multiple radar targets inside and outside the landslide area, and the history of deformation
of the individual targets. The geographical pattern of the ground deformation was found consistent with independent
topographic information. The deformation time series of the individual targets were compared to the rainfall history in
the area. Results revealed the lack of an immediate effect of rainfall on the ground deformation, and confirmed the
existence of a complex temporal interaction between the rainfall and the ground deformation histories in the landslide
area. Availability of very long, spatially distributed time series of surface deformation has provided an unprecedented
opportunity to investigate the history of the active landslide area.
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