We propose a probabilistic model to determine landslide hazard at the basin scale. The model predicts where landslides will occur, how frequently they will occur, and how large they will be. We test ...the model in the Staffora River basin, in the northern Apennines, Italy. For the study area, we prepare a multi-temporal inventory map through the interpretation of multiple sets of aerial photographs taken between 1955 and 1999. We partition the basin into 2243 geo-morpho-hydrological units, and obtain the probability of spatial occurrence of landslides by discriminant analysis of thematic variables, including morphological, lithological, structural and land use. For each mapping unit, we obtain the landslide recurrence by dividing the total number of landslide events inventoried in the unit by the time span of the investigated period. Assuming that landslide recurrence will remain the same in the future, and adopting a Poisson probability model, we determine the exceedance probability of having one or more landslides in each mapping unit, for different periods. We obtain the probability of landslide size by analysing the frequency–area statistics of landslides, obtained from the multi-temporal inventory map. Assuming independence, we obtain a quantitative estimate of landslide hazard for each mapping unit as the joint probability of landslide size, of landslide temporal occurrence and of landslide spatial occurrence.
We tested the possibility of using digital, color aerial ortho-photographs and monoscopic, panchromatic satellite images of comparable spatial and radiometric resolution, to map recent landslides in ...Italy and to update existing measures of landslide mobilization. In a 90-km
2 area in Umbria, central Apennines, rainfall resulted in abundant landslides in the period from September 2004 to June 2005. Analysis of the rainfall record determined the approximate dates of landslide occurrence and revealed that the slope failures occurred in response to moderately wet rainfall periods. The slope failures occurred primarily in cultivated terrain and left subtle morphological and land cover signatures, making the recognition and mapping of the individual landslides problematic. Despite the difficulty with the identification of the landslides without the use of stereoscopic visualization, visual analysis of the aerial and satellite images allowed mapping 457 new landslides, ranging in area 3.0
×
10
1
<
A
L
<
2.5
×
10
4 m
2, for a total landslide area
A
LT
=
6.92
×
10
5 m
2. To identify the landslides, the investigators adopted the interpretation criteria commonly used to identify and map landslides on aerial photography. The result confirms that monoscopic, very high resolution images taken by airborne and satellite sensors can be used to prepare landslide maps even where slope failures are difficult to detect, provided the imagery has sufficient geometric and radiometric resolutions. The different dates of the aerial (March 2005) and the satellite (June–July 2005) images allowed the temporal segmentation of the landslide information, and studying the statistics of landslide area and volume for different periods. Compared to pre-existing information on the abundance and size of the landslides in the area, the inventory obtained by studying the aerial and satellite images proved more complete. The new mapping showed 145% more landslides and 85% more landslide area than a pre-existing reconnaissance inventory. As a result of the improved mapping, the rate of landslide mobilization for the 2004–2005 landslide season was determined to be
φ
L
=
27.1
mm year
−
1
, 30% higher than a previous estimate for the same period. This seasonal rate of landslide mobilization is significantly larger than the long-term regional erosion rate in the central Apennines. The accelerated rate is attributed to agricultural practices that favor slope instability.
A high resolution Digital Elevation Model with a ground resolution of 2 m×2 m (DEM2) was obtained for the Collazzone area, central Umbria, through weighted linear interpolation of elevation points ...acquired by Airborne Lidar Swath Mapping. Acquisition of the elevation data was performed on 3 May 2004, following a rainfall period that resulted in numerous landslides. A reconnaissance field survey conducted immediately after the rainfall period allowed mapping 70 landslides in the study area, for a total landslide area of 2.7×105 m2. Topographic derivative maps obtained from the DEM2 were used to update the reconnaissance landslide inventory map in 22 selected sub-areas. The revised inventory map shows 27% more landslides and 39% less total landslide area, corresponding to a smaller average landslide size. Discrepancies between the reconnaissance and the revised inventory maps were attributed to mapping errors and imprecision chiefly in the reconnaissance field inventory. Landslides identified exploiting the Lidar elevation data matched the local topography more accurately than the same landslides mapped using the existing topographic maps. Reasons for the difference include an incomplete or inaccurate view of the landslides in the field, an unfaithful representation of topography in the based maps, and the limited time available to map the landslides in the field. The high resolution DEM2 was compared to a coarser resolution (10 m×10 m) DEM10 to establish how well the two DEMs captured the topographic signature of landslides. Results indicate that the improved topographic information provided by DEM2 was significant in identifying recent rainfall-induced landslides, and was less significant in improving the representation of stable slopes.
We present the results of the application of a recently proposed model to determine landslide hazard. The model predicts where landslides will occur, how frequently they will occur, and how large ...they will be in a given area. For the Collazzone area, in the central Italian Apennines, we prepared a multi-temporal inventory map through the interpretation of multiple sets of aerial photographs taken between 1941 and 1997 and field surveys conducted in the period between 1998 and 2004. We then partitioned the 79 square kilometres study area into 894 slope units, and obtained the probability of spatial occurrence of landslides by discriminant analysis of thematic variables, including morphology, lithology, structure and land use. For each slope unit, we computed the expected landslide recurrence by dividing the total number of landslide events inventoried in the terrain unit by the time span of the investigated period. Assuming landslide recurrence was constant, and adopting a Poisson probability model, we determined the exceedance probability of having one or more landslides in each slope unit, for different periods. We obtained the probability of landslide size, a proxy for landslide magnitude, by analysing the frequency-area statistics of landslides, obtained from the multi-temporal inventory map. Lastly, assuming independence, we determined landslide hazard for each slope unit as the joint probability of landslide size, of landslide temporal occurrence, and of landslide spatial occurrence.
We present an approach to measure 3-D surface deformations caused by large, rapid-moving landslides using the amplitude information of high-resolution, X-band synthetic aperture radar (SAR) images. ...We exploit SAR data captured by the COSMO-SkyMed satellites to measure the deformation produced by the 3 December 2013 Montescaglioso landslide, southern Italy. The deformation produced by the deep-seated landslide exceeded 10 m and caused the disruption of a main road, a few homes and commercial buildings. The results open up the possibility of obtaining 3-D surface deformation maps shortly after the occurrence of large, rapid-moving landslides using high-resolution SAR data.
Landslide inventories provide the knowledge basis for many geomorphological applications and also planning and emergency management. Detailed landslide inventories should also be prepared where ...pre-existing inventories are available, as knowledge updates. In this paper, we present a new geomorphological landslide inventory for an area of the High Agri Valley, Southern Italian Apennines. The map was prepared through systematic interpretation of historical aerial photographs testing extensive use of anaglyph glasses in StereoPhoto Maker freeware. A total of 2124 landslides were classified based on the type of movement, estimated depth, estimated relative age and three levels of uncertainty, providing landslide attributes and map constraints useful for land planning and hazard studies. The map also documents the relationships between landslides and fluvial landforms of different generations, recording important information to investigate the geomorphological evolution of the area further. We expect that landslide mapping in similar environments will benefit from the workflow here presented.
Systematic and timely documentation of triggered (i.e. event) landslides is fundamental to build extensive datasets worldwide that may help define and/or validate trends in response to climate ...change. More in general, preparation of landslide inventories is a crucial activity since it provides the basic data for any subsequent analysis. In this work we present an event landslide inventory map (E-LIM) that was prepared through a systematic reconnaissance field survey in about 1 month after an extreme rainfall event hit an area of about 5000 km
in the Marche-Umbria regions (central Italy). The inventory reports evidence of 1687 triggered landslides in an area of ~550 km
. All slope failures were classified according to type of movement and involved material, and documented with field pictures, wherever possible. The database of the inventory described in this paper as well as the collection of selected field pictures associated with each feature is publicly available at figshare.
Analyses of historical records of landslides and climate variables are useful tools to search for correlations between damaging landslide events and their triggers. In this work, we investigate the ...temporal and geographical relationships between two long-term historical series of catalogued landslide occurrences and daily rainfall data in Umbria, a central Italian region, from 1928 to 2001. Moreover, we search for changes in the frequency and density of landslides, and in the characteristics of the associated rainfall events. Using a consolidated approach, partially modified, we find that the rainfall events that have produced rainfall-induced landslides in Umbria changed in space and time during observation period and between two considered sub-periods (1928–1975 and 1976–2001). In particular, we find that: (i) the monthly distribution of landslides associated with rainfall events is quite different than that of all landslides in the regional catalogue; (ii) the spatial and temporal distribution of REL changed from the older (most events occurred in winter) to the recent period (most events occurred in autumn); (iii) the recent most rainfall events associated with landslides are characterized by a lower cumulated rainfall and a shorter duration, sign of an increased propensity of the regional territory to produce landslides over time.
We used landslide information for 13 study areas in Italy and morphometric information obtained from the 3-arcseconds shuttle radar topography mission digital elevation model (SRTM DEM) to determine ...areas where landslide susceptibility is expected to be negligible in Italy and in the landmasses surrounding the Mediterranean Sea. The morphometric information consisted of the local terrain slope which was computed in a square 3 3-cell moving window, and in the regional relative relief computed in a circular 15 15-cell moving window. We tested three different models to classify the "non-susceptible" landslide areas, including a linear model (LNR), a quantile linear model (QLR), and a quantile, non-linear model (QNL). We tested the performance of the three models using independent landslide information presented by the Italian Landslide Inventory (Inventario Fenomeni Franosi in Italia - IFFI). Best results were obtained using the QNL model. The corresponding zonation of non-susceptible landslide areas was intersected in a geographic information system (GIS) with geographical census data for Italy. The result determined that 57.5% of the population of Italy (in 2001) was located in areas where landslide susceptibility is expected to be negligible. We applied the QNL model to the landmasses surrounding the Mediterranean Sea, and we tested the synoptic non-susceptibility zonation using independent landslide information for three study areas in Spain. Results showed that the QNL model was capable of determining where landslide susceptibility is expected to be negligible in the validation areas in Spain. We expect our results to be applicable in similar study areas, facilitating the identification of non-susceptible landslide areas, at the synoptic scale.
A 1:5,000 scale geological map and 31 geological cross-sections are presented for the surroundings of Amatrice village (central Apennines, Italy), epicentral area of the first damaging earthquake of ...the 2016-2017 seismic sequence. This detailed geological dataset focuses on: (i) the extent, the thickness, and the internal stratigraphic architecture of the Quaternary continental deposits; (ii) the bedding and the thickness of the Miocene substratum; and (iii) the spatial distribution of the main fault systems. The provided dataset would update the available regional geological maps in deciphering the syn-to-post-orogenic history of the Amatrice Basin. Eventually, the accuracy of the geological mapping would represent a basic tool for interpreting and integrating the multidisciplinary dataset deriving from post-seismic activities.
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