j ourna l homepage: www.e lsev ie r.com/ locate /geomorph
Seasonal landslide mapping and estimation of landslide mobilization rates usingaerial and satellite images
F. Fiorucci a,b,⁎, M. Cardinali a, R. Carlà c, M. Rossi a, A.C. Mondini a,b, L. Santurri c, F. Ardizzone a, F. Guzzetti a
a CNR IRPI, via Madonna Alta 126, 06128 Perugia, Italyb Dipartimento di Scienze della Terra, Università degli Studi di Perugia, piazza dell'Università 1, 06123 Perugia, Italyc CNR IFAC, via Madonna del Piano 10, 50019 Sesto Fiorentino (FI), Italy
⁎ Corresponding author at: CNR IRPI, via Madonna ATel.: +39 075 5014427; fax: +39 075 5014420.
Article history:Received 14 July 2010Received in revised form 22 December 2010Accepted 14 January 2011Available online 25 January 2011
Keywords:LandslideMappingLandslide volumeRate of mobilizationSatellite
We tested the possibility of using digital, color aerial ortho-photographs and monoscopic, panchromaticsatellite images of comparable spatial and radiometric resolution, to map recent landslides in Italy and toupdate existing measures of landslide mobilization. In a 90-km2 area in Umbria, central Apennines, rainfallresulted in abundant landslides in the period from September 2004 to June 2005. Analysis of the rainfallrecord determined the approximate dates of landslide occurrence and revealed that the slope failuresoccurred in response to moderately wet rainfall periods. The slope failures occurred primarily in cultivatedterrain and left subtle morphological and land cover signatures, making the recognition and mapping of theindividual landslides problematic. Despite the difficulty with the identification of the landslides without theuse of stereoscopic visualization, visual analysis of the aerial and satellite images allowed mapping 457 newlandslides, ranging in area 3.0×101bALb2.5×104 m2, for a total landslide area ALT=6.92×105m2. To identifythe landslides, the investigators adopted the interpretation criteria commonly used to identify and maplandslides on aerial photography. The result confirms that monoscopic, very high resolution images taken byairborne and satellite sensors can be used to prepare landslide maps even where slope failures are difficult todetect, provided the imagery has sufficient geometric and radiometric resolutions. The different dates of theaerial (March 2005) and the satellite (June–July 2005) images allowed the temporal segmentation of thelandslide information, and studying the statistics of landslide area and volume for different periods. Comparedto pre-existing information on the abundance and size of the landslides in the area, the inventory obtained bystudying the aerial and satellite images proved more complete. The new mapping showed 145% morelandslides and 85% more landslide area than a pre-existing reconnaissance inventory. As a result of theimproved mapping, the rate of landslide mobilization for the 2004–2005 landslide season was determined tobe φL=27.1 mm year−1, 30% higher than a previous estimate for the same period. This seasonal rate oflandslide 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 landslide event-inventory map shows the effects of a singlelandslide trigger, such as an earthquake (Harp and Jibson, 1996;Esposito et al., 2000; Lin et al., 2004; Saba et al., 2010), a rainfall event(Bucknam et al., 2001; Guzzetti et al., 2004, 2006a; Dapporto et al.,2005; Cardinali et al., 2006; Tsai et al., 2010), or a rapid snowmeltevent (Cardinali et al., 2001; Kawagoe et al., 2009). Differenttechniques are used to compile landslide event-inventory maps,including: interpretation of stereoscopic aerial photographs takenshortly after an event (Bucknam et al., 2001; Cardinali et al., 2001;Guzzetti et al., 2004), visual or digital analysis of high-resolution
DEMs obtained from airborne Lidar sensors (McKeana and Roering,2004; Ardizzone et al., 2007; Corsini et al., 2007; Schulz, 2007; VanDen Eeckhaut et al., 2007; Kasai et al., 2009), and reconnaissance fieldsurveys (Dapporto et al., 2005; Cardinali et al., 2006; Mahdavifar et al.,2006; Santangelo et al., 2010). A combination of these techniques isoften used. A multi-temporal inventory consists of multiple landslidemaps prepared for different events or periods for the same area(Guzzetti et al., 2005, 2006a).
Successful production of event-based and multi-temporal landslideinventories is often hampered by the lack of a timely coverage of post-event aerial photographs. To overcome this limitation, investigatorshave attempted to exploit satellite images to detect and map landslides(e.g., Huang and Chen, 1991; Mantovani et al., 1996; Lin et al., 2002;Hervás et al., 2003; Cheng et al., 2004; Metternicht et al., 2005; NicholandWong, 2005a, 2005b; Lee and Lee, 2006; Hong et al., 2007;Weirichand Blesius, 2007; Saba et al., 2010). Monoscopic, high-resolution (HR)
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and very high resolution (VHR) optical images can be examined visuallyto detect individual landslides or groups of landslides, where thelandslide "signature" i.e., the visual evidence of the slope failure on theimage, is sufficiently clear (Marcelino et al., 2009). VHR images have aspatial resolution smaller than 1 m for panchromatic and smaller than4 m for multispectral, and HR images have a resolution in the range of1–4 m for panchromatic and 4–30 m for multispectral (EuropeanCommission, 2010). These spatial resolutions are compatible with thesize of most landslides (Malamud et al., 2004). Where pre- and post-event satellite optical images are available, change detection techni-ques can be used to help identify the landslide areas (Mantovani et al.,1996; Nichol and Wong, 2005a,b; Lee and Lee, 2006; Weirich andBlesius, 2007). Images acquired by Synthetic Aperture Radar (SAR)satellite sensors can also be used to detect landslides that havemodified significantly the surface morphology or the land cover(Czuchlewski et al., 2003; Singhroy and Molch, 2004).
In this work, we present the results of an experiment aimed attesting the possibility of using digital, color aerial ortho-photographsand VHR panchromatic satellite images to detect and map rainfall-induced landslides in an area of central Italy, where recent slopefailures have left subtle signatures. Following a description of thestudy area (Section 2), we present the landslide information, and theaerial and satellite imagery available to us (Section 3). Next, wediscuss the production of a seasonal landslide inventory for the periodfrom September 2004 to June 2005 (Section 4), and how landslideevents were singled from rainfall records in this period (Section 5).Next, we use the seasonal inventory to obtain statistics of landslidearea AL and volume VL, to determine landslide magnitudes mL, and tomeasure landslide mobilization rates φL (Guzzetti et al., 2009) fordifferent periods (Section 6). We conclude by examining theadvantages and limitations of aerial ortho-photographs and VHRmonoscopic satellite images for landslide mapping, and discussinghow the new landslide statistics and the associated measures oflandslide mobilization contribute to the understating of the recentevolution of the landscape in the study area (Section 7).
2. Study area
The study area is located between 42°47′N and 42°56′N, andbetween 12°22′E and 12°31′E, in Umbria, central Italy (Fig. 1). The
Fig. 1. Location map. Red polygon in the center is the study area. Blue dots showlocations of the Todi and the Bastardo rain gauges.
area extends for about 90 km2, including 78.9 km2 of hilly terrain,with elevation between 145 and 634 m, and terrain gradientcomputed from a 10×10 m DEM in the range from 0° to 63.7°(mean value=9.9°). Sedimentary rocks Cretaceous to Recent in agecrop out in the area, comprising recent fluvial deposits, continentalgravel, sand and clay, travertine, layered sandstone and marl, andthinly layered limestone. Climate is Mediterranean, with precipitationfalling mostly in the period from October to December and fromFebruary to May (Fig. 2).
Landslides in the area are caused primarily by meteorologicaltriggers, including prolonged rainfall and rapid snowmelt, and can beclassified as rapid to very slow, shallow soil slides and flows, deep-seated slides and flows, and compound movements (Guzzetti et al.,2006a,b, 2009; Ardizzone et al., 2007; Galli et al., 2008). After failure,landslides move for relatively short distances (a few meters to a fewtens of meters), and deposit the displacedmaterial on or at the bottomof the slope (Guzzetti et al., 2009). Shallow landslides occur primarilyon cultivated or abandoned areas, and are rare in forested terrain. Inthe cultivated areas, mechanical ploughing and harrowing obliteratelandslides features. For this reason, the lifetime of individual shallowlandslides in the study area rarely exceeds a few seasons, althoughreactivations and new slope failures where previous landslides haveoccurred are common.
3. Materials
3.1. Existing landslide information
For the study area, a multi-temporal inventory map, at 1:10,000scale, was available. The inventory was prepared through theinterpretation of multiple sets of stereoscopic, vertical aerial photo-graphs taken in the period 1941–1997, and by field surveys in theperiod 2003–2005 (Guzzetti et al., 2006a,b; Ardizzone et al., 2007;Galli et al., 2008). In the multi-temporal inventory, landslides areclassified based on the date of the aerial photographs, or the date orperiod of the field surveys or of the triggering events that haveresulted in slope failures. In the inventory, landslides are organized inseparate temporal layers, and slope failures pertaining to the sameevent or period are stored in the same temporal layer (Guzzetti et al.,2009).
In the multi-temporal inventory, a specific temporal layer showsrainfall-induced landslides in the period between October andDecember 2004. The landslide information was obtained through areconnaissance survey of the area, driving and walking along main,secondary, and farm roads (Cardinali et al., 2006). The investigatorsstopped where single or multiple landslides were identified, and at
Fig. 2. Monthly rainfall for the Todi rain gauge. Black line shows monthly averages(1921–2005). Vertical bars show total monthly rainfall between September 2004 andJune 2005. Yellow bars aremonths below the long-term average. Violet bars aremonthsabove the long-term average.
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viewing points to check individual and multiple slopes. In the field,single and stereoscopic color photographs of each landslide or groupof landslides were takenwith digital hand-held (nonmetric) cameras.In the laboratory, the photographs were used to position theindividual landslides on the base maps, to help characterize the typeand the size of the individual failures, and to evaluate the terrainconditions (e.g., slope, aspect, and land cover types). Landslidesidentified in the field and in the photographs were mapped on1:10,000 scale topographic base maps. The reconnaissance surveyalong the roads did not allow for a full coverage of the study area.Locally, slopes were not entirely visible from the viewing points, andan undetermined number of landslides was not identified andmapped. As a result, the reconnaissance inventory is incomplete,and the levels of completeness and accuracy of the map are unknown.We refer to the reconnaissance inventory as landslide map A (Fig. 3A,Table 1).
3.2. TerraItaly™ color aerial ortho-photograph
TerraItaly™ 2005 is a digital coverage of ortho-photographsproduced for the entire Italian territory by BLOM - Compagnia GeneraleRipreseaeree S.p.A. (http://www.TerraItaly.it/). The coverage wasobtained by flying a Leica ADS40 digital imaging device from analtitude of about 6300m. The ADS40 sensor is a linear charge coupleddevice (CCD) capable of collecting panchromatic (black-and-white)and multispectral images in the visible and in the near-infraredspectral region. The airborne “pushbroom” (along track) imagingsensor acquires simultaneously in three spectral bands, from 0.429 to0.900 μm, with corner stereoscopic off-nadir angles of 28.4° forwardand −14.2° backward. The digital panchromatic and multispectralimages were geo-referenced and ortho-rectified using a DEM with aground resolution of 40×40 m. Accuracy of the ortho-rectification inthe study area is unknown. For TerraItaly™, the nominal pixel size onthe ground is 0.5×0.5 m, allowing for the production of 1:10,000 scaledigital ortho-photographs that, according to the producer, can beenlarged to 1:2000 scale for improved interpretation and analysis.
For the study area, the ortho-photograph used to recognize andmap the landslides was the result of a mosaic of two sets of images.The first set was acquired on an unknown day of March 2005 to cover94% of the study area, and the second set was obtained on anunknown day of June 2005 to cover the remaining 6% of the area. Bothacquisitions were cloud free and with no haze. For the analysis, onlythe area covered by the first set of images was used (Fig. 3B).
3.3. Ikonos panchromatic images
The Ikonos satellite was launched on 24 September 1999 tobecome the first Earth observation commercial satellite to providepublicly available very high resolution images. Operating from a680 km sun synchronous orbit, Ikonos collects VHR panchromatic andmultispectral images in four bands (blue, green, red, near-infrared),with 11-bit radiometric resolution, along a swath of ~11 km. Theimaging sensor operates in the spectral range 0.445–0.853 μm,comparable (but not identical) to the range covered by the ADS40airborne sensor. At nadir, the ground sampling distance (GSD) of thesensor is 0.82 m for panchromatic and 3.2 m for multispectral images,allowing the production of pan-sharpened images with a nominal1×1 m ground resolution. The revisiting rate is 3–5 days off-nadir and144 days for true-nadir. At 45° latitude (i.e., approximately thelatitude of the study area), the revisiting rate is 3 days, at 1 m GSD.
To detect and map landslides in the study area, a mosaic of twoIkonos images was used (Table 2). The first image was taken on 28June 2005 and covers 35% of the study area, and the second image wasobtained on 4 July 2005 and covers 53% of the area. The remaining 12%of the study area was not covered by the satellite images (Fig. 3C).First, the two Ikonos images were registered using the associated
Rationale Polynomial Coefficients (RPC) files, and a 10×10 m DEMobtained through the interpolation of 5-m (and locally 1-m) contourlines shown in the topographic base maps used to prepare map A.Next, image registration was improved through a rigid translationapplied to a ground control point (GCP). Accuracy of the geo-locationwas determined considering two sets of check points (CPs), includingeight CPs selected in the Ikonos image taken on 28 June 2005, and 20CPs selected in the image taken on 4 July 2005. The lesser number ofCPs used for the first satellite image is justified because the areacovered by the image is smaller. The mean absolute error was 0.8 m(standard deviation σ=0.1 m) for the first image, and 1.1 m(σ=0.4 m) for the second image. The geo-location accuracy,measured by the 90% probability circular error (Alger, 2003; Helderet al., 2003), was 1.3 m for the first image and 2.0 m for the secondimage. These location errors are smaller than the errors commonlyaccepted for landslide mapping at 1:10,000 scale (Malamud et al.,2004; Santangelo et al., 2010).
4. Landslide mapping
When preparing a landslide inventory map through the interpre-tation of aerial photographs, stereoscopic visualization is important toobtain high-quality three-dimensional vision of the terrain (Rib andLiang, 1978; van Zuidam, 1985), a fundamental feature to identify themorphological signature of a landslide (Pike, 1988). Withoutstereoscopic vision, recognition and mapping of individual landslideson aerial photographs or satellite images of comparable character-istics is difficult, particularly where landslides have not left clear(unambiguous) morphological and land cover signatures.
4.1. Photo-interpretation criteria
For the study area, the TerraItaly™ color aerial ortho-photographand the Ikonos panchromatic satellite images were used to recognizeandmap fresh (at the date of the imagery) landslides. To recognize theindividual slope failures in the digital images, we used the samecriteria commonly adopted by geomorphologists to identify land-slides on aerial photographs, including visual analysis and heuristicinterpretation of the shape, size, color, tone, mottling, texture, andpattern of individual features, or sets of features, shown on the aerialand the satellite images (Ray, 1960;Miller, 1961; Allum, 1966; Rib andLiang, 1978; van Zuidam, 1985).
Fig. 4 shows examples of four typical shallow landslides in thestudy area identified visually on the aerial (Fig. 4A, B) and the satellite(Fig. 4C, D) images. The four prototype landslides are used to discussthe general criteria that allowed recognizing and mapping individualslope failures on the monoscopic digital images. Fig. 4A portrays acultivated area where, at the date of the aerial ortho-photograph(March 2005, Table 2), corn had not grown fully. In the image,alternating alignments of light and dark color tones, the result ofmechanical ploughing and sowing, define a clear linear pattern withdistinct mottling, which depends on the spacing and height of thevegetation. The interruption (or alteration) of the linear pattern alonga distinct, continuous and irregular narrow band allows therecognition of the landslide, and to map the landslide boundary.Fig. 4B shows an area in part cultivated (upper part) and in partforested (lower part). As in the case of Fig. 4A, at the date of the ortho-photograph corn had not grown fully in the cultivated area. In theimage, the forested area has a dark-green tone with a typical, coarse,point texture, and the cultivated area shows a homogeneous textureresulting from the close and regular alternation of light and dark colortones, the result of mechanical cultivation. In the centre of the image,the clear, ashen area that interrupts the green tones and the regulartexture along a distinct boundary is easily interpreted as a shallowlandslide.
Fig. 3. Multi-temporal landslide inventory map for the study area. (A) 153 landslides mapped through a reconnaissance field survey in December 2004. (B) 381 landslides mappedthrough visual interpretation of TerraItaly™ color ortho-photographs taken in March 2005. (C) 161 landslides mapped through visual interpretation of Ikonos panchromatic imagestaken on 28 June and 4 July 2005. (D) 457 landslides collectively representing the seasonal inventory for the period September 2004 to June 2005. Colored inset shows areas coveredby the different inventories.
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Fig. 4C shows a ploughed area, characterized by a light tone, a fineand homogeneous texture, and no clear pattern. Fig. 4D shows asimilar cultivated area, characterized by a light, mottled tone with a
fine and homogeneous texture and no distinct pattern, and an areacovered by densely spaced shrubs and low trees. In the two digitalimages, landslides occurred in the cultivated areas, and were
12/2004 3/2005 6/2005 12/2004 3/2005 12/2004 6/2005Area covered km2 90.0 84.6 80 90 90Hilly area km2 78.9 74.4 72.8 78.9 78.9NL # 153 381 161 444 457Min AL m2 51 30 81 30 30Max AL m2 4.79×104 2.53×104 2.53×104 2.53×104 2.53×104
Mean AL m2 2517 1512 2048 1521 1521Std. dev. AL m2 4328 2532 3225 2438 2421ALT m2 3.85×105 5.76×105 3.30×105 6.71×105 6.92×105
δ(AL) # km−2 1.9 5.1 2.2 5.6 5.8
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recognized andmapped based on changes in the gray tone, and on thedisruption of the regular texture along sharp or fuzzy boundaries, theresult of changes in land cover produced by the slope failures.
The photo-interpretation criteria discussed before and used toidentify landslides through the visual inspection of the digital imagesvaried slightly for the color aerial ortho-photograph (Fig. 4A, B) andthe panchromatic satellite images (Fig. 4C, D). For the ortho-photograph, local differences (variations) in color were the mostimportant criterion for landslide identification. For the panchromaticimages, the main criterion for landslide recognition was the variationand interruption along distinct boundaries of the gray tones. Further,the interpretation of shadows that highlighted local variations intopography, associated to the presence of specific landslide features(e.g., crown and toe areas, lateral shear zones and pressure ridges),was important for the identification and mapping of the landslides.Shadows were particularly useful when analyzing the satellitepanchromatic images (Fig. 4C, D), and less useful when studying thecolor ortho-photographs (Fig. 4A, B). The latter is partially aconsequence of the reduced radiometric resolution of the TerraItaly™image.
4.2. TerraItaly™ inventory map
Using the color ortho-photograph obtained by processing imagestaken in March 2005 (Table 2), and adopting the photo-interpretationcriteria discussed before, a total of 381 landslides were identified in74.4 km2 of the hills in the study area (Table 1). We refer to theTerraItaly™ inventory map as landslide map B (Fig. 3B). In this map,individual landslides ranged in size 3.0×101bALb2.5×104 m2, for atotal landslide area ALT=5.8×105 m2. This corresponds to 0.7% of thestudy area, and an average of 5.1 landslides per square kilometer ofthe hilly terrain (Table 1). In cultivated areas, several small landslideswith ALb1.2×102 m2 exhibited a distinct visual (radiometric)signature that differentiated them from the surrounding (stable)
Table 2Characteristics of the TerraItaly™ and the Ikonos images used in this study.
TerraItaly™ Ikonos
Frame 1 Frame 2
Date of acquisition March 2005 28 June 2005 4 July 2005Radiometric resolution 12 bit 11 bit 11 bit
Flying altitude 6.3 km 681 km 681 kmGeographicalresolution
0.56×0.56 m 0.88×0.85 m 0.90×0.97 mresampled to0.5×0.5 m
resampled to1×1 m
resampled to1×1 m
DTM for ortho-normalization 40×40 m 10×10 m 10×10 m
terrain. Field inspections in December 2004 did not identify landslidesin forested terrain (Cardinali et al., 2006; Ardizzone et al., 2007);however, small landslides may have occurred in the forests, and wentundetected. We conclude that the inventory compiled using theTerraItaly™ ortho-photograph is reasonably complete, for landslideslarger than ALN1.5×102 m2.
Of the 381 landslides shown in the TerraItaly™ inventory map(Fig. 3B), 140 landslides (36.7%) were also shown in the reconnaissanceinventory (map A), while 241 landslides (63.3%) were not shown in thereconnaissance inventory (Fig. 3A). The 241 landslides shown only inthe TerraItaly™ inventory range in area 5.0×101bALb1.7×104m2, for atotal landslide area ALT=2.8×105 m2, and include: (i) new failuresoccurred in the period from January 2005 andMarch 2005 (i.e., the dateof the TerraItaly™ digital image), and (ii) landslides not shown in thereconnaissance inventory. The proportion of “new” vs. “unrecognized”landslides is unknown.
Inspection of the 140 individual landslides shown in thereconnaissance (Fig. 3A) and the TerraItaly™ (Fig. 3B) inventoriesrevealed differences in the size, shape, and geographical position ofthe landslides (Ardizzone et al., 2007; Santangelo et al., 2010).Adopting the error indices proposed by Carrara et al. (1992) and Galliet al. (2008), the mapping mismatch exceeded 75%, collectively. Thisis not surprising for landslide mapping obtained through theinterpretation of aerial photographs (Carrara et al., 1992; Ardizzoneet al., 2002; Galli et al., 2008). Considering the different techniquesused to compile the inventories, mapping of the individual landslidesthrough the visual interpretation of the ortho-photograph (Fig. 3B) isconsidered more accurate and reliable than the reconnaissance fieldmapping (Fig. 3A). In the TerraItaly™ inventory map, the 140landslides range in area 3.0×101bALb2.5×104 m2, for a totallandslide area ALT=2.9×105 m2.
4.3. Ikonos inventory map
Using the panchromatic Ikonos image and adopting the same photo-interpretation criteria discussed before, a total of 161 landslides wereidentified in 72.8 km2 of the hills in the study area (Fig. 3C, Table 1),including 148 landslides shown in the landslide inventory compiledexploiting theTerraItaly™ortho-photograph, and13new landslides notshown in the TerraItaly™ image. We refer to the Ikonos inventory mapas landslidemapC (Fig. 3C). In landslidemapC, theareaof the individuallandslides was 8.1×101bALb2.5×104 m2, for a total landslide areaALT=3.3×105m2. This corresponds to0.4%of the study area, an averageof 2.2 landslides per square kilometer of thehilly terrain (Table 1).Of the148 landslides shown in both inventories, 133 (89.9%) appearedidentical in the TerraItaly™ (color) and the Ikonos (panchromatic)images. These landslides were not modified (e.g., reactivated) in theperiod between March and June 2005. Eight landslides (5.4%) have alarger area, and seven (4.7%) have a smaller area in the map preparedusing the Ikonos image, compared to the map prepared using theTerraItaly™ ortho-photograph. The former are landslides that were
Fig. 4. Examples of landslides recognized in the TerraItaly™ color ortho-photographs (A and B) and in the Ikonos panchromatic images (C and D). Dotted yellow lines outline themapped landslides for improved readability. A and B, images by TerraItaly™ Blom Compagnia Generale Ripreseaeree S.p.A.-Parma.
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reactivated in theperiodbetweenMarchand June2005, and the latter arelandslides destroyed by human activity (e.g., ploughing), or partiallycovered by new or taller vegetation that concealed the (faint) landslidetopographic signature. New and reactivated landslides in the periodfromMarch to June 2005 totalled 21, ranging in size 3.0×102bALb6.7×103m2,with ALT=2.9×104m2. Of the 21 landslides, 13 failureswere notseen in the TerraItaly™ inventory. These are considered new landslidesoccurred in the period from March to June 2005.
4.4. Seasonal landslide inventory map
The three landslide inventory maps prepared (i) based on thereconnaissance field survey conducted in December 2004 (Fig. 3A),(ii) exploiting the TerraItaly™ digital ortho-photograph taken inMarch 2005 (Fig. 3B), and (iii) using the Ikonos images taken in lateJune and early July 2005 (Fig. 3C), were combined in a single seasonalinventory to cover the landslide period from September 2004 to June2005 (10months) (Fig. 3D). Combination of the three inventories wasperformed in two steps.
First, the landslide information shown in the reconnaissanceinventory (Fig. 3A) was integrated with the information shown in theTerraItaly™ inventory (Fig. 3B). This intermediate inventory com-prises 444 landslides (in 78.9 km2), with 3.0×101bALb2.5×104 m2,for a total landslide area ALT=6.7×105m2 (Table 1). The intermediateinventory covers the landslide period September 2004–March 2005,and comprises: (i) 140 landslides shown in map B and showndifferently in map A, (ii) 241 landslides shown in map B that were notrecognized during the reconnaissance survey (map A), and (iii) 63landslides mapped during the reconnaissance survey (map A) and notrecognized in the TerraItaly™ ortho-photograph (map B). The latterslope failures were small in size (76% have ALb2.0×103 m2) and weredestroyed by mechanical ploughing and other agricultural practices,or concealed by the vegetation growth. For landslides showndifferently in the reconnaissance (map A) and in the TerraItaly™(map B) inventories, the latter were selected for inclusion in theintermediate inventory. This is justified by the better quality of theTerraItaly™ map (see Discussion).
Next, the landslide information shown in the intermediateinventory was merged with the information shown in the Ikonosinventory (Fig. 3C) to obtain a seasonal inventory spanning the 10-month period between September 2004 and June 2005. The seasonalinventory comprises: (i) 63 landslides (13.8%) shown solely in the
reconnaissance inventory (map A), (ii) 233 landslides (51.0%) shownin the intermediate inventory and no longer visible in the Ikonosimages (map C), (iii) 133 landslides (29.1%) shown in the interme-diate inventory that remained unchanged in map C, (iv) 21 landslides(4.6%) shown only in map C, and (v) seven landslides (1.5%) thatexhibited a smaller area in the map prepared using the Ikonos images(map C), compared to the map prepared using the TerraItaly™ ortho-photograph (map B). For the 63 slope failures shown only in map A,the landslide size is probably exaggerated, and the landslide positionmay be imprecise (Ardizzone et al., 2007; Santangelo et al., 2010). The233 failures shown in map B and no longer visible in map C werecanceled by human actions (chiefly mechanical ploughing andharrowing) or concealed by the seasonal vegetation. Collectively,the seasonal inventory lists 457 landslides, corresponding to anaverage density of about 5.8 landslides per square kilometer of hillyterrain (Fig. 3D). In the seasonal inventory, individual landslides rangein area 3.0×101bALb2.5×104 m2, with ALT=6.9×105 m2 (Table 1).
5. Analysis of rainfall data
The date of failure is not known for the landslides mapped in thestudy area in the period from September 2004 to June 2005. Evidenceexists that the landslides were caused by rainfall (Cardinali et al.,2006; Ardizzone et al., 2007), and rainfall measurements for two raingauges near the study area were used to identify the periods ofprobable landslide occurrence. The first rain gauge used for theanalysis is located in Todi, 3 km south of the study area, and thesecond rain gauge is in Bastardo, 8 km east of the study area (Fig. 1).Rainfall history covers the 85-year period 1921–2005 for the Todi raingauge and the 14-year period 1992–2005 for the Bastardo rain gauge.Because (i) morphology and elevation in the study area andwhere thetwo rain gauges are located is similar, and (ii) the distance of the tworain gauges from the study area is limited, we consider the rain gaugesrepresentative of the rainfall conditions in the study area.
Exploiting the time series of 30-min cumulated rainfall measure-ments for the two rain gauges, multiple rainfall events were singledout between September 2004 and June 2005. For the purpose, arainfall event was defined as a period of consecutive hours (from 0.5to 85 h) with a cumulated (total) rainfall in a 24-h period exceeding1 mm. An automatic procedure parsed the rainfall time series, andsingled out 47 rainfall events for the Todi rain gauge (green bars inFig. 5), and 43 rainfall events for the Bastardo rain gauge (purple bars
Fig. 5. Rainfall events in the period from 1 September 2004 to 30 June 2005 for the Bastardo (purple) and Todi (green) rain gauges (see Fig. 1 for location). Bars are shown at the endof the rainfall events. Dots mark rainfall events that have exceeded the intensity–duration (ID) rainfall threshold for possible landslide occurrence of Brunetti et al. (2010). Coloredbars show periods of the reconnaissance inventory, the TerraItalyv ortho-photographs, and the Ikonos images.
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in Fig. 5). For each event, the procedure calculated: (i) the totalcumulated rainfall C, (ii) the duration of the rainfall event D, and(iii) the rainfall mean intensity I. Values for the rainfall duration andmean intensity were compared to the rainfall intensity–duration (ID)threshold for possible landslide occurrence defined for Italy byBrunetti et al. (2010). The comparison revealed that at least fiverainfall events measured at the Todi rain gauge (green dots in Fig. 5),and at least nine events recorded at the Bastardo rain gauge (purpledots in Fig. 5) might have resulted in landslides.
Considering the landslide periods covered by the differentinventories (Table 1), and the rainfall history from September 2004to June 2005 (Fig. 5), three rainfall periods can be singled out: (i) the4-month period from September to December 2004 (period I), (ii) the3-month period from January to March 2005 (period II), and (iii), the3-month period from April to June 2005 (period III) (Table 3). Duringthe first period (September to December 2004), the ID rainfallthreshold of Brunetti et al. (2010) was exceeded four times at the Todirain gauge, and six times at the Bastardo rain gauge (Fig. 5). In thesecond period (January to March 2005), the ID threshold was
Table 3Rainfall and landslide statistics for different periods between September 2004 and June 200
Period I II
Sep. 2004 Dec. 2004 Jan. 2005 Mar. 20
Length months 4 3days 122 90
Todi rain gaugeRd # 42 30C mm 424.5 134.0Ĉ mm 373.4 203.0Ī mm day−1 10.1 4.5
Bastardo rain gaugeRd # 40 26C mm 587.0 147.0Ĉ mm 393.4 152.3Ī mm day−1 14.7 5.6
Rainfall data for the Todi and the Bastardo rain gauges (Fig. 1). Rd, number of rainy days; C,1992–2005); Ī, mean daily intensity for the period; NL, number of landslides; ALT, total landslin italics are uncertain.
exceeded only on 4 March 2005 at the Todi rain gauge. During thethird period (April to June 2005), the rainfall threshold was exceededtwo times at the Bastardo rain gauge, and one time at the Todi raingauge.
6. Analysis of the inventory
6.1. Statistics of landslide size
For each landslide in the inventory, the planimetric area wasobtained in a GIS. Table 1 lists summary statistics for the mappedlandslides, and Fig. 6A–D portrays the probability density of landslidearea, p(AL), for four periods from September 2004 to June 2005. InFig. 6A–D, colored dotted lines are approximations of the probabilitydensities of landslide areas obtained through kernel density estima-tion (KDE, Silverman, 1986; Scott, 1992; Venables and Ripley, 2002).Inspection of the plots reveals that the probability densities oflandslide areas p(AL) are reasonably well represented by the doublePareto (black line, Stark and Hovius, 2001) and the inverse Gamma
cumulated rainfall in the period; Ĉ, average for the period (Todi, 1921–2005; Bastardo,ide area; VLT, total landslide volume;mL, landslide magnitude; u, undetermined. Figures
Fig. 6. Statistics of landslide size. Upper graphs show probability density of landslide area, p(AL). Lower graphs show probability density of landslide volume, p(VL). Dotted lines areempirical densities obtained through kernel density estimation (KDE). Black lines are Double Pareto (Stark and Hovius, 2001), and gray lines are Inverse Gamma (Malamud et al.,2004) models of p(AL) obtained through maximum likelihood estimation. Volume of individual landslides obtained from landslide area using the relationship VL=0.074×AL
1.450
(Guzzetti et al., 2009).
66 F. Fiorucci et al. / Geomorphology 129 (2011) 59–70
(gray line, Malamud et al., 2004) functions used to model theprobability distribution of landslide areas. The two distributionsprovide similar (albeit not identical) results (Table 4). Differences(and uncertainties) are larger for the (incomplete) reconnaissanceinventory (map A) and for the dataset with the least number oflandslides, and are reduced for the larger – and more complete –
datasets. We attribute the differences to the size and completeness ofthe inventories, and the (slightly) different shape of the two modeldistributions. Lack of a distinct “rollover” for the reconnaissanceinventory (Fig. 6A) is attributed to the incompleteness of theinventory (particularly for small landslides) and to the fact that thearea of several landslides shown in this inventory is larger than the“exact” (and unknown) landslide area (Ardizzone et al., 2007;Santangelo et al., 2010). The two causes combine to distort the p(AL).
Adopting the relationship to link landslide volume VL to landslidearea AL proposed by Guzzetti et al. (2009), the volume of theindividual landslides in the inventory was obtained from thecorresponding area. Table 3 lists the total volume of mobilizedlandslide material computed for the different periods, and Fig. 6E–Hillustrates the corresponding empirical probability densities oflandslide volumes p(VL) obtained through KDE. The p(VL) valueswere obtained from the corresponding p(AL) values through a non-
Table 4Parameters estimated for the double Pareto (Stark and Hovius, 2001) and the inverse Gam
Values in paranthesis are standard errors (ε) of α, the scaling exponent. ĀL is the size of the
linear transformation, and the differences in the empirical distribu-tions of landslide volumes have the same causes of the differences inthe distributions of landslide areas.
The total (estimated) landslide volume mobilized in the periodfrom September 2004 to June 2005 was VLT=2.14×106 m3,corresponding to an average landslide depth of ~3.1 m (~1.5 mconsidering AL and VL of the single landslides), and a mean thicknessof the mobilized material over the entire area of ~3 cm. The figure is15 times larger than the figure of ~46 cm obtained by Guzzetti et al.(2009) for the study area in the (longer) period 1942–2005. Thedifference is significant, and is attributed to the lack of occurrence oflarge (for the study area VLN105 m3, Guzzetti et al., 2006a, b, 2009)landslides in the 2004–2005 landslide season (Table 1). In theexamined landslide season, the two largest landslides (0.4% of thetotal number) accounted for 10% of the total landslide volume (and6.8% of the total landslide area), and the 23 largest landslides (5.0% ofthe total number) accounted for 50% of the total landslide volume(and 31.5% of the total landslide area). This confirms that the fewlargest landslides dominate the total landslide volume VLT in an area(Hovius et al., 1997; Guzzetti et al., 2008, 2009).
The majority of the landslide volume was mobilized in the 7-monthperiod from September 2004 to March 2005, VLT=2.09×106 m3, by
ma (Malamud et al., 2004) models for the probability density of landslide area, p(AL).
Table 5Comparison of statistcs obtained using the empirical relationships to link landslide areaand volume proposed by (A) Guzzetti et al. (2009) and (B) Larsen et al. (2010).
Period Sep. 2004Mar. 2005
Apr. 2005Jun. 2005
Sep. 2004Jun. 2005
VLT [m3] A 2.09×106 7.20×104 2.14×106
B 1.51×106 5.58×104 1.55×106
mL [–] A 6.32 4.86 6.33B 6.18 4.75 6.19
ϕL [mm year−1] A 26.48 0.91 27.12B 19.13 0.70 19.64
67F. Fiorucci et al. / Geomorphology 129 (2011) 59–70
multiple rainfall events. Inspection of the rainfall history (Fig. 5), and ofthe dates of the rainfall events that may have resulted in slopeinstabilities suggests that landslides were triggered chiefly in the3-month wet period from October to December 2004. Unfortunately,the accuracy and completeness of the reconnaissance inventory (mapA) is not sufficient to determine the exact proportion of landslidevolumemobilized in the first 4 months (September to December 2004)and in the next 3months (January toMarch 2005). One to three rainfallevents betweenApril andearlyMay2005 resulted inVLT=7.20×104m3
The magnitude of a landslide event, or period, is a measure of theenergy dissipated by slope failures in the event, or period. Guzzettiet al. (2009) proposed to use the logarithm (base 10) of the totallandslide volume to measure the magnitude of a landslide event, orperiod, in an area,mL=log10(VLT). The same approach was adopted inthis study to determine and compare the landslide magnitude fordifferent periods. Using the landslide information discussed before(Table 3), the landslide season September 2004 to June 2005 hadmL=6.33. Analysis of the rainfall history in the examined period(Fig. 5) suggests that most of the magnitude was concentrated in theperiod October–December 2004. The less intense landslide periodApril–May 2005 resulted in a lower magnitude, mL=4.86 (Table 3).
6.3. Landslide mobilization rates
Landslide mobilization rates φL measure the velocity of landslidemobilization during a period in an area (Guzzetti et al., 2009). In thiswork, mobilization rates were calculated by dividing the total volumeof landslidematerial VLT in a period, by the length of the period, and bythe extent of the study area (7.89×107 m2 of hilly terrain). FromSeptember 2004 to June 2005, φL=27.1 mm in the 10-month period.Since landslides did not occur in the study area in the period July–August 2005, the yearly mobilization rate for the landslide season2004–2005 was also φL=27.1 mm year−1. Considering that most ofthe landslide material (N95%) was mobilized probably in the 3-monthperiod October–December 2004, the average monthly mobilizationrate for this accelerated landslide period averaged φL=8.8 mmmonth−1. The 3-month landslide period in the spring of 2005resulted in a significantly lower average monthly mobilization rate,φL=0.3 mm month−1.
6.4. Sensitivity
The volume of the individual landslides VL was obtained from thelandslide area AL using the empirical relationship VL=0.074×AL
1.450
(Guzzetti et al., 2009). The estimates of VLT,mL, and φL, depend on thisrelationship. To evaluate the sensitivity of the results, the calculationswere repeated using a different relationship, VL=0.146×AL
1.332,proposed recently by Larsen et al. (2010). Using the new relationship,the total volume of landslide material VLT was between 22.5% and27.5% smaller, and the landslide magnitude mL was approximately2.2% lower than the same figures obtained using the relationship ofGuzzetti et al. (2009) (Table 5). Similarly, in the period September2004 to June 2005, the landslide mobilization rate φL obtained withthe new relationship was 19.6 mm year−1, a reduction of 27.6% fromthe figure obtained with the old relationship (27.1 mm year−1). Thedifferences, and particularly the (larger) differences observed for VLT
and φL, are smaller than the differences introduced by the moreaccurate landslide mapping (Table 5). We conclude that to obtainreliable statistics of VLT, mL, and φL, availability of an accurateinventory is of primary importance.
7. Discussion
7.1. Landslide recognition and mapping
In the literature, most of the attempts to detect andmap landslidesusing satellite images (e.g., Huang and Chen, 1991; Mantovani et al.,1996; Gupta and Saha, 2001; Lin et al., 2002; Zhou et al., 2002; Herváset al., 2003; Cheng et al., 2004; Metternicht et al., 2005; Nichol andWong, 2005a,b; Singhroy, 2005; Lee and Lee, 2006; Weirich andBlesius, 2007) have consisted in the recognition and mapping of slopefailures that have left clear and easily recognizable signs, essentiallyevident changes in land cover (e.g., from dense forest to exposed soiland rock). In our experiment, aerial ortho-photographs and VHRmonoscopic satellite images were used to detect and map landslidesthat did not necessarily result in distinct (e.g., complete, extensive)morphological or land cover changes (Fig. 4).
The inventory produced through the visual interpretation of theaerial and the satellite images was more accurate and complete (i.e.,superior) than the pre-existing reconnaissance inventory obtainedthrough (unsystematic) field surveys. Overall, the new mappingshowed 145% more landslides, and 85% more landslide area thanpreviously recognized (Guzzetti et al., 2006a,b, 2009; Galli et al., 2008).The size of the smallest landslide detected using the TerraItaly™ ortho-photograph was 30 m2 (landslide length lL=7.5 m, landslide widthwL=8 m), smaller than the size of the smallest landslide detected usingthe Ikonos images (AL=81 m2, lL=20 m, wL=5 m) (Table 1). Acomparative analysis of the two inventories indicated that the Ikonosinventory missed several landslides with ALb4.5×102 m2, including 23very small landslides in forested terrain, 13 landslides in vegetatedescarpments, and 25 landslides in cultivated areas. The TerraItaly™inventory shows 146 landslides with ALb4.5×102 m2, with a cumula-tive landslide area of 3.47×104m2, 6.0% of the total landslide area in theinventory (Table 1). For ALb4.5×102 m2, the Ikonos inventory showsonly 45 landslides, with a cumulative area of 1.23×104 m2, 3.7% of ALT.Many of the slope failures that went undetected in the Ikonos imageswere small shallow landslides covered by vegetation, which had grownnoticeably between March and June 2005. The smallest landslidedetected using the Ikonos images, and not visible in the TerraItaly™ortho-photograph, has AL=3.0×102 m2. This was a new landslideoccurred between April and June 2005.
The possibility of recognizing slope failures on digital imagesdepends largely on the spatial resolution of the images: 0.5×0.5 m forTerraItaly™ and 1×1 m for Ikonos (Table 2).We argue that the spatialresolution of both images was adequate to detect andmapmost of thelandslides in the study area. Considering that the total landslide areaALT, and the total landslide volume VLT (and the relatedmeasures ofmL
and φL) are dominated by the few largest failures (Hovius et al., 1997;Guzzetti et al., 2008, 2009), we conclude that the seasonal inventoryobtained through the visual interpretation of the aerial ortho-photographs and the VHR satellite images is sufficiently completefor regional geomorphological investigations.
The area covered by a single VHR satellite image (~ 11×11 km forIkonos, at mid-latitudes) is comparable to the area covered by a small-
68 F. Fiorucci et al. / Geomorphology 129 (2011) 59–70
scale aerial photograph (~ 11×11 km for 1:50,000 scale), andsignificantly larger than the area covered by a large-scale aerialphotograph (~ 3.5×3.5 km for 1:15,000 scale photographs). Thisfacilitates accurate landslide mapping over large areas. The spatialresolution of the Ikonos panchromatic images (~1×1 m) provedadequate for the visual recognition of landslide features by expertgeomorphologists, and for the production of landslide inventorymapsat 1:10,000 scale. At this scale, 1 mm on the map (shorter than thedistance that one can reasonably measure or draw systematically at1:10,000 scale) corresponds to 1 m on the ground; in many cases adistance shorter than our ability to locate the boundary of a landslide(Santangelo et al., 2010). More modern VHR satellite sensors canprovide even higher spatial resolutions (e.g., GSD is 41 cm for GeoEye-1, 50 cm for WorldView-1 and WorldView-2, and 60 cm for Quick-Bird), well suited for the compilation of seasonal, or event landslideinventory maps (Mondini et al., submitted).
The Ikonos images used in this experimentof landslidedetection andmapping were panchromatic (grey tone) images. The correspondingmultispectral images were too coarse for effective landslide detectionand mapping (GSD=3.2 m), and were not used. Use of pan-sharpened(“fused”) images did not provide better results (e.g., easier landsliderecognition, improvedmappingdetail) than theuse of thepanchromaticimages. The photo-interpreters preferred the gray tone (panchromatic)images to the color (pan-sharpened) images because ofmultiple causes,including: (i) the quality of the images, (ii) the type of the sensor, (iii)the coarser spatial resolution of the multispectral information, (iv) thereduced performance of the algorithm used tomerge the panchromaticand multispectral radiometric information, (v) the local morphologyand land cover types, and (vi) the longpractice of thephoto-interpreterswith gray-scale aerial photographs for landslide recognition andmapping. Use of improved multispectral sensors, of better pan-sharpening algorithms, and advanced training of the photo-interpreterswith pan-sharpened images may change the result.
The experiment indicated that aerial ortho-photographs andmonoscopic satellite images of sufficient geographical (GSD≤1 m)and radiometric resolutions can be used to obtain accurate andstatistically complete (Malamud et al., 2004) seasonal landslideinventories, even in areas where slope failures leave only subtlemorphological and land use signatures. This is an important steptowards the systematic production of multi-temporal landslideinventories in different physiographic environments. Previous expe-rience had demonstrated that multi-temporal inventory maps aredifficult to prepare (Galli et al., 2008), chiefly because of the lack ofrepeated coverage of images of adequate characteristics. Modern VHRsatellite sensors have GSDb1 m, and revisiting times from a few daysto a few months (3 to 144 days for Ikonos). These operationalcharacteristics allow the production of multi-temporal inventorieswith unparalleled frequency of the temporal coverage.
In landscapes dominated by slopewasting processes,multi-temporalinventories can capture andmeasure terrain modifications produced byslope failures, providing valuable information to erosion studies (Hoviuset al., 1997; Lavé and Burbank, 2004; Korup, 2005; Imaizumi and Sidle,2007; Guzzetti et al., 2009; Larsen et al., 2010) and landslide hazardmodeling (Guzzetti et al., 2005, 2006a; van Westen et al., 2005; Wittet al., 2010). Availability of repeated coverage of aerial and satelliteimages for the same landslide season, or period, can result in changinglandslide maps, and proves that a single, definitive inventory cannot beprepared in active landscapes. Susceptibility and hazard modelsprepared for active landslide areas must cope with the changinglandslide information provided bymulti-temporal inventories (Guzzettiet al., 2006b; van Westen et al., 2005; Witt et al., 2010).
7.2. Statistics of landslide size
Using the temporal information in the landslide inventory, it waspossible to estimate the probability density distributions of landslide
area p(AL) and volume p(VL), for different periods (Fig. 6). Inspectionof the plots shows similarities and differences in the densitydistributions of landslide area, p(AL). The main similarity is that allthe empirical distributions exhibit the same general shape, with thedensity for large and very large landslides obeying a negative powerlaw trend. This was expected (Stark and Hovius, 2001; Guzzetti et al.,2002; Malamud et al., 2004). Closer comparison of the plots revealsthe differences. For the reconnaissance inventory, the empirical p(AL)(dotted yellow line in Fig. 6A) does not show the rollover for thesmallest landslides typical of statistically complete landslide inven-tories (Stark and Hovius, 2001; Guzzetti et al., 2002; Malamud et al.,2004). When compared to the empirical distribution for the seasonalinventory (dotted purple line in Fig. 6D), the power law tail for thereconnaissance inventory is steeper (Table 4), for both the doublePareto (Stark and Hovius, 2001) and the inverse Gamma (Malamudet al., 2004) models. This is a consequence of the different proportionof small and large landslides in the two inventories that have distortedthe p(AL) in the reconnaissance inventory. The same occurs for thedataset covering the April-June 2005 period (Fig. 6C). For thisinventory, the empirical p(AL) (dotted red line in Fig. 6C) does notshow a distinct rollover, and the tail of the distribution is steeper thanexpected (Table 4). This is attributed to the reduced size of thedataset. Similar considerations can bemade for the empirical distribu-tions of landslide volume, p(VL).
7.3. Geomorphologic implications
Guzzetti et al. (2009) used a multi-temporal landslide inventorycovering the 69-year period 1937–2005 to determine average(“multi-decades”) and event-based (“seasonal”) landslide mobiliza-tion rates in the study area. Using this extended landslide dataset, theauthors determined that the multi-decades average rate of landslidemobilization was φL=8.8 mm year−1, and that during particularlyactive landslide periods (e.g., between 1937 and 1941) φLN50 mmyear−1, six times higher than the multi-decades average. As part oftheir analysis, Guzzetti et al. (2009) used the same reconnaissanceinventory identified as map A in this study (Fig. 3A), and determinedthat φL=18.8 mm year−1 in the 2004–2005 landslide season. Basedon this analysis, and considering that most of the landslide materialwas mobilized in the 3-month period October–December 2004(Fig. 5), the average monthly mobilization rate for the period ofincreased landslide activity was φL=6.3 mm month−1. Analysis ofthe new and improved mapping results obtained through the visualinterpretation of the aerial and the satellite images (Fig. 3D) hasdemonstrated that the landslide mobilization rate for the 2004–2005landslide season was φL=27.1 mm year−1, 30% higher than previ-ously measured. Considering the 3-month period of augmentedlandslide activity, φL=8.8 mm month−1. The latter figure is ~40%higher than previously determined.
Long-term measures or estimates of erosion or uplift are notavailable for the study area, the neighboring territory, or the Umbriaregion. Thus, a direct comparison with the short-time erosionestimates obtained in this study is not possible. However, estimatesof erosion, river incision, and uplift rates for several sites, or areas, inthe northern and central Apennines since about 1 Ma are available(Balestrieri et al., 2003; Mancini et al., 2007; Mariani et al., 2007; Cyrand Granger, 2008; Cyr et al., 2010). These estimates concur inindicating that the long-term erosion rate ranges from 0.2 to 0.5 mmyear−1. The figure is in reasonably good agreement with estimates ofriver incision (0.2–2.0 mm year−1) (Cyr and Granger, 2008; Cyr et al.,2010), uplift (0.1–1.0 mmyear−1) (Mariani et al., 2007;Mancini et al.,2007; Cyr and Granger, 2008; Cyr et al., 2010), exhumation (Bartolini,2003), and erosion obtained from measurements of sediment yield(0.1–0.4 mm year−1) (Cyr et al., 2010).
The regional value for the long-term average of the rate of erosion(~ 0.4 mm year−1, Cyr et al., 2010) is significantly lower than the
69F. Fiorucci et al. / Geomorphology 129 (2011) 59–70
short-term estimates of landslide mobilization obtained for the studyarea. Specifically, the long-term (1 Ma) average is one order ofmagnitude larger than the 69-year average (8.8 mm year−1, from 1937to 2005), and two orders of magnitude larger than the estimates for the2004–2005 landslide season (27.1 mm year−1) and for the acceleratedlandslide period between 1937 and 1941 (N50 mm year−1, Guzzettiet al., 2009). Although the estimates obtained in this study are oflandslidemobilization (Guzzetti et al., 2009) and not of landslide erosion(i.e., the displaced landslide material is deposited on or at the bottom ofthe slope and is not eroded completely from the failed slope), wemaintain that the observed rates are abnormally large.We speculate thatthe remarkably high yearly rate of landslidemobilization observed in thestudy area for the recent period is the result of agricultural and land usepractices that favor erosion and slope instability (Torri et al., 2006).
8. Conclusions
Digital, color aerial ortho-photographs taken inMarch 2005 andVHRpanchromatic images obtained by the Ikonos satellite in late June andearly July 2005 were used to compile a multi-temporal landslideinventory covering the landslide season September 2004 to June 2005.Visual interpretation of the aerial and satellite images proved effectivefor the detection and mapping of recent landslides, including slopefailures that did not leave distinct, immediately recognizable (i.e., self-evident) morphological or land cover signatures. This is an originalresult, and an important step towards the systematic production ofmulti-temporal landslide inventories in different physiographic environ-ments. The ability to exploit monoscopic, panchromatic satellite imageryto prepare multi-temporal landslide inventories opens unprecedentedopportunities for the study of landscapes dominated by slope processes.
For the detection and mapping of the landslides on the aerialortho-photographs and the monoscopic satellite images, the inter-preters used established criteria commonly adopted tomap landslidesfrom stereoscopic aerial photographs (Ray, 1960; Miller, 1961; Allum,1966; Rib and Liang, 1978; van Zuidam, 1985). This is also a relevantresult, because it shows that geomorphologists trained in aerialphoto-interpretation can use effectively VHR panchromatic satelliteimages to map landslides triggered by an event, or a sequence ofevents in a period, where images of sufficient geographical andradiometric resolution exist. The result opens new possibilities for thesystematic mapping and monitoring of landslide activity, and forconstructing time series of landslide occurrence in large areas. This isimportant for landslide hazard modeling and validation (Guzzettiet al., 2006a,b).
Analysis of the seasonal inventory obtained through the visualinterpretation of the aerial and the satellite images indicate that therate of landslide mobilization for the 2004–2005 landslide season was27 mmyear−1, 40% higher than previously recognized (Guzzetti et al.,2009). Study of the rainfall record revealed that the slope failureswere caused by rainfall events that were not “extremes” in the record.The finding indicates that numerous landslides in the area occur inresponse to moderately wet rainfall events, or periods. This isimportant information for hazard assessment and landslide forecast-ing using empirical thresholds (Brunetti et al., 2010). Comparison ofthe long-term regional rate of erosion (Cyr et al., 2010) with the short-term seasonal rate of landslide mobilization revealed that the former(0.4 mmyear−1) is significantly smaller than the latter (27 mmyear−1).The recent accelerated rate of landslide mobilization is considered aresult of agricultural practices that favor erosion and slope instability(Torri et al., 2006).
Acknowledgements
This work was conducted in the framework of the ASI MORFEOproject. F.F., A.C.M. and L.S. were supported by ASI grants. We thankthe editor and an anonymous referee for their constructive comments.
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