Exporting a Google Earth™ aided earth-flow susceptibility model: a test in central Sicily

Costanzo, Darío; Cappadonia, Chiara; Conoscenti, Christian; Rotigliano, Edoardo

doi:10.1007/s11069-011-9870-0

Cited by 33 publications

(21 citation statements)

References 14 publications

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“…Webley et al (2009) used GE to display the volcanic ash dispersion process in the North Pacific region forecasted using the Puff model. Costanzo et al (2012) produced a landslide archive by exploiting GE for assessing landslide susceptibility in central Sicily, Italy. Ruan and Jia (2013) and Madadi et al (2015) employed GE to depict and display possible extent of flooding.…”

Section: Geib and Taubenböckmentioning

confidence: 99%

“Internet+” approach to mapping exposure and seismic vulnerability of buildings in a context of rapid socioeconomic growth: a case study in Tangshan, China

Sun

et al. 2016

Nat Hazards

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This paper presents the development of an ''Internet?'' approach to mapping exposure and seismic vulnerability of buildings in a context of rapid socioeconomic growth. This approach is a combination of the following interdependent components: (1) extraction of footprint areas of a large number of buildings from high-resolution Google Earth images; (2) estimation of floor numbers of these buildings with an integrated use of high-resolution Google Earth images, Tencent/Baidu Street Views, crowdsourcing data, and associated building-relevant local knowledge; and (3) identification of structural types of these buildings by a combined use of crowdsourcing data and associated buildingrelevant local knowledge. The efficacy of this ''Internet?'' approach was demonstrated through an application in Tangshan, China. Field-based verification indicated that the overall mean absolute percentage error of the proposed ''Internet?'' approach in assessing the total floor area of the addressed buildings was 4.64 %. The verification also showed that the overall consistency between the estimated structural types using the proposed approach and the actual structural types of the buildings with structural type uncertainties could reach 97.54 %, with a kappa coefficient of 0.94. Because of its good accuracy, noteworthy speed, substantial labor savings, negligible cost and distinctive capability in covering large areas in near real time, this ''Internet?'' approach might have promising prospects in actual seismic loss risk reduction challenges.

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Section: Geib and Taubenböckmentioning

confidence: 99%

“Internet+” approach to mapping exposure and seismic vulnerability of buildings in a context of rapid socioeconomic growth: a case study in Tangshan, China

Sun

et al. 2016

Nat Hazards

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“…The landslide inventory was prepared (Costanzo et al 2012a) by a remote Google Earth™-aided recognition survey, exploiting highresolution images of the area (catalog ID: 1010010008265000, date: Jun. 11, 2008, sensor: QB02, band info: Pan_MS1; catalog ID: 10100100071CDC00, date: Aug. 28, 2007, sensor: QB02, band info: Pan_MS1).…”

Section: Landslidesmentioning

confidence: 99%

“…Examples of previous studies Conditional analysis (CA) Clerici et al (2010, Conoscenti et al (2008, Costanzo et al (2012a, 2012b, Irigaray et al (2007), Jiménez-Peralvárez et al (2009), Rotigliano et al (2011, 2012, Vergari et al (2011) Discriminant analysis (DA) Baeza and Corominas (2001), Carrara (1983), Carrara et al (2008), Guzzetti et al (2006), Rossi et al (2010. ) Binary logistic regression (BLR) Atkinson and Massari (1998), Ayalew and Yamagishi (2005), Bai et al (2010), Can et al (2005), Carrara et al (2008), Chauan et al (2010), Conforti et al (2012), Dai and Lee (2002), Davis and Ohlmacher (2002), Erener and Düzgün (2010), Mathew et al (2009), Nandi and Shakoor (2009), Nefeslioglu et al (2008, Ohlmacher and Davis (2003), Van den Eckhaut et al (2006 Classification and regression trees (CART) Felicísimo et al (2012), Vorpahl et al (2012) Artificial neuronal networks (ANN) Aleotti and Chowdhury (1999), Ermini et al (2005), Lee et al (2004), Pradhan and Lee (2010) Original Paper exploited to compare the fitting of the model having only the constant term (all the β p are set to 0) with the fitting of the model that includes all the considered predictors with their estimated non-null coefficients so as to verify if the increase in likelihood is significant; in this case, at least one of the p coefficients is to be expected as different from zero …”

Section: Statistical Techniquementioning

confidence: 99%

“…Moreover, we decided to test a very simple approach to automatically define these diagnostic areas (Costanzo et al 2012a(Costanzo et al , 2012bRotigliano et al 2011): we first generated a landslide identification point (LIP) for each landslide by piking from the 2-m DEM the highest cells along the boundary of the polygons delimiting the landslide area, so that LIPs are positioned along the central sectors of the crown areas (Fig. 2b); we then identify the diagnostic areas in a buffer area of 8 m around the LIPs.…”

Section: Statistical Techniquementioning

confidence: 99%

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Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy)

et al. 2013

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Forward logistic regression has allowed us to derive an earth-flow susceptibility model for the Tumarrano river basin, which was defined by modeling the statistical relationships between an archive of 760 events and a set of 20 predictors. For each landslide in the inventory, a landslide identification point (LIP) was automatically produced as corresponding to the highest point along the boundary of the landslide polygons, and unstable conditions were assigned to cells at a distance up to 8m. An equal number of stable cells (out of landslides) was then randomly extracted and appended to the LIPs to prepare the dataset for logistic regression. A model building strategy was applied to enlarge the area included in training the model and to verify the sensitivity of the regressed models with respect to the locations of the selected stable cells. A suite of 16 models was prepared by randomly extracting different unoverlapping stable cell subsets that have been appended to the unstable ones. Models were finally submitted to forward logistic regression and validated. The results showed satisfying and stable error rates (0.236 on average, with a standard deviation of 0.007) and areas under the receiver operating characteristic (ROC) curve (AUCs) (0.839 for training and 0.817 for test datasets) as well as factor selections (ranks and coefficients). As regards the predictors, steepness and large-profile and local-plan topographic curvatures were systematically selected. Clayey outcropping lithology, midslope drainage, local and midslope ridges, and canyon landforms were also very frequently (from eight to 15 times) included in the models by the forward selection procedures. The model-building strategy allowed us to produce a performing earth-flow susceptibility model, whose model fitting, prediction skill, and robustness were estimated on the basis of validation procedures, demonstrating the independence of the regressed model on the specific selection of the stable cells.

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“…A third scheme, frequently adopted for model spatial transferability or exportation (e.g. Von Ruette et al, 2011;Costanzo et al, 2012a;Lombardo et al, 2014;Petschko et al, 2014), is based on the adoption of two different catchments or areas for calibration and validation (spatial partition).…”

Section: Introductionmentioning

confidence: 99%

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

Cama

Lombardo

Conoscenti

et al. 2015

Nat. Hazards Earth Syst. Sci.

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Abstract. The main assumption on which landslide susceptibility assessment by means of stochastic modelling lies is that the past is the key to the future. As a consequence, a stochastic model able to classify past known landslide events should be able to predict a future unknown scenario as well. However, storm-triggered multiple debris flow events in the Mediterranean region could pose some limits on the operative validity of such an expectation, as they are typically resultant of a randomness in time recurrence and magnitude and a great spatial variability, even at the scale of small catchments. This is the case for the 2007 and 2009 storm events, which recently hit north-eastern Sicily with different intensities, resulting in largely different disaster scenarios.The study area is the small catchment of the Itala torrent (10 km 2 ), which drains from the southern Peloritani Mountains eastward to the Ionian Sea, in the territory of the Messina province (Sicily, Italy). Landslides have been mapped by integrating remote and field surveys, producing two event inventories which include 73 debris flows, activated in 2007, and 616 debris flows, triggered by the 2009 storm. Logistic regression was applied in order to obtain susceptibility models which utilize a set of predictors derived from a 2 m cell digital elevation model and a 1 : 50 000 scale geologic map. The research topic was explored by performing two types of validation procedures: self-validation, based on the random partition of each event inventory, and chronovalidation, based on the time partition of the landslide in- Both of the two predictions resulted in largely acceptable performances in terms of fitting, skill and reliability. However, a loss of performance and differences in the selected predictors arose between the self-validated and the chronovalidated models. These are interpreted as effects of the nonlinearity in the domain of the trigger intensity of the relationships between predictors and slope response, as well as in terms of the different spatial paths of the two triggering storms at the catchment scale.

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Exporting a Google Earth™ aided earth-flow susceptibility model: a test in central Sicily

Cited by 33 publications

References 14 publications

“Internet+” approach to mapping exposure and seismic vulnerability of buildings in a context of rapid socioeconomic growth: a case study in Tangshan, China

“Internet+” approach to mapping exposure and seismic vulnerability of buildings in a context of rapid socioeconomic growth: a case study in Tangshan, China

Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy)

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

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