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Gulling is a complex process depending on several factors and involving a wide range of sub-processes. Different types of gullies were distinguished and described in literature. Their contribution to soil erosion changes in relation with the typology and their presence is influenced by different controlling factors. Mapping and classifying gullies is crucial for monitoring soil erosion. So far, no systematic definition of morphological characteristics of the different types of gullies and of their controlling factors has been made. The present work aims to suggest an innovative approach to automatically classify gullies by integrating remote sensing, GIS and a classification algorithm. The study was carried out in three subcatchments (20km2) of the Platani River basin, located in southwest Sicily (Italy). Two gullies inventories (2014 and 2008 years), containing more than 400 erosion features, were prepared by integrating Google Earth and aerial orthophotographs images and further field checks. Once mapped, gullies were classified by using the location in the landscape, the morphology and the dominant erosion process leading to their formation as criteria. Several primary and secondary topographic attributes were selected as independent variables in the classification model. The Classification and Regression Tree (CART) algorithm was used to predict the location of the different types of gullies and describe the influence of the different factors taking part of the model. The results, described in terms of AUC values, show high model accuracy. CARTbased gully classification is quicker and more objective than traditional methods. Moreover, the suggested method provided important information about which is the dominant erosion process leading to gullies formation.

The “Ecological Cross-­‐border Networks Malta-­‐Sicily” (RE.MA.SI.) is an international research project founded by the Italia-­‐Malta 2007-­‐2013 Programme. A part of the project aims to carry out a multidisciplinary study for the identification and enhancement of geosites in the Maltese Islands and Sicily. Some areas have been picked out; these areas are spectacular and interesting for the tourists and can make a significant contribution to the understanding the geological and geomorphological history. Field surveys took place for the identification and classification of the most significant geomorphosites and the verification of the collected bibliographical data; in particular, the identification and selection process concentrates on the following range of values: scientific, additional and use. By application of these already tested qualitative guidelines, the obtained territorial data have been put on in a standard forms in order to create a common geomorphosite inventory and a GIS-­‐database. An example of this approach for the Torre Salsa natural reserve located on the southern coast.

In this study, statistical models of earth-flow susceptibility were prepared using logistic regression. The analyses were carried out in a small (51 km2) basin of western Sicily, where 1,376 earth-flows were identified. To predict the spatial distribution of the mapped landslides, outcropping lithology and seven topographic attributes were exploited as explanatory variables. Before calculating these variables, a reconstruction of the pre-failure topography was performed. To evaluate the predictive skill and the robustness of the models, two groups made of five random subsets of earth-flows and stable cells were prepared. Absences of the first group were selected as individual cells whereas those of the second group where picked up using circular areas randomly distributed within the stable portions of the basin. The largely satisfactory accuracy and stability of the models demonstrated the reliability of the reconstructed pre-landslide topography. Furthermore, models prepared with the circular groups of absences showed better predictive skills.

In the recent years advanced statistical methods and GIS tools have been frequently used to assess landslide susceptibility. The latter is estimated by establishing statistical relationships between landscape characteristics and spatial distribution of past slope-failures. These are mapped mainly by recognizing changes on slope morphology produced by gravity. Despite of this, most of the researches on landslide susceptibility do not consider that slope-failures modify topography and associate high probability of landsliding with topographic characteristics that differ from those that led slope-failures. In this research we analyzed landslide susceptibility in the basin of the Malvello river with two statistical methods: binary logistic regression and multivariate adaptive regression splines. The study area, which extends for 51 km2 in western Sicily (Italy), is characterized by large outcroppings of clays and marls and is severely affected by shallow landslides. Bedrock lithology and land use were included as predictive variables, in addition to a set of primary and secondary topographic attributes. The latter were derived from a digital elevation model where altitude of areas hosting landslides was interpolated from adjacent undisturbed portions of the slopes. We assume that these artificial surfaces represent the old topography more efficiently respect to the morphology of depletion and accumulation zones of landslides. Ten random samples, with the same number of positive and negative cases, were used to train and test the susceptibility models. Accuracy, as well as overfitting and robustness of the models, were evaluated by drawing receiver operating characteristic (ROC) curves and calculating the area under the ROC curve. In addition of comparing the performance of the statistical methods, the validation results allowed us to highlight advantages/drawbacks of reconstructing original topography of slope-failures when mapping landslide susceptibility.

Malta and Sicily show common geological and geomorphological features, having been also physically linked not later than the Last Glacial Maximum. At present both islands have a high tourist vocation, but their environmental potential is not fully exploited for attracting tourists and visitors. In the framework of an international research project, “Ecological Cross-border Networks Malta-Sicily” (RE.MA.SI.), a multidisciplinary study for the identification, selection and enhancement of geosites in the Maltese and Sicilian islands was carried out. A number of sites both in Malta and Sicily have been identified and qualitatively and quantitatively assessed applying a methodology already tested in previous studies. The aim was to see whether any of these sites could be considered as geosites of geomorphological interest (geomorphosites) according to scientific, additional and use values. In order to create a common geomorphosite inventory and a GIS-database, the standard forms of the Italian Institute for Environmental Protection and Research (ISPRA) were used. The recognised and assessed geomorphosites mainly include coastal and kart features, but also landslides, badlands and mud volcanoes are encountered due to their scientific interest and scenic value. The research enabled us to highlight that geomorphosites can become a relevant resource for social and economic development in Malta and Sicily, and can provide the ground for bridging these islands through a new integrated approach with foresees common methods and actions for geoheritage exploitation and enforcement of conservation strategies.

The SUFRA project is based on a three level susceptibility mapping. According to the availability of more detailed data, the three scale for susceptibility mapping are increased respect to the ones suggested by the TIER group to 1:100,000, 1:50,000 and 1:25,000/1:10,000. The mapping levels exploit climatic, soil use (CORINE2009) and seismic informative layers, differentiating in the details of the core data (geology and topography), in the quality and resolution of the landslide inventory and in the modelling approach (Tab. 1). SUFRA_100 is based on a heuristic approach which is applied by processing a geologic layer (produced by ARTA integrating pre-CARG 1:100,000 geologic maps); the DEM exploited are IGMI 250m and the mapping units are 1km side square cells. Models are validated with respect to the PAI LIPs (Landslide Identification Points) which are reclassified adopting a simplified scheme. Output cuts of SUFRA100 will be referred to administrative boundaries (provinces). SUFRA50 is based on statistical analysis of new CARG geologic maps and 20m (ITA2000) - 2m (ATA2007) DEM. The mapping units are 500m and 50m cells, hydrographic and hydro-morphometric units. The landslide inventory is the IFFI2012_LIPs (first level) which is the result of the conversion in IFFI format of the PAI archive, which will be supported by remote landslide mapping (exploiting the ATA2007 aerial photos), according to the IFFI first level approach. Validation of the models will be performed exploiting both random spatial partition and temporal partition methods. Output cuts of SUFRA50 will be based on physiographic (basin) and administrative (municipalities) boundaries. SUFRA10/25 is based on statistical analysis of new CARG geologic maps (remotely and field adapted) and 2m (ATA2007) DEM. The mapping units are the slope units (SLUs) which are derived by further partitioning the hydro-morphometric units so to obtain closed morphodynamic units. The landslide inventories is the IFFI2012 which is the results of a field supported (on focus) landslide remote systematic mapping, according to the IFFI full level approach. Examples of SUFRA_100, SUFRA_50 and SUFRA_10 are presented for some representative key sector of Sicily. First results attest for the feasibility and goodness of the proposed protocol. The SUFRA program aims at enabling the regional governmental administration to cope with landslide prevision, which is the required operational concept in land management and planning. PAI has been a great advance with respect to the “pre-SARNO” conditions, but it is very exposed to fail: it is a blind approach for new activations; it is critically dependent on the quality of the landslide inventories; it cannot project the susceptibility outside the landslide areas

Oggigiorno, la valutazione della diversa importanza delle variabili geoambientali nel determinare le condizioni di suscettibilità da frana di un’area è uno dei problemi più attuali della geologia. L’uso ed il confronto di due differenti approcci statistici, ha consentito di stimare le condizioni di predisposizione all’instabilità gravitativa dei versanti, per un esteso settore settentrionale della catena appenninica siciliana, ricadente all’interno delle tavolette I.G.M.I. nn. 259 I SE “Scillato” e 259 II NE “Caltavuturo”. L’area oggetto della sperimentazione, estesa circa 200 Km2, è stata suddivisa in maniera semi-automatica in 1827 unità idro-morfologiche o unità di versante. Per ciascun’unità di versante, è stato definito un set di 15 proprietà fisico-ambientali (caratteristiche geologiche, di uso del suolo, attributi topografici primari e secondari), che rappresentano le variabili indipendenti capaci di descrivere i fattori di controllo della stabilità dei versanti. Nell’area era disponibile un archivio di 233 frane di colamento che erano state riconosciute sulla base di fotointerpretazione e controlli di campo condotti nel 1999. In occasione della presente ricerca, utilizzando immagini GoogleTM relative al 2008, è stato realizzato un nuovo archivio eventi contenente 55 attivazioni successive. Per caratterizzare le condizioni di stabilità di ciascuna unità di mappatura, si sono utilizzate due differenti aree diagnostiche: il centroide di ogni area in frana (LC) e l’intera sua area di rottura (AR). Per definire il modello di suscettibilità sono state utilizzate due differenti tecniche di regressione: la Forward Logistic Regression (FLR) e Multivariate Adaptive Regression Splines (MARS). In particolare, si sono utilizzate le frane mappate al 1999 per allenare i modelli e le successive 55 attivazione per la validazione degli stessi. I due metodi sono stati applicati per ciascuna delle due aree diagnostiche, al fine di confrontare anche, per una stessa metodologia, la capacità previsionale offerta. I modelli di suscettibilità ricavati mostrano caratteristiche comuni, selezionando quali variabili indipendenti di maggiore impatto sulle condizioni di stabilità delle unità di mappatura: la pendenza e la lunghezza dell’unità di versante, l’uso del suolo, il comportamento litotecnico atteso dei terreni, la litologia affiorante, le condizioni geomorfologiche, la curvatura longitudinale al versante e l’indice di erosione potenziale. I modelli di regressione costruiti utilizzando quale area diagnostica il punto centroide del movimento franoso hanno performance previsionali (AUC = 0.693) inferiori ai modelli di regressione logistica implementati utilizzando l’intera area di rottura (AUC = 0.722). Il metodo MARS, pur selezionando le stesse variabili predittive, ha raggiunto maggiori prestazioni sia con il modello di previsione costruito sui punti centroidi (AUC > 0.90), sia con quello che utilizza l’intera area di rottura (ROC = 0.84), con un errore GCV sempre minore di 0.09. Entrambi gli approcci statistici hanno prodotto complessi algoritmi che riflettono la complessità dell’area oggetto dello studio e l’interdipendenza tra il set di variabili geoambientali e la franosità. Dai risultati ottenuti con entrambi gli approcci, si evidenzia che l’uso delle unità di versante come unità di mappatura di base, unitamente al punto centroide quale area di attivazione dell’unità morfologica, si presta alla costruzione di un modello di suscettibilità con buone prestazioni previsionali, consentendo un indubbio risparmio in termini di risorse economiche e temporali e, anche e soprattutto, di limitare fortemente gli eventuali errori di mappatura delle forme franose derivate dall’intervento di un operatore.

The studied area is located within two Sicilian districts– Scillato and Caltavuturo. Studied area is 200 km2. This territory is affected by large landslides very strongly. In the course of the research these districts were divided into 2 million square units of 10 × 10 m. Values of landslide factors were assessed within each unit. Using method of logistic regression allowed us to select the most significant factors and to make the landslide susceptibility map. Landslide hazard analys was made with 3 models, which analyze different diagnostic areas (places, where landslides are trigger usually). The results indicate that the using of the buffer zone with radius 50 m around the highest point of the landslide show the most accurate results.

On the 1st of October 2009, the area centred on the village of Giampilieri (Messina), on the Ionian side of the Peloritan belt, suffered thousands of landslides activated in the time lapse of about five hours, which caused 36 victims, more than 100 injured and more than 0.5M€ of damage to structures. This unprecedented phenomenon was triggered by an exceptional meteorological event, recorded at the foothills with 250mm of rain in just 8 hours; this amount of rainfall was cumulated to two previous events (16/IX: 75mm; 23/IX: 190mm) for a total amount of more than 500mm in less than two weeks. Due to the peculiar triggering conditions a huge number of debris flows involved the shallow weathered layer of the outcropping lithologies, consisting of phyllites of the Mandanici Units, mica schists of the Mela Units and medium to high grade Varisic metamorphic rocks. The purpose of this study is to evaluate the exportability of a landslide susceptibility model, obtained by using logistic regression method, within a training hydrographic unit (the “Torrente Briga” catchment) to predict the landslide spatial distribution in a test hydrographic units (the “Torrente Giampilieri” catchment). Both the basins extend for about 10km2. Exporting procedures for susceptibility model are in fact of great importance to optimize the survey costs or when facing phenomena which are locally triggered, such as the ones activated under extreme rainfall events; in fact, in this cases the landslide scenario used to train the statistical model is local and spatially more limited than the extension of while investigated area. In this research we prepared a susceptibility model by means of forward logistic regression in the “Torrente Briga” catchment (871 landslides, regressing an optimized set of computed physicalenvironmental predictors and obtaining the log-function which was then applied to the “Torrente Giampilieri” catchment (1121 landslides). By using a 2m cell DEM (from which we calculated and tested a large set of primary and secondary topographic attributes) and some thematic maps (geology and land use), the following predictors have been selected: height, slope, stream power index, topographic wetness index, profile and plan curvatures, landform classification, outcropping geology, landuse. Unstable slope conditions were assigned to the cells within a 2.9m neighbourhood of the landslide identification points (located on the highest point of the landslide areas). Models were built by merging the unstable cells with an equal number of randomly selected stable cells. To assess the sensitivity of the models with respect to the selection, 8 extractions were performed for each of the two basins, obtaining 8 models for the “Torrente Briga” area and 8x8 exporting combination, for the “Torrente Giampilieri”. The results attest for a high performance of the models, as we obtained excellent AUC both for the the 8 Briga models (>0.84) and the 64 exported Giampilieri models (>0.8). High steepness, low height, plan concave and profile convex curvatures, together with south and south-west verging slopes, are the main controlling factors of debris flow initiations in the two areas.

L’attuale versione del PAI (Piano Stralcio per l'Assetto Idrogeologico) disponibile per il territorio siciliano è fortemente dipendente dallo scenario di dissesti passati censiti e catalogati, sulla base dei quali, utilizzando un sistema di matrici di valutazione, è possibile ricavare le condizioni di rischio geomorfologico associato. Questo stadio costituisce un primo grande avanzamento delle conoscenze a partire dal quale è ora necessario procedere alla valutazione della suscettibilità da frana e all’adozione dunque di uno strumento di analisi territoriale con carattere previsionale. La realizzazione di una cartografia della suscettibilità da frana a scala regionale pone d’altra parte una serie di problemi di approccio e di tecnica realizzativa, che derivano dalla necessità di trovare un compromesso tra estensione territoriale e risoluzione dei modelli previsionali e, dunque, di risoluzione dei dati necessari in ingresso. Il progetto SUFRA muove dalla stessa analisi metodologica del progetto TIER ma, alla luce della disponibilità per il territorio siciliano di tematismi geologici con maggiore dettaglio e inventari delle forme franose densamente popolati, diverge da questo sia nei dati in ingresso sia nei metodi di costruzione dei modelli, che vengono implementati per la valutazione della suscettibilità nei tre livelli in Sicilia. Tutti i livelli di mappatura sfruttano strati informativi con informazioni riguardo alla geologia, al clima, all’uso del suolo e ai dati sismici. Il progetto SUFRA mira dunque a realizzare una cartografia a varia scala della suscettibilità, articolata secondo i tre livelli: SUFRA100 (scala 1:100,000), SUFRA50 (scala 1:50,000) e SUFRA25/10 (scala 1:25,000 o 1:10,000). SUFRA100 è basato su un approccio euristico utilizzando un DEM con cella di 250 metri derivato dall’IGMI. Le unità di mappatura sono celle quadrate di 1 km di estensione. I modelli sono validati rispetto ai LIP (punti identificazione della frana) che vengono riclassificati con adozione di una semplificazione in due sole principali tipologie (frane di scarpata e frane di versante) dell’archivio PAI, disponibile per tutto il territorio regionale. La rappresentazione di uscita, a questo livello saranno limiti amministrativi come quelli comunali o provinciali. SUFRA50 si basa sull'analisi statistica sfruttando la disponibilità delle nuove carte geologiche (CARG) e un DEM ottenuto ricampionando a celle di 20 metri di lato il DEM ATA2007/08. L’inventario delle frane, utilizzato per questo livello è l’IFFI2012_LIP (solamente il primo livello) che è il risultato della conversione in formato IFFI dell'archivio PAI, che sarà supportato e implementato da un controllo remoto delle aree individuate (sfruttando le foto aeree ATA2007). La rappresentazione di uscita del SUFRA50 sarà basata su confini fisiografici (bacini idrografici) e/o amministrative (comuni). SUFRA10/25 si basa sull'analisi statistica delle carte CARG (verificate con un lavoro di campagna) e sfruttando l’alta risoluzione del DEM ATA2007. Le unità di mappatura di base corrispondono alle unità di versante (SLU), derivate da una nuova ripartizione delle unità idromorfometriche in modo da ottenere unità con coerenza morfodinamica. Gli inventari di frana sono il risultato di una successiva implementazione dell’archivio IFFI2012, controllato e verificato da remoto e con indagini di campo. Esempi di SUFRA_100, SUFRA_50 e SUFRA_10 sono stati realizzati per qualche settore rappresentativo di diversi contesti geologici e geomorfologici Siciliani. I primi risultati attestano per la fattibilità e la bontà del protocollo proposto.

Exploiting Google EarthTM to assess a landslide susceptibility model: a test in central Sicily. A landslide susceptibility multivariate model, based on the conditional analysis approach, has been derived in the Tumarrano river basin (about 78 km2), by intersecting a GIS grid layer, expressing some selected geo-environmental conditions (outcropping lithology, steepness, plan curvature and topographic wetness index), and a landslide vector archive, produced by a Google EarthTM aided remote survey. The analysis of the Google EarthTM images dated at 2006, allowed to recognize 733 landslides (30 rotational slides and 703 flows), almost exclusively affecting clay and sandy clay rocks. Validation procedures produced largely satisfactory results, which were analyzed in the domain of the success and prediction rate curves. The research confirms the goodness of the susceptibility assessment method, as well as the powerful of Google EarthTM as a tool to manage the need of new, detailed and multi-temporal landslide archives.

A GIS-analysis was carried out in a test basin of southern Sicily, the Magazzolo River basin, in order to assess susceptibility conditions to gully erosion phenomena. The linear density of ephemeral and permanent gullies computed within each class of nine environmental variables was used to generate a gully erosion susceptibility map for the area. A validation procedure carried out in order to test the reliability of the adopted method highlighted a clear correlation between the occurrence of gullies and the computed susceptibility levels

A seguito di un evento estremo di pioggia verificatosi il 1 ottobre 2009 in un settore circoscritto del versante ionico della Provincia di Messina, si sono sviluppati numerosi eventi franosi classificabili per la maggior parte come colate di fango e detrito a rapida evoluzione (debris-mud flows). Dai primi sopralluoghi effettuati, in assenza ancora di un censimento sistematico, si sono riconosciuti eventi in numero di oltre 500, che hanno interessato sia nei versanti interni dei bacini idrografici che sversano nello Ionio, sia sui rilievi direttamente prospicienti la costa. L’elevato numero di eventi occorsi ha consentito di dettagliare dal punto di vista morfologico i fenomeni avvenuti e di compiere preliminari analisi statistiche relative alle aree di innesco in rapporto con parametri geomorfologici, morfometrici, litologici e dello spessore di copertura. I rilievi sono stati condotti direttamente sul terreno, dall’alto tramite sorvoli su elicotteri messi a disposizione dalla Protezione Civile Nazionale, da ortofoto del 2000 e del 2008, da immagini satellitari. I dati così acquisiti successivamente sono stati successivamente elaborati e digitalizzati al fine di redigere una “Carta di censimento preliminare delle aree di innesco”, rappresentate da punti, di seguito indicati, in analogia con il sistema di archiviazione IFFI, con il termine “PIFF”, con particolare riferimento alle aree nell’immediato intorno degli abitati coinvolti. Tali dati sono stati poi trasposti in ambiente GIS, al fine sia di realizzare un database continuamente aggiornabile, condizione di primaria importanza in ragione della estrema dinamicità di modellamento dei versanti, sia di disporre di un database che consentisse di passare dall’analisi dei fenomeni all’analisi della pericolosità. In tal senso sono state elaborate le prime analisi statistico-descrittive relative ai 113 fenomeni censiti la cui distribuzione spaziale è stata messa in relazione con la variabilità geografica di parametri geologici, morfometrici, morfologici e di uso del suolo. Vengono inoltre espresse alcune considerazioni preliminari circa le soglie di innesco dei fenomeni.

L’area di Scopello (Sicilia Nord-Occidentale) è da tempo oggetto di studi che, sulla base di osservazioni e rilievi geomorfologici, hanno consentito sia di riconoscere la presenza di frane superficiali e di fenomeni di deformazione gravitativa profonda di versante (DGPV), sia di ipotizzare cause e tipologie dei movimenti in atto, giungendo alla definizione di un primo modello interpretativo. I movimenti franosi si sviluppano lungo i settori costieri dell’area di Scopello, dove è a giorno un piano di sovrascorrimento del Miocene superiore, che vede la sovrapposizione di unità rigide, costituite da successioni carbonatiche meso-cenozoiche, su unità duttili, costituite, al tetto, da argille-marnose del Miocene medio-superiore. Le unità argillo-marnose sono interessate da processi di scalzamento ad opera dell’erosione marina, che determinano condizioni di instabilità nei pendii. L’assetto strutturale ereditato, l’erosione del mare ed i movimenti tettonici plio-quaternari sono le cause che hanno consentito l’attivazione di fenomeni di DGPV, per le cui velocità non sono tuttavia disponibili valutazioni quantitative di riferimento. Il presente lavoro riferisce sulla realizzazione di una rete di monitoraggio GPS dei fenomeni gravitativi in atto nell’era di Scopello e sull’esecuzione del «rilievo di zero», evidenziando come la precisione sul posizionamento 3-D dei suoi vertici sia adeguata alle ampiezze di massimo spostamento attese e, quindi, come la rete possa consentire, con rilievi successivi, il monitoraggio del campo degli spostamenti. The Scopello area (north-western Sicily; fig. 1) is affected by several landslide phenomena (AGNESI et alii, 1995), involving both surface mass movements and, mainly, Deep-seated Gravitational Slope Deformation (DsGSD) phenomena (see GOGUEL, 1978; SAVAGE & VARNES, 1987; DRAMIS & SORRISO-VALVO, 1994; CANCELLI & CASAGLI, 1995; HUTCHINSON, 1995; SORRISO-VALVO, 1995; CROSTA, 1996). The phenomena involve the external margin of an overthrust plane (ABATE et alii, 1998a), where a rigid carbonate tectonic unit (Trias-Oligocene) overlies a ductile clayey substratum (Middle-Upper Miocene). The upper carbonate bodies (fig. 2) are marked by an inner imbricate structure and are intensely faulted and fractured, determining the presence of several disjointed blocks of various size, constituting favorable conditions for DsGSD movements to occur. Within the carbonate relief the DsGSD phenomena are responsible for the existence of double crests, depression alignments and river valleys truncated at their head, due to differential settlements. The outermost sectors of the carbonatic slopes (figs. 2-3), affected by block movements, lateral spreads and large topples or falls, are the sites of rotated and/or rafted blocks, great escarpments and open or closed trenches and cracks (AGNESI et alii, 1989). Finally, the marlyclayey slopes show diagnostic landforms of flow and/or slide type surface movements (AGNESI et alii, 1987). The causes of the landslides (AGNESI et alii, 2000; AGNESI et alii, 2003) are: superposition of carbonate rocks over marly-clayey rocks; undercutting processes induced by coastal erosion along the landslide foot/tip; Quaternary tectonic movements. As a geomorphological model has already been established for the landslide, a multidisciplinary study has been implemented, in order to define best both its geometric and kinematic characteristics. The final aim of this study is to gain a mathematical model which allows the phenomena to be correctly described, so that fore- and back- projection can be implemented. The project involves geophysical surveys (resistivity prospecting), geodetic monitoring measurements (GPS) as well as geomorphological, hydrogeological and geotechnical analysis and modeling. In the present paper all the study activities needed in order to implement a monitoring network, requiring a multidisciplinary approach in order to optimize its geometry, are discussed: detailed geomorphological analysis and modeling; resistivity geophysical survey; design and setting up of a geodetic monitoring network. In order to define a monitoring network for the landslide, a detailed geomorphological analysis has been carried out, so that bodies having a different expected kinematic response have been discriminated at a detailed scale (fig. 2). A resistivity survey was also carried out to verify the geometry of the ductile substratum in the area (fig. 4). The GPS network (fig. 2) consists on 27 3D vertices, 7 of which (fig. 2): SC01, SC02, SC03, SC04, SC05, SC06 e SC07) are located outside the landslide area, so that these can constitute a reference system, in respect to which the displacements of the other 20 vertices can be calculated. The coordinates of SC05 point have been calculated by connections to two vertices (TRAP and VALD in fig. 1) of the IGM95 geodetic network. The non-fixed vertices are located in areas characterized by: a) outermost areas of the margin of the carbonate slab about to undergo disarticulation (SC26 e SC27); b) isolated carbonate bodies moving toward the sea (SC08, SC09, SC10, SC15, SC16, SC17, SC18, SC19, SC24 e SC25); c) large landslide cemented debris masses (SC14, SC20, SC21, SC22 e SC23); d) marly-clay substratum (SC11, SC12 e SC13). The expected movement ranges from: very slow (mm/year) rotational -translational (a); varying velocity (mm/year-cm/year) translational, rotational-translational or rotational, depending on the depth to which «the roots» of the carbonate blocks extend inside the substratum (b); slow (cm/year) translational (c); slow to quite fast (dm/year) rotational-translational (d). A preliminary survey has been carried out, in order to calculate the coordinates of the vertices SC02, SC04, SC05, SC06 that are not subject to the landslide movements and to individuate a polygon including all the vertices (fig. 5). Data have been collected in two 3 hours sessions, using geodetic double frequency receivers with a rate of 15s. Data collected have been processed using the software SKI-Pro, with standard parameters but using a cut-off angle of 20° to lower the noise and the average multipath and compensated fixing the SC05 vertex. The redundancy of the this survey is equal to 2, which is enough to give a good accuracy and reliability to the network (CARDINALI et alii, 1993). The uncertainty in 2D and 1D of the fixed vertices (at a confidence level of 95%) is shown in fig. 5, suggesting a precision of less than 1 cm for their location. The survey on the whole net has been carried out using GPS 7 receivers (4 on the fixed points and 3 on as many sites positioned on the landslide body) with sessions 6-9 hours long. A total of 92 baselines were measured having a redundancy of 4. The survey was carried out between 20th and 25th of June 2000, according to the time schedule of fig. 6 and the quality of data tested according to ESTER & MEERTENS (1999), verifying acceptable values for the multipaths, both in L1 and L2 (fig. 7). Sixteen independent recording sessions with duration of 3-4 hours have been completed and the frequency distribution of the 92 baseline lengths (fig. 8) is characterized by an average of 1.9 km. The residuals for the 138 resolved triangles is of about 3 p.p.m. The uncertainty in 2D and 1D (at a confidence level of 95%) is presented in fig. 9, showing error ellipses having semiaxes less than 6 mm long and the minor to major axis ratio greater than 0.8; the uncertainty for the heights is greater by a factor two. Residuals after compensation for the 138 triangles (figs. 10a-b) are much lower and generally don’t exceed 1 p.p.m., thanks to a good geometry both of the network and of the satellite configuration during the recording sessions. The research carried out allowed refinement of the geomorphological model that had been defined in the past for the gravitational phenomena at Scopello, on the basis of which a geodetic monitoring network was defined. Data of the zero survey have been processed showing that the monitoring system is able to describe the kinematic evolution of the area with a precision suitable for DsGSD phenomena.

In the studies of landslide susceptibility assessment, which have been developed in recent years, statistical methods have increasingly been applied. Among all, the BLR (Binary Logistic Regression) certainly finds a more extensive application while MARS (Multivariate Adaptive Regression Splines), despite the good performance and the innovation of the strategies of analysis, only recently began to be employed as a statistical tool for predicting landslide occurrence. The purpose of this research was to evaluate the predictive performance and identify possible drawbacks of the two statistical techniques mentioned above, focusing in particular on the prediction of debris flows. To this aim, an inventory of debris flows triggered by the passage of the hurricane IDA and the low-pressure system associated with it 96E, on 7 th and 8 th November 2009, in an area of about 26 km 2 close to the Caldera Ilopango, El Salvador (CA), was employed. Two validation strategies have been applied to both statistical techniques, thus obtaining four models – BLR (I), MARS (I), BLR (II) and MARS (II) – to be compared in pairs. Model performance was assessed in terms of AUC (area under the receiver operating characteristic (ROC) curve), Sensitivity, Specificity, Positive Prediction Value and Negative Prediction Value. Moreover, to evaluate the robustness of the modelling procedure, 50 replicates were created for each model and standard deviation was calculated for each of them. The results show that both techniques allow for obtaining good or excellent performances so that it is not possible to define one of the two techniques as absolutely better. However, the validation procedure reveals slightly better performance of the MARS models, with greater sensitivity and greater discrimination among True Negatives (TNs).

Urban-geomorphology studies in historical cities provide a significant contribution towards the broad definition of the Anthropocene, perhaps even including its consideration as a new unit of geological time. Specific methodological approaches to recognize and map landforms in urban environments, where human-induced geomorphic processes have often overcome the natural ones, are proposed. This paper reports the results from, and comparison of, studies conducted in coastal historical cities facing the core of the Mediterranean Sea – that is, Genoa, Rome, Naples, Palermo (Italy) and Patras (Greece). Their settlements were facilitated by similar climatic and geographical contexts, with high grounds functional for defence, as well as by the availability of rocks useful as construction materials, which were excavated both in opencast and underground quarries. Over centuries, urbanization has also required the levelling of relief, which was performed by the excavation of heights, filling of depressions and by slope terracing. Consequently, highly modified hydrographic networks, whose streams were dammed, diverted, modified in a culvert or simply buried, characterize the selected cities. Their urban growth, which has been driven by maritime commercial activities, has determined anthropogenic coastal progradation through port and defence or waterfront works. Aggradation of artificial ground has also occurred as a consequence of repeated destruction because of both human and natural events, and subsequent reconstruction even over ruins, buried depressions and shallow cavities. As a result, the selected cities represent anthropogenic landscapes that have been predominately shaped by several human-driven processes, sometimes over centuries. Each landform represents the current result, often from multiple activities with opposing geomorphic effects. Beyond academic progress, we believe that detecting and mapping these landforms and processes should be compulsory, even in risk-assessment urban planning, because of the increase of both hazards and vulnerability as a result of climate-change-induced extreme events and extensive urbanization, respectively.

In this work, we assessed gully erosion susceptibility in two adjacent cultivated catchments of Sicily (Italy) by employing multivariate adaptive regression splines and a set of geo-environmental variables. To explore the influence of hydrological connectivity on gully occurrence, we measured the changes of performance occurred when adding one by one nine predictors reflecting terrain connectivity to a base model that included contributing area and slope gradient. Receiver operating characteristic (ROC) curves and the area under the ROC curve were used to evaluate model performance. Gully predictive models were trained in both the catchments and submitted to internal (in the calibration catchment) and external (in the adjacent one) validation, using samples extracted both from all cells of the catchments and only from cells located along flow concentration axes. Model evaluation on the entire catchments shows outstanding predictive performance of models that either include or do not include the predictors selected to reflect potential hydrological connectivity. Conversely, area under the ROC curve values measured on flow concentration axes reveals that almost all the additional predictors improve the performance of the base model, but the most enhanced increase of accuracy occurs when upstream drainage density of each landscape position is included as predictor of gully occurrence.