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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.

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.

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

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.

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

The Madonie Natural Park is characterized by relevant zoological and botanic aspects and by geological features so remarkable that since 2001 it has been incorporated into the European Geoparks Network. The Park is marked by a wide massif know as Madonie Mountains. In this area, segment of the Maghrebide-Apenninic chain, successions of Meso-Cenozoic lithologies and late- and post-orogenic deposits occur. The geomorphological setting is extremely varied and includes many landscapes characterising several Sicilian areas; it results from the interaction between geomorphological processes, tectonic movements and climatic changes. It is possible to identify five distinct sectors, each corresponding to a particular landscape unit marked by a typical assemblage of landforms, related to the geological and structural setting and to a distinctive geomorphological agent.

The morphologic set up of Sicily is extremely varied and can generally be considered representative of different geomorphological contexts characterizing the Mediterranean area. Paleoenvironmental reconstructions regarding the Middle Pleistocene to Holocene are mostly based on paleontologic informations and geomorphologic data. The geodynamic evolution of Sicily, characterized by particularly active neotectonics that has caused the raising and emerging of large areas, has interacted with the significant climatic fluctuations and eustatic oscillations, aiding remarkably in the landscape evolution. Tree different period are analyzed: the environment during Middle Pleistocene and Early-Upper Pleistocene (1,000,000- 75,000 y B.P.; from Pleniglacial to Tardiglacial (75,000 – 10,000 y B.P.) and from Holocene to Present (from 10,000 B.P. to present time).

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.

Malta and Sicily, which lie at the centre of the Mediterranean Sea, share a long history and have unique geological and geomorphological features which make them attractive destinations for geotourism. In the framework of an international research project, a study for the identification, selection and assessment of the rich geological heritage of Malta and Sicily was carried out, aiming to create a geosite network between these islands. Based on the experience and outputs achieved in previous investigations on geoheritage assessment carried out in various morpho-climatic contexts, an integrated methodology was applied for the selection, numerical assessment and ranking of geosites. The selection phase was based on three main criteria&mdash, scientific, additional and use values&mdash, and led to the establishment of a list of 42 geosites (20 in Malta and 22 in Sicily). Besides being spectacular and attractive for tourists, these sites represent the main geomorphological contexts and the various stages of regional morphogenesis of the study areas. The sites selected were assessed quantitatively and ranked according to management and tourism criteria. The results provide both the necessary basic knowledge for joint conservation actions and policies in Malta and Sicily and the elements for creating a link between Malta and Sicily through geoheritage appraisal and tourism development.