Assistant Professor at Land and Agroforest Environments Dept., University of Padova (Italy). Adjunct Professor at Polytechnic University of Marche (Italy). Visiting Professor at Disaster Prevention Research Center, National Cheng Kung University, Taiwan (R.O.C.).
Expert in geomorphometry, analysis of hydrogeomorphic processes in high altitude alpine regions, channel network extraction, semi-automatic methodologies for morphologic features extraction by high resolution topography, distributed slope stability models (quasi-dynamic shallow landsliding models), LiDAR technology and its derived applications by using resolution DTMs (Digital Terrain Models), GIS. He was a Marie Curie Fellow at the level of Experienced Researcher at the Hellenic Centre for Marine Research (Greece) in 2010, and Visting Scholar at Geography Department of National Taiwan University (Taiwan, R.O.C.) in 2010, St. Antony Falls Laboratory (University of Minnesota, USA) in 2008, and Civil and Environmental Engineering Department (Utah State University, USA) in 2005. He teaches "Integrated Watershed Management" at University of Padova (Italy), and "Hydraulic Forest Management" at Marche Polytechnic University (Italy). He served as Editor of the journal Geomorphology in the Special Issue “Understanding earth surface processes from remotely sensed Digital Terrain Processes”, and as session Convener at AGU (American Geophysical Union) Fall Meeting and at EGU (European Geosciences Union) General Assembly in the topic of Remotely Sensed DTMs for earth surface processes analysis. He is a regular member of American Geophysical Union and Geological Society of America, European Geosciences Union, and British Society for Geomorphology.
Abstract: The availability of high resolution topographic data is strategic for quantitative and qualitative analysis of river environment. The topographic data derived by traditional regional cartography are often too coarse for detailed recognition and mapping of surface morphologic features, while the more accurate data derived by GPS or theodolite are expensive and time-consuming. The airborne laser altimetry technology (LiDAR, Light Detection And Ranging) provides high-resolution topographic data over large areas with high vertical and horizontal accuracy, thus can signifi cantly contribute to a better representation of land surface. A valuable characteristic of this technology, which marks advantages over the traditional topographic survey techniques, is the capability to derive a high-resolution (~ 1 m) Digital Terrain Model (DTM) from the bare ground LiDAR data, by fi ltering vegetation and man-made features points (buildings, bridges) from raw data. The complex morphology, the wide range of land cover categories, and the presence of deep water bodies, make the airborne LiDAR application in river environments more complicated than application in different contexts. The aim of this work is to highlight the capabilities but also the limitations of airborne LiDAR in river studies, presenting some relevant researches and the main methodological aspects of this technology in fl uvial environment.
Abstract: Potential forest-related information can be obtained from processing data obtained from laser scanning sensors making this technology extremely useful for forest management and environmental assessment. It is thoroughly documented in recent literature how specific forest characteristics can be estimated at stand, plot and single tree level using laser scanner surveys at corresponding scales. The high resolution models of the canopy surface and of the bare earth (terrain), as well as the information obtained related to the structure of the volume between these two surfaces, concur at offering a more complete source of information not only for direct forestry-related applications, but also for connected disciplines such as hydrology, engineering, forest disturbances analysis and ecological assessment. Having accurate and spatially distributed information over the above mentioned aspects give land assessment and management added value data to work with. Correct utilization of laser scanner data can lead to the assessment of many characteristics usually obtained by ground surveys. Ground-plots require significant expenditure in terms of human effort, economical investment and can be distributed on large areas only in limited number. The following paper shows the efforts which are being undertaken by scientific research towards testing laser scanner applications for forest and environmental sciences.
Abstract: Drainage networks in agrarian landscape within floodplains constitute surface's discontinuities that are expected to affect hydrological response during floods. Drainage network recognition and quantification of water storage capacity within channels are, therefore, crucial for watershed planning and management. These evaluations require accurate spatial information for the area of interest and in most cases, when studying large catchments, broad datasets of ditches locations and descriptions are not available. In order to characterize drainage networks for large areas, the availability of high resolution topography derived by airborne laser scanner (LiDAR) represents a new and effective tool. Nowadays LiDAR DTMs covering large areas are readily available for public authorities, and there is a greater and more widespread interest in the application of such information for the development of automated methods aimed at solving geomorphological and hydrological problems. While LiDAR DTMs reliability in steep landscape has been proven by several recent studies, only few researches have been conducted to take into account the effectiveness of these data in agrarian low relief landscapes. The goal of this research is to propose a semi-automatic approach based on a LiDAR DTM to (1) detect drainage networks in agrarian/floodplain context, and (2) to estimate some of the network summary statistics (network length, width, drainage density and storage capacity). The procedure is applied in two typical alluvial-plain areas in the North East of Italy, and tested comparing automatically derived network with surveyed ones. The results underline the capability of high resolution DTMs for drainage network detection and characterization in the context of agrarian landscapes within floodplains, opening at the same time new challenges to evaluate some hydrological processes in these areas
Abstract: In recent years, new remote-sensed technologies, such as airborne and terrestrial laser scanner, have improved the detail and the quality of topographic information, providing topographical high-resolution and high-quality data over larger areas better than other technologies. A new generation of high-resolution (B3 m) digital terrain models (DTMs) is now available for different areas and is widely used by researchers, offering new opportunities for the scientific community. These data call for the development of a new generation of methodologies for an objective extraction of geomorphic features, such as channel heads, channel networks, bank geometry, debris-flow channel, debris-flow deposits, scree slope, landslide and erosion scars, etc. A high-resolution DTM is able to detect the divergence/convergence of areas related to unchannelized/channelized processes with better detail than a coarse DTM. In this work, we tested the performance of new methodologies for an objective extraction of geomorphic features related to shallow landsliding processes (landslide crowns), and bank erosion in a complex mountainous terrain. Giving a procedure that automatically recognizes these geomorphic features can offer a strategic tool to map natural hazard and to ease the planning and the assessment of alpine regions. The methodologies proposed are based on the detection of thresholds derived by the statistical analysis of variability of landform curvature. The study was conducted on an area located in the Eastern Italian Alps, where an accurate field survey on shallow landsliding, erosive channelized processes, and a high-quality set of both terrestrial and airborne laser scanner elevation data is available. The analysis was conducted using a high-resolution DTM and different smoothing factors for landform curvature calculation in order to test the most suitable scale of curvature calculation for the recognition of the selected features. The results revealed that (1) curvature calculation is strongly scaledependent, and an appropriate scale for derivation of the local geometry has to be selected according to the scale of the features to be detected; (2) such approach is useful to automatically detect and highlight the location of shallow slope failures and bank erosion, and it can assist the interpreter/operator to correctly recognize and delineate such phenomena. These results highlight opportunities but also challenges in fully automated methodologies for geomorphic feature extraction and recognition.
Abstract: This work analyses the prominent characteristics of flash flood regimes in two Mediterranean areas: the North-Western Mediterranean region, which includes Catalonia, France and Northern Italy, and the South-Eastern Mediterranean region, which includes Israel. The two regions are characterized by similarities in the hydro-meteorological monitoring infrastructure, which permits to ensure homogeneity in the data collection procedures. The analysis is articulated into two parts. The first part is based on use of flood peak data, catchment area and occurrence date for 99 events (69 from the North-Western region and 30 from the South-Eastern region). Analysis is carried out in terms of relationship of flood peaks with catchment area and seasonality. Results show that the envelope curve for the South-Eastern region exhibits a more pronounced decreasing with catchment size with respect to the curve of the North-Western region. The differences between the two relationships reflect changes in the effects of storm coverage and hydrological characteristics between the two regions. Seasonality analysis shows that the events in the North-Western region tend to occur between August to November, whereas those in the South-Eastern area tend to occur in the period between October and May, reflecting the relevant patterns in the synoptic conditions leading the intense precipitation events. In the second part, the focus is on the rainfall-runoff relationships for 13 selected major flash flood events (8 from the North-Western area and 5 from the South-Eastern area) for which rainfall and runoff properties are available. These flash floods are characterised in terms of climatic features of the impacted catchments, duration and amount of the generating rainfall, and runoff ratio. Results show that the rainfall duration is shorter and rainfall depth lower in the South-Eastern region. The runoff ratios are rather low in both regions whereas they are more variable in the South-Eastern area. No clear relationship between runoff ratio and rainfall depth is observed in the sample of floods, showing the major influence of rainfall intensity and initial wetness condition in the runoff generation for these events.
Abstract: Threshold conditions for channel initiation are evaluated by using gridded elevation data derived from a lidar survey, a reliable algorithm for the determination of surface flow paths, and field observations of channel heads for a study area located in the eastern Italian Alps. These threshold conditions are determined by considering the channel heads observed across a portion of the study area and computing the related values of (1) drainage area A, (2) area-slope function AS2, S being the local slope, and (3) Strahler order Ïâ 8 of surface flow paths extracted from gridded elevation data. Attention is focused on the dependence of the obtained threshold values on the size of grid cells involved, and on the ability of the identified threshold conditions to provide reliable predictions of channel heads across the entire study area. The results indicate that the threshold values of A, AS2, and Ïâ are all significantly dependent on grid cell size, and the uncertainty in the determination of threshold values of Ïâ is significantly smaller than that affecting the determination of threshold values of A and AS2. The comparison between predicted and observed channel heads indicates that the considered methods display variable reliability and sensitivity over different drainage basins and grid cell sizes, with a general tendency to predict more channel heads than can be observed in the field. Acceptable predictions are normally obtained where channel heads are formed essentially by surface erosion. More comprehensive methods seem, however, to be needed to predict channel heads affected by groundwater seeping upwards.
Abstract: We present an index-based shallow landsliding susceptibility model which allows explicit incorporation of local heavy rainfall statistical properties. The model, called Quasi-Dynamic Shallow Landsliding Model (QD-SLaM), is developed upon a theory for coupled shallow subsurface flow and landsliding of the soil mantle. The model uses a 'quasi-dynamic' wetness index to predict the spatial distribution of soil saturation in response to a rainfall of specified duration, and can take into account the spatial variability of soil properties. The rainfall predicted to cause instability in each topographic element is characterized by intensity and duration. The incorporation of a scaling model for the rainfall frequency-duration relationship provides a parsimonious and robust way to include heavy rainfall statistical properties into shallow landsliding modelling. The model is used to determine the return period of the critical rainfall needed to cause instability for each topographic element. The model is validated over six different study sites, where detailed inventories of shallow landslides are available. Two study sites are located in the north of Taiwan, and four are located in the Italian Alps. The sites are characterized by different climates and by different duration of the landslide-triggering storms. Model results are evaluated against the surveyed landslides and compared to those obtained by using a steady-state model, resembling SHALSTAB. It is shown that QD-SLaM improves significantly over the steady-state approach in predicting existing landslides as represented in the considered landslide inventory. Moreover, the improvement is higher for the cases where the landslide-triggering storm duration is short with respect to the length of time required for every point on a catchment to reach subsurface drainage equilibrium. The results of our work highlight the capability of the model to incorporate a robust description of the heavy rainfall properties in the analysis and mapping of shallow landsliding susceptibility by using an index-style approach.
Abstract: A statistical approach to LiDAR derived topographic attributes for the automatic extraction of channel network and for the choice of the scale to apply for parameter evaluation is presented in this paper. The basis of this approach is to use distribution analysis and statistical descriptors to identify channels where terrain geometry denotes significant convergences. Two case study areas with different morphology and degree of organization are used with their 1 m LiDAR Digital Terrain Models (DTMs). Topographic attribute maps (curvature and openness) for various window sizes are derived from the DTMs in order to detect surface convergences. A statistical analysis on value distributions considering each window size is carried out for the choice of the optimum kernel. We propose a three-step method to extract the network based (a) on the normalization and overlapping of openness and minimum curvature to highlight the more likely surface convergences, (b) a weighting of the upslope area according to these normalized maps to identify drainage flow paths and flow accumulation consistent with terrain geometry, (c) the standard score normalization of the weighted upslope area and the use of standard score values as non subjective threshold for channel network identification. As a final step for optimal definition and representation of the whole network, a noise-filtering and connection procedure is applied. The advantage of the proposed methodology, and the efficiency and accurate localization of extracted features are demonstrated using LiDAR data of two different areas and comparing both extractions with field surveyed networks.
Abstract: The next generation of digital elevation data (â¤3 m resolution) calls for the development of new algorithms for the objective extraction of geomorphic features, such as channel networks, channel heads, bank geometry, landslide scars, and service roads. In this work, we test the performance of two newly developed algorithms for the extraction of geomorphic features: the waveletâbased extraction methodology developed by Lashermes et al. (2007) and the GeoNet nonlinear diffusion and geodesic paths methodology proposed by Passalacqua et al. (2010). The study area is part of the Rio Cordon basin, a headwater alpine catchment located in the Dolomites, a mountainous region in the eastern Italian Alps. The aim of this work is to compare the capability of the two new algorithms in extracting the channel network and capturing channel heads, relevant channel disruptions corresponding to landslides, and representative channel cross sections. The extracted channel networks are also compared to the ones obtained using classical methodologies on the basis of an area threshold and an areaâslope threshold. A highâresolution digital terrain model of 1 m served as the basis for such analysis. The results suggest that, although the waveletâbased methodology performs well in the channel network extraction and is able to detect channel heads and channel disruptions, the local
nonlinear filter together with the global geodesic optimization used in GeoNet is more robust and computationally efficient while achieving better localization and extraction of
features, especially in areas where gentle slopes prevail. We conclude that these new methodologies should be considered as valid alternatives to classical methodologies for
channel network extraction from lidar, in addition to offering the potential for calibrationfree channel source identification and also extraction of additional features of interest.
Abstract: This study uses landform curvature as an approach for channel network extraction. We considered a study area located in the eastern Italian Alps where a high-quality set of LiDAR data was available and where channel heads and related channel network were mapped in the field. In the analysis, we derived 1-m DTMs from different ground LiDAR point densities, and we used different smoothing factors for the landscape curvature calculation in order to test the suitability of the LiDAR point density and the derived curvature maps for the recognition of channel network. This methodology is based on threshold values of the curvature calculated as multiples (1â3 times) of the standard deviation of the curvature. Our analyses suggested that(i) the window size for curvature calculations has to be a function of the size of the features to be detected, (ii) a coarse ground LiDAR point density could be as useful as a finer one for the recognition of main channel network features and (iii)rougher curvature maps are not optimal as they do not explore a sufficient range at which features occur, while smoother curvature maps overcome this problem and are more appropriate for the extraction of surveyed channels.
Abstract: Attraverso i dati topografici ad altissima risoluzione è possibile riconoscere le forme morfologiche caratteristiche della superficie terrestre. In questo contributo la curvatura delle superfici è stata impiegata come metodo utile per
lâidentificazione ed estrazione della rete idrografica in unâarea di studio situata nelle Alpi orientali, dove sono disponibili dati lidar ad altissima risoluzione.
Abstract: Understanding earth surface processes relies on modern digital terrain representations and depends strongly on the quality of the topographic data. In the last decade, a range of new remote sensing techniques has led to a dramatic increase in terrain information. Both Terrestrial Laser Scanner (TLS) and Airborne Laser Swath Mapping technology (ALSM), using LiDAR (Light Detection And Ranging) technology, now provide high resolution topographic data with notable advantages over traditional survey techniques. A valuable characteristic of these technologies is their capability to produce sub-meter resolution Digital Terrain Models (DTMs), and high-quality land cover information (Digital Surface Models, DSMs) over large areas. This special issue brings together studies on the innovative use of remotely sensed DTMs for Earth science applications for obtaining new understanding of earth surface processes. The idea for this issue arose from a session on âRemotely sensed DTM for Hydrogeomorphic Applicationsâ convened the proponents as well as William E. Dietrich and Salvatore Grimaldi, during the 2007 Annual Fall Meeting of the American Geophysical Union, held in San Francisco, California. The three oral and one poster sessions attracted 41 abstracts from North America, Europe and Asia, providing an opportunity to review methods, discuss challenges, and evaluate recent technological advances in the use of remotely sensed topographic data for Earth surface processes. Some of the posters and presentations discussed during the meeting sessions were developed into the papers appearing in this special issue. Our goal is to share advances in landscape analysis that are significantly improved by the use of different remotely sensed data sets.We decided to consider a range of remotely sensed technologies in order to offer the scientific community different options and perspectives from recent work. The sequence of contributions is arranged according to the spatial characteristics of the techniques and methods concerned: from very high resolution data collected by TLS, to a progressively coarser resolution data from LiDAR, STRM, and ASTER remotely sensed technology.
Abstract: The search for the optimal spatial scale for observing landforms to understand physical processes is a
fundamental issue in geomorphology. Topographic attributes derived from Digital Terrain Models (DTMs)
such as slope, curvature and drainage area provide a basis for topographic analyses. The slopeâarea
relationship has been used to distinguish diffusive (hillslope) from linear (valley) processes, and to infer
dominant sediment transport processes. In addition, curvature is also useful in distinguishing the dominant
landform process. Recent topographic survey techniques such as LiDAR have permitted detailed topographic
analysis by providing high-quality DTMs. This study uses LiDAR-derived DTMs with a spatial scale between 1
and 30 m in order to find the optimal scale for observation of dominant landform processes in a headwater
basin in the eastern Italian Alps where shallow landsliding and debris flows are dominant. The analysis
considered the scaling regimes of local slope versus drainage area, the spatial distribution of curvature, and
field observations of channel head locations. The results indicate that: i) hillslope-to-valley transitions in
slopeâarea diagrams become clearer as the DTM grid size decreases due to the better representation of
hillslope morphology, and the topographic signature of valley incision by debris flows and landslides is also
best displayed with finer DTMs; ii) regarding the channel head distribution in the slopeâarea diagrams, the
scaling regimes of local slope versus drainage area obtained with grid sizes of 1, 3, and 5 m are more
consistent with field data; and iii) the use of thresholds of standard deviation of curvature, particularly at the
finest grid size, were proven as a useful and objective methodology for recognizing hollows and related
channel heads.
Abstract: Slope is one of the most important distinguishing features for channel morphology. Variations in the
computation of slope from a digital elevation model can affect a wide range of hydrogeomorphically derived
applications. We compare different methods for computing channel slope using LiDAR-derived digital terrain
models (DTMs) with varying resolutions. We chose a headwater basin of the Eastern Italian Alps,
characterized by a dense ephemeral colluvial network and a main alluvial channel as our study area. The
identified alluvial morphologies are characteristic of steep mountain streams, namely, cascades and step
pools. Field surveys were carried out along the main channel and in some small tributaries. Results indicate
that a single method for slope calculation cannot estimate channel slope at the hydrographic network scale.
The differential geometry approach for slope calculation tends to overestimate field-surveyed channel slope
values for all the DTM resolutions (1, 2, 5 m). When a trigonometric approach for slope calculation is applied,
2 and 5 m DTM resolutions give more consistent results. Nevertheless, a reliable channel slope can be derived
from a DTM with an appropriate resolution by choosing a suitable method only after considering the channel
width.
Abstract: High resolution topographic data have the potential to differentiate the main morphological features of a landscape. For the purpose of this study the Digital Terrain Models (DTMs) ranging between 1m and 20m (cell size) were derived from the last pulse LiDAR data by filtering the vegetation points. We tested the effects of different resolutions in the analysis of river morphology, and potential slope stability. The study was conducted in two headwater catchments located in the eastern Italian Alps where a high-quality set of LiDAR data was available. The results indicated for higher DTM resolutions an improved effectiveness in the recognition of river
morphology. Otherwise the progressive finer DTM resolution does not necessarily improve the interpretation of slope stability processes especially if landslides occur at a spatial scale significantly greater than cell size.
Abstract: A model for the prediction of topographic and climatic control on shallow landsliding in mountainous terrain is enhanced to analyse the impact of upslope rocky outcrops on downslope shallow landsliding. The model uses a âgeneralised quasi-dynamic wetness indexâ to describe runoff propagation on bare rock surfaces connected to downslope soil-mantled topographic elements. This approach yields a simple enhanced model capable of describing the influence of upslope bedrock outcrops on the pattern of downslope soil saturation. The model is applied in both diagnostic and predictive modes to a small catchment in the eastern Italian Alps for which a detailed inventory of shallow landslides in areas dominated by rocky outcrops is available. In the diagnostic
mode, the model is used with satisfactory results to reproduce the pattern of instability generated by an intense short-duration storm occurred on 14 September 1994, which triggered a large percentage of the surveyed landslides. In the predictive mode, the model is used for hazard assessment, and the return time of the critical rainfall needed to cause instability for each topographic element is determined. Modelling results obtained in the predictive mode are evaluated against all the surveyed landslides. It is revealed that the generalised quasi-dynamic model offers considerable improvement over the non-generalised quasi-dynamic model and the
steady-state model in predicting existing landslides as represented in the considered landslide inventory.
Abstract: High-resolution topographic data have the potential to differentiate the main morphological features of a landscape. This paper analyses the capability of airborne LiDAR-derived data in the recognition of channel-bed morphology. For the purpose of this study, 0.5 m and 1 m resolution Digital Terrain Models (DTMs) were derived from the last pulse LiDAR data obtained by filtering the vegetation points. The analysis was carried out both at 1-D scale, i.e. along the longitudinal channel profile, and at 2-D scale, taking into account the whole extent of the channel bed. The 1-D approach analyzed the residuals of elevations orthogonal to the regression line drawn along the channel profile and the standard deviation of local slope. The 2-D analysis was based on two roughness indexes, consisting on the local variability of the elevation and slope of the channel bed. The study was conducted in a headwater catchment located in the Eastern Italian Alps. The results suggested a good capability of LiDAR data in the recognition of river morphology giving the potential to distinguish the riffle-pool and step-pool reaches.
Abstract: This paper introduces a new approach for determining the most likely initiation points for landslides from potential instability mapped using a terrain stability model. This approach identifies the location with critical stability index from a terrain stability model on each downslope path from ridge to valley. Any measure of terrain stability may be used with this approach, which here is illustrated using results from SINMAP, and from simply taking slope as an index of potential instability. The relative density of most likely landslide initiation points within and outside mapped landslide scars provides a way to evaluate the effectiveness of a terrain stability measure, even when mapped landslide scars include run out zones, rather than just initiation locations. This relative density was used to evaluate the utility of high resolution terrain data derived from airborne laser altimetry (LIDAR) for a small basin located in the Northeastern Region of Italy. Digital Terrain Models were derived from the LIDAR data for a range of grid cell sizes (from 2 to 50 m). We found appreciable differences between the density of most likely landslide initiation points within and outside mapped landslides with ratios as large as three or more with the highest ratios for a digital terrain model grid cell size of 10 m. This leads to two conclusions: (1) The relative density from a most likely landslide initiation point approach is useful for quantifying the effectiveness of a terrain stability map when mapped landslides do not or can not differentiate between initiation, runout, and depositional areas; and (2) in this study area, where landslides occurred in complexes that were sometimes more than 100 m wide, a digital terrain model scale of 10m is optimal. Digital terrain model scales larger than 10m result in loss of resolution that degrades the results, while for digital terrain model scales smaller than 10 m the physical processes responsible for triggering landslides are obscured by smaller scale terrain variability.
