Leonardo Disperati

Inner Northern Apennines

The Northern Apennines are a fold—thrust belt formed during the Tertiary by the tectonic superposition from W to E of the Ligurides on the Tuscan nappe and on the Tuscan metamorphic complex (Boccaletti et al., 1971; Alvarez et al., 1974; Kligfield, 1979). The ophiolite-bearing Ligurides derived from the southern extension of the Ligurian—Piedmont ocean, from which similar mafic components of the Western Alps also derive (Fig. 14.1). The Tuscan units derived from the continental palaeomargin of the Adria microplate and contain a Hercynian continental basement with its upper Carboniferous Tertiary cover (Vai, this vol., Ch. 10).

Estimation of canopy attributes in beech forests using true colour digital images from a small fixed-wing UAV

Abstract Accurate estimates of forest canopy are essential for the characterization of forest ecosystems. Remotely-sensed techniques provide a unique way to obtain estimates over spatially extensive areas, but their application is limited by the spectral and temporal resolution available from these systems, which is often not suited to meet regional or local objectives. The use of unmanned aerial vehicles (UAV) as remote sensing platforms has recently gained increasing attention, but their applications in forestry are still at an experimental stage. In this study we described a methodology to obtain rapid and reliable estimates of forest canopy from a small UAV equipped with a commercial RGB camera. The red, green and blue digital numbers were converted to the green leaf algorithm (GLA) and to the CIE L*a*b* colour space to obtain estimates of canopy cover, foliage clumping and leaf area index (L) from aerial images. Canopy attributes were compared with in situ estimates obtained from two digital canopy photographic techniques (cover and fisheye photography). The method was tested in beech forests. UAV images accurately quantified canopy cover even in very dense stand conditions, despite a tendency to not detecting small within-crown gaps in aerial images, leading to a measurement of a quantity much closer to crown cover estimated from in situ cover photography. Estimates of L from UAV images significantly agreed with that obtained from fisheye images, but the accuracy of UAV estimates is influenced by the appropriate assumption of leaf angle distribution. We concluded that true colour UAV images can be effectively used to obtain rapid, cheap and meaningful estimates of forest canopy attributes at medium-large scales. UAV can combine the advantage of high resolution imagery with quick turnaround series, being therefore suitable for routine forest stand monitoring and real-time applications.

The Wonji fault belt (Main Ethiopian Rift): structural and geomorphological constraints and GPS monitoring

Abstract The Wonji Fault Belt (WFB), Main Ethiopian Rift, forms a network of faults oriented NNE-SSW with a Quaternary direction of extension oriented c. N95° E. Faults are spaced between 0.5 and 2 km, show a fresh steep scarp, recent activity and slip rates of up to 2.0 mm a−1. This high value of deformation along the rift floor with respect to the plate separation rates suggests that most of the active strain could be accommodated by magma-induced faulting within the rift. However, the mountain front morphology associated with a displacement of 300–400 m since the Middle Pleistocene, tilted-blocks, brittle-seismic fault rock fabric and historical earthquakes with M>6 support a tectonic origin of the Asela boundary fault. Therefore, we propose a model that considers the possible coexistence of both magmatic deformation at the rift floor and brittle faulting at the rift margin. We also report the data relative to a GPS network installed in December 2004, along two transects across the WFB, between Asela and the Ziway Lake.

A Geomatics Approach to Multitemporal Shoreline Analysis in Western Mediterranean: The Case of Platamona-Maritza Beach (Northwest Sardinia, Italy)

Abstract VIRDIS, S.G.P.; OGGIANO, G., and DISPERATI, L., 2012. A geomatics approach to multitemporal shoreline analysis in Western Mediterranean: the case of Platamona-Maritza Beach (northwest Sardinia, Italy). This study examines the shoreline change evolution at two spatiotemporal scales over ca. 18 km of Platamona-Maritza beach (northwest Sardinia, Italy). A multitemporal dataset spanning the period 1955–2010 was used and made up of archival aerial photographs, orthophotos, satellite imagery, light detection and ranging data, terrestrial laser scanner, Global Positioning System, and recent and historical topographic maps. We integrated this dataset by implementing a repeatable processing procedure, for which the global sensitivity for shoreline change estimation was evaluated (ca. 10 m as a product of time interval and change rate). The applied methodology indicates that the wet/dry boundary can be used as a good shoreline proxy. It is also demonstrated how reliable shoreline positional uncertainty (from 1.9 to 8.6 m) can be estimated whenever a large set of multitemporal data is available and geomatic tools are properly integrated. Results showed how shoreline evolutionary trends affected the entire coastal zone and roughly migrated from east to west, with the largest rates between 1970 and 2000. Where long-term analysis provided higher erosion rates, a sediment volumetric change was estimated, although only for a 2-year window and for a 300-m-wide stretch of beach. An analysis of geomorphic features along the coast also indicates variable longshore drift direction as a consequence of changes in a combination of wind, wave, and current regimes. A direct relationship between the evolutionary trend of hydrometric and pluviometric data recorded at the study site highlight that, although rainfall regime is nearly constant, average discharge of main rivers underwent a dramatic decrease since 1965 in conjunction with land use change and upstream dam construction for agriculture and urban development. Therefore, it has been argued that river sediment supply also was reduced; hence, besides other natural and anthropogenic causes, it likely influenced erosion and accretion events in the southern sector of the Gulf of Asinara.

Geomatics for Integrated Coastal Zone Management: multitemporal shoreline analysis and future regional perspective for the Portuguese Central Region

ABSTRACT Cenci, L., Disperati, L., Sousa, L.P., Phillips, M. and Alves, F.L., 2013. Geomatics for Integrated Coastal Zone Management: multitemporal shoreline analysis and future regional perspective for the Portuguese Central Region. Proceedings 12th International Coastal Symposium (Plymouth, England), Journal of Coastal Research, Special Issue No. 65, pp. 1349–1354, ISSN 0749-0208.--> Shoreline mapping and change detection are critical for Integrated Coastal Zone Management (ICZM) and all that it represents. This research utilized previous studies that combined both Remote Sensing and Geographical Information System (GIS) techniques to assess, map and forecast shoreline evolution from short-term perspectives. The study area is located in the central region of Portugal, between the counties of Ovar and Marinha Grande (circa 140 km) and the time period assessed was from 1984 to 2011. Historical data were used to calculate advance and retreat rates in order to support environmental scenarios for the Portuguese Central Regions Coastal Management Plan. To ensure accuracy, a repeatable procedure was validated using Landsat TM and ETM+ satellite images, which were subsequently enhanced and elaborated by Remote Sensing analyses to detect and extract shorelines. They were subsequently integrated within an Esri ArcGIS software application (DSAS - Digital Shoreline Analysis System) to determine and predict rates of coastline change. Graphical DSAS plots identified coastline phases and shifts and were used to simulate the 2022 coastline scenario. These results will be integrated into the Coastal Zone Management Plan (Horizon – 2022). Importantly this methodological planning approach provides visual coastline change information for regional decision-makers and stakeholders.

Spectral characterization of coastal sediments using Field Spectral Libraries, Airborne Hyperspectral Images and Topographic LiDAR Data (FHyL)

Abstract Beach dune systems are important for coastal zone ecosystems as they provide natural sea defences that dissipate wave energy. Geomorphological models of this near-shore topography require site-specific sediment composition, grain size and moisture content as inputs. Hyperspectral, field radiometry and LiDAR remote sensing can be used as tools by providing synoptic maps of these properties. However, multi-remote sensing of near-shore beach images can only be interpreted if there are adequate bio-geophysical or empirical models for information extraction. Our aim was thus to model the effects of varying sediment properties on the reflectance in both field and laboratory conditions within the FHyL (Field Spectral Libraries, Airborne Hyperspectral Images and Topographic LiDAR) procedure, using a multisource dataset (airborne Hyperspectral – MIVIS and topographic LiDAR – Hawk-eye II and field radiometry). The methodology consisted of (i) acquisition of simultaneous multi-source datasets (airborne Hyperspectral – MIVIS and topographic LiDAR – Hawk-eye) (ii) hyperspectral measurements of sediment mixtures with varying physical characteristics (moisture, grain size and minerals) in field and laboratory conditions, (iii) determination and quantification of specific absorption features, and (iv) correlation between the absorption features and physical parameters cited above. Results showed the potential of hyperspectral signals to assess the effect of moisture, grain-size and mineral composition on sediment properties.

Remote sensing for coastal risk reduction purposes: Optical and microwave data fusion for shoreline evolution monitoring and modelling

Coastal zones are fragile and dynamic environments, most of the time largely urbanized and particularly vulnerable to natural hazards. Therefore, coastal areas are often exposed to high risk and shoreline position monitoring and modelling is required to mitigate it. In this context, satellite data are fundamental to provide synoptic and multitemporal information useful to map and model shoreline position through time. The aim of this work was to study the shoreline evolution of two selected areas, in Portugal and in Italy. Shoreline historical rates were obtained by analyzing Landsat images from mid-80s up to 2011. Subsequently, short-term scenarios (2014) were predicted and their accuracy was assessed by comparing 2014 modelled and observed shoreline positions. After that, Landsat 8 and Sentinel 1 images were exploited to extract and compare 2015 shoreline positions in a Data Fusion context. Finally, results were interpreted for their implications in the coastal risk reduction framework.

Integrating remote sensing and GIS techniques for monitoring and modeling shoreline evolution to support coastal risk management

Abstract The precise delineation of coastal areas subject to past, present, and future erosive processes plays a fundamental role in coastal risk management. Within this framework, satellite data represent a valuable synoptic and multi-temporal information source. Therefore, this research integrated remote sensing and GIS techniques for mapping and modeling shoreline evolution through time. Long-term shoreline’s proxy rates of advance and retreat were determined using Landsat data from the mid-1980s to 2011 and subsequently, a short-term scenario (3 years) was predicted and validated. Two different coastal environments, Oceanic and Mediterranean, were investigated. In the first, different proxies were analyzed, thereby enabling a multi-proxy analysis. Findings showed that the method provided more accurate results in higher energy environments (Oceanic) and where the coastline is not urbanized. Results also highlighted the importance of performing multi-proxy analyses in given study areas, to more reliably define shoreline modeling. Importantly, during the analyses, particular attention was given to assessing uncertainty, which is crucial when outcomes of scientific research are considered for management.

Sensitivity Analysis for Shallow Landsliding Susceptibility Assessment in Northern Tuscany

In two areas located in the north-western part of Tuscany, central Italy, Lunigiana and Garfagnana, noticeable heavy rainfall events occurred in the last years. During these events, the rainfall amounts and intensities triggered a great number of shallow landslides, causing damages, injuries and human losses. Steep slopes and deep valleys induced a persistently high relief of energy and high shallow landsliding susceptibility. In the present paper, the authors considered 4 heavy rainfall events that affected the area in 2009–2011. They carried out an analysis including a statistical modelling of spatial landslide occurrence by using Random Forest classifiers (RFc) after model selection by means of a stepwise AIC (Akaike Information Criterion) procedure. Event landslides occurrences permitted to build four event-specific RFc training sets, considering a large number of predictors reliable to characterize landslide susceptibility. Furthermore, the analysis took into account some relevant meteorological variables directly linked to the events themselves. An exploratory evaluation of the skills of a numerical weather prediction (NWP) model was conducted, to give a reliable supply to the RFc framework by using its weather forecast. For one selected event, a shallow landslide hazard model with meteorological inputs was validated. The preliminary results are shown and discussed.

Bi-temporal change analysis of satellite imagery to detect landslides triggered by intense rainfall events

This paper presents the results of implementation of bi-temporal change analysis methods to RapidEye satellite imagery to support the detection, at regional scale, of landslides caused by two intense rainfall events of 2009 and 2011, in Northern Tuscany. Image geometric and radiometric pre-processing were applied. Then, bi-temporal image ratioing of Difference Vegetation Index (DVI) and bi-temporal spectral transformations (Principal Component Analysis - PCA; Independent Component Analysis - ICA) were implemented. Finally, unsupervised and supervised classification allowed us to obtain thematic representation of areas of changes which supported the identification of almost hundred landslides in the study area.