The availability of high-resolution Digital Surface Models of coastal environments is of increasing interest for scientists involved in the study of the coastal system processes. Among the range of terrestrial and aerial methods available to produce such a dataset, this study tests the utility of the Structure from Motion (SfM) approach to low-altitude aerial imageries collected by Unmanned Aerial Vehicle (UAV). The SfM image-based approach was selected whilst searching for a rapid, inexpensive, and highly automated method, able to produce 3D information from unstructured aerial images. In particular, it was used to generate a dense point cloud and successively a high-resolution Digital Surface Models (DSM) of a beach dune system in Marina di Ravenna (Italy). The quality of the elevation dataset produced by the UAV-SfM was initially evaluated by comparison with point cloud generated by a Terrestrial Laser Scanning (TLS) surveys. Such a comparison served to highlight an average difference in the vertical values of 0.05 m (RMS = 0.19 m). However, although the points cloud comparison is the best approach to investigate the absolute or relative correspondence between UAV and TLS methods, the assessment of geomorphic features is usually based on multi-temporal surfaces analysis, where an interpolation process is required. DSMs were therefore generated from UAV and TLS points clouds and vertical absolute accuracies assessed by comparison with a Global Navigation Satellite System (GNSS) survey. The vertical comparison of UAV and TLS DSMs with respect to GNSS measurements pointed out an average distance at cm-level (RMS = 0.011 m). The successive point by point direct comparison between UAV and TLS elevations show a very small average distance, 0.015 m, with RMS = 0.220 m. Larger values are encountered in areas where sudden changes in topography are present. The UAV-based approach was demonstrated to be a straightforward one and accuracy of the vertical dataset was comparable with results obtained by TLS technology.

This study focuses on landslide susceptibility mapping in the Daunia area (Apulian Apennines, Italy) and achieves this by using a multivariate statistical method and data processing in a Geographical Information System (GIS). The Logistic Regression (hereafter LR) method was chosen to produce a susceptibility map over an area of 130 000 ha where small settlements are historically threatened by landslide phenomena. By means of LR analysis, the tendency to landslide occurrences was, therefore, assessed by relating a landslide inventory (dependent variable) to a series of causal factors (independent variables) which were managed in the GIS, while the statistical analyses were performed by means of the SPSS (Statistical Package for the Social Sciences) software. The LR analysis produced a reliable susceptibility map of the investigated area and the probability level of landslide occurrence was ranked in four classes. The overall performance achieved by the LR analysis was assessed by local comparison between the expected susceptibility and an independent dataset extrapolated from the landslide inventory. Of the samples classified as susceptible to landslide occurrences, 85% correspond to areas where landslide phenomena have actually occurred. In addition, the consideration of the regression coefficients provided by the analysis demonstrated that a major role is played by the "land cover" and "lithology" causal factors in determining the occurrence and distribution of landslide phenomena in the Apulian Apennines.

The feasibility of unmanned-aerial-vehicle-based photogrammetry was assessed for the reconstruction of high-resolution topography and geomorphic features of quarries by nadir and off-nadir imagery. The test site was a quarry located in the rural area of Turi (Bari, southern Italy). Two processing scenarios were created. Nadir images were initially used, and images acquired with off-nadir angles were added. An accurate set of ground control points (GCPs) were surveyed for both georeferencing purposes and validation processes. In the reconstruction of the surfaces, an accuracy of a few centimeters was achieved in the final positioning of point clouds representing the main geometries of quarry environment. However, greatest differences were found along the edges or the lines characterized by sudden slope changes. To better understand such results, some characteristic quarry shapes depicted by both the scenarios were compared to those surveyed by a total station used as an independent benchmark technique. It allowed to define the benefits introduced by the joint use of nadir and oblique images in the delineation of quarry shapes, surface discontinuities and better descriptions of sub-vertical walls. Beside the evaluation of benefits introduced by use of oblique cameras, the effectiveness of the proposed methodology was also discussed with alternative technologies. Unmanned aerial platforms represent an effective solution, with the need for few accurate GCPs.

This article compares the land surface temperature (LST) and sky view factor (SVF) as indicator of the impact of urban morphology on the urban heat island effect. For this purpose, the work investigates the dependence of this relationship to spatial resolutions varying from coarse to very fine and related scaling effect. For this purpose, several daytime Landsat 8, Advanced Spaceborne Thermal Emission and Reflection Radiometer and airborne TASI-600 images acquired in different months of the year were used to retrieve LST. Moreover, a three-dimensional building vector database was used to produce SVF maps at different spatial resolutions, according to the thermal ones. The results show a positive relationship between LST and SVF; these trends are almost superimposable for same-sensor images, but vary for images at different resolutions. This is likely due to the interference on the SVF and LST values of micro-scale factors, e.g. thermal properties of building materials, anthropogenic heat, humidity and pollutants, and others, which in a macro-scale analysis are smoothed becoming irrelevant. Therefore, a digital database able to represent urban features at suitable spatial scale is a requirement for a reliable analysis. In particular, when airborne thermal data are used, the lack of a reliable cartographic digital data could represent a limiting factor. Specifically, the data set used in the computation of SVF and other geographical parameters involved in the LST assessment should be consistent with the resolution of airborne thermal data.

Comprehension of vulnerability to coastal erosion in dynamic coastal environments strongly depends on accurate and frequent detection of shoreline position. The monitoring of such environments could benefit from the semi-automatic shoreline delineation method, especially in terms of time, cost, and labour-intensiveness. This article explores the potential of using a semi-automatic approach in delineating a proxy-based shoreline by processing high-resolution multispectral WorldView-2 satellite imagery. We studied the potential and differences of basic and easily accessible standard classification methods for shoreline detection. In particular we explored the use of high spatial and spectral resolution satellite imagery for shoreline extraction. The case study was carried out on a 40 km coastal stretch facing the Northern Adriatic Sea (Italy) and belonging to the Municipality of Ravenna. In this area a frequent monitoring of shoreline position is required because of the extreme vulnerability to erosion phenomena that have resulted in a general trend of coastal retreat over recent decades. The wet/dry shorelines were delineated between the classes of wet and dry sand, resulting from different supervised (Parallelepiped, Gaussian Maximum Likelihood, Minimum-Distance-to-Means, and Mahalanobis distance) image classification techniques and the unsupervised Iterative Self-Organizing Data Analysis Technique (ISODATA). In order to assign reliability to outcomes, the extrapolated shorelines were compared to reference shorelines visually identified by an expert, by assessing the average mean distance between them. In addition, the correlation between offset rates and different types of coast was investigated to examine the influence of specific coastal features on shoreline extraction capability. The results highlighted a high level of compatibility. The average median distance between reference shorelines and those resulting from the classification methods was less than 5.6 m (Maximum likelihood), whereas a valuable distance of just 2.2 m was detected from ISODATA and Mahalanobis. Heterogeneous coastal stretches exhibited a larger offset between extracted and reference shorelines than the homogeneous ones. To finally evaluate the coastal evolution of the area, results from Mahalanobis classification were compared to a shoreline derived from airborne light detection and ranging (lidar) data. The fine spatial resolution provided by both methodologies allowed a detailed Digital Shoreline Analysis System (DSAS) comparison, detecting an erosive trend within a wide portion of the study area.