Statistical Deviations in Shoreline Detection Obtained with Direct and Remote Observations

Coastal Sensitivity/Vulnerability Characterization and Adaptation Strategies: A Review

Coastal area constitutes a vulnerable environment and requires special attention to preserve ecosystems and human activities therein. To this aim, many studies have been devoted both in past and recent years to analyzing the main factors affecting coastal vulnerability and susceptibility. Among the most used approaches, the Coastal Vulnerability Index (CVI) accounts for all relevant variables that characterize the coastal environment dealing with: (i) forcing actions (waves, tidal range, sea-level rise, etc.), (ii) morphological characteristics (geomorphology, foreshore slope, dune features, etc.), (iii) socio-economic, ecological and cultural aspects (tourism activities, natural habitats, etc.). Each variable is evaluated at each portion of the investigated coast, and associated with a vulnerability level which usually ranges from 1 (very low vulnerability), to 5 (very high vulnerability). Following a susceptibility/vulnerability analysis of a coastal stretch, specific strategies must be chosen and implemented to favor coastal resilience and adaptation, spanning from hard solutions (e.g., groins, breakwaters, etc.) to soft solutions (e.g., beach and dune nourishment projects), to the relocation option and the establishment of accommodation strategies (e.g., emergency preparedness).

Shoreline Extraction Based on an Active Connection Matrix (ACM) Image Enhancement Strategy

Coastal environments are facing constant changes over time due to their dynamic nature and geological, geomorphological, hydrodynamic, biological, climatic and anthropogenic factors. For these reasons, the monitoring of these areas is crucial for the safeguarding of the cultural heritage and the populations living there. The focus of this paper is shoreline extraction by means of an experimental algorithm, called J-Net Dynamic (Semeion Research Center of Sciences of Communication, Rome, Italy). It was tested on two types of image: a very high resolution (VHR) multispectral image (WorldView-2) and a high resolution (HR) radar synthetic aperture radar (SAR) image (Sentinel-1). The extracted shorelines were compared with those manually digitized for both images independently. The results obtained with the J-Net Dynamic algorithm were also compared with common algorithms, widely used in the literature, including the WorldView water index and the Canny edge detector. The results show that the experimental algorithm is more effective than the others, as it improves shoreline extraction accuracy both in the optical and SAR images.

Automatic Shoreline Detection from Eight-Band VHR Satellite Imagery

Coastal erosion, which is naturally present in many areas of the world, can be significantly increased by factors such as the reduced transport of sediments as a result of hydraulic works carried out to minimize flooding. Erosion has a significant impact on both marine ecosystems and human activities; for this reason, several international projects have been developed to study monitoring techniques and propose operational methodologies. The increasing number of available high-resolution satellite platforms (i.e., Copernicus Sentinel) and algorithms to treat them allows the study of original approaches for the monitoring of the land in general and for the study of the coastline in particular. The present project aims to define a methodology for identifying the instantaneous shoreline, through images acquired from the WorldView 2 satellite, on eight spectral bands, with a geometric resolution of 0.5 m for the panchromatic image and 1.8 m for the multispectral one. A pixel-based classification methodology is used to identify the various types of land cover and to make combinations between the eight available bands. The experiments were carried out on a coastal area with contrasting morphologies. The eight bands in which the images are taken produce good results both in the classification process and in the combination of the bands, through the algorithms of normalized difference vegetation index (NDVI), normalized difference water index (NDWI), spectral angle mapper (SAM), and matched filtering (MF), with regard to the identification of the various soil coverings and, in particular, the separation line between dry and wet sand. In addition, the real applicability of an algorithm that extracts bathymetry in shallow water using the “coastal blue” band was tested. These data refer to the instantaneous shoreline and could be corrected in the future with morphological and tidal data of the coastal areas under study.