Francesco Giuseppe Figliomeni

In recent decades several methods have been developed to extract coastlines from remotely sensed images. In fact, this is one of the principal fields of remote sensing research that continues to receive attention, as testified by the thousands of scientific articles present in the main databases, such as SCOPUS, WoS, etc. The main issue is to automatize the whole process or at least a great part of it, so as to minimize the human error connected to photointerpretation and identification of training sites to support the classification of objects (basically soil and water) present in the observed scene. This article proposes a new fully automatic methodological approach for coastline extraction: it is based on the unsupervised classification of the most decorrelated fictitious band derived from Principal Component Analysis (PCA) applied to the satellite images. The experiments are carried out on datasets characterized by images with different geometric resolution, i.e., Landsat 9 Operational Land Imager (OLI) multispectral images (pixel size: 30 m), a Sentinel-2 dataset including blue, green, red and Near Infrared (NIR) bands (pixel size: 10 m) and a Sentinel-2 dataset including red edge, narrow NIR and Short-Wave Infrared (SWIR) bands (pixel size: 20 m). The results are very encouraging, given that the comparison between each extracted coastline and the corresponding real one generates, in all cases, residues that present a Root Mean Squared Error (RMSE) lower than the pixel size of the considered dataset. In addition, the PCA results are better than those achieved with Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) applications.

In recent decades, research has been developed to estimate near-shore bathymetry depth values using satellite imagery. Visible and infrared bands are used to derive elevation profile estimates, so to obtain bathymetric in rapid way without mobilisation of persons or equipment and saving the costs. For consequence, Satellite Derived Bathymetry (SDB) is seen as a valid approach for shallow waters survey: strongly supported by the activity of scholars and researchers, multiple methods are available in the literature. This article aims to investigate and compare different SDB methods for sea depth extraction from Sentinel-2 satellite multispectral images, with particular attention to the accuracy of the results. The experiments are conducted on imagery including Blue, Green, Red and Near Infrared bands, with 10 m resolution, concerning the Bay of Pozzuoli (Italy). After removing the glint, the effects caused by the reflection of sunlight through single scattering from sea surface, three methods are applied: Band Ratio method (BRM), 3rd-degree polynomial regression line method (3DPM), and principal component analysis method (PCAM). 3DPM can be seen as a variant of the BRM where the linear law that interprets the correlation between the band ratio values and the depth values is replaced by the third order function. Models are trained using depth data extracted from an Electronic Navigational Chart (ENC) at 1:7,500 scale, which is also used to verify result accuracy. The experiments demonstrate that the 3DPM is better able to obtain a more precise bathymetric model, confirming the greater adaptability of the 3rd order function to interpretate the variability of the interaction of light with water along the water column.

The coastal environment represents a resource from both a natural and economic point of view, but it is subject to continuous transformations due to climate change, human activities, and natural risks. Remote sensing techniques have enormous potential in monitoring coastal areas. However, one of the main tasks is accurately identifying the boundary between waterbodies such as oceans, seas, lakes or rivers, and the land surface. The aim of this research is to evaluate the accuracy of coastline extraction using different datasets. The images used come from UAV-RGB and the Landsat-9 and Sentinel-2 satellites. The method applied for extracting the coast feature involves a first phase of application of the Normalized Difference Water Index (NDWI), only for satellite data, and consequent application of the maximum likelihood classification, with automatic vectorization. To carry out a direct comparison with the extracted data, a coastline obtained through a field survey using a Global Navigation Satellite System (GNSS) device was used. The results are very satisfactory as they meet the minimum requirements specified by the International Hydrographic Organization (IHO) S-44. Both the UAV and the Sentinel-2 reach the maximum order, called the Exclusive order (Total Horizontal Uncertainty (THU) of 5 m with a confidence level of 95%), while the Landsat-9 falls into the Special order (THU of 10 m with a confidence level of 95%).

The coastal environment is a natural and economic resource of extraordinary value, but it is constantly modifying and susceptible to climate change, human activities and natural hazards. Remote sensing techniques have proved to be excellent for coastal area monitoring, but the main issue is to detect the borderline between water bodies (ocean, sea, lake or river) and land. This research aims to define a rapid and accurate methodological approach, based on the k-means algorithm, to classify the remotely sensed images in an unsupervised way to distinguish water body pixels and detect coastline. Landsat 8 Operational Land Imager (OLI) multispectral satellite images were considered. The proposal requires applying the k-means algorithm only to the most appropriate multispectral bands, rather than using the entire dataset. In fact, by using only suitable bands to detect the differences between water and no-water (vegetation and bare soil), more accurate results were obtained. For this scope, a new index based on the optimum index factor (OIF) was applied to identify the three best-performing bands for the purpose. The direct comparison between the automatically extracted coastline and the manually digitized one was used to evaluate the product accuracy. The results were very satisfactory and the combination involving bands B2 (blue), B5 (near infrared), and B6 (short-wave infrared-1) provided the best performance.

Due to its dynamic nature, coastline requires a continuous monitoring action, especially in presence of natural phenomena such as erosion and sea level rise. Earth observation from space is definitely useful for monitoring coastline variations, particularly in areas that are difficult to access. In addition to satellite images, other sources of information are maps that are very useful to support studies aimed at reconstructing the dynamics underway starting from periods even prior to remote sensing from space. This article aims to define a new methodological approach to automate the process of extracting the coastline, both from maps and satellite images, so as to eliminate the human error related to photointerpretation and manual vectorization. Experiments are carried out on Landsat 9 satellite images with cell resolution 30 m concerning the North-West of Campania Region and two raster maps in scale 1:25,000 included in the same area. The layer resulting from the application of Normalized Difference Water Index (NDWI) to the satellite dataset is submitted to K-means algorithm for distinguishing land and sea. The coastline is automatically extracted as separation between those two classes. The same unsupervised process is applied to the raster maps with the same objective. Tests on the accuracy of the resulting coastlines confirm the quality of the proposed approach.

Punta Licosa promontory is located in the northern part of the Cilento coast, in the southern Tyrrhenian basin. This promontory is bordered by sea cliffs connected to a wide shore platform sloping slightly towards the sea. This area has been considered stable at least since Late Pleistocene, as testified by a series of evidence well known in the literature. The aim of this research is to reconstruct the main coastal changes that have occurred in this area since the middle Holocene by means of the literature data, aerial photo interpretation, satellite images, GPS measurements, direct underwater surveys, GIS elaborations of high-resolution DTMs, bathymetric data and high-resolution orthophotos taken by UAV. Particular attention was paid to the wide platform positioned between −7.2 ± 1.2 m MSL and the present MSL, this being the coastal landform interpreted as the main consequence of sea cliff retreat. The elevation of this landform was compared with the GIA models calculated for the southern Tyrrhenian area, allowing establishing that it was shaped during the last 7.6 ± 1.1 ky BP. Moreover, the interpretation of archaeological and geomorphological markers led to the reconstruction of the shoreline evolution of this coastal sector since 7.6 ky BP. This research evaluates the cliff retreat under the effect of Holocene RSL variation on Cilento promontories, located in the western Mediterranean and characterised by the presence of monophasic platforms, and the applied method can be considered more effective and less complex and expensive if compared to other effective approaches such as those based on the usage of cosmogenic nuclides.

The acquisition of bathymetric data in shallower waters is difficult to attain using traditional survey methods because the areas to investigate may not be accessible to hydrographic vessels, due to the risk of grounding. For this reason, the use of satellite detection of depth data (satellite-derived bathymetry, SDB) constitutes a particularly useful and also economically advantageous alternative. In fact, this approach based on analytical modelling of light penetration through the water column in different multispectral bands allows to cover a big area against relatively low investment in time and resources. Particularly, the empirical method named band ratio method (BRM) is based on the degrees of absorption at different bands. The accuracy of the SDB is not comparable with that of traditional surveys, but we can certainly improve it by choosing satellite images with high geometric resolution. This article aims to investigate BRM applied to high geometric resolution images, IKONOS-2, concerning the Bay of Pozzuoli (Italy), and improve the accuracy of results performing the determination of the relation between band ratio and depth. Two non-linear functions such as the exponential function and the 3rd degree polynomial (3DP) are proposed, instead of regression line, to approximate the relationship between the values of the reflectance ratios and the true depth values collected in measured points. Those are derived from an Electronic Navigational Chart produced by the Italian Hydrographic Office. The results demonstrate that the adopted approach allows to enhance the accuracy of the SDB, specifically, 3DP supplies the most performing bathymetric model derived by multispectral IKONOS-2 images.