3D models, particularly useful in many technical and scientific applications, can be easily created starting from cartographic data, using the tools provided by Geographic Information Systems (GIS). If you want to create a 3D model concerning both the territory and the marine areas adj acent to it, the necessary cartographic data are often present in different documentary sources. This requires some preliminary operations in order to homogenize these data, bringing them to the same cartographic datum, proj ection and scale of representation. This article aims to define an efficient procedure for the construction of a 3D model of Ischia Island and the surrounding seabed at medium scale. For the territorial part, a Digital Elevation Model (DEM) with a resolution of 2 meters is used. For the bathymetric part, the data is taken from an Electronic Navigational Chart (ENC) at a scale of 1:30,000. The two types of data differ both in the detail of information and in the altimetric datum. The territorial data is processed in order to have the elevation information at the same scale of representation as the nautical chart. The bathymetric data is traced back to the same altimetric datum of the DEM. The elevation and depth data, reported at the same projection (Universal Transverse of Mercator, UTM) and datum (WGS84), are interpolated by Ordinary Kriging imposing a resolution of 15 meters. The verifications of the level of accuracy through Cross-validation confirm the validity of the procedure performed and the quality of the model obtained.

Precise and timely land-cover identification plays an important role in effective environmental monitoring and land management. This study compares theperformanceoffive machine-learningclassifiers –supportvectormachine (SVM), decision tree (DT), normal Bayes (NB), random forest (RF), and k-nearest neighbor (k-NN) – in the land-cover mapping of the Agro Nocerino Sarnese area (Southern Italy) using high-resolution SPOT 7 pan-sharpened multispectral images with a pixel size of 1.5 m × 1.5 m. The data set consisted of blue, green, red, and near-infrared (NIR) bands and was processed with Orfeo ToolBox (OTB) software. Two data sets were analyzed: DS-3B (which included only the visible bands [blue, green, and red]), and DS-4B (which also included the NIR band). A comparison of the classifiers’ performances across various land-cover classes was conducted in order to assess their respective classification accuracy. The results showed that SVM and k-NN achieved the highest overall accuracy levels (93% and 92%, respectively) using only the visible bands, whereas the decision tree classifier performed best when the NIR band was included. Random forest achieved excellent accuracy in vegetation classes (88–99%) but struggled with misclassifications in bare soil and man-made classes such as buildings and roads. These results emphasized the significant impact of data set characteristics on classifier performance as well as the importance of band selection and pan-sharpening techniques in high-resolution land-cover mapping.

Capturing the thermal emission from the Earth, satellite sensors permit to derive sea surface temperature (SST). The infrared radiance intercepted by the sensor also depends on other variables, such as the surface emissivity, viewing geometry and the atmospheric contribution. All variables that affect atmospheric absorption and emission must be carefully considered. The brightness temperature of the surface measured by satellite sensor needs transformation to obtain SST. To remove the noise generated by the atmosphere, an equation can be used to derive a set of SST algorithm coefficients that can be applied to the brightness temperature image. Temperature data from specific points are necessary for the scope. This article aims to demonstrate that, in absence of an adequate temperature dataset, SST can be derived by MODIS thermal images (pixel dimensions: 1 km x 1 km) using Copernicus data even if it has a lower spatial resolution (0.0625° x 0.0625°). The study area includes an extended part of the Mediterranean Sea which contains the Strait of Sicily, the Adriatic, Ionian, and Tyrrhenian Seas. The Pathfinder algorithm developed by NOAA based on an equation with pre-established coefficients is also applied for comparison. Temperatures recorded by buoys are used to test the result accuracy. The experiments testify the good performance of the adopted approach.

Several scientific, technical and practical applications concerning the Italian coasts require the use of a unitary cartographic representation of the entire area. It is therefore necessary to select not only the geodetic datum itself, but also a single cartographic projection. This article aims to identify, among different possible solutions, the cartographic projection capable of guaranteeing minimal deformations. In fact, each projection introduces distortions (e.g., to distances, areas, angles between directions). In relation to the objectives pursued, a projection that limits or even nullifies the values of a specific type of deformation (for example those on areas or angles) may be preferable. It is clear that a study of the variability of distortions is useful: tools available in Geographic Information Systems (GISs) are suitable for this purpose. In this article the following representations, all conformal (i.e., without angle deformations), are considered and analysed using QGIS software: Universal Transverse Mercator (UTM) based on a single zone for the entire area considered, Mercator of the world, Mercator with one standard parallel, Lambert Conformal Conic with one standard parallel. The experiments demonstrate that UTM is the most suitable for the Italian coastal zones to limit both area and distance deformations preserving the shape.