The monitoring of burned areas can easily be performed using satellite multispectral images: several indices are available in the literature for highlighting the differences between healthy vegetation areas and burned areas, in consideration of their different signatures. However, these indices may have limitations determined, for example, by the presence of clouds or water bodies that produce false alarms. To avoid these inaccuracies and optimize the results, this work proposes a new index for detecting burned areas named Normalized Burn Ratio Plus (NBR+), based on the involvement of Sentinel-2 bands. The efficiency of this index is verified by comparing it with five other existing indices, all applied on an area with a surface of about 500 km² and covering the north-eastern part of Sicily (Italy). To achieve this aim, both a uni-temporal approach (single date image) and a bi-temporal approach (two date images) are adopted. The maximum likelihood classifier (MLC) is applied to each resulting index map to define the threshold separating burned pixels from non-burned ones. To evaluate the efficiency of the indices, confusion matrices are constructed and compared with each other. The NBR+ shows excellent results, especially because it excludes a large part of the areas incorrectly classified as burned by other indices, despite being clouds or water bodies.

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.

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.