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.

In relation to 3D bathymetric modelling, this article aims to analyze the performance of Kriging approaches in dependence of the location and density of the measured depth points. The experiments were carried out on a multi-beam sonar (MBS) dataset that includes 240,000 soundings covering a sea-bottom area near Giglio Island (Italy). Seven subsets were derived in random way from the initial regular MBS dataset, selecting an increasing number of points uniformly spaced. Seven models were generated for both Ordinary Kriging and Universal Kriging. Each model was submitted to leave-one-out cross-validation to define the exactness of the predictive values and compared with the initial grid to better evaluate the accuracy in dependence of the point number and dissemination. To investigate this relationship, a new index called MVI (Morphological Variation Index) was introduced as a measurement of the level of variation of seabed morphology. The results validate the efficiency of the Kriging methods and remark the influence of the dataset distribution on the 3D model, highlighting MVI as a useful index to represent the seabed variation as a unique value. Finally, in no rugged areas using 1 point every 1000 m², the RMSE of the differences between measured and interpolated values falls below 1 m, while a further increment of soundings is required in the presence of a high level of variation of seabed morphology.

Pan-sharpening methods allow the transfer of higher resolution panchromatic images to multispectral ones concerning the same scene. Different approaches are available in the literature, and only a part of these approaches is included in remote sensing software for automatic application. In addition, the quality of the results supplied by a specific method varies according to the characteristics of the scene; for consequence, different algorithms must be compared to find the best performing one. Nevertheless, pan-sharpening methods can be applied using GIS basic functions in the absence of specific pan-sharpening tools, but this operation is expensive and time-consuming. This paper aims to explain the approach implemented in Quantum GIS (QGIS) for automatic pan-sharpening of Pléiades images. The experiments are carried out on data concerning the Greek island named Lesbo. In total, 14 different pan-sharpening methods are applied to reduce pixel dimensions of the four multispectral bands from 2 m to 0.5 m. The automatic procedure involves basic functions already included in GIS software; it also permits the evaluation of the quality of the resulting images supplying the values of appropriate indices. The results demonstrate that the approach provides the user with the highest performing method every time, so the best possible fused products are obtained with minimal effort in a reduced timeframe.

Coastal and Marine Geographic Information Systems (CMGISs) permit to collect, manage, and analyze a great amount of heterogeneous data concerning coastal, sea, and ocean environments, e.g., nautical charts, topographic maps, remotely sensed images. To integrate those heterogeneous layers in CMGIS, particular attention is necessary to ensure the perfect geo-localization of data, which is a basic requirement for the correct spatial analysis. In fact, the above-mentioned types of information sources are usually available in different cartographic projections, geodetic datum, and scale of representation. Therefore, automatic conversions supplied by Geographic Information System (GIS) software for layer overlay do not produce results with adequate positional accuracy. This paper aims to describe methodological aspects concerning different data integration in CMGIS in order to enhance its capability to handle topics of coastal and marine applications. Experiments are carried out to build a CMGIS of the Campania Region (Italy) harmonizing different data (maps and satellite images), which are heterogeneous for datum (World Geodetic System 1984 and European Datum 1950), projection (Mercator and Universal Transverse of Mercator), and scale of representation (large and medium scale). Results demonstrate that automatic conversion carried out by GIS software are insufficient to ensure levels of positional accuracy adequate for large scale representation. Therefore, additional operations such as those proposed in this work are necessary.

Nowadays, within the built environment, railway infrastructures play a key role to sustain national policies oriented toward promoting sustainable mobility. For this reason, national institutions and infrastructure managers need to increase their awareness in relation to the current and future climate risks on their representative systems. Among climate change impacts, preventing the effects of sea-level rise (SLR) on coastal railway infrastructures is a priority. The first step in the climate change adaptation policy cycle is the development of an ad hoc climate risk assessment. In this view, this research develops a vulnerability and a risk assessment metric to identify the hotspots within a national coastal railway due to the SLR impacts. The proposed methodology required different steps to quantify the SLR projections and the vulnerability characteristics of the assets, in terms of sensitivity and adaptive capacity. The investigated case study is the coastal railway infrastructure in Italy, thanks to an initial approach of co-design participative processes with the national Infrastructure Manager: Rete Ferroviaria Italiana (RFI). The results of this application, although not included in the paper due to confidential reasons imposed by the infrastructure manager — led to a clear identification of the areas and the coastal railway sections which are exposed to high levels of risks and of the places which require priority actions for urgent adaptation in a view of climate proof infrastructures.