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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Accurate coastline position is of fundamental importance for many applications concerning coastal zone monitoring, management, and planning. For example, coastal erosion phenomena require a careful and continuous monitoring due to the dynamic nature of the coastline which can undergo sudden and significant changes in position and shape over time. Various techniques allow acquiring the coastline, among these the use of multispectral optical sensors operating from satellites is one of the most widespread. With the advent of high and very high geometric resolution (VHR) satellites, it is possible to obtain images with a pixel size of less than 1 m that allow extracting accurate coastlines. The purpose of this article is to define a fast approach to investigate the degree of accuracy of one of the most popular techniques for the automatic extraction of the coastline, based on the Normalized Difference Water Index (NDWI) use. In this study, the coastline is achieved from VHR Pléiades imagery (2 m for multispectral and 0.5 m for panchromatic). Therefore, NDWI is obtained and processed both from initial images and pan-sharpened images. The resulting coastlines are submitted to smoothing and their accuracy is therefore evaluated. For this purpose, a reference coastline is manually achieved from panchromatic image. Two different methods are proposed for coastline accuracy evaluation, both based on the geometrical analysis of the polygons generated by the intersection between the extracted coastline and the reference one. This study demonstrates that the proposed methods permit to easily evaluate the accuracy of the extracted coastline; in addition, the results confirm the effectiveness of NDWI and highlight the limited benefits of pan-sharpened images for this index application. [ABSTRACT FROM AUTHOR]

In recent years, the demand for very high geometric resolution satellite images has increased significantly. The pan-sharpening techniques, which are part of the data fusion techniques, enable the increase in the geometric resolution of multispectral images using panchromatic imagery of the same scene. However, it is not trivial to choose a suitable pan-sharpening algorithm: there are several, but none of these is universally recognized as the best for any type of sensor, in addition to the fact that they can provide different results with regard to the investigated scene. This article focuses on the latter aspect: analyzing pan-sharpening algorithms in relation to different land covers. A dataset of GeoEye-1 images is selected from which four study areas (frames) are extracted: one natural, one rural, one urban and one semi-urban. The type of study area is determined considering the quantity of vegetation included in it based on the normalized difference vegetation index (NDVI). Nine pan-sharpening methods are applied to each frame and the resulting pan-sharpened images are compared by means of spectral and spatial quality indicators. Multicriteria analysis permits to define the best performing method related to each specific area as well as the most suitable one, considering the co-presence of different land covers in the analyzed scene. Brovey transformation fast supplies the best results among the methods analyzed in this study. [ABSTRACT FROM AUTHOR]

In this paper, we present several study cases focused on marine, oceanographic, and atmospheric environments, which would greatly benefit from the use of a deployable system for small satellite observations. As opposed to the large standard ones, small satellites have become an effective and affordable alternative access to space, owing to their lower costs, innovative design and technology, and higher revisiting times, when launched in a constellation configuration. One of the biggest challenges is created by the small satellite instrumentation working in the visible (VIS), infrared (IR), and microwave (MW) spectral ranges, for which the resolution of the acquired data depends on the physical dimension of the telescope and the antenna collecting the signal. In this respect, a deployable payload, fitting the limited size and mass imposed by the small satellite architecture, once unfolded in space, can reach performances similar to those of larger satellites. In this study, we show how ecology and Earth Observations can benefit from data acquired by small satellites, and how they can be further improved thanks to deployable payloads. We focus on DORA—Deployable Optics for Remote sensing Applications—in the VIS to TIR spectral range, and on a planned application in the MW spectral range, and we carry out a radiometric analysis to verify its performances for Earth Observation studies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]