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Hyperspectral PRISMA images are new and have not yet been evaluated for their ability to detect marine plastic litter. The hyperspectral PRISMA images have a fine spectral resolution, however, their spatial resolution is not high enough to enable the discrimination of small plastic objects in the ocean. Pansharpening with the panchromatic data enhances their spatial resolution and makes their detection capabilities a technological challenge. This study exploits, for the first time, the potential of using satellite hyperspectral data in detecting small-sized marine plastic litter. Controlled experiments with plastic targets of various sizes constructed from several materials have been conducted. The required pre-processing steps have been defined and 13 pansharpening methods have been applied and evaluated for their ability to spectrally discriminate plastics from water. Among them, the PCA-based substitution efficiently separates plastic spectra from water without producing duplicate edges, or pixelation. Plastic targets with size equivalent to 8% of the original hyperspectral image pixel coverage are easily detected. The same targets can also be observed in the panchromatic image, however, they cannot be detected solely by the panchromatic information as they are confused with other appearances. Exploiting spectra derived from the pan-sharpened hyperspectral images, an index combining methodology has been developed, which enables the detection of plastic objects. Although spectra of plastic materials present similarities with water spectra, some spectral characteristics can be utilized for producing marine plastic litter indexes. Based on these indexes, the index combining methodology has successfully detected the plastic targets and differentiated them from other materials.INDEX TERMS PRISMA satellite data, hyperspectral imaging, pansharpening, marine pollution, plastic litter detection, indexes, controlled experiments, spectral analysis, image denoising.

Desertification is a complex environmental phenomenon that affects many regions worldwide, including the Mediterranean area. Its effects, primarily resulting from climate variations and also influenced by human-induced changes, impact upon potential regional progress due to significant economic losses, social problems and ecological damage. The aim of this study was the identification of sensitive areas to desertification at watershed scale, in the Bradano River basin (Basilicata, southern Italy). The analysis was carried out by means of the model developed within the European project MEDALUS (MEditerranean Desertification And Land USe), which identifies prone areas to desertification through the Environmentally Sensitive Areas (ESAs) index. The model parameters were implemented and processed using a GIS-based approach to evaluate climate, soil, vegetation and management system quality factors, which represent the input for the ESAs assessment. The results indicate that 35% of the study area is highly sensitive to desertification, 49% of the study area has moderate sensitivity to desertification, 12% has low sensitivity and only 4% is non-sensitive to desertification.

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