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The World Health Organization estimates that 100 thousand people in the world die every year from asbestos-related cancers and more than 300 thousand European citizens are expected to die from asbestos-related mesothelioma by 2030. Both the European and the Italian legislations have banned the manufacture, importation, processing and distribution in commerce of asbestos-containing products and have recommended action plans for the safe removal of asbestos from public and private buildings. This paper describes the quantitative mapping of asbestos-cement covers over a large mountainous region of Italian Western Alps using the Multispectral Infrared and Visible Imaging Spectrometer sensor. A very large data set made up of 61 airborne transect strips covering 3263 km2 were processed to support the identification of buildings with asbestos-cement roofing, promoted by the Valle d'Aosta Autonomous Region with the support of the Regional Environmental Protection Agency. Results showed an overall mapping accuracy of 80%, in terms of asbestos-cement surface detected. The influence of topography on the classification's accuracy suggested that even in high relief landscapes, the spatial resolution of data is the major source of errors and the smaller asbestos-cement covers were not detected or misclassified.

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In this article, we present the use of the Automatic Ground control points Extraction technique (AGE) for increasing the automation in the geometric correction of high-resolution satellite imagery. The method is based on an image-to-image matching between the satellite data and an already geocoded image (i.e., a digital orthophoto). By using an adaptive least squares matching algorithm which implements a very robust outlier rejection technique, AGE can automatically measure many hundreds of topographic features (TFs) on the images, whose cartographic coordinates are derived from the geocoded image and elevations are extracted from an associated digital elevation model (DEM). The AGE technique has been tested for different high-resolution data: (a) 0.62-meter QuickBird panchromatic data (basic imagery processing level), (b) 2.5-meter SPOT-5/HRG panchromatic supermode data (standard 1B processing level), and (c) 1-meter Ikonos panchromatic data (standard Geo product processing level) collected in the Northern of Italy, both in flat (Torino Caselle test site) and mountain areas (Lecco test site). Regardless the relative image resolution between the satellite and the aerial data (1-meter) and regardless the processing level of the original satellite data, a similar TFs density has been obtained for both the QuickBird and the SPOT-5/HRG data (4.4 GCPs/km 2 and 4.1 GCPs/km 2) respectively, with a geometric accuracy for the GCPs extracted of 0.90 m for QuickBird and 3.90 m for SPOT-5/HRG. For the Ikonos imagery, AGE extracted a more dense set of GCPs (8.7 GCPs/km 2) but with a lower accuracy (3.19 m). The TFs identified with AGE can be used as GCPs for the rational polynomial coefficients (RPCs) computation and, therefore, for implementing a full automatic orthoimage generation procedure. By using the commercial off-the-shelf software PCI Geomatica ® v.9.1, orthoimages have been generated for all datasets. The geometric accuracy was verified on a set of 30 manually measured independent check points (CPs) and assessed a precision of 4.99 m RMSE for QuickBird, 5.99 m RMSE for SPOT-5/HRG, and 8.65 m RMSE for Ikonos. The use of a non-conventional image orthorectification technique implementing a neural network GCPs regularization, tested for the SPOT-5/HRG data, showed the full potential of the AGE method, allowing to obtain a 3.83 m RMSE orthoprojection in a fully automated way.

This study presents a first assessment of the Top-Of-Atmosphere (TOA) radiances measured in the visible and near-infrared (VNIR) wavelengths from PRISMA (PRecursore IperSpettrale della Missione Applicativa), the new hyperspectral satellite sensor of the Italian Space Agency in orbit since March 2019. In particular, the radiometrically calibrated PRISMA Level 1 TOA radiances were compared to the TOA radiances simulated with a radiative transfer code, starting from in situ measurements of water reflectance. In situ data were obtained from a set of fixed position autonomous radiometers covering a wide range of water types, encompassing coastal and inland waters. A total of nine match-ups between PRISMA and in situ measurements distributed from July 2019 to June 2020 were analysed. Recognising the role of Sentinel-2 for inland and coastal waters applications, the TOA radiances measured from concurrent Sentinel-2 observations were added to the comparison. The results overall demonstrated that PRISMA VNIR sensor is providing TOA radiances with the same magnitude and shape of those in situ simulated (spectral angle difference, SA, between 0.80 and 3.39; root mean square difference, RMSD, between 0.98 and 4.76 [mW m−2 sr−1 nm−1]), with slightly larger differences at shorter wavelengths. The PRISMA TOA radiances were also found very similar to Sentinel-2 data (RMSD < 3.78 [mW m−2 sr−1 nm−1]), and encourage a synergic use of both sensors for aquatic applications. Further analyses with a higher number of match-ups between PRISMA, in situ and Sentinel-2 data are however recommended to fully characterize the on-orbit calibration of PRISMA for its exploitation in aquatic ecosystem mapping.