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Mapping Asbestos-Cement Roofing with Hyperspectral Remote Sensing over a Large Mountain Region of the Italian Western Alps

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 manuf...

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Autores principales: Frassy, Federico, Candiani, Gabriele, Rusmini, Marco, Maianti, Pieralberto, Marchesi, Andrea, Nodari, Francesco Rota, Via, Giorgio Dalla, Albonico, Carlo, Gianinetto, Marco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208152/
https://www.ncbi.nlm.nih.gov/pubmed/25166502
http://dx.doi.org/10.3390/s140915900
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author Frassy, Federico
Candiani, Gabriele
Rusmini, Marco
Maianti, Pieralberto
Marchesi, Andrea
Nodari, Francesco Rota
Via, Giorgio Dalla
Albonico, Carlo
Gianinetto, Marco
author_facet Frassy, Federico
Candiani, Gabriele
Rusmini, Marco
Maianti, Pieralberto
Marchesi, Andrea
Nodari, Francesco Rota
Via, Giorgio Dalla
Albonico, Carlo
Gianinetto, Marco
author_sort Frassy, Federico
collection PubMed
description 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 km(2) 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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spelling pubmed-42081522014-10-24 Mapping Asbestos-Cement Roofing with Hyperspectral Remote Sensing over a Large Mountain Region of the Italian Western Alps Frassy, Federico Candiani, Gabriele Rusmini, Marco Maianti, Pieralberto Marchesi, Andrea Nodari, Francesco Rota Via, Giorgio Dalla Albonico, Carlo Gianinetto, Marco Sensors (Basel) Article 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 km(2) 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. MDPI 2014-08-27 /pmc/articles/PMC4208152/ /pubmed/25166502 http://dx.doi.org/10.3390/s140915900 Text en © 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Frassy, Federico
Candiani, Gabriele
Rusmini, Marco
Maianti, Pieralberto
Marchesi, Andrea
Nodari, Francesco Rota
Via, Giorgio Dalla
Albonico, Carlo
Gianinetto, Marco
Mapping Asbestos-Cement Roofing with Hyperspectral Remote Sensing over a Large Mountain Region of the Italian Western Alps
title Mapping Asbestos-Cement Roofing with Hyperspectral Remote Sensing over a Large Mountain Region of the Italian Western Alps
title_full Mapping Asbestos-Cement Roofing with Hyperspectral Remote Sensing over a Large Mountain Region of the Italian Western Alps
title_fullStr Mapping Asbestos-Cement Roofing with Hyperspectral Remote Sensing over a Large Mountain Region of the Italian Western Alps
title_full_unstemmed Mapping Asbestos-Cement Roofing with Hyperspectral Remote Sensing over a Large Mountain Region of the Italian Western Alps
title_short Mapping Asbestos-Cement Roofing with Hyperspectral Remote Sensing over a Large Mountain Region of the Italian Western Alps
title_sort mapping asbestos-cement roofing with hyperspectral remote sensing over a large mountain region of the italian western alps
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208152/
https://www.ncbi.nlm.nih.gov/pubmed/25166502
http://dx.doi.org/10.3390/s140915900
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