{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T21:35:58Z","timestamp":1781991358840,"version":"3.54.5"},"reference-count":54,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2020,12,29]],"date-time":"2020-12-29T00:00:00Z","timestamp":1609200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"University of Padova\u2019s VAIA-FRONT"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The frequency of extreme storm events has significantly increased in the past decades, causing significant damage to European forests. To mitigate the impacts of extreme events, a rapid assessment of forest damage is crucial, and satellite data are an optimal candidate for this task. The integration of satellite data in the operational phase of monitoring forest damage can exploit the complementarity of optical and Synthetic Aperture Radar (SAR) open datasets from the Copernicus programme. This study illustrates the testing of Sentinel 1 and Sentinel 2 data for the detection of areas impacted by the Vaia storm in Northern Italy. The use of multispectral Sentinel 2 provided the best performance, with classification overall accuracy (OA) values up to 86 percent; however, optical data use is seriously hampered by cloud cover that can persist for months after the event and in most cases cannot be considered an appropriate tool if a fast response is required. The results obtained using SAR Sentinel 1 were slightly less accurate (OA up to 68 percent), but the method was able to provide valuable information rapidly, mainly because the acquisition of this dataset is weather independent. Overall, for a fast assessment Sentinel 1 is the better of the two methods where multispectral and ground data are able to further refine the initial SAR-based assessment.<\/jats:p>","DOI":"10.1093\/forestry\/cpaa043","type":"journal-article","created":{"date-parts":[[2020,11,7]],"date-time":"2020-11-07T12:11:45Z","timestamp":1604751105000},"page":"407-416","source":"Crossref","is-referenced-by-count":36,"title":["Satellite open data to monitor forest damage caused by extreme climate-induced events: a case study of the Vaia storm in Northern Italy"],"prefix":"10.1093","volume":"94","author":[{"given":"Gaia","family":"Vaglio Laurin","sequence":"first","affiliation":[{"name":"Department for Innovation in Biological, Agro-Food and Forest Systems, University of Tuscia, Viterbo 01100, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saverio","family":"Francini","sequence":"additional","affiliation":[{"name":"Department of Agricultural, Food and Forestry Systems, Universit\u00e0 degli Studi di Firenze, Firenze 50145, Italy"},{"name":"Dipartimento di Bioscienze e Territorio, Universit\u00e0 degli Studi del Molise, Pesche, Isernia Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tania","family":"Luti","sequence":"additional","affiliation":[{"name":"Department of Earth Science, Universit\u00e0 degli Studi di Firenze, Firenze 50121, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gherardo","family":"Chirici","sequence":"additional","affiliation":[{"name":"Department of Agricultural, Food and Forestry Systems, Universit\u00e0 degli Studi di Firenze, Firenze 50145, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Pirotti","sequence":"additional","affiliation":[{"name":"Interdepartmental Research Center of Geomatics (CIRGEO), University of Padova, Legnaro 35020, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dario","family":"Papale","sequence":"additional","affiliation":[{"name":"Department for Innovation in Biological, Agro-Food and Forest Systems, University of Tuscia, Viterbo 01100, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,12,29]]},"reference":[{"key":"2021050406364105400_ref1","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.rse.2013.12.020","article-title":"Landsat remote sensing of forest windfall disturbance","volume":"143","author":"Baumann","year":"2014","journal-title":"Remote Sens. 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