{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T15:37:59Z","timestamp":1766158679672,"version":"build-2065373602"},"reference-count":119,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T00:00:00Z","timestamp":1616544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In the light of the \u201cBiological Diversity\u201d concept, habitats are cardinal pieces for biodiversity quantitative estimation at a local and global scale. In Europe EUNIS (European Nature Information System) is a system tool for habitat identification and assessment. Earth Observation (EO) data, which are acquired by satellite sensors, offer new opportunities for environmental sciences and they are revolutionizing the methodologies applied. These are providing unprecedented insights for habitat monitoring and for evaluating the Sustainable Development Goals (SDGs) indicators. This paper shows the results of a novel approach for a spatially explicit habitat mapping in Italy at a national scale, using a supervised machine learning model (SMLM), through the combination of vegetation plot database (as response variable), and both spectral and environmental predictors. The procedure integrates forest habitat data in Italy from the European Vegetation Archive (EVA), with Sentinel-2 imagery processing (vegetation indices time series, spectral indices, and single bands spectral signals) and environmental data variables (i.e., climatic and topographic), to parameterize a Random Forests (RF) classifier. The obtained results classify 24 forest habitats according to the EUNIS III level: 12 broadleaved deciduous (T1), 4 broadleaved evergreen (T2) and eight needleleaved forest habitats (T3), and achieved an overall accuracy of 87% at the EUNIS II level classes (T1, T2, T3), and an overall accuracy of 76.14% at the EUNIS III level. The highest overall accuracy value was obtained for the broadleaved evergreen forest equal to 91%, followed by 76% and 68% for needleleaved and broadleaved deciduous habitat forests, respectively. The results of the proposed methodology open the way to increase the EUNIS habitat categories to be mapped together with their geographical extent, and to test different semi-supervised machine learning algorithms and ensemble modelling methods.<\/jats:p>","DOI":"10.3390\/rs13071231","type":"journal-article","created":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T21:36:51Z","timestamp":1616621811000},"page":"1231","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Earth Observation and Biodiversity Big Data for Forest Habitat Types Classification and Mapping"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2346-8346","authenticated-orcid":false,"given":"Emiliano","family":"Agrillo","sequence":"first","affiliation":[{"name":"Italian National Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Roma, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1419-3734","authenticated-orcid":false,"given":"Federico","family":"Filipponi","sequence":"additional","affiliation":[{"name":"Italian National Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Roma, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6406-4527","authenticated-orcid":false,"given":"Alice","family":"Pezzarossa","sequence":"additional","affiliation":[{"name":"Italian National Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Roma, Italy"}]},{"given":"Laura","family":"Casella","sequence":"additional","affiliation":[{"name":"Italian National Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Roma, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0427-248X","authenticated-orcid":false,"given":"Daniela","family":"Smiraglia","sequence":"additional","affiliation":[{"name":"Italian National Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Roma, Italy"}]},{"given":"Arianna","family":"Orasi","sequence":"additional","affiliation":[{"name":"Italian National Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Roma, Italy"}]},{"given":"Fabio","family":"Attorre","sequence":"additional","affiliation":[{"name":"Department of Environmental Biology, University of Roma \u201cLa Sapienza\u201d, Piazzale Aldo Moro 5, 00185 Roma, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0865-4124","authenticated-orcid":false,"given":"Andrea","family":"Taramelli","sequence":"additional","affiliation":[{"name":"Italian National Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Roma, Italy"},{"name":"IUSS-Istituto Universitario di Studi Superiori di Pavia, Palazzo del Broletto-Piazza della Vittoria 15, 27100 Pavia, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1126\/science.1259855","article-title":"Planetary boundaries: Guiding human development on a changing planet","volume":"347","author":"Steffen","year":"2015","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1038\/nature11118","article-title":"A global synthesis reveals biodiversity loss as a major driver of ecosystem change","volume":"486","author":"Hooper","year":"2012","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Theriault, J., Young, L., and Barrett, L.F. 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