{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T05:37:32Z","timestamp":1774935452299,"version":"3.50.1"},"reference-count":73,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T00:00:00Z","timestamp":1675728000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Innovation in Biology, Agri-Food and Forest Systems (DIBAF), University of Tuscia"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Afforestation processes, natural and anthropogenic, involve the conversion of other land uses to forest, and they represent one of the most important land use transformations, influencing numerous ecosystem services. Although remotely sensed data are commonly used to monitor forest disturbance, only a few reported studies have used these data to monitor afforestation. The objectives of this study were two fold: (1) to develop and illustrate a method that exploits the 1985\u20132019 Landsat time series for predicting afforestation areas at 30 m resolution at the national scale, and (2) to estimate afforestation areas statistically rigorously within Italian administrative regions and land elevation classes. We used a Landsat best-available-pixel time series (1985\u20132019) to calculate a set of temporal predictors that, together with the random forests prediction technique, facilitated construction of a map of afforested areas in Italy. Then, the map was used to guide selection of an estimation sample dataset which, after a complex photointerpretation phase, was used to estimate afforestation areas and associated confidence intervals. The classification approach achieved an accuracy of 87%. At the national level, the afforestation area between 1985 and 2019 covered 2.8 \u00b1 0.2 million ha, corresponding to a potential C-sequestration of 200 million t. The administrative region with the largest afforested area was Sardinia, with 260,670 \u00b1 58,522 ha, while the smallest area of 28,644 \u00b1 12,114 ha was in Valle d\u2019Aosta. Considering elevation classes of 200 m, the greatest afforestation area was between 400 and 600 m above sea level, where it was 549,497 \u00b1 84,979 ha. Our results help to understand the afforestation process in Italy between 1985 and 2019 in relation to geographical location and altitude, and they could be the basis of further studies on the species composition of afforestation areas and land management conditions.<\/jats:p>","DOI":"10.3390\/rs15040923","type":"journal-article","created":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T05:37:31Z","timestamp":1675834651000},"page":"923","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Estimating Afforestation Area Using Landsat Time Series and Photointerpreted Datasets"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5460-1245","authenticated-orcid":false,"given":"Alice","family":"Cavalli","sequence":"first","affiliation":[{"name":"Department of Innovation in Biology, Agri-Food and Forest Systems (DIBAF), University of Tuscia, Via San Camillo de Lellis SNC, 01100 Viterbo, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6991-0289","authenticated-orcid":false,"given":"Saverio","family":"Francini","sequence":"additional","affiliation":[{"name":"Fondazione per il Futuro delle Citt\u00e0, 50133 Firenze, Italy"},{"name":"Department of Agricultural, Food and Forestry Systems, University of Florence, 50145 Firenze, Italy"}]},{"given":"Ronald E.","family":"McRoberts","sequence":"additional","affiliation":[{"name":"Department of Forest Resources, University of Minnesota, Saint Paul, MN 55108, USA"}]},{"given":"Valentina","family":"Falanga","sequence":"additional","affiliation":[{"name":"Department of Biosciences and Territory, University of Molise, C\/da Fonte Lappone, 86090 Pesche, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7283-116X","authenticated-orcid":false,"given":"Luca","family":"Congedo","sequence":"additional","affiliation":[{"name":"Italian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Rome, Italy"}]},{"given":"Paolo","family":"De Fioravante","sequence":"additional","affiliation":[{"name":"Italian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4325-951X","authenticated-orcid":false,"given":"Mauro","family":"Maesano","sequence":"additional","affiliation":[{"name":"Department of Innovation in Biology, Agri-Food and Forest Systems (DIBAF), University of Tuscia, Via San Camillo de Lellis SNC, 01100 Viterbo, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3415-6105","authenticated-orcid":false,"given":"Michele","family":"Munaf\u00f2","sequence":"additional","affiliation":[{"name":"Italian Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0669-5726","authenticated-orcid":false,"given":"Gherardo","family":"Chirici","sequence":"additional","affiliation":[{"name":"Fondazione per il Futuro delle Citt\u00e0, 50133 Firenze, Italy"},{"name":"Department of Agricultural, Food and Forestry Systems, University of Florence, 50145 Firenze, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0357-4360","authenticated-orcid":false,"given":"Giuseppe","family":"Scarascia Mugnozza","sequence":"additional","affiliation":[{"name":"Department of Innovation in Biology, Agri-Food and Forest Systems (DIBAF), University of Tuscia, Via San Camillo de Lellis SNC, 01100 Viterbo, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"FAO (2003). 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