{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T18:30:43Z","timestamp":1775845843625,"version":"3.50.1"},"reference-count":137,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,18]],"date-time":"2023-06-18T00:00:00Z","timestamp":1687046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Oradea"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The assessment of changes in forest coverage is crucial for managing protected forest areas, particularly in the face of climate change. This study monitored forest cover dynamics in a 6535 ha mountain area located in north-west Romania as part of the Apuseni Natural Park from 2003 to 2019. Two approaches were used: vectorization from orthophotos and Google Earth images (in 2003, 2005, 2009, 2012, 2014, 2016, 2017, and 2019) and satellite imagery (Landsat 5 TM, 7 ETM, and 8 OLI) pre-processed to Surface Reflectance (SR) format from the same years. We employed four standard classifiers: Support Vector Machine (SVM), Random Forest (RF), Maximum Likelihood Classification (MLC), Spectral Angle Mapper (SAM), and three combined methods: Linear Spectral Unmixing (LSU) with Natural Breaks (NB), Otsu Method (OM) and SVM, to extract and classify forest areas. Our study had two objectives: 1) to accurately assess changes in forest cover over a 17-year period and 2) to determine the most efficient methods for extracting and classifying forest areas. We validated the results using performance metrics that quantify both thematic and spatial accuracy. Our results indicate a 9% loss of forest cover in the study area, representing 577 ha with an average decrease ratio of 33.9 ha\/year\u22121. Of all the methods used, SVM produced the best results (with an average score of 88% for Overall Quality (OQ)), followed by RF (with a mean value of 86% for OQ).<\/jats:p>","DOI":"10.3390\/rs15123168","type":"journal-article","created":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T01:59:51Z","timestamp":1687139991000},"page":"3168","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Monitoring Forest Cover Dynamics Using Orthophotos and Satellite Imagery"],"prefix":"10.3390","volume":"15","author":[{"given":"Lucian","family":"Blaga","sequence":"first","affiliation":[{"name":"Department of Geography, Tourism and Territorial Planning, Faculty of Geography, Tourism and Sport, The Territorial Studies and Analyses Centre (CSAT), University of Oradea, 410087 Oradea, Romania"}]},{"given":"Dorina Camelia","family":"Ilie\u0219","sequence":"additional","affiliation":[{"name":"Department of Geography, Tourism and Territorial Planning, Faculty of Geography, Tourism and Sport, The Territorial Studies and Analyses Centre (CSAT), University of Oradea, 410087 Oradea, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1712-4926","authenticated-orcid":false,"given":"Jan A.","family":"Wendt","sequence":"additional","affiliation":[{"name":"Institute of Socio-Economic Geography and Spatial Management, University of Gdansk, 80309 Gdansk, Poland"}]},{"given":"Ioan","family":"Rus","sequence":"additional","affiliation":[{"name":"Faculty of Geography, Babes-Bolyai University, 400006 Cluj-Napoca, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4041-2918","authenticated-orcid":false,"given":"Kai","family":"Zhu","sequence":"additional","affiliation":[{"name":"Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7880-9860","authenticated-orcid":false,"given":"L\u00f3r\u00e1nt D\u00e9nes","family":"D\u00e1vid","sequence":"additional","affiliation":[{"name":"Faculty of Economics and Business, John von Neumann University, 6000 Kecskemet, Hungary"},{"name":"Institute of Rural Development and Sustainable Economy, Hungarian University of Agriculture and Life Sciences, 2100 Godollo, Hungary"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.foreco.2018.11.033","article-title":"Impacts of forests and forestation on hydrological services in the Andes: A systematic review","volume":"433","author":"Bonnesoeur","year":"2019","journal-title":"For. 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