{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T18:00:27Z","timestamp":1776276027349,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T00:00:00Z","timestamp":1617840000000},"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>This study assessed the accuracy of land cover change (2000\u20132018) maps compiled from Landsat images with either automated digital processing or with visual interpretation for a tropical forest area in Indonesia. The accuracy assessment used a two-stage stratified random sampling involving a confusion matrix for assessing map accuracy and by estimating areas of land cover change classes and associated uncertainty. The reference data were high-resolution images from SPOT 6\/7 and high-resolution images finer than 5 m obtained from Open Foris Collect Earth. Results showed that the map derived from automated digital processing had lower accuracy (overall accuracy 73\u201377%) compared to the map based on visual interpretation (overall accuracy 80\u201384%). The automated digital processing map error was in differentiating between native forest and plantation areas. While the visual interpretation map had a higher accuracy, it did not consistently differentiate between native forest and shrub areas. Future improvement of the digital map requires more accurate differentiation between forest and plantation to better support national forest monitoring systems for sustainable forest management.<\/jats:p>","DOI":"10.3390\/rs13081446","type":"journal-article","created":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T21:27:44Z","timestamp":1617917264000},"page":"1446","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Assessing Accuracy of Land Cover Change Maps Derived from Automated Digital Processing and Visual Interpretation in Tropical Forests in Indonesia"],"prefix":"10.3390","volume":"13","author":[{"given":"Inggit Lolita","family":"Sari","sequence":"first","affiliation":[{"name":"Remote Sensing Technology and Data Center, National Institute of Aeronautics and Space of Indonesia (LAPAN), Jakarta 13710, Indonesia"},{"name":"School of Ecosystem and Forest Sciences, Faculty of Science, The University of Melbourne, Creswick, VIC 3363, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4675-0486","authenticated-orcid":false,"given":"Christopher J.","family":"Weston","sequence":"additional","affiliation":[{"name":"School of Ecosystem and Forest Sciences, Faculty of Science, The University of Melbourne, Creswick, VIC 3363, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6913-1258","authenticated-orcid":false,"given":"Glenn J.","family":"Newnham","sequence":"additional","affiliation":[{"name":"CSIRO Land and Water, Private Bag 10, Clayton South, VIC 3169, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4556-3478","authenticated-orcid":false,"given":"Liubov","family":"Volkova","sequence":"additional","affiliation":[{"name":"School of Ecosystem and Forest Sciences, Faculty of Science, The University of Melbourne, Creswick, VIC 3363, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2540","DOI":"10.1111\/gcb.12605","article-title":"Determination of tropical deforestation rates and related carbon losses from 1990 to 2010","volume":"20","author":"Achard","year":"2014","journal-title":"Glob. 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