{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T15:36:23Z","timestamp":1766158583882,"version":"build-2065373602"},"reference-count":58,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T00:00:00Z","timestamp":1602720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF\u20102018R1D1A1B07041203"],"award-info":[{"award-number":["NRF\u20102018R1D1A1B07041203"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003719","name":"Korea Aerospace Research Institute","doi-asserted-by":"publisher","award":["Development of precision analysis technology for forest change using satellite images and machine learning"],"award-info":[{"award-number":["Development of precision analysis technology for forest change using satellite images and machine learning"]}],"id":[{"id":"10.13039\/501100003719","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Human-induced deforestation has a major impact on forest ecosystems and therefore its detection and analysis methods should be improved. This study classified landscape affected by human-induced deforestation efficiently using high-resolution remote sensing and deep-learning. The SegNet and U-Net algorithms were selected for application with high-resolution remote sensing data obtained by the Kompsat-3 satellite. Land and forest cover maps were used as base data to construct accurate deep-learning datasets of deforested areas at high spatial resolution, and digital maps and a softwood database were used as reference data. Sites were classified into forest and non-forest areas, and a total of 13 areas (2 forest and 11 non-forest) were selected for analysis. Overall, U-Net was more accurate than SegNet (74.8% vs. 63.3%). The U-Net algorithm was about 11.5% more accurate than the SegNet algorithm, although SegNet performed better for the hardwood and bare land classes. The SegNet algorithm misclassified many forest areas, but no non-forest area. There was reduced accuracy of the U-Net algorithm due to misclassification among sub-items, but U-Net performed very well at the forest\/non-forest area classification level, with 98.4% accuracy for forest areas and 88.5% for non-forest areas. Thus, deep-learning modeling has great potential for estimating human-induced deforestation in mountain areas. The findings of this study will contribute to more efficient monitoring of damaged mountain forests and the determination of policy priorities for mountain area restoration.<\/jats:p>","DOI":"10.3390\/rs12203372","type":"journal-article","created":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T09:02:03Z","timestamp":1602752523000},"page":"3372","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Classification of Landscape Affected by Deforestation Using High-Resolution Remote Sensing Data and Deep-Learning Techniques"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9048-0850","authenticated-orcid":false,"given":"Seong-Hyeok","family":"Lee","sequence":"first","affiliation":[{"name":"Center for Environmental Data Strategy, Korea Environment Institute, Sejong 30147, Korea"}]},{"given":"Kuk-Jin","family":"Han","sequence":"additional","affiliation":[{"name":"Center for Environmental Data Strategy, Korea Environment Institute, Sejong 30147, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3543-7416","authenticated-orcid":false,"given":"Kwon","family":"Lee","sequence":"additional","affiliation":[{"name":"MindForge, Seoul 08377, Korea"}]},{"given":"Kwang-Jae","family":"Lee","sequence":"additional","affiliation":[{"name":"Korea Aerospace Research Institute, Daejeon 34133, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1902-4374","authenticated-orcid":false,"given":"Kwan-Young","family":"Oh","sequence":"additional","affiliation":[{"name":"Korea Aerospace Research Institute, Daejeon 34133, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1226-3460","authenticated-orcid":false,"given":"Moung-Jin","family":"Lee","sequence":"additional","affiliation":[{"name":"Center for Environmental Data Strategy, Korea Environment Institute, Sejong 30147, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,15]]},"reference":[{"key":"ref_1","unstructured":"(2020, June 17). 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