{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:35:34Z","timestamp":1776274534289,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,6,25]],"date-time":"2020-06-25T00:00:00Z","timestamp":1593043200000},"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>The most recent forest-type map of the Korean Peninsula was produced in 1910. That of South Korea alone was produced since 1972; however, the forest type information of North Korea, which is an inaccessible region, is not known due to the separation after the Korean War. In this study, we developed a model to classify the five dominant tree species in North Korea (Korean red pine, Korean pine, Japanese larch, needle fir, and Oak) using satellite data and machine-learning techniques. The model was applied to the Gwangneung Forest area in South Korea; the Mt. Baekdu area of China, which borders North Korea; and to Goseong-gun, at the border of South Korea and North Korea, to evaluate the model\u2019s applicability to North Korea. Eighty-three percent accuracy was achieved in the classification of the Gwangneung Forest area. In classifying forest types in the Mt. Baekdu area and Goseong-gun, even higher accuracies of 91% and 90% were achieved, respectively. These results confirm the model\u2019s regional applicability. To expand the model for application to North Korea, a new model was developed by integrating training data from the three study areas. The integrated model\u2019s classification of forest types in Goseong-gun (South Korea) was relatively accurate (80%); thus, the model was utilized to produce a map of the predicted dominant tree species in Goseong-gun (North Korea).<\/jats:p>","DOI":"10.3390\/rs12122049","type":"journal-article","created":{"date-parts":[[2020,6,25]],"date-time":"2020-06-25T10:36:54Z","timestamp":1593081414000},"page":"2049","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Machine Learning for Tree Species Classification Using Sentinel-2 Spectral Information, Crown Texture, and Environmental Variables"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9026-5763","authenticated-orcid":false,"given":"Joongbin","family":"Lim","sequence":"first","affiliation":[{"name":"Inter-Korean Forest Research Team, Division of Global Forestry, Department of Forest Policy and Economics, National Institute of Forest Science, Seoul 02455, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyoung-Min","family":"Kim","sequence":"additional","affiliation":[{"name":"Inter-Korean Forest Research Team, Division of Global Forestry, Department of Forest Policy and Economics, National Institute of Forest Science, Seoul 02455, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eun-Hee","family":"Kim","sequence":"additional","affiliation":[{"name":"Inter-Korean Forest Research Team, Division of Global Forestry, Department of Forest Policy and Economics, National Institute of Forest Science, Seoul 02455, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8919-2624","authenticated-orcid":false,"given":"Ri","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Geography, Yanbian University, Yanji 133002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,25]]},"reference":[{"key":"ref_1","unstructured":"Kim, K.M., and Lee, S.H. 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