{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T18:22:22Z","timestamp":1768414942465,"version":"3.49.0"},"reference-count":16,"publisher":"Wiley","license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003621","name":"Ministry of Science, ICT and Future Planning","doi-asserted-by":"publisher","award":["NRF-2017R1A2B4003258"],"award-info":[{"award-number":["NRF-2017R1A2B4003258"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003621","name":"Ministry of Science, ICT and Future Planning","doi-asserted-by":"publisher","award":["NRF-2015M1A3A3A02013416"],"award-info":[{"award-number":["NRF-2015M1A3A3A02013416"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003621","name":"Ministry of Science, ICT and Future Planning","doi-asserted-by":"publisher","award":["NRF-2017R1A2B4003258"],"award-info":[{"award-number":["NRF-2017R1A2B4003258"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003621","name":"Ministry of Science, ICT and Future Planning","doi-asserted-by":"publisher","award":["NRF-2015M1A3A3A02013416"],"award-info":[{"award-number":["NRF-2015M1A3A3A02013416"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2017R1A2B4003258"],"award-info":[{"award-number":["NRF-2017R1A2B4003258"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2015M1A3A3A02013416"],"award-info":[{"award-number":["NRF-2015M1A3A3A02013416"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Sensors"],"published-print":{"date-parts":[[2018]]},"abstract":"<jats:p>Recently, numerous studies have attempted to determine forest height using remote sensing techniques that not only have the benefits of fast data acquisition, processing, and analysis but are also cost-effective. However, if there was insufficient data to apply the latest remote sensing techniques, we need to consider many kinds of datasets as possible. In this study, we tried to determine forest height using discrete-return LiDAR data, SRTM, satellite L-band SAR data, and Optical data. We experimented with the differences between LiDAR DSM and DTM, as well as SRTM DSM and LiDAR DTM. In addition, we applied an SBAS algorithm and linear regression to the dataset. From the quantitative evaluation, the RMSE and <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> of the LiDAR-derived forest height (3.22\u2009m and 0.43, resp.) and the SRTM-derived forest height (2.90\u2009m and 0.50, resp.) were both reasonably good, especially when we consider data acquisition time differences and measurement errors in mountainous areas. Moreover, we slightly improved the RMSE and <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> from 2.90\u2009m and 0.50, respectively, to 2.75\u2009m and 0.54, respectively, by correcting the SRTM using the SBAS algorithm. Furthermore, we merged the datasets using linear regression and obtained improved forest heights with RMSE and <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> values of 2.68\u2009m and 0.56, respectively. To generate a forest height map, we used NDVI from Optical imagery and masked heights below 2\u2009m from each sensor. Thus, we excluded urban areas, \u201cbare earth surfaces,\u201d and mountain streams from each sensor\u2019s imagery. Finally, we generated a forest height map by overlapping the datasets. The results of this study indicate that each sensor has the potential for not only determining forest height but also extracting complementary forest area information. Furthermore, this study demonstrates the potential for improvement using the SBAS algorithm and linear regression.<\/jats:p>","DOI":"10.1155\/2018\/1593129","type":"journal-article","created":{"date-parts":[[2018,4,11]],"date-time":"2018-04-11T19:31:02Z","timestamp":1523475062000},"page":"1-9","source":"Crossref","is-referenced-by-count":15,"title":["Forest Canopy Height Estimation Using Multiplatform Remote Sensing Dataset"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5541-2617","authenticated-orcid":true,"given":"Won-Jin","family":"Lee","sequence":"first","affiliation":[{"name":"Earthquake and Volcano Research Division, Korea Meteorological Administration, 61 16-Gil, Yeouidaebang-ro, Dongjak-gu, Seoul 07062, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7235-3225","authenticated-orcid":true,"given":"Chang-Wook","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of Science Education, 1 Kangwondaehak-gil, Chuncheon, 24341 Gangwon, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/8076271"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.3390\/f5050992"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2009.11.002"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2017.11.017"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1029\/2009JG000997"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolind.2016.10.001"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1016\/j.foreco.2008.11.022"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2007.02.001"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/485264"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1191\/0309133303pp360ra"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2007.907602"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2002.803792"},{"issue":"i","key":"13","first-page":"309","volume":"sp-351","year":"1973","journal-title":"Third ERTS Symposium NASA"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1080\/0143116032000160499"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1155\/2009\/864108"},{"issue":"2","key":"16","first-page":"165","volume":"24","year":"2008","journal-title":"Korean Society of Remote Sensing"}],"container-title":["Journal of Sensors"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/js\/2018\/1593129.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/js\/2018\/1593129.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/js\/2018\/1593129.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,4,11]],"date-time":"2018-04-11T19:31:06Z","timestamp":1523475066000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/js\/2018\/1593129\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":16,"alternative-id":["1593129","1593129"],"URL":"https:\/\/doi.org\/10.1155\/2018\/1593129","relation":{},"ISSN":["1687-725X","1687-7268"],"issn-type":[{"value":"1687-725X","type":"print"},{"value":"1687-7268","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]}}}