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DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 1729 | - |
dc.citation.number | 6-1 | - |
dc.citation.startPage | 1719 | - |
dc.citation.title | Korean Journal of Remote Sensing | - |
dc.citation.volume | 37 | - |
dc.contributor.author | Lee, Jaese | - |
dc.contributor.author | Kang, Yoojin | - |
dc.contributor.author | Son, Bokyung | - |
dc.contributor.author | Im, Jungho | - |
dc.contributor.author | Jang, Keunchang | - |
dc.date.accessioned | 2023-12-21T14:50:02Z | - |
dc.date.available | 2023-12-21T14:50:02Z | - |
dc.date.created | 2021-12-28 | - |
dc.date.issued | 2021-12 | - |
dc.description.abstract | Leaf area index (LAI) provides valuable information necessary for sustainable and effective management of forests. Although global high resolution LAI data are provided by European Space Agency using Sentinel-2 satellite images, they have not considered forest characteristics in model development and have not been evaluated for various forest ecosystems in South Korea. In this study, we proposed a LAI estimation model combining machine learning and the PROSAIL radiative transfer model using Sentinel-2 satellite data over a local forest area in South Korea. LAI-2200C was used to measure in situ LAI data. The proposed LAI estimation model was compared to the existing Sentinel-2 LAI product. The results showed that the proposed model outperformed the existing Sentinel-2 LAI product, yielding a difference of bias ∼ 0.97 and a difference of root-mean-square-error - 0.81 on average, respectively, which improved the underestimation of the existing product. The proposed LAI estimation model provided promising results, implying its use for effective LAI estimation over forests in South Korea. © 2021 Korean Society of Remote Sensing. All Rights Reserved. | - |
dc.identifier.bibliographicCitation | Korean Journal of Remote Sensing, v.37, no.6-1, pp.1719 - 1729 | - |
dc.identifier.doi | 10.7780/kjrs.2021.37.6.1.19 | - |
dc.identifier.issn | 1225-6161 | - |
dc.identifier.scopusid | 2-s2.0-85127113670 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/55658 | - |
dc.language | 한국어 | - |
dc.publisher | 대한원격탐사학회 | - |
dc.title.alternative | 광학위성영상을 이용한 기계학습/PROSAIL 모델 기반 엽면적지수 추정 | - |
dc.title | Estimation of Leaf Area Index Based on Machine Learning/PROSAIL Using Optical Satellite Imagery | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.identifier.kciid | ART002795453 | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | LAI | - |
dc.subject.keywordAuthor | random forest | - |
dc.subject.keywordAuthor | PROSAIL | - |
dc.subject.keywordAuthor | Sentinel-2 | - |
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