Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.number | 17 | - |
dc.citation.startPage | 4361 | - |
dc.citation.title | REMOTE SENSING | - |
dc.citation.volume | 14 | - |
dc.contributor.author | Wang, Man | - |
dc.contributor.author | Im, Jungho | - |
dc.contributor.author | Zhao, Yinghui | - |
dc.contributor.author | Zhen, Zhen | - |
dc.date.accessioned | 2023-12-21T13:41:28Z | - |
dc.date.available | 2023-12-21T13:41:28Z | - |
dc.date.created | 2022-09-19 | - |
dc.date.issued | 2022-09 | - |
dc.description.abstract | Individual-tree aboveground biomass (AGB) estimation is vital for precision forestry and still worth exploring using multi-platform LiDAR data for high accuracy and efficiency. Based on the unmanned aerial vehicle and terrestrial LiDAR data, this study explores the feasibility of the individual tree AGB estimation of Changbai larch (Larix olgensis Henry) of eight plots from three different regions in Maoershan Forest Farm of Heilongjiang, China, using nonlinear mixed effect model with hierarchical Bayesian approach. Results showed that the fused LiDAR data estimated the individual tree parameters (i.e., diameter at breast height (DBH), tree height (TH), and crown projection area (CPA)) with high accuracies (all R-2 > 0.9 and relatively low RMSE and rRMSE) using region-based hierarchical cross-section analysis (RHCSA) algorithm. Considering regions as random variables, the nonlinear mixed-effects AGB model with three predictor variables (i.e., DBH, TH, and CPA) performed better than its corresponding nonlinear model. In addition, the hierarchical Bayesian method provided better model-fitting performances and more stable parameter estimates than the classical method (i.e., nonlinear mixed-effect model), especially for small sample sizes (e.g., <50). This methodology (i.e., multi-platform LiDAR data and the hierarchical Bayesian method) provides a potential solution for non-destructive individual-tree AGB modeling with small sample size and high accuracy in both forestry and remote sensing communities. | - |
dc.identifier.bibliographicCitation | REMOTE SENSING, v.14, no.17, pp.4361 | - |
dc.identifier.doi | 10.3390/rs14174361 | - |
dc.identifier.issn | 2072-4292 | - |
dc.identifier.scopusid | 2-s2.0-85137942731 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/59315 | - |
dc.identifier.wosid | 000851702300001 | - |
dc.language | 영어 | - |
dc.publisher | MDPI | - |
dc.title | Multi-Platform LiDAR for Non-Destructive Individual Aboveground Biomass Estimation for Changbai Larch (Larix olgensis Henry) Using a Hierarchical Bayesian Approach | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology | - |
dc.relation.journalResearchArea | Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | hierarchical Bayesian | - |
dc.subject.keywordAuthor | UAV-LiDAR | - |
dc.subject.keywordAuthor | TLS | - |
dc.subject.keywordAuthor | AGB | - |
dc.subject.keywordAuthor | mixed-effect model | - |
dc.subject.keywordPlus | RUN LENGTH CONTROL | - |
dc.subject.keywordPlus | AIRBORNE LIDAR | - |
dc.subject.keywordPlus | FOREST BIOMASS | - |
dc.subject.keywordPlus | ALLOMETRIC EQUATIONS | - |
dc.subject.keywordPlus | EFFECTS MODELS | - |
dc.subject.keywordPlus | TREE DETECTION | - |
dc.subject.keywordPlus | HEIGHT | - |
dc.subject.keywordPlus | TERRESTRIAL | - |
dc.subject.keywordPlus | SEGMENTATION | - |
dc.subject.keywordPlus | CANOPY | - |
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Tel : 052-217-1404 / Email : scholarworks@unist.ac.kr
Copyright (c) 2023 by UNIST LIBRARY. All rights reserved.
ScholarWorks@UNIST was established as an OAK Project for the National Library of Korea.