Full metadata record
DC Field | Value | Language |
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dc.citation.number | 16 | - |
dc.citation.startPage | 1906 | - |
dc.citation.title | REMOTE SENSING | - |
dc.citation.volume | 11 | - |
dc.contributor.author | Li, Siqi | - |
dc.contributor.author | Quackenbush, Lindi J. | - |
dc.contributor.author | Im, Jungho | - |
dc.date.accessioned | 2023-12-21T18:48:34Z | - |
dc.date.available | 2023-12-21T18:48:34Z | - |
dc.date.created | 2019-10-01 | - |
dc.date.issued | 2019-08 | - |
dc.description.abstract | Accurately estimating aboveground biomass (AGB) is important in many applications, including monitoring carbon stocks, investigating deforestation and forest degradation, and designing sustainable forest management strategies. Although lidar provides critical three-dimensional forest structure information for estimating AGB, acquiring comprehensive lidar coverage is often cost prohibitive. This research focused on developing a lidar sampling framework to support AGB estimation from Landsat images. Two sampling strategies, systematic and classification-based, were tested and compared. The proposed strategies were implemented over a temperate forest study site in northern New York State and the processes were then validated at a similar site located in central New York State. Our results demonstrated that while the inclusion of lidar data using systematic or classification-based sampling supports AGB estimation, the systematic sampling selection method was highly dependent on site conditions and had higher accuracy variability. Of the 12 systematic sampling plans, R-2 values ranged from 0.14 to 0.41 and plot root mean square error (RMSE) ranged from 84.2 to 93.9 Mg ha(-1). The classification-based sampling outperformed 75% of the systematic sampling strategies at the primary site with R-2 of 0.26 and RMSE of 70.1 Mg ha(-1). The classification-based lidar sampling strategy was relatively easy to apply and was readily transferable to a new study site. Adopting this method at the validation site, the classification-based sampling also worked effectively, with an R-2 of 0.40 and an RMSE of 108.2 Mg ha(-1) compared to the full lidar coverage model with an R-2 of 0.58 and an RMSE of 96.0 Mg ha(-1). This study evaluated different lidar sample selection methods to identify an efficient and effective approach to reduce the volume and cost of lidar acquisitions. The forest type classification-based sampling method described in this study could facilitate cost-effective lidar data collection in future studies. | - |
dc.identifier.bibliographicCitation | REMOTE SENSING, v.11, no.16, pp.1906 | - |
dc.identifier.doi | 10.3390/rs11161906 | - |
dc.identifier.issn | 2072-4292 | - |
dc.identifier.scopusid | 2-s2.0-85071559197 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/27844 | - |
dc.identifier.url | https://www.mdpi.com/2072-4292/11/16/1906 | - |
dc.identifier.wosid | 000484387600069 | - |
dc.language | 영어 | - |
dc.publisher | MDPI | - |
dc.title | Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat Imagery | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.relation.journalWebOfScienceCategory | Remote Sensing | - |
dc.relation.journalResearchArea | Remote Sensing | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | systematic sampling | - |
dc.subject.keywordAuthor | classification-based sampling | - |
dc.subject.keywordAuthor | forest types | - |
dc.subject.keywordAuthor | data fusion | - |
dc.subject.keywordAuthor | regression | - |
dc.subject.keywordAuthor | random forest | - |
dc.subject.keywordPlus | MODEL-ASSISTED ESTIMATION | - |
dc.subject.keywordPlus | INVENTORY DATA | - |
dc.subject.keywordPlus | TIME-SERIES | - |
dc.subject.keywordPlus | STEM VOLUME | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | INTEGRATION | - |
dc.subject.keywordPlus | HEIGHT | - |
dc.subject.keywordPlus | COVER | - |
dc.subject.keywordPlus | AREA | - |
dc.subject.keywordPlus | COMBINATIONS | - |
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