Show simple item record

dc.contributorVirginia Tech. Department of Forest Resources and Environmental Conservation
dc.contributor.authorShrestha, Rupesh
dc.contributor.authorWynne, Randolph H.
dc.coverage.spatialOklahoma
dc.date.accessioned2015-09-27T23:48:13Z
dc.date.available2015-09-27T23:48:13Z
dc.date.issued2012-02-15
dc.identifier.citationShrestha, R., & Wynne, R. H. (2012). Estimating Biophysical Parameters of Individual Trees in an Urban Environment Using Small Footprint Discrete-Return Imaging Lidar. Remote Sensing, 4(2), 484-508.
dc.identifier.issn2072-4292
dc.identifier.urihttp://hdl.handle.net/10919/56649
dc.description.abstractQuantification of biophysical parameters of urban trees is important for urban planning, and for assessing carbon sequestration and ecosystem services. Airborne lidar has been used extensively in recent years to estimate biophysical parameters of trees in forested ecosystems. However, similar studies are largely lacking for individual trees in urban landscapes. Prediction models to estimate biophysical parameters such as height, crown area, diameter at breast height, and biomass for over two thousand individual trees were developed using best subsets multiple linear regression for a study area in central Oklahoma, USA using point cloud distributional metrics from an Optech ALTM 2050 lidar system. A high level of accuracy was attained for estimating individual tree height (R2 = 0.89), dbh (R2 = 0.82), crown diameter (R2 = 0.90), and biomass (R2 = 0.67) using lidar-based metrics for pooled data of all tree species. More variance was explained in species-specific estimates of biomass (R2 = 0.68 for Juniperus virginiana to 0.84 for Ulmus parviflora) than in estimates from broadleaf deciduous (R2 = 0.63) and coniferous (R2 = 0.45) taxonomic groups-or the data set analysed as a whole (R2 = 0.67). The metric crown area performed particularly well for most of the species-specific biomass equations, which suggests that tree crowns should be delineated accurately, whether manually or using automatic individual tree detection algorithms, to obtain a good estimation of biomass using lidar-based metrics.
dc.description.sponsorshipUnited States. National Aeronautics and Space Administration – NNS06AA57G
dc.description.sponsorshipNational Institute of Food and Agriculture (U.S.) - NIFA PINEMAP Project
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherMDPI
dc.rightsCreative Commons Attribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBiomass
dc.subjectCarbon
dc.subjectRemote Sensing
dc.subjectUrban forestry
dc.subjectAirborne laser scanning
dc.titleEstimating biophysical parameters of individual trees in an urban environment using small footprint discrete-return imaging lidaren_US
dc.typeArticle - Refereed
dc.date.accessed2014-04-08
dc.title.serialRemote Sensing
dc.identifier.doihttps://doi.org/10.3390/rs4020484
dc.type.dcmitypeText


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Creative Commons Attribution 4.0 International
License: Creative Commons Attribution 4.0 International