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Precision Forestry: Using LiDAR to Optimize Row Thinning in Pinus taeda (L.) Plantations

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dc.contributor.authorPlatt, Erik Jamesen
dc.contributor.committeechairCarter, David Robert Jamesen
dc.contributor.committeememberCoates, Thomas Adamen
dc.contributor.committeememberAust, Wallace M.en
dc.contributor.departmentForest Resources and Environmental Conservationen
dc.date.accessioned2024-01-25T09:00:18Zen
dc.date.available2024-01-25T09:00:18Zen
dc.date.issued2024-01-24en
dc.description.abstractPrecision forestry uses information collecting techniques to detect within stand variability and inform sub-stand management treatments, such as stand thinning guidelines. This study uses LiDAR to assess individual-tree stem volumes in Pinus taeda L. plantations in the southeast US. Currently, starting rows in commercial row thinning operations are arbitrarily selected, but the study used LiDAR collected stem volume data to inform starting row selection. Three study sites were measured to provide evidence of between-row volume variability. The primary study site was set up in an alternative treatment design. Two treatments were tested: a fourth row removal scenario which removed the most volume of the four possible scenarios versus a fourth row removal which targeted the least amount of volume removed. Between-row volume variability was shown in all study sites and LiDAR data accurately assessed volume in the primary study site. The primary site saw the two blocks homogenized by their thinning treatments, demonstrating the ability to increase or decrease residual volumes using targeted row selection . Targeted row removal retained more volume and larger trees and may lead to higher harvest yields and shorter rotations. Timber managers across the globe are increasingly using remote sensing to inventory stands, thus LiDAR-informed volume acquisition may be an additional application to increase the efficiency and productivity of forests.en
dc.description.abstractgeneralLoblolly pine (Pinus taeda L.) is the most important commercial timber species in the southeastern US. Commonly grown in plantations and planted in rows, these forest tracts often receive at least one "commercial thinning" (i.e. profit yielding thinning) between planting and harvesting. This thinning typically removes every fourth row as well as undesirable individuals in residual rows, thus providing residual trees more space and resources to grow. Due to the high costs of manually inventorying these stands, row removal selection is arbitrary and may fail to fully address stem volume variability among rows. LiDAR (light detection and ranging) is a technology that creates 3D models from sent and returned light signals. This technology was aerially employed to inventory a P. taeda plantation in the Virginia Piedmont and models were developed to measure stem volumes from collected data. These stem volumes were consolidated into row volumes and were used to inform row selection during commercial row thinning operations. Targeted volume removal showed the ability of LiDAR application to alter residual stand volumes. Through low volume removal, residual volumes were increased. Additionally, more large trees were retained. Large trees are especially important as they have a competitive advantage in growth response post-thinning. These results have the potential to increase harvest yields and therefore pine plantation productivity and efficiency.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:38903en
dc.identifier.urihttps://hdl.handle.net/10919/117674en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectSilvicultureen
dc.subjectRemote Sensingen
dc.subjectVariabilityen
dc.subjectCommercialen
dc.subjectOperator Selecten
dc.titlePrecision Forestry: Using LiDAR to Optimize Row Thinning in Pinus taeda (L.) Plantationsen
dc.typeThesisen
thesis.degree.disciplineForestryen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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