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

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Date
2024-01-24
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Virginia Tech
Abstract

Precision 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.

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Keywords
Silviculture, Remote Sensing, Variability, Commercial, Operator Select
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