Quantifying Forest Biomass and Genetic Contribution using Light Detection and Ranging

Abstract

The growing focus on the role of forests in carbon sequestration highlights the importance of accurately and efficiently measuring biophysical traits, such as diameter at breast height (DBH) and tree height. Understanding genetic contributions to trait variation is crucial for enhancing carbon storage through genetic improvement of forest trees. Light detection and ranging (LiDAR) has been used to estimate DBH and tree height; however, few studies have explored the heritability of these traits or assessed the accuracy of biomass increment selections based on these traits. Therefore, this study aimed to leverage LiDAR to measure DBH and tree height, estimate tree heritability, and evaluate the accuracy of timber volume selections based on these traits using 60-year-old larch as the study material. Unmanned aerial vehicle (UAV) and backpack LiDAR were compared against hand-measured values. The accuracy of DBH estimations using backpack LiDAR resulted in a root mean square error (RMSE) of 2.7 cm and a coefficient of determination of 0.67. Conversely, the accuracy achieved with UAV LiDAR was 4.0 cm in RMSE and a 0.24 coefficient of determination. The heritability of DBH was found to be higher for backpack LiDAR than for UAV LiDAR and even exceeded that of hand measurements. Comparisons of the accuracy of timber volume selections based on the measured traits demonstrated comparable performances between the backpack and UAV LiDAR. Overall, these findings underscore the potential of using LiDAR remote sensing to quantitatively measure forest tree biomass and facilitate their genetic improvement of carbon-sequestration ability based on these measurements.

Plant phenotyping relevance

LiDARによる樹木DBH・樹高・バイオマス関連形質の測定を中心に、UAVおよび背負い式LiDARを手測定と比較検証しており、森林樹木の表現型取得手法として方法的に実質的である。

Code and data availability

The supplied blocks describe LiDAR point cloud acquisition, DBH estimation, and heritability analysis, but contain no data or code availability statement, no public repository deposit, and no author-provided URL for the paper's phenotype datasets, point clouds, or analysis scripts. The only URLs present are the preprin

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