Predicting Tree-Level Diameter and Volume for Radiata Pine Using UAV LiDAR-Derived Metrics Across a National Trial Series in New Zealand
The rapid development of UAV-LiDAR and data processing capabilities is likely to enable accurate individual-tree inventories in the near future, requiring few on-ground calibration measurements. Using data collected from 20 radiata pine trials dispersed across New Zealand, the objective of this stud...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/8/1456 |
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| Summary: | The rapid development of UAV-LiDAR and data processing capabilities is likely to enable accurate individual-tree inventories in the near future, requiring few on-ground calibration measurements. Using data collected from 20 radiata pine trials dispersed across New Zealand, the objective of this study was to determine the accuracy of high-density UAV-LiDAR for the prediction of tree diameter and volume, under a range of data calibration scenarios. Using all measurements for the calibration (a range of 335–4703 tree measurements across the 20 sites), accurate random forest models for each of the 20 sites were created from a diverse range of LiDAR metrics that characterised the horizontal and vertical structures of the canopy. Averaged across the 20 sites, predictions had a mean <i>R</i><sup>2</sup> and relative RMSE (rRMSE) of, respectively, 0.713 and 9.699% for the tree diameter and 0.746 and 19.57% for the tree volume. Reductions in the numbers of calibration trees per trial had little effect on model accuracy until only 300 trees/site were used; however, accurate, unbiased predictions were still possible using as few as 100 trees/site. More generally, applicable random forest models for both tree dimensions were constructed by collating all of the data and tested using leave-one-site-out cross-validation to determine the accuracy of the model predictions when calibration measurements were not available. The predictions using this approach were reasonable but less accurate and more biased than with the use of calibration data, with a mean <i>R</i><sup>2</sup> and rRMSE of, respectively, 0.631 and 15.12% for the tree diameter and 0.631 and 35.6% for the volume. Our research aims to facilitate the transition from a plot-based to tree-level inventory in plantation forests and contribute to the future development of a generalised model that could accurately predict tree dimensions from UAV-LiDAR, relying on minimal field measurements. |
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| ISSN: | 2072-4292 |