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Remote Sensing
Volume 12
Issue 24
10.3390/rs12244039
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Article

An Assessment of High-Density UAV Point Clouds for the Measurement of Young Forestry Trials

by
Robin J. L. Hartley
1,*,
Ellen Mae Leonardo
1,
Peter Massam
1,
Michael S. Watt
2,
Honey Jane Estarija
1,
Liam Wright
1,
Nathanael Melia
1,3 and
Grant D. Pearse
1
1
Scion, 49 Sala Street, Private Bag 3020, Rotorua 3046, New Zealand
2
Scion, P.O. Box 29237, Fendalton, Christchurch 8041, New Zealand
3
School of Geography, Environment and Earth Sciences, Victoria University of Wellington, Kelburn, Wellington 6012, New Zealand
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(24), 4039; https://doi.org/10.3390/rs12244039
Submission received: 10 November 2020 / Revised: 7 December 2020 / Accepted: 7 December 2020 / Published: 10 December 2020
(This article belongs to the Special Issue Individual Tree Detection and Characterisation from UAV Data)
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Abstract

The measurement of forestry trials is a costly and time-consuming process. Over the past few years, unmanned aerial vehicles (UAVs) have provided some significant developments that could improve cost and time efficiencies. However, little research has examined the accuracies of these technologies for measuring young trees. This study compared the data captured by a UAV laser scanning system (ULS), and UAV structure from motion photogrammetry (SfM), with traditional field-measured heights in a series of forestry trials in the central North Island of New Zealand. Data were captured from UAVs, and then processed into point clouds, from which heights were derived and compared to field measurements. The results show that predictions from both ULS and SfM were very strongly correlated to tree heights (R2 = 0.99, RMSE = 5.91%, and R2 = 0.94, RMSE = 18.5%, respectively) but that the height underprediction was markedly lower for ULS than SfM (Mean Bias Error = 0.05 vs. 0.38 m). Integration of a ULS DTM to the SfM made a minor improvement in precision (R2 = 0.95, RMSE = 16.5%). Through plotting error against tree height, we identified a minimum threshold of 1 m, under which the accuracy of height measurements using ULS and SfM significantly declines. Our results show that SfM and ULS data collected from UAV remote sensing can be used to accurately measure height in young forestry trials. It is hoped that this study will give foresters and tree breeders the confidence to start to operationalise this technology for monitoring trials.
Keywords: UAV; forestry trials; ULS; structure-from-motion; lidar; small trees; tree height UAV; forestry trials; ULS; structure-from-motion; lidar; small trees; tree height
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MDPI and ACS Style

Hartley, R.J.L.; Leonardo, E.M.; Massam, P.; Watt, M.S.; Estarija, H.J.; Wright, L.; Melia, N.; Pearse, G.D. An Assessment of High-Density UAV Point Clouds for the Measurement of Young Forestry Trials. Remote Sens. 2020, 12, 4039. https://doi.org/10.3390/rs12244039

AMA Style

Hartley RJL, Leonardo EM, Massam P, Watt MS, Estarija HJ, Wright L, Melia N, Pearse GD. An Assessment of High-Density UAV Point Clouds for the Measurement of Young Forestry Trials. Remote Sensing. 2020; 12(24):4039. https://doi.org/10.3390/rs12244039

Chicago/Turabian Style

Hartley, Robin J. L., Ellen Mae Leonardo, Peter Massam, Michael S. Watt, Honey Jane Estarija, Liam Wright, Nathanael Melia, and Grant D. Pearse. 2020. "An Assessment of High-Density UAV Point Clouds for the Measurement of Young Forestry Trials" Remote Sensing 12, no. 24: 4039. https://doi.org/10.3390/rs12244039

APA Style

Hartley, R. J. L., Leonardo, E. M., Massam, P., Watt, M. S., Estarija, H. J., Wright, L., Melia, N., & Pearse, G. D. (2020). An Assessment of High-Density UAV Point Clouds for the Measurement of Young Forestry Trials. Remote Sensing, 12(24), 4039. https://doi.org/10.3390/rs12244039

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