Unmanned aerial vehicle-based structure from motion biomass inventory estimates

Emily Bedell; Monique Leslie; Katie Fankhauser; Jonathan Burnett; Michael G. Wing; Evan A. Thomas

doi:10.1117/1.JRS.11.026026

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31 May 2017 Unmanned aerial vehicle-based structure from motion biomass inventory estimates

Emily Bedell, Monique Leslie, Katie Fankhauser, Jonathan Burnett, Michael G. Wing, Evan A. Thomas

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J. of Applied Remote Sensing, 11(2), 026026 (2017). https://doi.org/10.1117/1.JRS.11.026026

Abstract

Riparian vegetation restoration efforts require cost-effective, accurate, and replicable impact assessments. We present a method to use an unmanned aerial vehicle (UAV) equipped with a GoPro digital camera to collect photogrammetric data of a 0.8-ha riparian restoration. A three-dimensional point cloud was created from the photos using “structure from motion” techniques. The point cloud was analyzed and compared to traditional, ground-based monitoring techniques. Ground-truth data were collected on 6.3% of the study site and averaged across the entire site to report stem heights in stems/ha in three height classes. The project site was divided into four analysis sections, one for derivation of parameters used in the UAV data analysis and the remaining three sections reserved for method validation. Comparing the ground-truth data to the UAV generated data produced an overall error of 21.6% and indicated an R2 value of 0.98. A Bland–Altman analysis indicated a 95% probability that the UAV stems/section result will be within 61 stems/section of the ground-truth data. The ground-truth data are reported with an 80% confidence interval of ±1032 stems/ha; thus, the UAV was able to estimate stems well within this confidence interval.

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Emily Bedell, Monique Leslie, Katie Fankhauser, Jonathan Burnett, Michael G. Wing, and Evan A. Thomas "Unmanned aerial vehicle-based structure from motion biomass inventory estimates," Journal of Applied Remote Sensing 11(2), 026026 (31 May 2017). https://doi.org/10.1117/1.JRS.11.026026

Received: 14 December 2016; Accepted: 12 May 2017; Published: 31 May 2017

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