Remote Sensing Applications and Decision Support

Unmanned aerial vehicle-based structure from motion biomass inventory estimates

[+] Author Affiliations
Emily Bedell, Evan A. Thomas

Portland State University, Department of Mechanical and Materials Engineering, Portland, Oregon, United States

Monique Leslie

The Freshwater Trust, Portland, Oregon, United States

Katie Fankhauser

Oregon Health and Science University, Department of Public Health and Preventive Medicine, Portland, Oregon, United States

Jonathan Burnett, Michael G. Wing

Oregon State University, Department of Forest Engineering, Resources, and Management, Corvallis, Oregon, United States

J. Appl. Remote Sens. 11(2), 026026 (May 31, 2017). doi:10.1117/1.JRS.11.026026
History: Received December 14, 2016; Accepted May 12, 2017
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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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© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Emily Bedell ; Monique Leslie ; Katie Fankhauser ; Jonathan Burnett ; Michael G. Wing, et al.
"Unmanned aerial vehicle-based structure from motion biomass inventory estimates", J. Appl. Remote Sens. 11(2), 026026 (May 31, 2017). ; http://dx.doi.org/10.1117/1.JRS.11.026026


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