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

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

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 2, 026026 (May 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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available