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Open AccessArticle

Measuring Vegetation Height in Linear Disturbances in the Boreal Forest with UAV Photogrammetry

Department of Geography, University of Calgary, Calgary, AB T2N 1N4, Canada
Department of Earth and Environment, Boston University, 685 Commonwealth Avenue, Boston, MA 02215, USA
Northern Forestry Centre, 5320 122 Street Northwest, Edmonton, AB T6H 3S5, Canada
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(12), 1257;
Received: 16 October 2017 / Revised: 28 November 2017 / Accepted: 29 November 2017 / Published: 3 December 2017
(This article belongs to the Special Issue Remote Sensing from Unmanned Aerial Vehicles (UAVs))
Monitoring vegetation recovery typically requires ground measurements of vegetation height, which is labor-intensive and time-consuming. Recently, unmanned aerial vehicles (UAVs) have shown great promise for characterizing vegetation in a cost-efficient way, but the literature on specific methods and cost savings is scant. In this study, we surveyed vegetation height on seismic lines in Alberta’s Boreal Forest using a point-intercept sampling strategy, and compared them to height estimates derived from UAV-based photogrammetric point clouds. In order to derive UAV-based vegetation height, we tested three different approaches to estimate terrain elevation: (1) UAV_RTK, where photogrammetric point clouds were normalized using terrain measurements obtained from a real-time kinematic global navigation satellite system (RTK GNSS) surveys; (2) UAV_LiDAR, where photogrammetric data were normalized using pre-existing LiDAR (Light Detection and Ranging) data; and (3) UAV_UAV, where UAV photogrammetry data were used alone. Comparisons were done at two scales: point level (n = 1743) and site level (n = 30). The point-level root-mean-square errors (RMSEs) of UAV_RTK, UAV_LiDAR, and UAV_UAV were 28 cm, 31 cm, and 30 cm, respectively. The site-level RMSEs were 11 cm, 15 cm, and 8 cm, respectively. At the aggregated site level, we found that UAV photogrammetry could replace traditional field-based vegetation surveys of mean vegetation height across the range of conditions assessed in this study, with an RMSE less than 10 cm. Cost analysis indicates that using UAV-based point clouds is more cost-effective than traditional field vegetation surveys. View Full-Text
Keywords: UAV; vegetation; point clouds; 3D; accuracy assessment; remote sensing UAV; vegetation; point clouds; 3D; accuracy assessment; remote sensing
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Chen, S.; McDermid, G.J.; Castilla, G.; Linke, J. Measuring Vegetation Height in Linear Disturbances in the Boreal Forest with UAV Photogrammetry. Remote Sens. 2017, 9, 1257.

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