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Open AccessArticle

Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure

1
Department of Geography and Environmental Systems, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
2
Smithsonian Tropical Research Institute, Roosevelt Ave., Ancón 0843-03092, Panama
3
Computer Science and Electrical Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Nicolas Baghdadi, Norman Kerle and Prasad S. Thenkabail
Remote Sens. 2015, 7(10), 13895-13920; https://doi.org/10.3390/rs71013895
Received: 27 July 2015 / Revised: 10 September 2015 / Accepted: 10 October 2015 / Published: 23 October 2015
Ecological remote sensing is being transformed by three-dimensional (3D), multispectral measurements of forest canopies by unmanned aerial vehicles (UAV) and computer vision structure from motion (SFM) algorithms. Yet applications of this technology have out-paced understanding of the relationship between collection method and data quality. Here, UAV-SFM remote sensing was used to produce 3D multispectral point clouds of Temperate Deciduous forests at different levels of UAV altitude, image overlap, weather, and image processing. Error in canopy height estimates was explained by the alignment of the canopy height model to the digital terrain model (R2 = 0.81) due to differences in lighting and image overlap. Accounting for this, no significant differences were observed in height error at different levels of lighting, altitude, and side overlap. Overall, accurate estimates of canopy height compared to field measurements (R2 = 0.86, RMSE = 3.6 m) and LIDAR (R2 = 0.99, RMSE = 3.0 m) were obtained under optimal conditions of clear lighting and high image overlap (>80%). Variation in point cloud quality appeared related to the behavior of SFM ‘image features’. Future research should consider the role of image features as the fundamental unit of SFM remote sensing, akin to the pixel of optical imaging and the laser pulse of LIDAR. View Full-Text
Keywords: Ecosynth; UAV; SFM; computer vision; canopy height; point cloud; optimal Ecosynth; UAV; SFM; computer vision; canopy height; point cloud; optimal
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MDPI and ACS Style

Dandois, J.P.; Olano, M.; Ellis, E.C. Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure. Remote Sens. 2015, 7, 13895-13920.

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