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Forests 2017, 8(9), 340;

Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest

Department of Forestry and Environmental Resources, North Carolina State University, 2800 Faucette Drive, Raleigh, NC 27695, USA
Department of Operations Research, North Carolina State University, 2310 Stinson Drive, Raleigh, NC 27695, USA
Department of Natural Resources and Society, University of Idaho, 708 South Deakin Street, Moscow, ID 83844, USA
USDA Forest Service Rocky Mountain Research Station, Forestry Sciences Laboratory, 1221 South Main Street, Moscow, ID 83843, USA
Department of Environmental Sciences and Engineering, UNC-Chapel Hill, 135 Dauer Drive, Chapel Hill, NC 27599, USA
Tecnosylva, Parque Tecnológico de León, 24009 León, Spain
Department of Horticultural Sciences, North Carolina State University, 2721 Founders Drive, Raleigh, NC 27695, USA
Author to whom correspondence should be addressed.
Received: 28 July 2017 / Revised: 8 September 2017 / Accepted: 8 September 2017 / Published: 11 September 2017
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Advances in Unmanned Aerial Vehicle (UAV) technology and data processing capabilities have made it feasible to obtain high-resolution imagery and three dimensional (3D) data which can be used for forest monitoring and assessing tree attributes. This study evaluates the applicability of low consumer grade cameras attached to UAVs and structure-from-motion (SfM) algorithm for automatic individual tree detection (ITD) using a local-maxima based algorithm on UAV-derived Canopy Height Models (CHMs). This study was conducted in a private forest at Cache Creek located east of Jackson city, Wyoming. Based on the UAV-imagery, we allocated 30 field plots of 20 m × 20 m. For each plot, the number of trees was counted manually using the UAV-derived orthomosaic for reference. A total of 367 reference trees were counted as part of this study and the algorithm detected 312 trees resulting in an accuracy higher than 85% (F-score of 0.86). Overall, the algorithm missed 55 trees (omission errors), and falsely detected 46 trees (commission errors) resulting in a total count of 358 trees. We further determined the impact of fixed tree window sizes (FWS) and fixed smoothing window sizes (SWS) on the ITD accuracy, and detected an inverse relationship between tree density and FWS. From our results, it can be concluded that ITD can be performed with an acceptable accuracy (F > 0.80) from UAV-derived CHMs in an open canopy forest, and has the potential to supplement future research directed towards estimation of above ground biomass and stem volume from UAV-imagery. View Full-Text
Keywords: structure from motion (SfM); 3D point cloud; remote sensing; local maxima; fixed tree window size structure from motion (SfM); 3D point cloud; remote sensing; local maxima; fixed tree window size

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Mohan, M.; Silva, C.A.; Klauberg, C.; Jat, P.; Catts, G.; Cardil, A.; Hudak, A.T.; Dia, M. Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest. Forests 2017, 8, 340.

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