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Open AccessArticle

Detection of Coniferous Seedlings in UAV Imagery

1
Department of Geography, 2500 University Dr. NW Calgary, University of Calgary, Calgary, AB T2N 1N4, Canada
2
Northern Forestry Centre, Canadian Forest Service, 5320 122 St. NW Edmonton, Victoria, AB T6H 3S5, Canada
*
Author to whom correspondence should be addressed.
Forests 2018, 9(7), 432; https://doi.org/10.3390/f9070432
Received: 26 June 2018 / Revised: 13 July 2018 / Accepted: 16 July 2018 / Published: 18 July 2018
(This article belongs to the Special Issue Forestry Applications of Unmanned Aerial Vehicles (UAVs))
Rapid assessment of forest regeneration using unmanned aerial vehicles (UAVs) is likely to decrease the cost of establishment surveys in a variety of resource industries. This research tests the feasibility of using UAVs to rapidly identify coniferous seedlings in replanted forest-harvest areas in Alberta, Canada. In developing our protocols, we gave special consideration to creating a workflow that could perform in an operational context, avoiding comprehensive wall-to-wall surveys and complex photogrammetric processing in favor of an efficient sampling-based approach, consumer-grade cameras, and straightforward image handling. Using simple spectral decision rules from a red, green, and blue (RGB) camera, we documented a seedling detection rate of 75.8 % (n = 149), on the basis of independent test data. While moderate imbalances between the omission and commission errors suggest that our workflow has a tendency to underestimate the seedling density in a harvest block, the plot-level associations with ground surveys were very high (Pearson’s r = 0.98; n = 14). Our results were promising enough to suggest that UAVs can be used to detect coniferous seedlings in an operational capacity with standard RGB cameras alone, although our workflow relies on seasonal leaf-off windows where seedlings are visible and spectrally distinct from their surroundings. In addition, the differential errors between the pine seedlings and spruce seedlings suggest that operational workflows could benefit from multiple decision rules designed to handle diversity in species and other sources of spectral variability. View Full-Text
Keywords: unmanned aerial vehicles; seedling detection; forest regeneration; reforestation; establishment survey; machine learning; multispectral classification unmanned aerial vehicles; seedling detection; forest regeneration; reforestation; establishment survey; machine learning; multispectral classification
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Feduck, C.; McDermid, G.J.; Castilla, G. Detection of Coniferous Seedlings in UAV Imagery. Forests 2018, 9, 432.

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