Detection of Coniferous Seedlings in UAV Imagery

• Published in: Forests 2018-07, Vol.9 (7), p.432
• Main Authors: Feduck, Corey, McDermid, Gregory, Castilla, Guillermo
• Format: Article
• Language: English
• Subjects: Accuracy; Aerial surveys; Algorithms; Assessments; Automation; Biodiversity; Cameras; Cost control; establishment survey; Feasibility studies; forest regeneration; Forestry; Forests; Harvest; Image detection; machine learning; multispectral classification; Photogrammetry; Planting density; Polls & surveys; reforestation; Regeneration; Remote sensing; seedling detection; Seedlings; Species diversity; Trees; Unmanned aerial vehicles; Vegetation; Vehicles; Workflow
• ISSN: 1999-4907; 1999-4907
• DOI: 10.3390/f9070432

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.