Abstract
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.
Highlights
The rapid development of natural resource extraction in Alberta, Canada, has created large numbers of anthropogenic disturbance features, such as roads, seismic lines, pipelines, and well sites, which have cumulative effects on wildlife habitat and biodiversity
Our results show that at the aggregated site level, point clouds derived from unmanned aerial vehicles (UAVs) photogrammetry alone could replace traditional field-based vegetation surveys of mean vegetation height across the range of conditions assessed in this study, with an root-mean-square errors (RMSEs) less than 10 cm
Our results show that at the aggregated site or plot level, there was no significant difference between UAV- and field-surveyed height estimates, suggesting that UAV photogrammetry alone 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
Summary
The rapid development of natural resource extraction in Alberta, Canada, has created large numbers of anthropogenic disturbance features, such as roads, seismic lines, pipelines, and well sites, which have cumulative effects on wildlife habitat and biodiversity. Linear features increase the spatial overlap between caribou and wolves, their main predator, and provide movement corridors for wolf packs to travel and hunt more efficiently [5]. Linear features provide forage and access for adaptable ungulates, such as moose, elk, and deer. These species would otherwise not be prominent in caribou habitat, and their presence in turn increases the carrying capacity for wolves and other predators [6,7]. Among the linear disturbances present in the Boreal Forest, seismic lines—narrow corridors created to deploy seismic equipment for geophysical exploration—have the greatest impact on caribou in Alberta, because of their very slow reforestation rates, high density (up to 10 km per km2), and wide distribution [8]
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