No abstract

No abstract

The oil and gas industry has grown significantly throughout the boreal and arctic ecosystems of North America. A major feature of the ecological footprint of oil and gas exploration is seismic lines—narrow corridors used to transport and deploy geophysical survey equipment. These lines, which traverse forests, tundra, uplands, and peatlands, were historically up to 10 m wide. Over the past decade, seismic lines have decreased in width (in some cases down to 1.75–3 m); however, their density has increased drastically and their construction is expected to continue in regions of Canada and the United States that are rich in oil and gas resources. We examine the literature related to the environmental impacts of, and restoration and reclamation efforts associated with, seismic lines in the boreal and arctic ecosystems of North America. With respect to conventional seismic lines, numerous studies report significant and persistent environmental changes along these lines and slow recovery of vegetation that translates into a lasting fragmentation of the landscape. This fragmentation has many ramifications for biodiversity and ecosystem processes, including significant implications for threatened woodland caribou herds. While modern, low-impact seismic lines have comparatively lower ecological effects at the site-level, their high density and associated potential edge effects suggest that their actual environmental impact may be underestimated. Seismic line restoration is a critical aspect of future integrated landscape management in hydrocarbon-rich regions of the boreal-arctic, and if widely applied, has the potential to benefit a wide range of species and maintain or re-establish key ecosystem services such as carbon sequestration and biodiversity.

Abstract-Outbreaks of insect pests periodically cause large losses of volume in Canada's forests.

No abstract

Monitoring tree regeneration in forest areas disturbed by resource extraction is a requirement for sustainably managing the boreal forest of Alberta, Canada. Small remotely piloted aircraft systems (sRPAS, a.k.a. drones) have the potential to decrease the cost of field surveys drastically, but produce large quantities of data that will require specialized processing techniques. In this study, we explored the possibility of using convolutional neural networks (CNNs) on this data for automatically detecting conifer seedlings along recovering seismic lines: a common legacy footprint from oil and gas exploration. We assessed three different CNN architectures, of which faster region-CNN (R-CNN) performed best (mean average precision 81%). Furthermore, we evaluated the effects of training-set size, season, seedling size, and spatial resolution on the detection performance. Our results indicate that drone imagery analyzed by artificial intelligence can be used to detect conifer seedling in regenerating sites with high accuracy, which increases with the size in pixels of the seedlings. By using a pre-trained network, the size of the training dataset can be reduced to a couple hundred seedlings without any significant loss of accuracy. Furthermore, we show that combining data from different seasons yields the best results. The proposed method is a first step towards automated monitoring of forest restoration/regeneration.

No 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.