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This paper presents a novel approach to continuously monitor very slow-moving translational landslides in mountainous terrain using conventional and experimental differential global navigation satellite system (d-GNSS) technologies. A key research question addressed is whether displacement trends captured by a radio-frequency "mobile" d-GNSS network compare with the spatial and temporal patterns in activity indicated by satellite interferometric synthetic aperture radar (InSAR) and unmanned aerial vehicle (UAV) photogrammetry. Field testing undertaken at Ripley Landslide, near Ashcroft in south-central British Columbia, Canada, demonstrates the applicability of new geospatial technologies to monitoring ground control points (GCPs) and railway infrastructure on a landslide with small and slow annual displacements (<10 cm/yr). Each technique records increased landslide activity and ground displacement in late winter and early spring. During this interval, river and groundwater levels are at their lowest levels, while ground saturation rapidly increases in response to the thawing of surficial earth materials, and the infiltration of snowmelt and runoff occurs by way of deep-penetrating tension cracks at the head scarp and across the main slide body. Research over the last decade provides vital information for government agencies, national railway companies, and other stakeholders to understand geohazard risk, predict landslide movement, improve the safety, security, and resilience of Canada's transportation infrastructure; and reduce risks to the economy, environment, natural resources, and public safety. [ABSTRACT FROM AUTHOR]

Recent advancements in geophysical exploration have been realized through reliably integrating unmanned aerial vehicle platforms with lightweight, high‐resolution magnetometer payloads. Unmanned aerial vehicle aeromagnetic surveys can provide a contemporary data product between the two end‐members of coverage and resolution attained using manned airborne and terrestrial magnetic surveys. This new data product is achievable because unmanned aerial vehicle platforms can safely traverse with magnetometer payloads at flight elevations closer to ground targets than manned airborne surveys, while also delivering an increased coverage rate compared to walking conventional terrestrial surveys. This is a promising new development for geophysical and mineral exploration applications, especially in variable terrains. A three‐dimensional unmanned aerial vehicle aeromagnetic survey was conducted within the Shebandowan Greenstone Belt, northwest of Thunder Bay, Ontario, Canada, in July 2017. A series of two‐dimensional grids (∼500 m × 700 m) were flown at approximate elevations of 35, 45 and 70 m above ground level using a Dà‐Jiāng Innovations multi‐rotor unmanned aerial vehicle (S900) and a GEM Systems, Inc., Potassium Vapour Magnetometer (GSMP‐35U). In total, over 48 line‐km of unmanned aerial vehicle aeromagnetic data were flown with a line spacing of 25 m. The collected aeromagnetic data were compared to a regional heliborne aeromagnetic survey flown at an elevation of approximately 85 m above the terrain, with a line spacing of 100 m, as well as a follow‐up terrestrial magnetic survey. The first vertical derivative of the gathered unmanned aerial vehicle total magnetic field data was calculated both directly between each of the different flight elevations, and indirectly by calculating the values predicted using upward continuation. This case study demonstrates that low flight elevation unmanned aerial vehicle aeromagnetic surveys can reliably collect industry standard total magnetic field measurements at an increased resolution when compared to manned airborne magnetic surveys. The enhanced interpretation potential provided by this approach also aided in delineating structural controls and hydrothermal fluid migration pathways (a pair of adjacent shear zones) related to gold mineralization on site. These structural features were not clearly resolved in the regional manned airborne magnetic data alone, further demonstrating the utility of applying high‐resolution unmanned aerial vehicle aeromagnetic surveys to mineral exploration applications. The conclusions and interpretations drawn from the unmanned aerial vehicle aeromagnetic data, coupled with historical data, were applied to make a new gold mineralization discovery on the site, assayed at 15.7 g/t. [ABSTRACT FROM AUTHOR]

Supraglacial streams are a significant part of the glacial hydrological system and important for understanding glacial hydrology and dynamics. Here we infer factors that influence the long-term development of perennial supraglacial streams, particularly in reference to canyon, incised and surface stream formation. Orthophotos and digital elevation models generated from high-resolution aerial imagery taken with unmanned aerial vehicles or piloted helicopters between 2010 and 2017 were used to compare seven streams on Fountain Glacier, Bylot Island, Canada over time. Results show canyon formation occurs from a combination of fluvial processes and the impact of solar radiation. The greater the discharge or slope, the faster the incision and higher the sinuosity. With greater sinuosity, the distance between the top of the valley banks increases, and cutoffs cause trapezoidal canyon-like valleys to form. Solar radiation causes the backward migration of the valley walls, further enhancing canyon area. Canyons are less likely to occur in areas of low discharge and slope. Less incised channels are also more likely to have water flow jumping the channel banks, changing the channel path. The presence of medial moraines and crevasses also increases rerouting of small streams. Lastly, windblown created snow-plugs may lead to stream diversion. [ABSTRACT FROM AUTHOR]

Wildfires are a dominant disturbance to boreal forests, and in North America, they typically cause widespread tree mortality. Forest fire burn severity is often measured at a plot scale using the Composite Burn Index (CBI), which was originally developed as a means of assigning severity levels to the Normalized Burn Ratio (NBR) computed from Landsat satellite imagery. Our study investigated the potential to map biophysical indicators of burn severity (residual green vegetation and charred organic surface) at very high (3 cm) resolution, using color orthomosaics and vegetation height models derived from UAV-based photographic surveys and Structure from Motion methods. These indicators were scaled to 30 m resolution Landsat pixel footprints and compared to the post-burn NBR (post-NBR) and differenced NBR (dNBR) ratios computed from pre- and post-fire Landsat imagery. The post-NBR showed the strongest relationship to both the fraction of charred surface (exponential R2 = 0.79) and the fraction of green crown vegetation above 5 m (exponential R2 = 0.81), while the dNBR was more closely related to the total green vegetation fraction (exponential R2 = 0.69). Additionally, the UAV green fraction and Landsat indices could individually explain more than 50% of the variance in the overall CBI measured in 39 plots. These results provide a proof-of-concept for using low-cost UAV photogrammetric mapping to quantify key measures of boreal burn severity at landscape scales, which could be used to calibrate and assign a biophysical meaning to Landsat spectral indices for mapping severity at regional scales. [ABSTRACT FROM AUTHOR]

Technological developments in the areas of robotics, autonomous flight controls, sensory equipment, and energy storage have enabled the rise of the commercial drone industry. Drones, also referred to as remotely piloted aircraft systems (RPAS) are a versatile tool used in applications ranging from geological surveying to package delivery and vary in size from a few grams to several tons. With their increasing utilization by individuals, corporations, and the government, more must be done to understand how the public perceives their use. Studies have been performed all over the world and show an increasing level of support for drones, largely due to greater awareness of their existence and how they work. The last major survey performed in Canada was done in 2014 and found the public was rather opposed to the use of drones and preferred traditionally piloted aircraft in all 38 applications polled. Much has changed over the past eight years as the findings presented herein show the public is supportive of the technology in most applications. Factors leading to a higher level of support included age, gender, prior drone knowledge, and being a user of the technology. Applications of drones that were perceived to further the public interest such as search and rescue, firefighting, and climate research were also viewed more positively. Most drone user groups were viewed favorably except for journalists and corporations. Together, these results show the changing sentiment and opinions of Canadians and can be of use to industry stakeholders and governments for effectively using drones and developing policies to regulate them. • The public is significantly more supportive of RPAS than in past Canadian studies. • The term " drone " is mostly associated with small commercial RPAS. • Participants were more supportive of drones than UAVs, suggesting a term influence. • Only three of the thirteen missions received more than 10% opposition. • Older individuals were more supportive of RPAS use when given the use circumstances. [ABSTRACT FROM AUTHOR]

Lichen is an important food source for caribou in Canada. Lichen mapping using remote sensing (RS) images could be a challenging task, however, as lichens generally appear in unevenly distributed, small patches, and could resemble surficial features. Moreover, collecting lichen labeled data (reference data) is expensive, which restricts the application of many robust supervised classification models that generally demand a large quantity of labeled data. The goal of this study was to investigate the potential of using a very-high-spatial resolution (1-cm) lichen map of a small sample site (e.g., generated based on a single UAV scene and using field data) to train a subsequent classifier to map caribou lichen over a much larger area (~0.04 km2 vs. ~195 km2) and a lower spatial resolution image (in this case, a 50-cm WorldView-2 image). The limited labeled data from the sample site were also partially noisy due to spatial and temporal mismatching issues. For this, we deployed a recently proposed Teacher-Student semi-supervised learning (SSL) approach (based on U-Net and U-Net++ networks) involving unlabeled data to assist with improving the model performance. Our experiments showed that it was possible to scale-up the UAV-derived lichen map to the WorldView-2 scale with reasonable accuracy (overall accuracy of 85.28% and F1-socre of 84.38%) without collecting any samples directly in the WorldView-2 scene. We also found that our noisy labels were partially beneficial to the SSL robustness because they improved the false positive rate compared to the use of a cleaner training set directly collected within the same area in the WorldView-2 image. As a result, this research opens new insights into how current very high-resolution, small-scale caribou lichen maps can be used for generating more accurate large-scale caribou lichen maps from high-resolution satellite imagery. [ABSTRACT FROM AUTHOR]