Unmanned Aerial Vehicle (UAV) systems, sensors, and photogrammetric processing techniques have enabled timely and highly detailed three-dimensional surface reconstructions at a scale that bridges the gap between conventional remote-sensing and field-scale observations. In this work 29 rotary and fixed-wing UAV surveys were conducted during multiple field campaigns, totaling 47 flights and over 14.3 km², to document permafrost thaw subsidence impacts on or close to road infrastructure in the Northwest Territories, Canada. This paper provides four case studies: (1) terrain models and orthomosaic time series revealed the morphology and daily to annual dynamics of thaw-driven mass wasting phenomenon (retrogressive thaw slumps; RTS). Scar zone cut volume estimates ranged between 3.2 × 10³ and 5.9 × 10⁶ m³. The annual net erosion of RTS surveyed ranged between 0.35 × 10³ and 0.39 × 10⁶ m³. The largest RTS produced a long debris tongue with an estimated volume of 1.9 × 10⁶ m³. Downslope transport of scar zone and embankment fill materials was visualized using flow vectors, while thermal imaging revealed areas of exposed ground ice and mobile lobes of saturated, thawed materials. (2) Stratigraphic models were developed for RTS headwalls, delineating ground-ice bodies and stratigraphic unconformities. (3) In poorly drained areas along road embankments, UAV surveys detected seasonal terrain uplift and settlement of up to 0.5 m (>1700 m² in extent) as a result of injection ice development. (4) Time series of terrain models highlighted the thaw-driven evolution of a borrow pit (6.4 × 10⁵ m³ cut volume) constructed in permafrost terrain, whereby fluvial and thaw-driven sediment transfer (1.1 and 3.9 × 10³ m³ a⁻¹ respectively) was observed and annual slope profile reconfiguration was monitored to gain management insights concerning site stabilization. Elevation model vertical accuracies were also assessed as part of the case studies and ranged between 0.02 and 0.13 m Root Mean Square Error. Photogrammetric models processed with Post-processed Kinematic image solutions achieved similar accuracies without ground control points over much larger and complex areas than previously reported. The high resolution of UAV surveys, and the capacity to derive quantitative time series provides novel insights into permafrost processes that are otherwise challenging to study. The timely emergence of these tools bridges field-based research and applied studies with broad-scale remote-sensing approaches during a period when climate change is transforming permafrost environments.

Ice-wedge networks underlie polygonal terrain and comprise the most widespread form of massive ground ice in continuous permafrost. Here, we show that climate-driven thaw of hilltop ice-wedge networks is rapidly transforming uplands across Banks Island in the Canadian Arctic Archipelago. Change detection using high-resolution WorldView images and historical air photos, coupled with 32-year Landsat reflectance trends, indicate broad-scale increases in ponding from ice-wedge thaw on hilltops, which has significantly affected at least 1500 km² of Banks Island and over 3.5% of the total upland area. Trajectories of change associated with this upland ice-wedge thermokarst include increased micro-relief, development of high-centred polygons, and, in areas of poor drainage, ponding and potential initiation of thaw lakes. Millennia of cooling climate have favoured ice-wedge growth, and an absence of ecosystem disturbance combined with surface denudation by solifluction has produced high Arctic uplands and slopes underlain by ice-wedge networks truncated at the permafrost table. The thin veneer of thermally-conductive mineral soils strongly links Arctic upland active-layer responses to summer warming. For these reasons, widespread and intense ice-wedge thermokarst on Arctic hilltops and slopes contrast more muted responses to warming reported in low and subarctic environments. Increasing field evidence of thermokarst highlights the inherent climate sensitivity of the Arctic permafrost terrain and the need for integrated approaches to monitor change and investigate the cascade of environmental consequences.

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.

Satellite remote sensing is a promising technology for monitoring natural and anthropogenic changes occurring in remote, northern environments. It offers the potential to scale-up ground-based, local environmental monitoring efforts to document disturbance types, and characterize their extents and frequencies at regional scales. Here we present a simple, but effective means of visually assessing landscape disturbances in northern environments using trend analysis of Landsat satellite image stacks. Linear trends of the Tasseled Cap brightness, greenness, and wetness indices, when composited into an RGB image, effectively distinguish diverse landscape changes based on additive color logic. Using a variety of reference datasets within Northwest Territories, Canada, we show that the trend composites are effective for identifying wildfire regeneration, tundra greening, fluvial dynamics, thermokarst processes including lake surface area changes and retrogressive thaw slumps, and the footprint of resource development operations and municipal development. Interpretation of the trend composites is aided by a color wheel legend and contextual information related to the size, shape, and location of change features. A companion paper in this issue (Olthof and Fraser) focuses on quantitative methods for classifying these changes.