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Recently, sensors deployed on unpiloted aerial systems (UAS) have provided snow depth estimates with high spatial resolution over watershed scales. While light detection and ranging (LiDAR) produces precise snow depth estimates for areas without vegetation cover, there has generally been poorer precision in forested areas. At a constant flight speed, the poorest precision within forests is observed beneath tree canopies that retain foliage into or through winter. The precision of lidar-derived elevation products is improved by increasing the sample size of ground returns but doing so reduces the spatial coverage of a mission due to limitations of battery power. We address the influence of flight speed on ground return density for baseline and snow-covered conditions and the subsequent effect on precision of snow depth estimates across a mixed landscape, while evaluating trade-offs between precision and bias. Prior to and following a snow event in December 2020, UAS flights were conducted at four different flight speeds over a region consisting of three contrasting land types: (1) open field, (2) deciduous forest, (3) conifer forest. For all cover types, we observed significant improvements in precision as flight speeds were reduced to 2 m s−1, as well as increases in the area over which a 2 cm snow depth precision was achieved. On the other hand, snow depth estimate differences were minimized at baseline flight speeds of 2 m s−1 and 4 m s−1 and snow-on flight speeds of 6 m s−1 over open fields and between 2 and 4 m s−1 over forest areas. Here, with consideration to precision and estimate bias within each cover type, we make recommendations for ideal flight speeds based on survey ground conditions and vegetation cover. 

Unoccupied aerial systems (UAS) are an established technique for collecting data on cold region phenomenon at high spatial and temporal resolutions. While many studies have focused on remote sensing applications for monitoring long term changes in cold regions, the role of UAS for detection, monitoring, and response to rapid changes and direct exposures resulting from abrupt hazards in cold regions is in its early days. This review discusses recent applications of UAS remote sensing platforms and sensors, with a focus on observation techniques rather than post-processing approaches, for abrupt, cold region hazards including permafrost collapse and event-based thaw, flooding, snow avalanches, winter storms, erosion, and ice jams. The pilot efforts highlighted in this review demonstrate the potential capacity for UAS remote sensing to complement existing data acquisition techniques for cold region hazards. In many cases, UASs were used alongside other remote sensing techniques (e.g., satellite, airborne, terrestrial) andin situsampling to supplement existing data or to collect additional types of data not included in existing datasets (e.g., thermal, meteorological). While the majority of UAS applications involved creation of digital elevation models or digital surface models using Structure-from-Motion (SfM) photogrammetry, this review describes other applications of UAS observations that help to assess risks, identify impacts, and enhance decision making. As the frequency and intensity of abrupt cold region hazards changes, it will become increasingly important to document and understand these changes to support scientific advances and hazard management. The decreasing cost and increasing accessibility of UAS technologies will create more opportunities to leverage these techniques to address current research gaps. Overcoming challenges related to implementation of new technologies, modifying operational restrictions, bridging gaps between data types and resolutions, and creating data tailored to risk communication and damage assessments will increase the potential for UAS applications to improve the understanding of risks and to reduce those risks associated with abrupt cold region hazards. In the future, cold region applications can benefit from the advances made by these early adopters who have identified exciting new avenues for advancing hazard research via innovative use of both emerging and existing sensors. 

Snow albedo, a measure of the amount of solar radiation that is reflected at the snow surface, plays a critical role in Earth’s climate and in regional hydrology because it is a primary driver of snowmelt timing. Satellite multi-spectral remote sensing provides a multi-decade record of land surface reflectance, from which snow albedo can be retrieved. However, this observational record is challenging to assess because discretein situobservations are not well suited for validation of snow properties at the spatial resolution of satellites (tens to hundreds of meters). For example, snow grain size, a primary driver of snow albedo, can vary at the sub-meter scale driven by changes in aspect, elevation, and vegetation. Here, we present a new uncrewed aerial vehicle hyperspectral imaging (UAV-HSI) method for mapping snow surface properties at high resolution (20 cm). A Resonon near-infrared HSI was flown on a DJI Matrice 600 Pro over the meadow encompassing Swamp Angel Study Plot in Senator Beck Basin, Colorado. Using a radiative transfer forward modeling approach, effective snow grain size and albedo maps were produced from measured surface reflectance. Coincident ground observations were used for validation; relative to retrievals from a field spectrometer the mean grain size difference was 2 μm, with an RMSE of 12 μm, and the mean broadband albedo was within 1% of that measured near the center of the flight area. Even though the snow surface was visually homogenous, the maps showed spatial variability and coherent patterns in the freshly fallen snow. To demonstrate the potential for UAV-HSI to be used to improve validation of satellite retrievals, the high-resolution maps were used to assess grain size and albedo retrievals, and subpixel variability, across 17 Landsat 9 OLI pixels from a satellite overpass with similar conditions two days following the flight. Although Landsat 9 did not capture the same range of values and spatial variability as the UAV-HSI, on average the comparison showed good agreement, with a mean grain size difference of 9 μm and the same broadband albedo (86%).