Accuracy assessment of late winter snow depth mapping for tundra environments using Structure-from-Motion photogrammetry

Walker, Branden; Wilcox, Evan J.; Marsh, Philip

doi:10.1139/as-2020-0006

Cited by 14 publications

(25 citation statements)

References 32 publications

(39 reference statements)

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“…Positive MBE values for both transects is consistent with the hypothesis that the snow depth would be overestimated because of the freeboard of floating ice. The RMSE for both transects is within the reported error for studies observing UAV SD on terrestrial sites (Walker et al, 2020), however the highly positive MBE indicates that the bias from freeboard can be addressed through the derived freeboard height.…”

Section: Snow Surface Validation and Freeboard Correctionsupporting

confidence: 66%

“…In similar studies that utilize snow depths observed using Magnaprobes to validate UAV-derived snow depths a simple extraction of the UAV snow depth (UAV SD ) raster by the point location yielded co-registration errors, as the horizontal accuracy of the Magnaprobe GPS (approximately 2.5 m) is much lower than the UAV (< 0.03 m) (Nolan et al, 2015;Walker et al, 2020). Therefore, to reduce the potential inclusion of erroneous snow depth information, Magnaprobe snow depths (Magnaprobe SD ) are validated against the average snow depth extracted from a 5 m buffer surrounding each insitu observation, which would approximate the spatial accuracy of the GPS location.…”

Section: Snow Surface Validation and Freeboard Correctionmentioning

confidence: 99%

“…Studies that utilize UAVs to retrieve snow depth over glaciers and open natural environments typically report a root mean square error (RMSE) for retrievals of less than 0.3 m (Vander Jagt et al, 2015;De Michele et al, 2016;Gindraux et al, 2017;Avanzi et al, 2018;Harder et al, 2020;Walker et al, 2020). In vegetated areas, the obstruction caused by the canopy results in larger error estimates of snow depth of up to 0.5 m (Bühler et al, 2016;Harder et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Unpiloted Aerial Vehicle Retrieval of Snow Depth Over Freshwater Lake Ice Using Structure From Motion

Gunn

Jones

Rangel

2021

Front. Remote Sens.

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The presence and thickness of snow overlying lake ice affects both the timing of melt and ice-free conditions, can contribute to overall ice thickness through its insulative capacity, and fosters the development of variable ice types. The use of UAVs to retrieve snow depths with high spatial resolution is necessary for the next generation of ultra-fine hydrological models, as the direct contribution of water from snow on lake ice is unknown. Such information is critical to the understanding of the physical processes of snow redistribution and capture in catchments on small lakes in the Arctic, which has been historically estimated from its relationship to terrestrial snowpack properties. In this study, we use a quad-copter UAV and SfM principles to retrieve and map snow depth at the winter maximum at high resolution over a the freshwater West Twin Lake on the Arctic Coastal Plain of northern Alaska. The accuracy of the snow depth retrievals is assessed using in-situ observations (n = 1,044), applying corrections to account for the freeboard of floating ice. The average snow depth from in-situ observations was used calculate a correction factor based on the freeboard of the ice to retrieve snow depth from UAV acquisitions (RMSE = 0.06 and 0.07 m for two transects on the lake. The retrieved snow depth map exhibits drift structures that have height deviations with a root mean square (RMS) of 0.08 m (correlation length = 13.8 m) for a transect on the west side of the lake, and an RMS of 0.07 m (correlation length = 18.7 m) on the east. Snow drifts present on the lake also correspond to previous investigations regarding the variability of snow on lakes, with a periodicity (separation) of 20 and 16 m for the west and east side of the lake, respectively. This study represents the first retrieval of snow depth on a frozen lake surface from a UAV using photogrammetry, and promotes the potential for high-resolution snow depth retrieval on small ponds and lakes that comprise a significant portion of landcover in Arctic environments.

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Section: Snow Surface Validation and Freeboard Correctionsupporting

confidence: 66%

Section: Snow Surface Validation and Freeboard Correctionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Unpiloted Aerial Vehicle Retrieval of Snow Depth Over Freshwater Lake Ice Using Structure From Motion

Gunn

Jones

Rangel

2021

Front. Remote Sens.

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“…For TVC, pit locations were chosen based on previous snow depth distribution (2016), slope and elevation. Multiple snow depth maps at 1m resolution from RPAS surveys conducted in March 2018 (Walker et al, 2020) were used to estimate snow depth distribution in TVC with total spatial coverage of 13 km 2 . Also, a small RPAS survey is available for CB with spatial coverage of 0.2 km 2 at 1 m resolution.…”

Section: Datamentioning

confidence: 99%

Characterizing Tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals

Meloche

Langlois

Rutter

et al. 2021

Preprint

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Abstract. Topography and vegetation play a major role in sub-pixel variability of Arctic snowpack properties, but are not considered in current passive microwave (PMW) satellite SWE retrievals. Simulation of sub-pixel variability of snow properties is also problematic when downscaling snow and climate models. In this study, we simplified observed variability of snowpack properties (depth, density, microstructure) in a two-layer model with mean values and distributions of two multi-year tundra dataset so they could be incorporated in SWE retrieval schemes. Spatial variation of snow depth was parametrized by a log-normal distribution with mean (μsd) values and coefficients of variation (CVsd). Snow depth variability (CVsd) was found to increase as a function of the area measured by a Remotely Piloted Aircraft System (RPAS). Distributions of snow specific area (SSA) and density were found for the wind slab (WS) and depth hoar (DH) layers. The mean depth hoar fraction (DHF) was found to be higher in Trail Valley Creek (TVC) than Cambridge Bay (CB) where TVC is at a lower latitude with a sub-arctic shrub tundra compared to CB which is a graminoid tundra. DHF were fitted with a gaussian process and predicted from snow depth. Simulations of brightness temperatures using the Snow Microwave Radiative Transfer (SMRT) model incorporating snow depth and DHF variation were evaluated with measurements from the Special Sensor Microwave/Imager and Sounder (SSMIS) sensor. Variation in snow depth (CVsd) is proposed as an effective parameter to account for sub-pixel variability in PMW emission, improving simulation by 8 K. Snow depth simulations using a CVsd of 0.9 best matched CVsd observations from spatial datasets for areas > 3 km2, which is comparable to the 3.125 km pixel size of the Equal-Area Scalable Earth (EASE) grid 2.0 enhanced resolution at 37 GHz.

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“…Airborne remote sensing methods are able to provide high resolution snow data, but also have certain limitations. For example, methods to map snow depth at high resolutions are available (Deems et al, 2013;Walker et al, 2020), but mapping of snow density or SWE are not (Koch et al, 2019). SWE along flight transects is available using airborne gamma methods but have limited applicability in the Arctic due to high cost.…”

Section: Introductionmentioning

confidence: 99%

Cosmic-ray neutron method for the continuous measurement of Arctic snow accumulation and melt

Jitnikovitch

Marsh

Walker

et al. 2021

Preprint

Self Cite

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Abstract. The Arctic is warming at two to three times the rate of the global average, significantly impacting snow accumulation and melt. Unfortunately, conventional methods to measure snow water equivalent (SWE), a key aspect of the Arctic snow cover, have numerous limitations that hinder our ability to document annual cycles, the impact of climate change, or to test predictive models. As a result, there is an urgent need for improved methods that measure Arctic SWE; allow for continuous, unmanned measurements over the entire winter; and allow measurements that are representative of spatially variable, Arctic snow covers. In-situ, or invasive, cosmic ray neutron sensors (CRNSs) may fill this observational gap, but few studies have tested these types of sensors or considered their applicability at remote sites in the Arctic. During the winters of 2016/17 and 2017/18 we tested an in-situ CRNS system at two locations in Canada; a cold, low- to high-SWE environment in the Canadian Arctic and at a warm, low-SWE landscape in Southern Ontario that allowed easier access for validation purposes. CRNS moderated neutron counts were compared to manual snow survey SWE values obtained during both winter seasons. Pearson correlation coefficients ranged from −0.89 to −0.98, while regression analyses provided R2 values from 0.79 to 0.96. RMSE of the CRNS-measured SWE averaged 2 mm at the southern Ontario site and ranged from 28 to 40 mm at the Arctic site. These data show that in-situ CRNS instruments are able to continuously measure SWE with sufficient accuracy, and have important applications for measuring SWE in a variety of environments, including remote Arctic locations. These sensors can provide important SWE data for testing snow and hydrological models, water resource management applications, and the validation of remote-sensing applications.

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Accuracy assessment of late winter snow depth mapping for tundra environments using Structure-from-Motion photogrammetry

Cited by 14 publications

References 32 publications

Unpiloted Aerial Vehicle Retrieval of Snow Depth Over Freshwater Lake Ice Using Structure From Motion

Unpiloted Aerial Vehicle Retrieval of Snow Depth Over Freshwater Lake Ice Using Structure From Motion

Characterizing Tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals

Cosmic-ray neutron method for the continuous measurement of Arctic snow accumulation and melt

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