Measuring snow ablation rates in alpine terrain with a mobile multioffset ground‐penetrating radar system

Mohr, Franziska; Jonas, Tobias

doi:10.1002/hyp.13259

Cited by 18 publications

(19 citation statements)

References 29 publications

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“…The range of applications for accurate high-resolution snow depth mapping is diverse. Snow depth or height of snowpack (HS) is defined as the vertical distance from the base to the surface of the snowpack (Fierz et al, 2009) and can vary significantly over short horizontal distances (Lundberg et al, 2010;Griessinger et al, 2018;Dong, 2018). Several fields rely on accurate information about how snow depth changes across a landscape.…”

Section: Introductionmentioning

confidence: 99%

Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping

et al. 2021

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Abstract. Snow depth has traditionally been estimated based on point measurements collected either manually or at automated weather stations. Point measurements, though, do not represent the high spatial variability in snow depths present in alpine terrain. Photogrammetric mapping techniques have progressed in recent years and are capable of accurately mapping snow depth in a spatially continuous manner, over larger areas and at various spatial resolutions. However, the strengths and weaknesses associated with specific platforms and photogrammetric techniques as well as the accuracy of the photogrammetric performance on snow surfaces have not yet been sufficiently investigated. Therefore, industry-standard photogrammetric platforms, including high-resolution satellite (Pléiades), airplane (Ultracam Eagle M3), unmanned aerial system (eBee+ RTK with SenseFly S.O.D.A. camera) and terrestrial (single lens reflex camera, Canon EOS 750D) platforms, were tested for snow depth mapping in the alpine Dischma valley (Switzerland) in spring 2018. Imagery was acquired with airborne and space-borne platforms over the entire valley, while unmanned aerial system (UAS) and terrestrial photogrammetric imagery was acquired over a subset of the valley. For independent validation of the photogrammetric products, snow depth was measured by probing as well as by using remote observations of fixed snow poles. When comparing snow depth maps with manual and snow pole measurements, the root mean square error (RMSE) values and the normalized median absolute deviation (NMAD) values were 0.52 and 0.47 m, respectively, for the satellite snow depth map, 0.17 and 0.17 m for the airplane snow depth map, and 0.16 and 0.11 m for the UAS snow depth map. The area covered by the terrestrial snow depth map only intersected with four manual measurements and did not generate statistically relevant measurements. When using the UAS snow depth map as a reference surface, the RMSE and NMAD values were 0.44 and 0.38 m for the satellite snow depth map, 0.12 and 0.11 m for the airplane snow depth map, and 0.21 and 0.19 m for the terrestrial snow depth map. When compared to the airplane dataset over a large part of the Dischma valley (40 km2), the snow depth map from the satellite yielded an RMSE value of 0.92 m and an NMAD value of 0.65 m. This study provides comparative measurements between photogrammetric platforms to evaluate their specific advantages and disadvantages for operational, spatially continuous snow depth mapping in alpine terrain over both small and large geographic areas.

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Section: Introductionmentioning

confidence: 99%

Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping

et al. 2021

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“…The range of applications for accurate high-resolution snow depth mapping is diverse. Snow depth is defined as the vertical distance from the base to the surface of the snowpack (Fierz et al, 2009) and can vary significantly over short distances horizontally within a meter-scale (Lundberg et al, 2010;Griessinger et al, 2018;Dong, 2018). Several fields rely on accurate information about how snow depth changes across a landscape.…”

Section: Introductionmentioning

confidence: 99%

Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping

Eberhard¹,

Sirguey²,

Miller³

et al. 2020

Preprint

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Abstract. Snow depth has traditionally been estimated based on point measurements collected either manually or at automated weather stations. Point measurements, though, do not represent the high spatial variability of snow depths present in alpine terrain. Photogrammetric mapping techniques have made significant progress in recent years and are suitable to accurately map snow depth in a spatially continuous manner, over larger areas, and at various spatial resolutions. However, the strengths and weaknesses associated with specific platforms and photogrammetric techniques, as well as the accuracy of the photogrammetric performance on snow surfaces have not yet been sufficiently investigated. Therefore, industry-standard photogrammetric platforms, including high-resolution satellites (Pléiades), airplanes (Ultracam), Unmanned Aerial Systems UAS (eBee+) and ground-based (single lens reflex camera), were tested in a timely manner for snow depth mapping in the alpine Dischma valley (Switzerland) in spring 2018. Imagery was acquired with airborne and space-borne platforms over the entire valley, while UAS and ground-based photogrammetric imagery were acquired over a subset of the valley. For independent validation of the photogrammetric products, snow depth was measured by probing, as well as using remote observations of fixed snow poles. When comparing snow depth maps with manual and snow pole measurements the root mean square errors (RMSEs) and the normalized median deviations (NMADs) are 0.52 m and 0.47 m for the Pléiades snow depth map, 0.17 m and 0.17 m for the Ultracam snow depth map, 0.16 m and 0.11 m for the UAS snow depth map. Ground-based had to few measurements to be statistically relevant. When using the eBee+ snow depth map as ground truth, the RMSEs and NMADs are 0.44 m and 0.38 m for the Pléiades snow depth map, 0.12 m and 0.11 m for the Ultracam snow depth map, 0.21 and 0.19 m for the ground-based snow depth map. Because of the accuracy and precision of the Ultracam dataset we finally compared the Ultracam snow depth map to the Pléiades snow depth map over a large part of the Dischma valley and calculated a RMSE of 0.92 m and a NMAD of 0.65 m. By comparing for the first time more than two platforms, this study provides comparative measurements between platforms to evaluate the specific advantages and disadvantages of them for operational, spatially continuous snow depth mapping in alpine terrain over small and large areas.

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“…Although spatially distributed measurements of snow depth using methods such as airborne light detection and ranging (LiDAR; Deems, Painter, & Finnegan, 2013;Painter et al, 2016) and structure-frommotion photogrammetry (Bühler, Adams, Bösch, & Stoffel, 2016;Nolan, Larsen, & Sturm, 2015;Redpath, Sirguey, & Cullen, 2018) are becoming more common and can be used to estimate areal SWE (Griessinger, Mohr, & Jonas, 2018;Painter et al, 2016), these are typically not longterm datasets. Established snow courses and point measurement sites therefore remain the basis for investigating the impacts of climate variability and change in many alpine landscapes.…”

Section: Introductionmentioning

confidence: 99%

Spatial controls on the distribution and dynamics of a marginal snowpack in the Australian Alps

Bilish

Callow

McGrath

et al. 2019

Hydrological Processes

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Seasonal snowpacks in marginal snow environments are typically warm and nearly isothermal, exhibiting high inter‐ and intra‐annual variability. Measurements of snow depth and snow water equivalent were made across a small subalpine catchment in the Australian Alps over two snow seasons in order to investigate the extent and implications of snowpack spatial variability in this marginal setting. The distribution and dynamics of the snowpack were found to be influenced by upwind terrain, vegetation, solar radiation, and slope. The role of upwind vegetation was quantified using a novel parameter based on gridded vegetation height. The elevation range of the catchment was relatively modest (185 m), and elevation impacted distribution but not dynamics. Two characteristic features of marginal snowpack behaviour are presented. Firstly, the evolution of the snowpack is described in terms of a relatively unstable accumulation state and a highly stable ablation state, as revealed by temporal variations in the mean and standard deviation of snow water equivalent. Secondly, the validity of partitioning the snow season into distinct accumulation and ablation phases is shown to be compromised in such a setting. Snow at the most marginal locations may undergo complete melt several times during a season and, even where snow cover is more persistent, ablation processes begin to have an effect on the distribution of the snowpack early in the season. Our results are consistent with previous research showing that individual point measurements are unable to fully represent the variability in the snowpack across a catchment, and we show that recognising and addressing this variability are particularly important for studies in marginal snow environments.

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Measuring snow ablation rates in alpine terrain with a mobile multioffset ground‐penetrating radar system

Cited by 18 publications

References 29 publications

Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping

Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping

Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping

Spatial controls on the distribution and dynamics of a marginal snowpack in the Australian Alps

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