Microscale variability of snow depth using U.A.S. technology

Abstract. We investigate the capabilities of photogrammetry-based surveys with Unmanned Aerial Systems (U.A.S.) to retrieve the snow depth distribution at cm resolution over a small alpine area (~300 000 m2). For this purpose, we have designed two field campaigns during the 2013/2014 winter season. In the first survey, realized at the beginning of the accumulation season, the digital elevation model of bare soil has been obtained. The second survey, made at the end of the accumulation season, allowed to determine the snow depth distribution as difference with respect to the previous aerial survey. 12 manual measurements of snow depth were collected at random positions in order to run a point comparison with U.A.S. measurements. The spatial integration of U.A.S. snow depth measurements allowed to estimate the snow volume accumulated over the area. We compare this volume estimation with the ones provided by classical interpolation techniques of the 12 point measurements. Results show that the U.A.S. technique provides an accurate estimation of point snow depth values (the average difference with reference to manual measurements is of −7.3 cm), and a distributed evaluation of the snow accumulation patterns. Moreover, the interpolation techniques considered return average differences in snow volume estimation, with respect to the one obtained through the U.A.S. technology, equal to ~21%.

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Using a fixed-wing UAS to map snow depth distribution: an evaluation at peak accumulation

The Cryosphere ◽

10.5194/tc-10-511-2016 ◽

2016 ◽

Vol 10 (2) ◽

pp. 511-522 ◽

Cited By ~ 41

Author(s):

Carlo De Michele ◽

Francesco Avanzi ◽

Daniele Passoni ◽

Riccardo Barzaghi ◽

Livio Pinto ◽

...

Keyword(s):

Standard Deviation ◽

Snow Depth ◽

Depth Distribution ◽

Aerial Survey ◽

Preliminary Evaluation ◽

Spatial Integration ◽

Rotor Systems ◽

Digital Elevation ◽

Elevation Model

Abstract. We investigate snow depth distribution at peak accumulation over a small Alpine area ( ∼  0.3 km2) using photogrammetry-based surveys with a fixed-wing unmanned aerial system (UAS). These devices are growing in popularity as inexpensive alternatives to existing techniques within the field of remote sensing, but the assessment of their performance in Alpine areas to map snow depth distribution is still an open issue. Moreover, several existing attempts to map snow depth using UASs have used multi-rotor systems, since they guarantee higher stability than fixed-wing systems. We designed two field campaigns: during the first survey, performed at the beginning of the accumulation season, the digital elevation model of the ground was obtained. A second survey, at peak accumulation, enabled us to estimate the snow depth distribution as a difference with respect to the previous aerial survey. Moreover, the spatial integration of UAS snow depth measurements enabled us to estimate the snow volume accumulated over the area. On the same day, we collected 12 probe measurements of snow depth at random positions within the case study to perform a preliminary evaluation of UAS-based snow depth. Results reveal that UAS estimations of point snow depth present an average difference with reference to manual measurements equal to −0.073 m and a RMSE equal to 0.14 m. We have also explored how some basic snow depth statistics (e.g., mean, standard deviation, minima and maxima) change with sampling resolution (from 5 cm up to ∼  100 m): for this case study, snow depth standard deviation (hence coefficient of variation) increases with decreasing cell size, but it stabilizes for resolutions smaller than 1 m. This provides a possible indication of sampling resolution in similar conditions.

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A snow-transport model for complex terrain

Journal of Glaciology ◽

10.3189/s0022143000002021 ◽

1998 ◽

Vol 44 (148) ◽

pp. 498-516 ◽

Cited By ~ 35

Author(s):

Glen E. Liston ◽

Matthew Sturm

Keyword(s):

Snow Depth ◽

Depth Distribution ◽

Snow Water Equivalent ◽

Transport Model ◽

Vegetation Type ◽

Cold Season ◽

Snow Accumulation ◽

Surface Shear Stress ◽

Surface Shear ◽

Snow Transport

AbstractAs part of the winter environment in middle- and high-latitude regions, the interactions between wind, vegetation, topography and snowfall produce snow covers of non-uniform depth and snow water-equivalent distribution. A physically based numerical snow-transport model (SnowTran-3D) is developed and used to simulate this three-dimensional snow-depth evolution over topographically variable terrain. The mass-transport model includes processes related to vegetation snow-holding capacity, topographic modification of wind speeds, snow-cover shear strength, wind-induced surface-shear stress, snow transport resulting from saltation and suspension, snow accumulation and erosion, and sublimation of the blowing and drifting snow. The model simulates the cold-season evolution of snow-depth distribution when forced with inputs of vegetation type and topography, and atmospheric foreings of air temperature, humidity, wind speed and direction, and precipitation. Model outputs include the spatial and temporal evolution of snow depth resulting from variations in precipitation, saltation and suspension transport, and sublimation. Using 4 years of snow-depth distribution observations from the foothills north of the Brooks Range in Arctic Alaska, the model is found to simulate closely the observed snow-depth distribution patterns and the interannual variability.

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Influence of snow depth distribution on surface roughness in alpine terrain: a multi-scale approach

The Cryosphere Discussions ◽

10.5194/tcd-7-4633-2013 ◽

2013 ◽

Vol 7 (5) ◽

pp. 4633-4680 ◽

Cited By ~ 1

Author(s):

J. Veitinger ◽

B. Sovilla ◽

R. S. Purves

Keyword(s):

Surface Roughness ◽

Snow Depth ◽

Depth Distribution ◽

Management Strategies ◽

Snow Accumulation ◽

Swiss Alps ◽

Transfer Energy ◽

Hazard Management ◽

Terrestrial Lidar ◽

Basin Scale

Abstract. In alpine terrain, the snow covered winter surface deviates from its underlying summer terrain due to the progressive smoothing caused by snow accumulation. Terrain smoothing is believed to be an important factor in avalanche formation, avalanche dynamics and affects surface heat transfer, energy balance as well as snow depth distribution. To characterize the effect of snow on terrain we use the concept of roughness. Roughness is calculated for several snow surfaces and its corresponding underlying terrain for three alpine basins in the Swiss Alps characterized by low medium and high terrain roughness. To this end, elevation models of winter and summer terrain are derived from high-resolution (1 m) measurements performed by airborne and terrestrial LIDAR. We showed that on basin scale terrain smoothing not only depends on mean snow depth in the basin but also on its variability. Terrain smoothing can be modelled in function of mean snow depth and its standard deviation using a power law. However, a relationship between terrain smoothing and snow depth does not exist on a pixel scale. Further we demonstrated the high persistence of snow surface roughness even in between winter seasons. Those persistent patterns might be very useful to improve the representation of a winter terrain without modelling of the snow cover distribution. This can potentially improve avalanche release area definition and in the long term natural hazard management strategies.

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Snowpack Distribution Using Topographical, Climatological and Winter Season Index Inputs

Atmosphere ◽

10.3390/atmos13010003 ◽

2021 ◽

Vol 13 (1) ◽

pp. 3

Author(s):

Douglas M. Hultstrand ◽

Steven R. Fassnacht ◽

John D. Stednick ◽

Christopher A. Hiemstra

Keyword(s):

Snow Depth ◽

Depth Distribution ◽

Generalized Additive Models ◽

Winter Season ◽

Regression Tree ◽

Distribution Patterns ◽

Time Frame ◽

Additive Models ◽

Depth Measurement ◽

Accumulation Index

A majority of the annual precipitation in many mountains falls as snow, and obtaining accurate estimates of the amount of water stored within the snowpack is important for water supply forecasting. Mountain topography can produce complex patterns of snow distribution, accumulation, and ablation, yet the interaction of topography and meteorological patterns tends to generate similar inter-annual snow depth distribution patterns. Here, we question whether snow depth patterns at or near peak accumulation are repeatable for a 10-year time frame and whether years with limited snow depth measurement can still be used to accurately represent snow depth and mean snow depth. We used snow depth measurements from the West Glacier Lake watershed, Wyoming, U.S.A., to investigate the distribution of snow depth. West Glacier Lake is a small (0.61 km2) windswept (mean of 8 m/s) watershed that ranges between 3277 m and 3493 m. Three interpolation methods were compared: (1) a binary regression tree, (2) multiple linear regression, and (3) generalized additive models. Generalized additive models using topographic parameters with measured snow depth presented the best estimates of the snow depth distribution and the basin mean amounts. The snow depth patterns near peak accumulation were found to be consistent inter-annually with an average annual correlation coefficient (r2) of 0.83, and scalable based on a winter season accumulation index (r2 = 0.75) based on the correlation between mean snow depth measurements to Brooklyn Lake snow telemetry (SNOTEL) snow depth data.

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Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry

Sensors ◽

10.3390/s19051027 ◽

2019 ◽

Vol 19 (5) ◽

pp. 1027 ◽

Cited By ~ 6

Author(s):

Theodora Lendzioch ◽

Jakub Langhammer ◽

Michal Jenicek

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Snow Depth ◽

Control Point ◽

Winter Season ◽

Snow Accumulation ◽

Plant Canopy ◽

Coverage Area ◽

Area Index ◽

Uav Images

This study presents a novel approach in the application of Unmanned Aerial Vehicle (UAV) imaging for the conjoint assessment of the snow depth and winter leaf area index (LAI), a structural property of vegetation, affecting the snow accumulation and snowmelt. The snow depth estimation, based on a multi-temporal set of high-resolution digital surface models (DSMs) of snow-free and of snow-covered conditions, taken in a partially healthy to insect-induced Norway spruce forest and meadow coverage area within the Šumava National Park (Šumava NP) in the Czech Republic, was assessed over a winter season. The UAV-derived DSMs featured a resolution of 0.73–1.98 cm/pix. By subtracting the DSMs, the snow depth was determined and compared with manual snow probes taken at ground control point (GCP) positions, the root mean square error (RMSE) ranged between 0.08 m and 0.15 m. A comparative analysis of UAV-based snow depth with a denser network of arranged manual snow depth measurements yielded an RMSE between 0.16 m and 0.32 m. LAI assessment, crucial for correct interpretation of the snow depth distribution in forested areas, was based on downward-looking UAV images taken in the forest regime. To identify the canopy characteristics from downward-looking UAV images, the snow background was used instead of the sky fraction. Two conventional methods for the effective winter LAI retrieval, the LAI-2200 plant canopy analyzer, and digital hemispherical photography (DHP) were used as a reference. Apparent was the effect of canopy density and ground properties on the accuracy of DSMs assessment based on UAV imaging when compared to the field survey. The results of UAV-based LAI values provided estimates were comparable to values derived from the LAI-2200 plant canopy analyzer and DHP. Comparison with the conventional survey indicated that spring snow depth was overestimated, and spring LAI was underestimated by using UAV photogrammetry method. Since the snow depth and the LAI parameters are essential for snowpack studies, this combined method here will be of great value in the future to simplify snow depth and LAI assessment of snow dynamics.

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Topographical effects of Snow Deposition on Restructured Land

Journal of Glaciology ◽

10.3189/s0022143000011114 ◽

1980 ◽

Vol 26 (94) ◽

pp. 518-518

Author(s):

E. Chaco ◽

M. Molnau

Keyword(s):

Snow Depth ◽

Winter Season ◽

Surface Mining ◽

Snow Accumulation ◽

Grid Data ◽

Grid Pattern ◽

Contour Maps ◽

Snow Properties ◽

Waste Dumps ◽

Snow Deposition

AbstractThe measurement of snow accumulation and distribution is one of the primary objectives of a study on the melt of snow-drifts and erosion in the phosphate mining region of south-eastern Idaho. The study area is located in an active phosphate mine and is limited to the sites of waste dumps, a product of the surface mining technique used in this area. Four sites are included in the overall study with one dump selected for intensive snow measurement. Snow deposition data have been collected for one winter season (November 1977—June 1978) on a grid pattern over this dump. The area of the study site has been expanded and similar measurements are planned for the coming snow season.The snow measurements were made monthly on a pre-established 23 m (75 ft) square grid overlaid on the dump. The analysis of the snow data consists of contour mapping of any one or all the snow properties measured—snow depth, density, or water equivalent. In addition, since the measurements are made on the same grid each month, mathematical manipulation of grid data allows contour maps of the residual of the monthly snow properties to be plotted. A similar analysis of terrain properties collected on the same grid results in contour maps displaying ground slope, concavity-convexity of the surface, aspect, or distance from snow- deposition obstacles.The aim of the analysis using these types of data is to arrive at a model which will compute patterns of snow accumulation and distribution on the ground surface given a description of terrain type and probable meteorological properties of the region. A preliminary comparison of the maps shows a similar pattern of snow deposition occurring each month with the exposed areas of the dump swept clean and the greatest snow depth occurring in the sheltered concavities.

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Topographic control of snowpack distribution in a small catchment in the central Spanish Pyrenees: intra- and inter-annual persistence

The Cryosphere Discussions ◽

10.5194/tcd-8-1937-2014 ◽

2014 ◽

Vol 8 (2) ◽

pp. 1937-1972 ◽

Cited By ~ 3

Author(s):

J. Revuelto ◽

J. I. López-Moreno ◽

C. Azorin-Molina ◽

S. M. Vicente-Serrano

Keyword(s):

Snow Depth ◽

Depth Distribution ◽

Laser Scanner ◽

Climatic Conditions ◽

Snow Accumulation ◽

Small Catchment ◽

Explanatory Variables ◽

Spanish Pyrenees ◽

Snow Distribution ◽

Variance Explained

Abstract. In this study we analyzed the relations between terrain characteristics and snow depth distribution in a small alpine catchment located in the central Spanish Pyrenees. Twelve field campaigns were conducted during 2012 and 2013, which were years characterized by very different climatic conditions. Snow depth was measured using a long range terrestrial laser scanner and analyses were performed at a spatial resolution of 5 m. Pearson's r correlation, multiple linear regressions and binary regression trees were used to analyze the influence of topography on the snow depth distribution. The analyses were used to identify the topographic variables that better explain the snow distribution in this catchment, and to assess whether their contributions were variable over intra- and inter-annual time scales. The topographic position index, which has rarely been used in these types of studies, most accurately explained the distribution of snow accumulation. Other variables affecting the snow depth distribution included the maximum upwind slope, elevation, and northing (or potential incoming solar radiation). The models developed to predict snow distribution in the basin for each of the 12 survey days were similar in terms of the most explanatory variables. However, the variance explained by the overall model and by each topographic variable, especially those making a lesser contribution, differed markedly between a year in which snow was abundant (2013) and a~year when snow was scarce (2012), and also differed between surveys in which snow accumulation or melting conditions dominated in the preceding days. The total variance explained by the models clearly decreased for those days on which the snow pack was thinner and more patchily distributed. Despite the differences in climatic conditions in the 2012 and 2013 snow seasons, some similarities in snow accumulation patterns were observed.

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Mapping snow depth in alpine terrain with unmanned aerial systems (UAS): potential and limitations

10.5194/tc-2015-220 ◽

2016 ◽

Cited By ~ 2

Author(s):

Y. Bühler ◽

M. S. Adams ◽

R. Bösch ◽

A. Stoffel

Keyword(s):

Snow Depth ◽

Laser Scanning ◽

Depth Distribution ◽

Cost Effective ◽

Test Site ◽

Measurement Technology ◽

Unmanned Aerial Systems ◽

Tall Grass ◽

Aerial Systems ◽

Better Than

Abstract. Detailed information on the spatiotemporal distribution, and variability of snow depth (HS) is a crucial input for numerous applications in hydrology, climatology, ecology and avalanche research. Nowadays, snow depth distribution is usually estimated by combining point measurements from weather stations or observers in the field with spatial interpolation algorithms. However, even a dense measurement network is not able to capture the large spatial variability of snow depth present in alpine terrain. Remote sensing methods, such as laser scanning or digital photogrammetry, have recently been successfully applied to map snow depth variability at local and regional scales. However, such data acquisition is costly if manned airplanes are involved. The effectiveness of ground-based measurements on the other hand is often hindered by occlusions, due to the complex terrain or acute viewing angles. In this paper, we investigate the application of unmanned aerial systems (UAS), in combination with structure-from-motion photogrammetry, to map snow depth distribution. Such systems have the advantage that they are comparatively cost-effective and can be applied very flexibly to cover otherwise inaccessible terrain. In this study, we map snow depth at two different locations: (a) a sheltered location at the bottom of the Flüela valley (1900 m a.s.l.) and (b) an exposed location on a peak (2500 m a.s.l.) in the ski resort Jakobshorn, both in the vicinity of Davos, Switzerland. At the first test site, we monitor the ablation on three different dates. We validate the photogrammetric snow depth maps using simultaneously acquired manual snow depth measurements. The resulting snow depth values have a root mean square error (RMSE) better than 0.07 to 0.15 m on meadows and rocks and a RMSE better than 0.30 m on sections covered by bushes or tall grass. This new measurement technology opens the door for efficient, flexible, repeatable and cost effective snow depth monitoring for various applications, investigating the worlds cryosphere.

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Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations

The Cryosphere ◽

10.5194/tc-10-1075-2016 ◽

2016 ◽

Vol 10 (3) ◽

pp. 1075-1088 ◽

Cited By ~ 73

Author(s):

Yves Bühler ◽

Marc S. Adams ◽

Ruedi Bösch ◽

Andreas Stoffel

Keyword(s):

Snow Depth ◽

Laser Scanning ◽

Depth Distribution ◽

Cost Effective ◽

Test Site ◽

Measurement Technology ◽

Unmanned Aerial Systems ◽

Tall Grass ◽

The One ◽

Aerial Systems

Abstract. Detailed information on the spatiotemporal snow depth distribution is a crucial input for numerous applications in hydrology, climatology, ecology and avalanche research. Today, snow depth distribution is usually estimated by combining point measurements from weather stations or observers in the field with spatial interpolation algorithms. However, even a dense measurement network like the one in Switzerland, with more than one measurement station per 10 km2 on average, is not able to capture the large spatial variability of snow depth present in alpine terrain.Remote sensing methods, such as laser scanning or digital photogrammetry, have recently been successfully applied to map snow depth variability at local and regional scales. However, in most countries such data acquisition is costly if manned airplanes are involved. The effectiveness of ground-based measurements on the other hand is often hindered by occlusions, due to the complex terrain or acute viewing angles. In this paper, we investigate the application of unmanned aerial systems (UASs), in combination with structure-from-motion photogrammetry, to map snow depth distribution. Compared to manual measurements, such systems are relatively cost-effective and can be applied very flexibly to cover terrain not accessible from the ground. In this study, we map snow depth at two different locations: (a) a sheltered location at the bottom of the Flüela valley (1900 m a.s.l.) and (b) an exposed location on a peak (2500 m a.s.l.) in the ski resort Jakobshorn, both in the vicinity of Davos, Switzerland. At the first test site, we monitor the ablation on three different dates. We validate the photogrammetric snow depth maps using simultaneously acquired manual snow depth measurements. The resulting snow depth values have a root mean square error (RMSE) of less than 0.07 to 0.15 m on meadows and rocks and a RMSE of less than 0.30 m on sections covered by bushes or tall grass, compared to manual probe measurements. This new measurement technology opens the door for efficient, flexible, repeatable and cost-effective snow depth monitoring over areas of several hectares for various applications, if the national and regional regulations permit the application of UASs.

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Topographical effects of Snow Deposition on Restructured Land

Journal of Glaciology ◽

10.1017/s0022143000011114 ◽

1980 ◽

Vol 26 (94) ◽

pp. 518

Author(s):

E. Chaco ◽

M. Molnau

Keyword(s):

Snow Depth ◽

Winter Season ◽

Surface Mining ◽

Snow Accumulation ◽

Grid Data ◽

Grid Pattern ◽

Contour Maps ◽

Snow Properties ◽

Waste Dumps ◽

Snow Deposition

Abstract The measurement of snow accumulation and distribution is one of the primary objectives of a study on the melt of snow-drifts and erosion in the phosphate mining region of south-eastern Idaho. The study area is located in an active phosphate mine and is limited to the sites of waste dumps, a product of the surface mining technique used in this area. Four sites are included in the overall study with one dump selected for intensive snow measurement. Snow deposition data have been collected for one winter season (November 1977—June 1978) on a grid pattern over this dump. The area of the study site has been expanded and similar measurements are planned for the coming snow season. The snow measurements were made monthly on a pre-established 23 m (75 ft) square grid overlaid on the dump. The analysis of the snow data consists of contour mapping of any one or all the snow properties measured—snow depth, density, or water equivalent. In addition, since the measurements are made on the same grid each month, mathematical manipulation of grid data allows contour maps of the residual of the monthly snow properties to be plotted. A similar analysis of terrain properties collected on the same grid results in contour maps displaying ground slope, concavity-convexity of the surface, aspect, or distance from snow- deposition obstacles. The aim of the analysis using these types of data is to arrive at a model which will compute patterns of snow accumulation and distribution on the ground surface given a description of terrain type and probable meteorological properties of the region. A preliminary comparison of the maps shows a similar pattern of snow deposition occurring each month with the exposed areas of the dump swept clean and the greatest snow depth occurring in the sheltered concavities.

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