scholarly journals Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data

2020 ◽  
Vol 14 (9) ◽  
pp. 2925-2940 ◽  
Author(s):  
César Deschamps-Berger ◽  
Simon Gascoin ◽  
Etienne Berthier ◽  
Jeffrey Deems ◽  
Ethan Gutmann ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Snow Depth ◽  
Laser Scanning ◽  
Unmanned Aircraft ◽  
Small Scale ◽  
High Mountain ◽  
Catchment Scale ◽  
Absolute Deviation ◽  
Covered Area ◽  
Tuolumne River

Abstract. Accurate knowledge of snow depth distributions in mountain catchments is critical for applications in hydrology and ecology. Recently, a method was proposed to map snow depth at meter-scale resolution from very-high-resolution stereo satellite imagery (e.g., Pléiades) with an accuracy close to 0.5 m. However, the validation was limited to probe measurements and unmanned aircraft vehicle (UAV) photogrammetry, which sampled a limited fraction of the topographic and snow depth variability. We improve upon this evaluation using accurate maps of the snow depth derived from Airborne Snow Observatory laser-scanning measurements in the Tuolumne river basin, USA. We find a good agreement between both datasets over a snow-covered area of 138 km2 on a 3 m grid, with a positive bias for a Pléiades snow depth of 0.08 m, a root mean square error of 0.80 m and a normalized median absolute deviation (NMAD) of 0.69 m. Satellite data capture the relationship between snow depth and elevation at the catchment scale and also small-scale features like snow drifts and avalanche deposits at a typical scale of tens of meters. The random error at the pixel level is lower in snow-free areas than in snow-covered areas, but it is reduced by a factor of 2 (NMAD of approximately 0.40 m for snow depth) when averaged to a 36 m grid. We conclude that satellite photogrammetry stands out as a convenient method to estimate the spatial distribution of snow depth in high mountain catchments.

scholarly journals Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne lidar data

2020 ◽  
Author(s):  
César Deschamps-Berger ◽  
Simon Gascoin ◽  
Etienne Berthier ◽  
Jeffrey Deems ◽  
Ethan Gutmann ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Random Error ◽  
Snow Depth ◽  
Depth Map ◽  
Airborne Lidar ◽  
Small Scale ◽  
High Mountain ◽  
Catchment Scale ◽  
Absolute Deviation ◽  
Tuolumne River

Abstract. An accurate knowledge of snow depth distribution in mountain catchments is critical for applications in hydrology and ecology. A recent new method was proposed to map the snow depth at meter-scale resolution from very-high resolution stereo satellite imagery (e.g., Pléiades) with an accuracy close to 0.50 m. However, the validation was mainly done using probe measurements which sampled a limited fraction of the topographic and snow depth variability. We deepen this evaluation using accurate maps of the snow depth derived from ASO airborne lidar measurements in the Tuolumne river basin, USA. We find a good agreement between both datasets over a snow-covered area of 137 km2 on a 3 m grid with a positive bias for Pléiades snow depth of 0.08 m, a root-mean-square error of 0.80 m and a normalized median absolute deviation of 0.69 m. Satellite data capture the relationship between snow depth and elevation at the catchment scale, and also small-scale features like snow drifts and avalanche deposits. The random error on snow depth can be reduced by a factor two (up to approximately 0.40 m) when the snow depth map is spatially averaged to a ~ 20 m grid. The random error at the pixel level is lower on snow-free areas than on snow-covered areas, but errors on both terrain type converge at coarser resolutions, which is important for further applications of the method in areas without snow depth reference data. We conclude that satellite photogrammetry stands out as an efficient method to estimate the spatial distribution of snow depth in high mountain catchments.

scholarly journals LiDAR snow cover studies on glacier surface: significance of snow- and ice dynamical processes

2013 ◽  
Vol 7 (2) ◽  
pp. 1787-1832 ◽  
Author(s):  
K. Helfricht ◽  
M. Kuhn ◽  
M. Keuschnig ◽  
A. Heilig
Keyword(s):  
Spatial Distribution ◽  
Standard Deviation ◽  
Snow Cover ◽  
Snow Depth ◽  
Laser Scanning ◽  
Snow Accumulation ◽  
Surface Elevation ◽  
High Mountain ◽  
Dynamical Processes ◽  
Elevation Changes

Abstract. The storage of water within the seasonal snow cover is a substantial source for runoff in high mountain catchments. Information about the spatial distribution of snow accumulation is necessary for calibration and validation of hydro-meteorological models. Generally only a small number of precipitation measurements deliver precipitation input for modeling in remote mountain areas. The spatial interpolation and extrapolation of measurements of precipitation is still difficult. Multi-temporal application of Light Detecting And Ranging (LiDAR) techniques from aircraft, so-called airborne laser scanning (ALS), enables to derive surface elevations changes even in inaccessible terrain. Within one snow accumulation season these surface elevation changes can be interpreted as snow depths as a first assumption for snow hydrological studies. However, dynamical processes in snow, firn and ice are contributing to surface elevation changes on glaciers. To evaluate the magnitude and significance of these processes on alpine glaciers in the present state, ALS derived surface elevation changes were compared to converted snow depths from 35.4 km of ground penetrating radar (GPR) profiles on four glaciers in the high alpine region of Ötztal Alps. LANDSAT data were used to distinguish between firn and ice areas of the glaciers. In firn areas submerging ice flow and densification of firn and snow are contributing to a mean relative deviation of ALS surface elevation changes from actually observed snow depths of −20.0% with a mean standard deviation of 17.1%. Deviations between ALS surface elevation changes and GPR snow depth are small along the profiles on the glacier tongues. At these areas mean absolute deviation of ALS surface elevation changes and GPR snow depth is 0.004 m with a mean standard deviation of 0.27 m. Emergence flow leads to distinct positive deviations only at the very front of the glacier tongues. Snow depths derived from ALS deviate less from actually measured snow depths than expected errors of in-situ measurements of solid precipitation. Hence, ALS derived snow depths are an important data source for both, spatial distribution and total sum of the snow cover volume stored on the investigated glaciers and in the corresponding high mountain catchments at the end of an accumulation season.

scholarly journals Mapping snow depth in open alpine terrain from stereo satellite imagery

2016 ◽  
Author(s):  
R. Marti ◽  
S. Gascoin ◽  
E. Berthier ◽  
M. de Pinel ◽  
T. Houet ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Snow Depth ◽  
Depth Map ◽  
Unmanned Aircraft ◽  
Mountainous Areas ◽  
Ground Control Points ◽  
Accuracy And Precision ◽  
Digital Elevation ◽  
Probe Measurements

Abstract. To date, there is no direct approach to map snow depth in mountainous areas from spaceborne sensors. Here, we examine the potential of very-high-resolution (VHR) stereo satellites to this purpose. Two triplets of 70 cm-resolution images were acquired by the Pléiades satellite over an open alpine catchment (14.5 km2) under snow-free and snow-covered conditions. The open-source software Ame's Stereo Pipeline (ASP) was used to match the stereo pairs without ground control points, to generate raw photogrammetric clouds and to convert them into high-resolution Digital Elevation Models (DEMs) at 1-m, 2-m, and 4-m resolutions. The DEMs difference (dDEM) were computed after 3D-coregistration, including a correction of a −0.48 m vertical bias. The bias-corrected dDEMs maps were compared to 451 snow probe measurements. The results show a decimetric accuracy and precision in the Pléiades-derived snow depths. The median of the residuals is −0.16 m, with a standard deviation (SD) of 0.58 m at a pixel size of 2 m. We compared the 2 m-Pléiades dDEM to a 2 m-dDEM that was based on a winged unmanned aircraft vehicle (UAV) photogrammetric survey that was performed on the same winter date over a portion of the catchment (3.1 km2). The UAV-derived snow depth map exhibit the same patterns as the Pléiades-derived snow map. The Pléiades images benefit from a very broad radiometric range (12 bits), allowing a high correlation success rate over the snow-covered areas. This study demonstrates the value of VHR stereo satellite imagery to map snow depth in remote mountainous areas without any field data.

scholarly journals ACCURACY OF SNOW DEPTH ESTIMATION IN MOUNTAIN AND PRAIRIE ENVIRONMENTS BY AN UNMANNED AERIAL VEHICLE

2016 ◽  
Author(s):  
Phillip Harder ◽  
Michael Schirmer ◽  
John Pomeroy ◽  
Warren Helgason
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Snow Depth ◽  
Laser Scanning ◽  
Low Cost ◽  
Depth Estimation ◽  
Snow Accumulation ◽  
Small Scale ◽  
Canadian Prairie ◽  
Aerial Vehicle ◽  
Mean Square Errors

Abstract. The quantification of the spatial distribution of snow is crucial to predict and assess snow as a water resource and understand land-atmosphere interactions in cold regions. Typical remote sensing approaches to quantify snow depth have focused on terrestrial and airborne laser scanning and recently airborne (manned and unmanned) photogrammetry. In this study photography from a small unmanned aerial vehicle (UAV) was used to generate digital surface models (DSMs) and orthomosaics for snowcovers at a cultivated agricultural Canadian Prairie and a sparsely-vegetated Rocky Mountain alpine ridgetop site using Structure from Motion (SfM). The ability of this method to quantify snow depth, changes in depth and its spatial variability was assessed for different terrain types over time. Root mean square errors in snow depth estimation from the DSMs were 8.8 cm for a short prairie grain stubble surface, 13.7 cm for a tall prairie grain stubble surface and 8.5 cm for an alpine mountain surface. This technique provided meaningful information on maximum snow accumulation and snow-covered area depletion at all sites, while temporal changes in snow depth could also be quantified at the alpine site due to the deeper snowpack and consequent higher signal-to noise-ratio. The application of SfM to UAV photographs can estimate snow depth in areas with snow depth > 30 cm – this restricts its utility for studies of the ablation of shallow, windblown snowpacks. Accuracy varied with surface characteristics, sunlight and wind speed during the flight, with the most consistent performance found for wind speeds < 6 m s−1, clear skies, high sun angles and surfaces with negligible vegetation cover. Relative to surfaces having greater contrast and more identifiable features, snow surfaces present unique challenges when applying SfM to imagery collected by a small UAV for the generation of DSMs. Regardless, the low cost, deployment mobility and the capability of repeat-on-demand flights that generate DSMs and orthomosaics of unprecedented spatial resolution provide exciting opportunities to quantify previously unobservable small-scale variability in snow depth and its dynamics.

scholarly journals Statistical modelling of the snow depth distribution on the catchment scale

2013 ◽  
Vol 10 (3) ◽  
pp. 3237-3282 ◽  
Author(s):  
T. Grünewald ◽  
J. Stötter ◽  
J. W. Pomeroy ◽  
R. Dadic ◽  
I. Moreno Baños ◽  
...  
Keyword(s):  
Snow Depth ◽  
Laser Scanning ◽  
Depth Distribution ◽  
Statistical Modelling ◽  
Snow Accumulation ◽  
Catchment Scale ◽  
Local Conditions ◽  
Large Variability ◽  
Explanatory Variables ◽  
Unit Scale

Abstract. The spatial distribution of alpine snow covers is characterized by a large variability. Taking this variability into account is important for many tasks including hydrology, glaciology, ecology or natural hazards. Statistical modelling is frequently applied to assess the spatial variability of the snow cover. For this study, we assembled seven data sets of high-resolution snow-depth measurements from different mountain regions around the world. All data were obtained from airborne laser scanning near the time of maximum seasonal snow accumulation. Topographic parameters were used to model the snow depth distribution on the catchment-scale by applying multiple linear regressions. We found that by averaging out the substantial spatial heterogeneity at the metre scales, i.e. individual drifts and aggregating snow accumulation at the landscape or hydrological response unit scale, that 30% to 91% of the snow depth variability can be explained by models that are calibrated to local conditions at the single study areas. As all sites were sparsely vegetated, only a few topographic variables were included as explanatory variables, including elevation, slope, the deviation of the aspect from north (northing), and a wind sheltering parameter. In most cases, elevation, slope and northing are very good predictors of snow distribution. A comparison of the models showed that importance of parameters and their coefficients differed among the catchments. A "global" model, combining all the data from all areas investigated, could still explain 23% of the variability. It appears that local statistical models cannot be transferred to different regions. However, there seem to be some temporal transferability, in which models developed on one peak snow season were good predictors for other peak snow seasons.

scholarly journals Statistical modelling of the snow depth distribution in open alpine terrain

2013 ◽  
Vol 17 (8) ◽  
pp. 3005-3021 ◽  
Author(s):  
T. Grünewald ◽  
J. Stötter ◽  
J. W. Pomeroy ◽  
R. Dadic ◽  
I. Moreno Baños ◽  
...  
Keyword(s):  
Snow Depth ◽  
Laser Scanning ◽  
Depth Distribution ◽  
Statistical Modelling ◽  
Snow Accumulation ◽  
Catchment Scale ◽  
Local Conditions ◽  
Large Variability ◽  
Explanatory Variables ◽  
Unit Scale

Abstract. The spatial distribution of alpine snow covers is characterised by large variability. Taking this variability into account is important for many tasks including hydrology, glaciology, ecology or natural hazards. Statistical modelling is frequently applied to assess the spatial variability of the snow cover. For this study, we assembled seven data sets of high-resolution snow-depth measurements from different mountain regions around the world. All data were obtained from airborne laser scanning near the time of maximum seasonal snow accumulation. Topographic parameters were used to model the snow depth distribution on the catchment-scale by applying multiple linear regressions. We found that by averaging out the substantial spatial heterogeneity at the metre scales, i.e. individual drifts and aggregating snow accumulation at the landscape or hydrological response unit scale (cell size 400 m), that 30 to 91% of the snow depth variability can be explained by models that are calibrated to local conditions at the single study areas. As all sites were sparsely vegetated, only a few topographic variables were included as explanatory variables, including elevation, slope, the deviation of the aspect from north (northing), and a wind sheltering parameter. In most cases, elevation, slope and northing are very good predictors of snow distribution. A comparison of the models showed that importance of parameters and their coefficients differed among the catchments. A "global" model, combining all the data from all areas investigated, could only explain 23% of the variability. It appears that local statistical models cannot be transferred to different regions. However, models developed on one peak snow season are good predictors for other peak snow seasons.

scholarly journals Mapping snow depth in open alpine terrain from stereo satellite imagery

2016 ◽  
Vol 10 (4) ◽  
pp. 1361-1380 ◽  
Author(s):  
R. Marti ◽  
S. Gascoin ◽  
E. Berthier ◽  
M. de Pinel ◽  
T. Houet ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Snow Depth ◽  
Depth Map ◽  
Unmanned Aircraft ◽  
Mountainous Areas ◽  
Ground Control Points ◽  
Accuracy And Precision ◽  
Digital Elevation ◽  
Probe Measurements

Abstract. To date, there is no definitive approach to map snow depth in mountainous areas from spaceborne sensors. Here, we examine the potential of very-high-resolution (VHR) optical stereo satellites to this purpose. Two triplets of 0.70 m resolution images were acquired by the Pléiades satellite over an open alpine catchment (14.5 km2) under snow-free and snow-covered conditions. The open-source software Ame's Stereo Pipeline (ASP) was used to match the stereo pairs without ground control points to generate raw photogrammetric clouds and to convert them into high-resolution digital elevation models (DEMs) at 1, 2, and 4 m resolutions. The DEM differences (dDEMs) were computed after 3-D coregistration, including a correction of a −0.48 m vertical bias. The bias-corrected dDEM maps were compared to 451 snow-probe measurements. The results show a decimetric accuracy and precision in the Pléiades-derived snow depths. The median of the residuals is −0.16 m, with a standard deviation (SD) of 0.58 m at a pixel size of 2 m. We compared the 2 m Pléiades dDEM to a 2 m dDEM that was based on a winged unmanned aircraft vehicle (UAV) photogrammetric survey that was performed on the same winter date over a portion of the catchment (3.1 km2). The UAV-derived snow depth map exhibits the same patterns as the Pléiades-derived snow map, with a median of −0.11 m and a SD of 0.62 m when compared to the snow-probe measurements. The Pléiades images benefit from a very broad radiometric range (12 bits), allowing a high correlation success rate over the snow-covered areas. This study demonstrates the value of VHR stereo satellite imagery to map snow depth in remote mountainous areas even when no field data are available.

scholarly journals Using a Groundwater Adjusted Water Balance Approach and Copulas to Evaluate Spatial Patterns and Dependence Structures in Remote Sensing Derived Evapotranspiration Products

2021 ◽  
Vol 13 (5) ◽  
pp. 853
Author(s):  
Mohsen Soltani ◽  
Julian Koch ◽  
Simon Stisen
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Principal Component ◽  
Small Scale ◽  
Dependence Structure ◽  
Test Case ◽  
Catchment Scale ◽  
Balance Approach

This study aims to improve the standard water balance evapotranspiration (WB ET) estimate, which is typically used as benchmark data for catchment-scale ET estimation, by accounting for net intercatchment groundwater flow in the ET calculation. Using the modified WB ET approach, we examine errors and shortcomings associated with the long-term annual mean (2002–2014) spatial patterns of three remote-sensing (RS) MODIS-based ET products from MODIS16, PML_V2, and TSEB algorithms at 1 km spatial resolution over Denmark, as a test case for small-scale, energy-limited regions. Our results indicate that the novel approach of adding groundwater net in water balance ET calculation results in a more trustworthy ET spatial pattern. This is especially relevant for smaller catchments where groundwater net can be a significant component of the catchment water balance. Nevertheless, large discrepancies are observed both amongst RS ET datasets and compared to modified water balance ET spatial pattern at the national scale; however, catchment-scale analysis highlights that difference in RS ET and WB ET decreases with increasing catchment size and that 90%, 87%, and 93% of all catchments have ∆ET < ±150 mm/year for MODIS16, PML_V2, and TSEB, respectively. In addition, Copula approach captures a nonlinear structure of the joint relationship with multiple densities amongst the RS/WB ET products, showing a complex dependence structure (correlation); however, among the three RS ET datasets, MODIS16 ET shows a closer spatial pattern to the modified WB ET, as identified by a principal component analysis also. This study will help improve the water balance approach by the addition of groundwater net in the ET estimation and contribute to better understand the true correlations amongst RS/WB ET products especially over energy-limited environments.

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