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

2021 ◽  
Vol 2 ◽  
Author(s):  
Grant E. Gunn ◽  
Benjamin M. Jones ◽  
Rodrigo C. Rangel
Keyword(s):  
High Resolution ◽  
Correlation Length ◽  
Snow Depth ◽  
Depth Map ◽  
The Arctic ◽  
The West ◽  
Lake Ice ◽  
Snowpack Properties ◽  
Northern Alaska

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.

scholarly journals EVALUATION OF DIGITAL ELEVATION MODELS FOR GEOMORPHOMETRIC ANALYSES ON DIFFERENT SCALES FOR NORTHERN CHILE

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1229-1235 ◽  
Author(s):  
T. Kramm ◽  
D. Hoffmeister
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
Thermal Emission ◽  
Digital Elevation Models ◽  
Regional Scale ◽  
Laser Altimeter ◽  
Absolute Deviation ◽  
Digital Elevation ◽  
Very High

<p><strong>Abstract.</strong> The resolution and accuracy of digital elevation models (DEMs) have direct influence on further geoscientific computations like landform classifications and hydrologic modelling results. Thus, it is crucial to analyse the accuracy of DEMs to select the most suitable elevation model regarding aim, accuracy and scale of the study. Nowadays several worldwide DEMs are available, as well as DEMs covering regional or local extents. In this study a variety of globally available elevation models were evaluated for an area of about 190,000&amp;thinsp;km<sup>2</sup>. Data from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m, Shuttle Radar Topography Mission (SRTM) 30&amp;thinsp;m and 90&amp;thinsp;m, Advanced Land Observing Satellite (ALOS) World 3D 30&amp;thinsp;m and TanDEM-X WorldDEM&amp;trade; &amp;ndash; 12&amp;thinsp;m and 90&amp;thinsp;m resolution were obtained. Additionally, several very high resolution DEM data were derived from stereo satellite imagery from SPOT 6/7 and Pléiades for smaller areas of about 100&amp;ndash;400&amp;thinsp;km<sup>2</sup> for each dataset. All datasets were evaluated with height points of the Geoscience Laser Altimeter System (GLAS) instrument aboard the NASA Ice, Cloud, and land Elevation (ICESat) satellite on a regional scale and with nine very high resolution elevation models from UAV-based photogrammetry on a very large scale. For all datasets the root mean square error (RMSE) and normalized median absolute deviation (NMAD) was calculated. Furthermore, the association of errors to specific terrain was conducted by assigning these errors to landforms from the topographic position index (TPI), topographic roughness index (TRI) and slope. For all datasets with a global availability the results show the highest overall accuracies for the TanDEM-X 12&amp;thinsp;m (RMSE: 2.3&amp;thinsp;m, NMAD: 0.8&amp;thinsp;m). The lowest accuracies were detected for the 30&amp;thinsp;m ASTER GDEM v3 (RMSE: 8.9&amp;thinsp;m, NMAD: 7.1&amp;thinsp;m). Depending on the landscape the accuracies are higher for all DEMs in flat landscapes and the errors rise significantly in rougher terrain. Local scale DEMs derived from stereo satellite imagery show a varying overall accuracy, mainly depending on the topography covered by the scene.</p>

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