scholarly journals Measuring glacier surface roughness using plot-scale, close-range digital photogrammetry

2014 ◽  
Vol 60 (223) ◽  
pp. 957-969 ◽  
Author(s):  
Tristram D.L. Irvine-Fynn ◽  
Enoc Sanz-Ablanedo ◽  
Nick Rutter ◽  
Mark W. Smith ◽  
Jim H. Chandler
Keyword(s):  
Surface Roughness ◽  
Temporal Variations ◽  
Horizontal Resolution ◽  
Spatial And Temporal Variation ◽  
Stereo Pair ◽  
Digital Photogrammetry ◽  
Glacier Surface ◽  
Ice Surface ◽  
Close Range ◽  
Ice Surface Roughness

AbstractGlacier roughness at sub-metre scales is an important control on the ice surface energy balance and has implications for scattering energy measured by remote-sensing instruments. Ice surface roughness is dynamic as a consequence of spatial and temporal variation in ablation. To date, studies relying on singular and/or spatially discrete two-dimensional profiles to describe ice surface roughness have failed to resolve common patterns or causes of variation in glacier surface morphology. Here we demonstrate the potential of close-range digital photogrammetry as a rapid and cost-effective method to retrieve three-dimensional data detailing plot-scale supraglacial topography. The photogrammetric approach here employed a calibrated, consumer-grade 5 Mpix digital camera repeatedly imaging a plot-scale (≤25 m2) ice surface area on Midtre Lovénbreen, Svalbard. From stereo-pair images, digital surface models (DSMs) with sub-centimetre horizontal resolution and 3 mm vertical precision were achieved at plot scales ≤4 m2. Extraction of roughness metrics including estimates of aerodynamic roughness length (z0) was readily achievable, and temporal variations in the glacier surface topography were captured. Close-range photogrammetry, with appropriate camera calibration and image acquisition geometry, is shown to be a robust method to record sub-centimetre variations in ablating ice topography. While the DSM plot area may be limited through use of stereo-pair images and issues of obliquity, emerging photogrammetric packages are likely to overcome such limitations.

scholarly journals C-Band Polarimetric Coherences and Ratios for Discriminating Sea Ice Roughness

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Mukesh Gupta ◽  
Randall K. Scharien ◽  
David G. Barber
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Two Dimensions ◽  
The Arctic ◽  
Rapid Decline ◽  
Ice Surface ◽  
Ice Roughness ◽  
Direct Measurements ◽  
Ice Surface Roughness ◽  
Spaceborne Radar

The rapid decline of sea ice in the Arctic has resulted in a variable sea ice roughness that necessitates improved methods for efficient observation using high-resolution spaceborne radar. The utility of C-band polarimetric backscatter, coherences, and ratios as a discriminator of ice surface roughness is evaluated. An existing one-dimensional backscatter model has been modified to two-dimensions (2D) by considering deviation in the orientation (i.e., the slopes) in azimuth and range direction of surface roughness simultaneously as an improvement in the model. It is shown theoretically that the circular coherence (ρRRLL) decreases exponentially with increasing surface roughness. The crosspolarized coherence (ρHHVH) is found to be less sensitive to surface roughness, whereas the copolarized coherence (ρVVHH) decreases at far-range incidence angles for all ice types. A complete validation of the adapted 2D model using direct measurements of surface roughness is suggested as an avenue for further research.

scholarly journals Effects of surface roughness and light-absorbing impurities on glacier surface albedo, August-one ice cap, Qilian Mountains, China

2020 ◽  
Author(s):  
Junfeng Liu ◽  
Rensheng Chen ◽  
Yongjian Ding ◽  
Chuntan Han ◽  
Yong Yang ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Large Scale ◽  
Surface Albedo ◽  
Flow Line ◽  
Glacier Surface ◽  
Negative Power ◽  
Mountain Glaciers ◽  
Ice Cap ◽  
Ice Surface ◽  
Exciting Potential

Abstract. Surface albedo is the main influence on surface melt for Qilian mountain glaciers. Fluctuations in surface albedo are due primarily to variations in micro scale surface roughness (ξ) and light-absorbing impurities (LAIs) in this region. However, combined ξ and LAIs effects over glacier surface albedo are rarely studied and surface roughness rarely considered in the albedo parameterization methods in this region. The present study was conducted in tandem with an intensive photogrammetric survey of glacier surface roughness, LAIs samples and albedo observations along the main flow-line of August-one ice cap during the 2018 melt season. Automatic photogrammetry of surface roughness and automatic observation of glacier surface albedo was conducted at middle of the ice cap in 2018. Detailed analysis indicates a negative power function and positive linear relationship exist between ξ and albedo for snow and ice surface, respectively. ξ could explain 68% of snow surface albedo and 38 % of ice surface albedo variation in melt season. Effective LAIs concentration (Cξ) calculated by consider ξ effect over LAIs deposition account for more than 63 % of albedo variation at ice surface. Using either ξ or Cξ to estimate ice surface albedo would be a great improvement over some current parameterization methods, such as assuming a constant mean ice surface albedo. A finer resolution of above 50 mm and above 100 mm is recommended for ice and snow ξ calculations, which explain more albedo variation than coarse resolutions below it. With advances in topographic surveys to improve the resolution, extent and availability of topographic datasets and surface roughness, appropriate parameterizations of albedo based on ξ have exciting potential to be applied over large scale snow cover and glacier.

Mapping Arctic Sea Ice Surface Roughness with Multi-angle Imaging SpectroRadiometry

2020 ◽  
Author(s):  
Thomas Johnson ◽  
Michel Tsamados ◽  
Jan-Peter Muller ◽  
Julienne Stroeve
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Distribution Functions ◽  
Arctic Sea Ice ◽  
Ice Age ◽  
Training Dataset ◽  
Support Vector ◽  
Laser Measurements ◽  
Ice Surface ◽  
Ice Surface Roughness

<div> <div> <p>Surface roughness is a crucial parameter in climate and oceanographic studies, constraining momentum transfer between the atmosphere and ocean, providing preconditioning for summer melt pond extent, while also closely related to ice age. High resolution roughness estimates from airborne laser measurements are limited in spatial and temporal coverage while pan-Arctic satellite roughness have remained elusive and do not extended over multi-decadal time-scales.  The MISR (Multi-angle Imaging SpectroRadiometer) instrument acquires optical imagery at 275m (red channel) and 1.1 km (all channels) resolutions from nine near-simultaneous camera view zenith angles sampling specular anisotropy, since 1999. Extending on previous work to model sea ice surface roughness from MISR angular reflectance signatures, a training dataset of cloud-free pixels and coincident probability distribution functions of lidar derived elevations from the Airborne Topographic Mapper (ATM) is generated. Surface roughness, defined as the standard deviation of the within-pixel elevations to a best-fit plane, is modelled using Support Vector Regression with a Radial Basis Function kernel, hyperparameters are tuned using GridSearchCV, and performance is assessed using nested cross-validation. We present derived instantaneous and monthly averaged sea ice roughness products at 1.1km and 17.6km resolution over the timespan of IceBridge campaigns (March and April for 2009-2018) on an EASE-2 (Equal-Area Scalable Earth) grid. These show considerable promise in detecting newly formed smooth ice from polynyas, and detailed surface features such as ridges and leads.</p> </div> </div><div> </div>

scholarly journals Sea-ice surface roughness estimates from airborne laser scanner and laser altimeter observations in Fram Strait and north of Svalbard

2015 ◽  
Vol 56 (69) ◽  
pp. 235-244 ◽  
Author(s):  
Justin F. Beckers ◽  
Angelika H.H. Renner ◽  
Gunnar Spreen ◽  
Sebastian Gerland ◽  
Christian Haas
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Laser Scanner ◽  
Ice Thickness ◽  
Fram Strait ◽  
Laser Altimeter ◽  
Airborne Laser Scanner ◽  
Ice Surface ◽  
Airborne Laser ◽  
Ice Surface Roughness

AbstractWe present sea-ice surface roughness estimates, i.e. the standard deviation of relative surface elevation, in the Arctic regions of Fram Strait and the Nansen Basin north of Svalbard acquired by an airborne laser scanner and a single-beam laser altimeter in 2010. We compare the scanner to the altimeter and compare the differences between the two survey regions. We estimate and correct sensor roll from the scanner data using the hyperbolic response of the scanner over a flat surface. Measurement surveys had to be longer than 5 km north of Svalbard and longer than 15 km in Fram Strait before the statistical distribution in surface roughness from the scanner and altimeter became similar. The shape of the surface roughness probability distributions agrees with those of airborne electromagnetic induction measurements of ice thickness. The ice in Fram Strait had a greater mean surface roughness, 0.16 m vs 0.09 m, and a wider distribution in roughness values than the ice in the Nansen Basin. An increase in surface roughness with increasing ice thickness was observed over fast ice found in Fram Strait near the coast of Greenland but not for the drift ice.

Late Summer Arctic Sea Ice Surface Roughness Signatures in C-Band SAR Data

2016 ◽  
Vol 9 (3) ◽  
pp. 1199-1215 ◽  
Author(s):  
Ane S. Fors ◽  
Camilla Brekke ◽  
Sebastian Gerland ◽  
Anthony P. Doulgeris ◽  
Justin F. Beckers
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Late Summer ◽  
Arctic Sea Ice ◽  
Ice Surface ◽  
Arctic Sea ◽  
Sar Data ◽  
Ice Surface Roughness

scholarly journals Parameterization of Centimeter-Scale Sea Ice Surface Roughness Using Terrestrial LiDAR

2015 ◽  
Vol 53 (3) ◽  
pp. 1271-1286 ◽  
Author(s):  
Jack C. Landy ◽  
Dustin Isleifson ◽  
Alexander S. Komarov ◽  
David G. Barber
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Terrestrial Lidar ◽  
Ice Surface ◽  
Ice Surface Roughness

scholarly journals Arctic Sea Ice Surface Roughness Derived from Multi-Angular Reflectance Satellite Imagery

2018 ◽  
Author(s):  
Anne W. Nolin
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Age ◽  
Ice Surface ◽  
Ice Roughness ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Angular Reflectance ◽  
Ice Surface Roughness

Sea ice surface roughness affects ice-atmosphere interactions, serves as an indicator of ice age, shows patterns of ice convergence and divergence, affects the spatial extent of summer melt ponds, and ice albedo. We have developed a method for mapping sea ice surface roughness using angular reflectance data from the Multi-angle Imaging SpectroRadiometer (MISR) and lidar-derived roughness measurements from the Airborne Topographic Mapper (ATM). Using an empirical data modeling approach, we derived estimates of Arctic sea ice roughness ranging from centimeters to decimeters meters within the MISR 275-m pixel size. Using independent ATM data for validation, we find that histograms of lidar and multi-angular roughness values are nearly identical for areas with roughness <20 cm but that for rougher regions, the MISR-derived roughness has a narrower range of values than the ATM data. The algorithm is able to accurately identify areas that transition between smooth and rough ice. Because of its coarser spatial scale, MISR-derived roughness data have a variance of about half that ATM roughness data.

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