Metrics for assessing snow surface roughness from digital imagery

Fassnacht, S. R.; Stednick, John D.; Deems, J. S.; Corrao, Mark V.

doi:10.1029/2008wr006986

Cited by 31 publications

(45 citation statements)

References 23 publications

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“…There are a large number of established parameters used to describe surface roughness [ Church , ; Manes et al ., ; Manninen , ; Fassnacht et al ., ; Hollaus et al ., ; Lacroix et al ., ; Rees and Arnold , , a good overview in Dong et al ., , , , ]. The material of the surface and the feature that is crucial for the application of interest defines which parameters are used.…”

Section: Introductionmentioning

confidence: 99%

The temporal and spatial variability in submeter scale surface roughness of seasonal snow in Sodankylä Finnish Lapland in 2009–2010

Anttila

Manninen

Karjalainen³

et al. 2014

JGR Atmospheres

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Seasonal snow surface roughness is an important parameter for remote sensing data analysis since it affects the scattering properties of the snow surface. To understand the phenomenon, snow surface roughness was measured near the town of Sodankylä, in Finnish Lapland, during winters 2009 and 2010 using a photogrammetry-based plate method. The images were automatically processed so that an approximately 1 m long horizontal profile was extracted from each image. The data set consists of 669 plate profiles from different times and canopy types. This large data set was used to study the temporal and spatial variability of seasonal snow surface roughness. The profiles were analyzed using parameters derived from the root mean square height (σ) and correlation length (L) as functions of measured length. Also, the autocorrelation functions were calculated and analyzed. The (σ) and (L) were found to be so strongly correlated (R 2~0 .97) that a more detailed analysis was made using only the scaling parameters derived from σ. These parameters are related to the distance dependence of the rms height. The results show that they react to different characteristics of the profiles and are therefore well able to distinguish between different types of snow. They also show a clear difference between midwinter snow and melting snow, and the effects of snowfall events and slower melting in forested areas are evident in the data.

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Section: Introductionmentioning

confidence: 99%

The temporal and spatial variability in submeter scale surface roughness of seasonal snow in Sodankylä Finnish Lapland in 2009–2010

Anttila

Manninen

Karjalainen³

et al. 2014

JGR Atmospheres

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“…The latter determines the rate of snowmelt, with consequences on the watershed runoff dynamic. Snow surface roughness varies at multiple scales (Fassnacht et al, 2009a), from grain scale variation (Fassnacht et al, 2009b) to large scale features that develop due to wind, topography, and underlying vegetation (Munro, 1989;Smeets et al, 1999). These different scales of snow and ice surface roughness variation are important to understanding surface-atmosphere interactions and for remote sensing in the visible and microwave portions of the spectrum.…”

Section: Introductionmentioning

confidence: 99%

“…Indices developed for soil science include the random roughness (Kuipers, 1957), the sum of the absolute slopes between various distance intervals (Currence and Lovely, 1970), the product of the microrelief index (mean absolute deviation of elevation from a reference plane) and the peak frequency (number of elevation peaks per unit transect length) (Romkens and Wang, 1986). Fassnacht et al (2009a) used these roughness indices to determine the roughness of a snowpack surface. These indices were compared to several roughness measures which quantify the spatial structure of the surface, including the semivariance (Brown, 1987), autocorrelation (Huang, 1998) and power spectral density (Currence and Lovely, 1970).…”

Section: Introductionmentioning

confidence: 99%

“…Due to the use of the brown board, rather than the black board used by Fassnacht et al (2009a,b), each image was examined to determine the optimal digital number threshold between snow and no-snow. Fassnacht et al (2009a) presented a number of metrics to analyse snow surface roughness. As per Fassnacht et al (2009b), this paper used random roughness (RR) and fractal dimension (D f ) measures to evaluate surface roughness.…”

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confidence: 99%

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The effect of aeolian deposition on the surface roughness of melting snow, Byers Peninsula, Antarctica

Fassnacht

Velasco

Meiman

et al. 2010

Hydrological Processes

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Abstract:The surface of the snowpack is the bottom boundary layer for air movement, and its roughness influences aerodynamics. The presence of aeolian deposits on a snowpack decreases its albedo and is shown to decrease the roughness of the surface. During snowmelt in the Lake Limnopolar basin on Byers Peninsula of Livingston Island of the South Shetland Islands, Antarctica, wind moved coarse soil grains (1-4 mm particles) from a bare, dry and snow-free area to an adjacent snowpack. This addition of large soil particles rapidly changed the snowpack surface characteristics. Within several days, the sun-cups, initially present on the melting snow surface, had been smoothed out in areas where soil was deposited on the snow surface. The differences in the snowpack surface were assessed using digital imagery of a roughness board inserted into the snow, both parallel and perpendicular to the dominant wind direction. The random roughness was twice as variable for the clean snow compared to the snow with soil; it was 27% more and 26% less perpendicular versus parallel to the wind for the clean snow and snow with soil, respectively. Variogram analysis showed that the clean snow had up to four different scales of roughness over the 55 ð 55 cm area of analysis, with fractal dimensions varying from 1Ð33 to 1Ð83. The snow with soil did not vary substantially from 0Ð1 to 55 cm with fractal dimensions of 1Ð65 in parallel and perpendicular to the wind.

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“…The thicknesses of each layer at a 1 cm horizontal resolution were calculated and two roughness indices were calculated for each layer boundary over a 50 cm horizontal moving window to replicate the footprint length of the radar. Roughness indices were determined following Fassnacht et al (2009), where layers were first detrended to remove slope influences, then random roughness (RR), the standard deviation of the elevations from the mean surface, and the sum of the absolute slopes (RM) were both calculated for each 50 cm window.…”

Section: Methodsmentioning

confidence: 99%

Observations of snowpack properties to evaluate ground-based microwave remote sensing

Rutter¹,

Marshall²,

Tape³

et al. 2010

Role of Hydrology in Managing Consequences of a Changing Global Environment

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Active microwave radar has been shown to have great potential for estimating snow water equivalent (SWE) globally from space. To help evaluate optimal active microwave sensor configurations to observe SWE, we evaluated ground-based Frequency Modulated Continuous Wave (FMCW) radar (12-18 GHz, cross-polarisation) using very high resolution in-situ observations of snowpack layering, dielectric permittivity and density over a 10 m snow trench on Toolik Lake, Alaska. Results showed that the thicknesses of layers within the 10 m trench were highly variable over short distances (< 1 m), even where total snow depth changed very little. Layer boundaries observed using NIR photography identified all bands of high radar backscatter. Although additional observations of density and dielectric permittivity helped to explain the causes of backscatter, not all snowpack properties which cause backscatter were coincident with strong vertical changes in density or permittivity. Further observations of high surface roughness in layer boundaries explained some areas of weak backscatter, nonetheless it was shown that a suite of coincident observations, rather than a single technique in isolation, were required to adequately explain the variability of backscatter and the influence of snowpack properties upon it.

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Metrics for assessing snow surface roughness from digital imagery

Cited by 31 publications

References 23 publications

The temporal and spatial variability in submeter scale surface roughness of seasonal snow in Sodankylä Finnish Lapland in 2009–2010

The temporal and spatial variability in submeter scale surface roughness of seasonal snow in Sodankylä Finnish Lapland in 2009–2010

The effect of aeolian deposition on the surface roughness of melting snow, Byers Peninsula, Antarctica

Observations of snowpack properties to evaluate ground-based microwave remote sensing

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