Geostatistical analysis of glacier-roughness data

Herzfeld, U. C.; Mayer, Helmut; Feller, Wolfgang; Mimler, Matthias

doi:10.3189/172756400781820769

Cited by 33 publications

(30 citation statements)

References 22 publications

(28 reference statements)

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“…Other methods employed to determine surface roughness include fourier analysis Stone and Dugundji (1965); Fox and Hayes (1984); Hubbard et al (2000); Siegert et al (2004); Taylor et al (2004); Bingham and Siegert (2009), geostatistics Philip and Watson (1986); Herzfeld et al (2000); Siska et al (2005) the fractal dimension of a surface Elliot (1989); Taud and Parrot (2005) and entropy Gorini (2009); Papasaika and Baltsavias (2009).…”

Section: Measures Of Surface Roughnessmentioning

confidence: 99%

“…Whilst surface roughness remains the most common generic term, a variety of terminology have been applied to its study, including ruggedness Beasom et al (1983); Washtell et al (2009) (2004); Wilson et al (2007), microrelief Stone and Dugundji (1965) or microtopography Herzfeld et al (2000). Throughout this article, we use the term surface roughness as an expression of the variability of a topographic surface at a given scale, where the scale of analysis is determined by the size of the landforms or geomorphic features of interest, either local or regional.…”

Section: Introductionmentioning

confidence: 99%

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Multiscale Analysis of Topographic Surface Roughness in the Midland Valley, Scotland

Grohmann

Smith

Riccomini

2011

IEEE Trans. Geosci. Remote Sensing

244

196

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Surface roughness is an important geomorphological variable which has been used in the earth and planetary sciences to infer material properties, current/past processes and the time elapsed since formation. No single definition exists, however within the context of geomorphometry we use surface roughness as a expression of the variability of a topographic surface at a given scale, where the scale of analysis is determined by the size of the landforms or geomorphic features of interest. Six techniques for the calculation of surface roughness were selected for an assessment of the parameter's behaviour at different spatial scales and dataset resolutions. Area ratio operated independently of scale, providing consistent results across spatial resolutions. Vector dispersion produced results with increasing roughness and homogenisation of terrain at coarser resolutions and larger window sizes. Standard deviation of residual topography tends to highlight local features and doesn't detect regional relief. Standard deviation of elevation correctly identified breaks-of-slope and was good at detecting regional relief. Standard deviation of slope (SDslope) also correctly identified smooth sloping areas and breaks-of-slope, providing the best results for geomorphological analysis. Standard deviation of profile curvature identified the breaks-of-slope, although not as strongly as SDslope and it is very sensitive to noise and spurious data. In general, SDslope offered good performance at a variety of scales, whilst the simplicity of calculation is perhaps its single greatest benefit.

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Section: Measures Of Surface Roughnessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiscale Analysis of Topographic Surface Roughness in the Midland Valley, Scotland

Grohmann

Smith

Riccomini

2011

IEEE Trans. Geosci. Remote Sensing

244

196

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“…Dabboor and Geldsetzer [99] utilize 23 statistical parameters. In Herzfeld et al [61,100], Herzfeld [55] we have introduced a large number of parameters to capture different levels of feature complexity that occurs at several different scales, however a too large number of individual parameters then may introduce unnecessary numerical complexity at the next level, the actual classification step. Therefore in Herzfeld et al [31], Herzfeld [55] we introduced summarizing classes of hyperparameters as a means to provide a hierarchical structure to the set of spatial parameters that may be useful in a given application.…”

Section: Recent Developmentsmentioning

confidence: 99%

“…Slope parameters (p1-type parameters) involve distance. Relative significance parameters (p2-type parameters) are independent of dimensions and thus facilitate comparison between data from different instruments and of different scales, for example, surface roughness from satellite SAR data may be compared directly to surface roughness from Glacier Roughness Sensor (GRS) data collected in the field (Herzfeld et al [61]), or to other field data as will be applied in this study (see, Herzfeld et al [31]). …”

Section: Mathematical Introduction Of Vario Parametersmentioning

confidence: 99%

Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data

et al. 2016

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Recent drastic reductions in the Arctic sea-ice cover have raised an interest in understanding the role of sea ice in the global system as well as pointed out a need to understand the physical processes that lead to such changes. Satellite remote-sensing data provide important information about remote ice areas, and Synthetic Aperture Radar (SAR) data have the advantages of penetration of the omnipresent cloud cover and of high spatial resolution. A challenge addressed in this paper is how to extract information on sea-ice types and sea-ice processes from SAR data. We introduce, validate and apply geostatistical and statistical approaches to automated classification of sea ice from SAR data, to be used as individual tools for mapping sea-ice properties and provinces or in combination. A key concept of the geostatistical classification method is the analysis of spatial surface structures and their anisotropies, more generally, of spatial surface roughness, at variable, intermediate-sized scales. The geostatistical approach utilizes vario parameters extracted from directional vario functions, the parameters can be mapped or combined into feature vectors for classification. The method is flexible with respect to window sizes and parameter types and detects anisotropies. In two applications to RADARSAT and ERS-2 SAR data from the area near Point Barrow, Alaska, it is demonstrated that vario-parameter maps may be utilized to distinguish regions of different sea-ice characteristics in the Beaufort Sea, the Chukchi Sea and in Elson Lagoon. In a third and a fourth case study the analysis is taken further by utilizing multi-parameter feature vectors as inputs for unsupervised and supervised statistical classification. Field measurements and high-resolution aerial observations serve as basis for validation of the geostatistical-statistical classification methods. A combination of supervised classification and vario-parameter mapping yields best results, correctly identifying several sea-ice provinces in the shore-fast ice and the pack ice. Notably, sea ice does not have to be static to be classifiable with respect to spatial structures. In consequence, the geostatistical-statistical classification may be applied to detect changes in ice dynamics, kinematics or environmental changes, such as increased melt ponding, increased snowfall or changes in the equilibrium line.

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“…Usually, surface roughness remains as the most common generic term. There was a variety of terminology has been applied to study the roughness, including ruggedness [8], microtopography [9] or rugosity [10]. In a general sense, roughness refers to the irregularity or variability of a topographic surface.…”

Section: Introductionmentioning

confidence: 99%

Quantitative studies of the morphology of the south Poland using Relief Index (RI)

Szypuła

2017

Open Geosciences

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Abstract:The aim of this study was to introduce a new morphometric index named Relief Index (RI). RI is the ratio of the total length of the contour lines and the surface area at which they occur. This easily calculated index provides an objective quantitative measure of relief variability as an important feature in geomorphological studies. To achieve this goal, a highly detailed morphometric analysis was carried out using a high-resolution (1m×1m) DEM. Twenty one sample areas in southern Poland were examined. These analyses showed RI, as a good tool for rapidly evaluating topography heterogeneity in division into relief classes. I distinguished 4 classes of the Relief Index that classify earth surface considering the variability of the relief. Results of the calculations demonstrated that there is a significant correlation between RI and the local relief and slopes, but there is no correlation between RI and planar curvatures and TWI. The relief of the sample areas were analysed using geomorphometric parameters (slopes, local relief, planar curvatures). Moreover the influence of the DEM resolution on Relief Index values was examined.

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Geostatistical analysis of glacier-roughness data

Cited by 33 publications

References 22 publications

Multiscale Analysis of Topographic Surface Roughness in the Midland Valley, Scotland

Multiscale Analysis of Topographic Surface Roughness in the Midland Valley, Scotland

Geostatistical and Statistical Classification of Sea-Ice Properties and Provinces from SAR Data

Quantitative studies of the morphology of the south Poland using Relief Index (RI)

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