Multiscale Terrain Analysis of Multibeam Bathymetry Data for Habitat Mapping on the Continental Slope

Wilson, Margaret F. J.; O’Connell, Brian; Brown, Colin; Guinan, Janine; Grehan, Anthony

doi:10.1080/01490410701295962

Cited by 788 publications

(596 citation statements)

References 66 publications

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“…Several authors define surface roughness in terms of the variability of elevation values, generally expressed as the absolute standard deviation of all values within a window, or as the deviation from a best-fit plane. It has been applied to data such as raw elevation points Hobson (1972), DEMs Evans (1984); Haneberg et al (2005); Glenn et al (2006);Frankel and Dolan (2007); Arrell and Carver (2009), satellite imagery Beyer et al (2003); Cord et al (2007), bathymetric data Dartnell and Gardner (2004); Valentine et al (2004); Lundblad et al (2006); Wilson et al (2007) or elevation profiles Van Der Veen et al (1998); Kreslavsky and Head (2000); Shepard et al (2001). Standard deviation of residual topography was used as a measure of roughness by Haneberg et al (2005); Haneberg (2007); Cavalli et al (2008), where the residual topography is the difference between the original and a smoothed DEM (an approach similar to the deviation of values from a best-fit plane).…”

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

253

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

253

196

View full text Add to dashboard Cite

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“…gov/; files: gt30e020n40, gt30e020n90, gt30w020n40, gt30w020n90, gt30w060n90). We use the Terrain Ruggedness Index as applied by Wilson et al (2007) by employing the focal function in the R package raster (formula provided in the help function of "terrain" in the raster package). The calculation is performed separately for each of the 1692 Mosaic locations that form our 115 Mosaic regional populations.…”

Section: General Conclusionmentioning

confidence: 99%

“…The first of these covariates is terrain ruggedness (Wilson et al 2007). 22 The second geographic variable is population potential (Stewart 1942), which accounts for the centrality and the accessibility of a region by determining the size of the population living close to the location of a region.…”

mentioning

confidence: 99%

Age heaping patterns in Mosaic data

Szołtysek

Poniat

Gruber

2017

Historical Methods: A Journal of Quantitative and Interdiscipli

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This paper analyzes the extent and nature of age-misreporting in the Mosaic data, currently one of the largest historical census microdata infrastructures for continental Europe. We use demographic measures known as the age heaping indexes to explore regional, periodic and sex-specific patterns of age misreporting across 115 Mosaic regional datafiles, from Catalonia to Moscow, during Europe's demographic ancien r egime and thereafter. The paper's second significant contribution is the comparison of Mosaic-based results to those derived from two other big census data projects-IPUMS and NAPP. Beyond this exploratory data analysis, we also investigate possible sources of variation in age heaping across Mosaic data by examining how it relates to variability in socioeconomic, institutional, and environmental conditions. Overall, our systematic inquiry into quality of age reporting in Mosaic consolidates the project's potentially transformative role in comparative historical family demography and suggests some avenues for future research.

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“…Among the 723 azooxanthellate scleractinian coral species currently known, the majority (532 species, 73.6%) are solitary in habit while the remainder (191,26.4%) are colonial. The distribution of DWC is governed by various factors including temperature and calcium carbonate saturation (Cairns, 2007), topographic relief (Mortensen and Buhl-Mortensen, 2004;Buhl-Mortensen et al, 2010;Guinan et al, 2009;Yesson et al, 2012), sedimentary regime and currents (Thiem et al, 2006;Bryan and Metaxas, 2006;Wilson et al, 2007;Dolan et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

In-situ observation of deep water corals in the northern Red Sea waters of Saudi Arabia

Qurban

Krishnakumar

Joydas

et al. 2014

Deep Sea Research Part I: Oceanographic Research Papers

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Multiscale Terrain Analysis of Multibeam Bathymetry Data for Habitat Mapping on the Continental Slope

Cited by 788 publications

References 66 publications

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

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

Age heaping patterns in Mosaic data

In-situ observation of deep water corals in the northern Red Sea waters of Saudi Arabia

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