Predicting ordinary kriging errors caused by surface roughness and dissectivity

Abstract

The magnitude of kriging errors varies in accordance with the surface properties. The purpose of this paper is to determine the association of ordinary kriging (OK) estimated errors with the local variability of surface roughness, and to analyse the suitability of probabilistic models for predicting the magnitude of OK errors from surface parameters. This task includes determining the terrain parameters in order to explain the variation in the magnitude of OK errors. The results of this research indicate that the higher order regression models, with complex interaction terms, were able to explain 95 per cent of the variation in the OK error magnitude using the least number of predictors. In addition, the results underscore the importance of the role of the local diversity of relief properties in increasing or decreasing the magnitude of interpolation errors. The newly developed dissectivity parameters provide useful information for terrain analysis. Our study also provides constructive guides to understanding the local variation of interpolation errors and their dependence on surface dissectivity.

Geotechnical evaluation of the alluvial soils for urban land management zonation in Gharbiya governorate, Egypt

“…Ordinary kriging (OK), the most popular geostatistical method, was appraised to define the best spatial estimation conditions and to produce spatial distribution maps of the studied parameters, taking into account all the available information through cross-validation analysis, multiple linear regression. OK uses spatial correlation to estimate a target variable Z 0 at a defined point x 0 of the space, using a set of values of the same parameter measured on n 0 near the estimation points x á (á = 1, n 0 ) (Journel and Huijbregts, 1978;Goovaerts, 1997;Siska et al, 2005). This method consists of a linear combination of the measures (Eq.…”

Multi-scale Analysis of Topographic Surface Roughness in the Midland Valley, Scotland

“…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).…”

Conflation of sparsely sampled and gridded elevation data: scale mismatch and semantic differences

“…Moreover, void-filling, noisy data smoothing and other related processing can also be viewed as specific types of data conflation when auxiliary data and information are incorporated to enhance utility of the dataset being processed. While image fusion and map conflation have been addressed in remote sensing and geographic information systems, respectively, a coherent framework and well-assessed techniques for data conflation remain underdeveloped, especially for heterogeneous (i.e., multi-source and complex lineage) and vast-area (e.g., regional and global scales) geospatial data, such as terrain data (Siska et al 2005, Karkee et al 2008, Papasaika et al 2011, Cheung et al 2012, whose conflation is hampered by inhomogeneity in data semantics and scale mismatch, as elaborated later. Since this paper deals with the terrain elevation data, in particular SRTM and GTOPO30 DEMs data, a brief introduction to them is necessary.…”