Non-stationary variogram models for geostatistical sampling optimisation: an empirical investigation using elevation data


Atkinson, P.M. and LLoyd, C.D. (2007) Non-stationary variogram models for geostatistical sampling optimisation: an empirical investigation using elevation data. Computers & Geosciences, 33, (10), 1285-1300. (doi:10.1016/j.cageo.2007.05.011).

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Description/Abstract

A problem with use of the geostatistical Kriging error for optimal sampling design is that the design does not adapt locally to the character of spatial variation. This is because a stationary variogram or covariance function is a parameter of the geostatistical model. The objective of this paper was to investigate the utility of non-stationary geostatistics for optimal sampling design. First, a contour data set of Wiltshire was split into 25 equal sub-regions and a local variogram was predicted for each. These variograms were fitted with models and the coefficients used in Kriging to select optimal sample spacings for each sub-region. Large differences existed between the designs for the whole region (based on the global variogram) and for the sub-regions (based on the local variograms). Second, a segmentation approach was used to divide a digital terrain model into separate segments. Segment-based variograms were predicted and fitted with models. Optimal sample spacings were then determined for the whole region and for the sub-regions. It was demonstrated that the global design was inadequate, grossly over-sampling some segments while under-sampling others.

Item Type: Article
ISSNs: 0098-3004 (print)
Related URLs:
Keywords: kriging, spatial structure, dem
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
G Geography. Anthropology. Recreation > GB Physical geography
Divisions: University Structure - Pre August 2011 > School of Geography > Remote Sensing and Spatial Analysis
ePrint ID: 52541
Date Deposited: 09 Jul 2008
Last Modified: 27 Mar 2014 18:35
URI: http://eprints.soton.ac.uk/id/eprint/52541

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