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ISPRS Int. J. Geo-Inf. 2014, 3(2), 817-852; doi:10.3390/ijgi3020817

The Sequential Generation of Gaussian Random Fields for Applications in the Geospatial Sciences

Sensor Geopositioning Center, National Geospatial-Intelligence Agency (contractors), 7500 GEOINT Dr, Springfield, VA 22150, USA
“Approval number” assigned by authors’ organization: PA case #14-350
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Author to whom correspondence should be addressed.
Received: 8 February 2014 / Revised: 21 May 2014 / Accepted: 26 May 2014 / Published: 16 June 2014

Abstract

This paper presents practical methods for the sequential generation or simulation of a Gaussian two-dimensional random field. The specific realizations typically correspond to geospatial errors or perturbations over a horizontal plane or grid. The errors are either scalar, such as vertical errors, or multivariate, such as , , and  errors. These realizations enable simulation-based performance assessment and tuning of various geospatial applications. Both homogeneous and non-homogeneous random fields are addressed. The sequential generation is very fast and compared to methods based on Cholesky decomposition of an a priori covariance matrix and Sequential Gaussian Simulation. The multi-grid point covariance matrix is also developed for all the above random fields, essential for the optimal performance of many geospatial applications ingesting data with these types of errors. View Full-Text
Keywords: geospatial; random field; errors; sequential; simulation; covariance matrix; strictly positive definite correlation function geospatial; random field; errors; sequential; simulation; covariance matrix; strictly positive definite correlation function
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Dolloff, J.; Doucette, P. The Sequential Generation of Gaussian Random Fields for Applications in the Geospatial Sciences. ISPRS Int. J. Geo-Inf. 2014, 3, 817-852.

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