Results 191 to 200 of about 3,459 (219)
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The semivariogram in remote sensing: An introduction
Remote Sensing of Environment, 1988Abstract The Earth's surface and remotely sensed imagery contain spatial information that, if quantified, could be used to optimize many sampling procedures in remote sensing. Until recently a suitable and simple technique for the spatial characterisation of surfaces was not readily available.
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Journal of environmental sciences (China), 2002
The spatial variation of soil nutrients in topsoil (0-20 cm) was analyzed using semivariogram in the Zunhua County of Hebei Province, China. The effect on semivariogram with randomly deleted data and kriged estimates using various reduced sample sizes was also analyzed.
X D, Guo, B J, Fu, K M, Ma, L D, Chen
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The spatial variation of soil nutrients in topsoil (0-20 cm) was analyzed using semivariogram in the Zunhua County of Hebei Province, China. The effect on semivariogram with randomly deleted data and kriged estimates using various reduced sample sizes was also analyzed.
X D, Guo, B J, Fu, K M, Ma, L D, Chen
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A Composite Likelihood Approach to Semivariogram Estimation
Journal of Agricultural, Biological, and Environmental Statistics, 1999This article proposes the use of estimating functions based on composite likelihood for the estimation of isotropic as well as geometrically anisotropic semivariogram parameters. The composite likelihood approach is objective, eliminating the specification of distance lags and lag tolerances associated with the commonly used moment estimator ...
Frank C. Curriero, Subhash Lele
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Semivariogram Models Based on Geometric Offsets
Mathematical Geology, 2006Kriging-based geostatistical models require a semivariogram model. Next to the initial decision of stationarity, the choice of an appropriate semivariogram model is the most important decision in a geostatistical study. Common practice consists of fitting experimental semivariograms with a nested combination of proven models such as the spherical ...
Michael J. Pyrcz, Clayton V. Deutsch
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The Effects of Influential Observations on Sample Semivariograms
Journal of Agricultural, Biological, and Environmental Statistics, 1997Optimal prediction of the values of regionalized variables or the means of random fields is often accomplished by using kriging methods. These methods rely on satisfactory estimation of the underlying spatial semivariograms and the fitting of semivariogram models.
Sabyasachi Basu +3 more
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Asymptotic normality of the Nadaraya–Watson semivariogram estimators
TEST, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Kriging and Semivariogram Deconvolution in the Presence of Irregular Geographical Units
Mathematical Geosciences, 2007This paper presents a methodology to conduct geostatistical variography and interpolation on areal data measured over geographical units (or blocks) with different sizes and shapes, while accounting for heterogeneous weight or kernel functions within those units.
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Spatio-Temporal Semivariograms as Neighborhood Definers
2022SPATIO-TEMPORAL SEMIVARIOGRAMS AS NEIGHBORHOOD DEFINERS. Brendan J. Hurley George Mason University, 2022 Dissertation Director: Dr. Timothy F. Leslie Spatial neighborhood definitions are a consistent source of disagreement among geographic scholars.
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Semivariogram estimation: asymptotic theory and applications
2016The semivariogram is a function characterizing the second-order dependence structure of an intrinsically stationary random field; its estimation has applications in spatial statistics, particularly in the construction of optimal predictors of the random field at unobserved locations.
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An Algorithm for Sampling Optimization for Semivariogram Estimation
1995This paper describes an algorithm for the optimal selection of sampling locations for semivariogram estimation. We assume that the semivariogram is estimated by fitting a parametric function of separation distance between observation sites to a selected subset of the squared differences of original observations (thereby restricting ourselves to ...
Werner G. Müller, Dale L. Zimmerman
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