Quantification of the spatial variability: calculation and interpretation of the variogram.

Quantify the spatial variability of a pollutant: calculation, interpretation and modeling of the variogram.

Quantify the spatial variability of a pollutant: calculation, interpretation and modeling of the variogram.

Quantify the spatial variability of a pollutant: calculation, interpretation and modeling of the variogram.

Quantify the spatial variability of a pollutant: calculation, interpretation and modeling of the variogram.

Quantify the spatial variability of a pollutant: calculation, interpretation and modeling of the variogram.

The variogram model may be validated in real-time to provide immediate feedback without the need to interpolate or simulate the real data.

For multivariate analysis, the selected model can be adjusted simultaneously to both the variograms and the covariograms.

But as indicated in the semi-variogram with the sill and range, it begins to reach its flat, asymptotic level.

This is because the variogram model influences the prediction of those unknown values during kriging interpolation.

The value that the semivariogram model attains at the range (the value on the -axis) is called the sill.

The value at which the semivariogram model attains the range (the value on the y-axis) is called the sill.

Comparative analysis, such as variogram analysis or correlogram analysis, is performed of the decomposed images to determine spatial relationships between regions of the images that are relatively homogeneous.

Because this type of interpolation technique uses the mathematical model from the semi-variogram, it’s one of the best forms of prediction today.

There are deviations of the points on the empirical semivariogram from the model; some points are above the model curve, and some points are below.