Applications in GIS(Kriging Interpolation)Dr. S.M. MalaekAssistant: M. Younesi

Interpolating a Surface FromSampled Point Data

Interpolating a Surface From Sampled Point DataAssumes a continuous surface that is sampledInterpolationEstimating the attribute values of locations that are within the range of available data using known data valuesExtrapolationEstimating the attribute values of locations outside the range of available data using known data values

Interpolating a Surface From Sampled Point DataInterpolation

Interpolating a Surface From Sampled Point DataExtrapolation

Sampling Strategies for InterpolationRegular SamplingRandom SamplingInterpolating a Surface From Sampled Point Data

Interpolating a Surface From Sampled Point DataLinear Interpolation

Interpolating a Surface From Sampled Point DataNon-Linear Interpolation

Interpolating a Surface From Sampled Point DataGlobal InterpolationUses all known sample points to estimate a value at an unsampled location

Interpolating a Surface From Sampled Point DataLocal InterpolationUses a neighborhood of sample points to estimate a value at an unsampled location

Trend Surface

Trend SurfaceGlobal methodInexactCan be linear or non-linearpredicting a z elevation value [dependent variable] with x and y location values [independent variables]

Trend Surface1st Order Trend SurfaceIn one dimension: z varies as a linear function of x

Trend Surface1st Order Trend SurfaceIn two dimensions: z varies as a linear function of x and y

Trend Surface

Inverse Distance Weighted (IDW)

Inverse Distance WeightedLocal methodExactCan be linear or non-linearThe weight (influence) of a sampled data value is inversely proportional to its distance from the estimated value

Inverse Distance Weighted(Example)

Inverse Distance Weighted(Example)

Inverse Distance Weighted(Example)

Geostatistics

GeostatisticsGeostatistics:The original purpose of geostatistics centered on estimating changes in ore grade within a mine. The principles have been applied to a variety of areas in geology and other scientific disciplines.

A unique aspect of geostatistics is the use of regionalized variables which are variables that fall between random variables and completely deterministic variables.

GeostatisticsRegionalized variables describe phenomena with geographical distribution (e.g. elevation of ground surface).

The phenomenon exhibit spatial continuity.

GeostatisticsIt is notalways possible to sample every location. Therefore, unknown values must be estimated from data taken at specific locations that can be sampled. The size, shape, orientation, and spatial arrangement of the sample locations are termed the support and influence the capability to predict the unknown samples.

Semivariance

SemivarianceRegionalized variable theory uses a related property called the semivariance to express the degree of relationship between points on a surface.

The semivariance is simply half the variance of the differences between all possible points spaced a constant distance apart.Semivariance is a measure of the degree of spatial dependence between samples (elevation(

Semivariancesemivariance :The magnitude of the semivariance between points depends on the distance between the points. A smaller distance yields a smaller semivariance and a larger distance results in a larger semivariance.

Calculating the Semivariance (Regularly Spaced PointsRegularly Spaced Points(Consider regularly spaced points distance (d) apart, the semivariance can be estimated for distances that are multiple of (d) (Simple form):

SemivarianceZi is the measurement of a regionalized variable taken at location i ,Zi+h is another measurement taken h intervals away dNh is number of separating distance = number of points Lag (if the points are located in a single profile)

Semivariance Sill: The distance at which the semivariance approaches a flat region. sill, is referred as the range or span of the regionalized variable.

Range: The range or span defines a neighborhood within which all data points are related to one another.

Calculating the Semivariance (Irregularly Spaced PointsRegularly Spaced Points(Here we are going to explore directional variograms. Directional variograms is defines the spatial variation among points separated by space lag h. The difference from the omnidirectional variograms is that h is a vector rather than a scalar. For example, if d={d1,d2}, then each pair of compared samples should be separated in E-W direction and in S-N direction.

Calculating the Semivariance (Irregularly Spaced PointsRegularly Spaced Points(In practice, it is difficult to find enough sample points which are separated by exactly the same lag vector [d]. The set of all possible lag vectors is usually partitioned into classes

Variogram

VariogramThe plot of the semivariances as a function of distance from a point is referred to as a semivariogram or variogram.

VariogramThe semivariance at a distance d = 0 should be zero, because there are no differences between points that are compared to themselves. However, as points are compared to increasingly distant points, the semivariance increases.

VariogramThe range is the greatest distance over which the value at a point on the surface is related to the value at another point. The range defines the maximum neighborhood over which control points should be selected to estimate a grid node.

Variogram (Models(It is a model semi-variogram and is usually called the spherical model. a is called the range of influence of a sample. C is called the sill of the semi-variogram.

Variogram (Models(Exponential Modelspherical and exponential with the same range and sill spherical and exponential with the same sill and the same initial slope

Kriging Interpolation

Kriging Interpolation

Kriging is named after the South African engineer, D. G. Krige, who first developed the method.

Kriging uses the semivariogram, in calculating estimates of the surface at the grid nodes.

Kriging Interpolation

The procedures involved in kriging incorporate measures of error and uncertainty when determining estimations. In the kriging method, every known data value and every missing data value has an associated variance. If C is constant (i.e. known value exactly), its variance is zero. Based on the semivariogram used, optimal weights are assigned to known values in order to calculate unknown ones. Since the variogram changes with distance, the weights depend on the known sample distribution.

Ordinary Kriging

Ordinary KrigingOrdinary kriging is the simplest form of kriging.

It uses dimensionless points to estimate other dimensionless points, e.g. elevation contour plots.

In Ordinary kriging, the regionalized variable is assumed to be stationary.

Punctual (Ordinary) KrigingIn our case Z, at point p, Ze (p) to be calculated using a weighted average of the known values or control points:

This estimated value will most likely differ from the actual value at point p, Za(p), and this difference is called the estimation error:

Punctual (Ordinary) KrigingIf no drift exists and the weights used in the estimation sum to one, then the estimated value is said to be unbiased. The scatter of the estimates about the true value is termed the error or estimation variance,

Punctual (Ordinary) Krigingkriging tries to choose the optimal weights that produce the minimum estimation error .Optimal weights, those that produce unbiased estimates and have a minimum estimation variance, are obtained by solving a set of simultaneous equations .

Punctual (Ordinary) KrigingA fourth variable is introduced called the Lagrange multiplier

Punctual (Ordinary) KrigingOnce the individual weights are known, an estimation can be made by And an estimation variance can be calculated by