Kriging is a spatial prediction method, which can predict at any location and return a measure of prediction confidence (the kriging standard error). There exist many variations of kriging, some of which can be complex, especially those that allow many of its parameters to vary spatially. To calibrate such a kriging model and to be able to interpret its results can therefore be quite daunting. ...

High temporal resolution information on burnt area is needed to improve fire behaviour and emissions models. We used theModerate Resolution Imaging Spectroradiometer (MODIS) thermal anomaly and active fire product (MO(Y)D14) as input to a kriging interpolation to derive continuousmaps of the timing of burnt area for 16 large wildland fires. For each fire, parameters for the kriging model were d...

We evaluate the performance and statistical accuracy of the fast Fourier transform method for unconditional and conditional simulation. The method is applied under difficult but realistic circumstances of a large field (1001 by 1001 points) with abundant conditioning criteria and a band limited, anisotropic, fractal-based statistical characterization (the von Kármán model). The simple Fourier u...

Kriging is a well-known prediction method. It interpolates the value of an unmeasured location from nearby measured locations. In a traditional Kriging interpolation, a client (an entity that is looking for a prediction for a specific location) asks help from a server (an entity that holds enough measurements collected for Kriging interpolations in a region). Predictions are estimated based on ...

The spatial pattern of precipitation is known to be highly dependent on meteorological conditions and relief. However, the relationships between precipitation and topography in mountainous areas are not very well known, partly because of the complex topography in these regions, and partly because of the sparsity of information available to study such relationships in high elevation areas. The p...

We present a method for efficiently fitting a time series of spatial functions to observed data. The method is closely related to kriging, which is an interpolation method based on a stochastic data model. While kriging is effective and versatile for estimating individual functions from observed data, it must be extended to incorporate temporal correlation. In this paper, we introduce temporal ...

In this paper, we applied the support vector machine (SVM) to the spatial interpolation of the multi-year average annual precipitation in the Three Gorges Region basin. By combining it with the inverse distance weighting and ordinary kriging method, we constructed the SVM residual inverse distance weighting, as well as the SVM residual kriging precipitation interpolation model and compared them...