While outcrop models can provide important information on reservoir architecture and heterogeneity, it is not entirely clear how such information can be used exhaustively in geostatistical reservoir modeling. Traditional, variogram-based geostatistics is inadequate in that regard since the variogram is too limiting in capturing geological heterogeneity from outcrops. A new field, termed multipl...

The probability of fire in a particular area depends on a range of environmental and geographic variables. Fire prevention planning can be assisted by the construction of models to identify the variables that have a significant influence on the occurrence of fires and by building maps showing the spatial probability distribution for fires occurring in specific geographic areas. We used generali...

It is shown that in model-based geostatistics, not all parameters in the Matérn class can be estimated consistently if data are observed in an increasing density in a Ž xed domain, regardless of the estimation methods used. Nevertheless, one quantity can be estimated consistently by the maximum likelihoodmethod, and this quantity is more important to spatial interpolation. The results are estab...

Annually much agricultural productions are lost by natural hazards such as flooding, extreme temperature, and drought. In this research we present a procedure to identify such hazards for rice cropping from remote sensing imagery. Extreme temperature is investigated by comparing actual temperatures with tolerance temperatures during the growing stage. Drought is investigated using the evapotran...

The packages geoR and geoRglm are contributed packages to the statistical software system R, implementing methods for model-based geostatistical data-analysis. In this paper we focus on the capabilities of the packages, the computational implementation and related issues, and indicate directions for future developments. geoR implements methods for Gaussian and transformed Gaussian models. The p...

Methods based on two-point geostatistics have been routinely used to interpolate random variables such as groundwater level and concentration and to estimate their values at un-sampled locations. These methods use the observed data to analyze spatial two-point correlations and ignore the higher order moments that may play a key role in the characterization of complex patterns. In this work, a m...