It is often useful to estimate obscured or missing remotely sensed data. Traditional interpolation methods, such as nearest-neighbor or bilinear resampling, do not take full advantage of the spatial information in the image. An alternative method, a geostatistical technique known as indicator krigingo is described and demonstrated using a Landsat Thematic Mapper image in southern Chiapas, Mexico. The image was first classified into  pasture and nonpasture land cover. For each pixel that was obscured by cloud or cloud shadow, the probability that it was pasture was assigned by the algorithm. An exponential omnidirectional variogram model was used to characterize the spatial continuity of the image for use in the kriging algorithm. Assuming a cutoff probability level of 50%, the error was shown to be 17% with no obvious spatial bias but with some tendency to categorize nonpasture as pasture (overestimation). While this is a promising result, the method's practical application in other missing data problems for remotely sensed images will depend on the amount and spatial pattern of the unobscured pixels and missing pixels and the success of the spatial continuity model used. <...>

Mineral resource evaluation requires defining geological domains that differentiate the types of mineralogy, alteration and lithology. Usual practice is to consider the domain boundaries as hard, i.e. data from across the boundaries are disregarded when estimating the grades within a given domain. This practice may hinder the quality of the estimates when a significant spatial correlation of the grades exists across the domain boundaries.

Geostatistics (also known as kriging) was developed for the mining industry during the 1960s and 1970s to estimate changes in ore grade. The principles of geostatistics are now applied to many applications that require statistically based interpolation techniques. Geostatistics provides a data value estimate for locations that cannot be sampled directly by examining data taken at locations that can be sampled.

Geostatistical techniques are extensively used for mineral resources assessment, including grade estimation and uncertainty analysis. Support is the term used in Geostatistics to describe the size or volume of a sample. For example, suppose a grade sample obtained from diamond drill holes (DDH). In this case, the support is a function of the core’s radius and length.

The objective of this paper is to show how well known the structure is for a particular uranium deposit. After presenting the average vertical variogram for all the holes, some of the individual variograms will be studied and we will show the influence of a few very rich holes on the overall variogram. This turns out to be poorly defined. The first order variogram, which also has been considered, is curiously similar to the usual variogram, whereas the structure of the translated logarithm proves to be better known. <...>

The Second European Conference on Geostatistics for Environmental Applications took place in Valencia, November 18-20, 1998. Two years have past from the first meeting in Lisbon and the geostatistical community has kept active in the environmental field. In these days of congress inflation, we feel that continuity can only be achieved by ensuring quality in the papers. For this reason, all papers in the book have been reviewed by, at least, two referees, and care has been taken to ensure that the reviewer comments have been incorporated in the final version of the manuscript. We are thankful to the members of the scientific committee for their timely review of the scripts. All in all, there are three keynote papers from experts in soil science, climatology and ecology and 43 contributed papers providing a good indication of the status of geostatistics as applied in the environmental field all over the world. We feel now confident that the geoENV conference series, seeded around a coffee table almost six years ago, will march firmly into the next century. <...>

Many geostatistical variables have sample distributions that are highly positively skewed. Because of this, significant deskewing of the histogram and reduction of variance occurs when going from sample to block support, where blocks are of larger volume than samples. When making estimates in both mining and non-mining applications we often wish to map the spatial distribution on the basis of block support rather than sample support.

From its inception as a separate discipline, geostatis tics sought recognition from practitioners, not from math ematicians or physicists. and rightfully so. Indeed, the theory was essentially established by the 1950's by Kol mogorov and Wiener and exposed by Matern (1960), Whit tle (1963), and Matheron (1965), among others. But there is a long, hard way between a concept expressed by matrix notations in a Hilbert space and its implementation and routine application. It is my opinion that the main con tribution of geostatistics has been and still is implementa. tion, an essential follow-up step much too often forsaken by theoreticians. <...>

Estimating mineral resources from drill hole data is an activity that is fraught with difficulty. Most classical statisticians would regard the data for  any ore reserve estimate as dangerously inadequate. This would often apply even in cases where the geologist felt that the deposit had been "overdrilled". <...>