The magnitude of kriging errors varies in accordance with the surface properties. The purpose of this paper is to determine the association of ordinary kriging (OK) estimated errors with the local variability of surface roughness, and to analyse the suitability of probabilistic models for predicting the magnitude of OK errors from surface parameters. This task includes determining the terrain parameters in order to explain the variation in the magnitude of OK errors. The results of this research indicate that the higher order regression models, with complex interaction terms, were able to explain 95 per cent of the variation in the OK error magnitude using the least number of predictors. In addition, the results underscore the importance of the role of the local diversity of relief properties in increasing or decreasing the magnitude of interpolation errors. The newly developed dissectivity parameters provide useful information for terrain analysis. Our study also provides constructive guides to understanding the local variation of interpolation errors and their dependence on surface dissectivity.

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Dolines or sinkholes are earth depressions that develop in soluble rocks complexes such as limestone, dolomite, gypsum, anhydrite, and halite; dolines appear in a variety of shapes from nearly circular to complex structures with highly curved perimeters. The occurrence of dolines in the studied karst area is not random; they are the results of geomorphic, hydrologic, and chemical processes that have caused partial subsidence, even the total collapse of the land surface when voids and caves are present in the bedrock and the regolith arch overbridging these voids is unstable. In the study area, the majority of collapses occur in the regolith (bedrock cover) that bridges voids in the bedrock. Because these collapsing dolines may result in property damage and even cause the loss of lives, there is a need to develop methods for evaluating karst hazards. These methods can then be used by planners and practitioners for urban and economic development, especially in regions with a growing population. The purpose of this project is threefold: 1) to develop a karst feature database, 2) to investigate critical indicators associated with doline collapse, and 3) to develop a doline susceptibility model for potential doline collapse based on external morphometric data. The study has revealed the presence of short range spatial dependence in the distribution of the dolines’ morphometric parameters such as circularity, the geographic orientation of the main doline axes, and the length-to-width doline ratios; therefore, geostatistics can be used to spatially evaluate the susceptibility of the karst area for doline collapse. The partial susceptibility estimates were combined into a final probability map enabling the identification of areas where, until now, undetected dolines may cause significant hazards.

The field of spatial analysis is a rapidly developing applied science. One area where this scientific approach can contribute to the analysis and interpretation of field sampling is in the study and distribution of pollen and spores. Pollen grains and spores have distinct spatial distributions that can be identified, analyzed, and modeled in association with significant environmental changes that have influence on resource appraisal and management. In this work the pollen is perceived as a regionalized variable and modeling procedures are used to reproduce spatial distribution patterns, and the continuity of pollen and spore data. As an example, we examine the spatial analysis of surface pollen rain from a desert environment in the Big Bend National Park region located in west Texas (Text- Figure 1).

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