Many environmental processes are heterogeneous in space (spatially non-stationary), due to factors such as topography, local pollutant emissions, and meteorology. Much of the commonly used spatial statistical methodology depends on simplifying assumptions such as spatial isotropy. Violations of these assumptions can cause problems, including incorrect error assessment of spatial estimates. This...

Sustainable forest management and conservation require understanding of underlying basic structural and competitive relationships. To gain insight into these relationships, we analyzed spatial continuity of tree basal area (BA) and crown projection area (CPA) on twelve 0.64-ha plots in four mixed bottomland hardwood stands in Louisiana, Arkansas, and Mississippi. Variogram range indicated that ...

There exists a growing number of spatial statistics software packages covering a broad range of methods including the classical technique of kriging following variogram fitting (for a review see e.g. Bivand and Gebhardt, 2000). However, most of these programs do not contain tools for assessing the fit of particular models of the spatial dependencies. Based upon a variogram cloud estimation meth...

For spatial data modeled using the customary variogram approach, likelihood-based or fully Bayesian inference has been conceded as infeasible for large sample size n. The diiculty in such a setting is that modeling the variogram requires modeling the joint covariance structure. For the sample, this is captured as an n n covariance matrix. The computation of the likelihood then requires the inve...

It is well-known that the interrelationship between atmosphere and topography in a mountainous country like Austria produces more or less complicated patterns of climate variables. In particular, the spatial variability of precipitation data has often been the topic of research. For this study, the daily data from more than 400 precipitation stations in Austria recorded over a period of 20 year...

Classification problems of functional data arise naturally in many applications. Several approaches have been considered for solving the problem of finding groups based on functional data. In this paper we are interested in detecting groups when the functional data are spatially correlated. Our methodology allows to find spatially homogeneous groups of sites when the observations at each sampli...

Dominant features of spatial data are connected structures or patterns that emerge from location-based variation and manifest at specific scales resolutions. To identify dominant features, we propose a sequential application multiresolution decomposition variogram function estimation. Multiresolution separates into additive components, in this way enables the recognition their features. A dedic...

post-mineralization activities may cause difficulties in the process of ore modeling in porphyry deposits. sungun, nw iran, is one of the porphyry copper deposits, in which dyke intrusions have made ore modeling more complicated than expected. among different kinds of dykes, two types were chosen and the consequent geostatistical analyses were applied on. in this study, simple directional vario...

Purpose. This paper aims to estimate phosphate ore grade in the Abu Tartur area, Western Desert, Egypt, using four Machine Learning Algorithms (MLA), Geostatistical Techniques (variogram and kriging models), GIS-analysis. Methods. Four machine-learning techniques include Optimizable Decision Tree (ODT), Support Vector (OSVM), Gaussian Process Regression (OGPR), Artificial Neural Network (ANN) a...

Digital soil mapping (DSM) approaches provide information by utilising the relationship between properties and environmental variables. Calibration of DSM models requires measurements that may often have substantial measurement errors which propagate to outputs need be accounted for. This study applied a geostatistical-based approach incorporates error variances in covariance structure spatial ...