The management of soil organic matter (SOM) is fundamental in agriculture for conservation and crop yields. However, addition the being dynamics, it heterogeneous, therefore, understanding spatial variability SOM essential. main objective this study was to evaluate a cassava cultivation under different management, seeking classify its dependence by geostatistics. A filed experiment conducted ou...

With the proposition of a Markov chain random field (MCRF) theory and its accompanying spatial measure – the transiogram, Markov chain has been extended into a nonlinear Markov chain-based geostatistical approach for one to multi-dimensional conditional (or unconditional) simulation, called Markov chain geostatistics (MCG). This new approach has nonlinear estimators, considers conditional indep...

Area-class maps are conventionally delineated by human hands based on limited observed (or high-quality) data and expert knowledge. It is recognized that areaclass maps contain spatial uncertainty because the classification of unobserved locations is not completely certain. Spatial uncertainty associated with an area-class map may be quantified by spatial statistical approaches through estimati...

INTRODUCTION A geostatistical approach to characterize MS-lesion patterns based on their geometrical properties is presented. METHODS A dataset of 259 binary MS-lesion masks in MNI space was subjected to directional variography. A model function was fit to express the observed spatial variability in x, y, z directions by the geostatistical parameters Range and Sill. RESULTS Parameters Range...

We describe a prototype approach to flexible modelling for maxima observed at sites in a spatial domain, based on fitting of max-stable processes derived from underlying Gaussian random fields. The models we propose have generalized extreme-value marginal distributions throughout the spatial domain, consistent with statistical theory for maxima in simpler cases, and can incorporate both geostat...

Biography Dr. Dan Cornford is a lecture in Computer Science and works in the Neural Computing Research Group at Aston University. Research interests are in the field of spatial statistics, space-time modelling and data assimilation. Lehel Csato is a post-doc in the same group working on an EPSRC grant (GR/R61857/01) looking at applying sparse sequential Gaussian processes to data assimilation. ...