Ecological processes act upon landscapes over a wide range of temporal and spatial extents. These processes interact to create complex and ever-changing landscape patterns. While remote sensing data (e.g. AVHRR, TM) has often been used to characterize such heterogeneity at the landscape scale, these sensors cannot resolve spatial pattern to the detail required for finer scale analyses, such as ...

The estimation of missing observations is an important research field which has practical applications in many science and engineering disciplines. In analyzing the variability of a particular data set which can be spatially related, classical statistical methods make no use of this type of information; whereas geostatistics accomodates the spatial information of the data set in its regression ...

An understanding of the Earth’s climate system benefits all sectors of the economy and environment. Several challenges faced when modeling the Earth’s climate system include: estimating geographical features of global datasets, making inferences from multiple data-products, and providing diagnostic tools for complex Earth models. Existing geostatistical approaches address these challenges by mo...

Traditionally, geostatistical algorithms are contained within specialist GIS and spatial statistics software. Such packages are often expensive, with relatively complex user interfaces and steep learning curves, and cannot be easily integrated into more complex process chains. In contrast, Service Oriented Architectures (SOAs) promote interoperability and loose coupling within distributed syste...

The newest version of the ITC GIS and Remote Sensing software ILWIS offers an integrated set of modules to perform geostatistics. Its functionality for image processing, geographic analysis and digital mapping combined with extensive geostatistical tools make it a modern product for education and research in the field of G.I.S. It incorporates spatial correlation tools like semi-and cross vario...

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...

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...