Scale in Spatial Information and Analysis.pdf
Introduction Scale issues Models and methods The book chapters Scale in representations and measurements Data modeling incorporating scale Multi-scale data acquisition Scale models Geostatistical models Convolution and de-convolution Extensions Spatial prediction and scaling Fundamentals of kriging Geostatistical scaling Indicator approaches Multi-scale data integration Multivariate geostatistics Handling multi-scale data Scale mismatch and its effects Scale and uncertainty characterization Accuracy metrics and spatial assessment Validation across scales Scale-explicit stochastic simulation Scale in terrain analysis Digital terrain analysis Scale sensitivity in derivatives Terrain data fusion Scale effects in uncertainty modeling Scale in categorical mapping Spatial and categorical scales Scalable models for area classes Area-class map conflation Discriminant models of uncertainty Scale in landscape dynamics Scale effects in change detection Scaling of predicted changes Scale in objects Methods for scaling of discrete objects Merging objects Uncertainty analysis in scaling and conflation Multi-scale data assimilation Kalman filters Multi-scale methods Prospects
Essential to the comprehension of spatial phenomena and application of spatial information, scale dependencies, along with their representations and analyses, must be properly and usefully incorporated for better-informed spatial problem-solving. This book provides a coherent synthesis of past and current research on scale issues in spatial information and analysis. Within a clear geostatistical framework, it describes those fundamental concepts, theories, and techniques essential for scale-dependent and sensitive handling of multi-source spatial data. It includes examples and illustrations for mathematical detail and outlines potential future developments in the field.