Spatial Uncertainty in Ecology Implications for Remote Sensing and GIS Applications

The huge growth in the use of geographic information systems, remote sensing platforms and spatial databases have made accurate spatial data more available for ecological and environmental models. Unfortunately, there has been too little analysis of the appropriate use of this data and the role of uncertainty in resulting ecological models. This is the first book to take an ecological perspective on uncertainty in spatial data. It applies principles and techniques from geography and other disciplines to ecological research. It brings the tools of cartography, cognition, spatial statistics, remote sensing and computer sciences to the ecologist using spatial data. After describing the uses of spatial data in ecological research, the authors discuss how to account for the effects of uncertainty in various methods of analysis. Carolyn T. Hunsaker is a research ecologist in the USDA Forest Service in Fresno, California. Michael F. Goodchild is Professor of Geography at the University of California, Santa Barbara. Mark A. Friedl is Assistant Professor in the Department of Geography and the Center for Remote Sensing at Boston University. Ted J. Case is Professor of Biology at the University of California, San Diego.

I Introduction.- 1 Introduction.- II Dimensions of Spatial Ecological Data.- 2 The Use and Uncertainties of Spatial Data for Landscape Models: An Overview with Examples from the Florida Everglades.- 3 Measuring and Predicting Species Presence: Coastal Sage Scrub Case Study.- 4 Incorporating Uncertainties in Animal Location and Map Classification into Habitat Relationships Modeling.- 5 An Introduction to Uncertainty Issues for Spatial Data Used in Ecological Applications.- III Methods.- 6 Mapping Ecological Uncertainty.- 7 A Cognitive View of Spatial Uncertainty.- 8 Delineation and Analysis of Vegetation Boundaries.- 9 Geostatistical Models of Uncertainty for Spatial Data.- 10 Uncertainty and Spatial Linear Models for Ecological Data.- 11 Characterizing Uncertainty in Digital Elevation Models.- 12 An Overview of Uncertainty in Optical Remotely Sensed Data for Ecological Applications.- 13 Modeling Forest Net Primary Productivity with Reduced Uncertainty by Remote Sensing of Cover Type and Leaf Area Index.- 14 Spatially Variable Thematic Accuracy: Beyond the Confusion Matrix.- 15 Modeling Spatial Variation of Classification Accuracy Under Fuzzy Logic.- 16 Alternative Set Theories for Uncertainty in Spatial Information.- 17 Roles of Meta-Information in Uncertainty Management.- 18 Uncertainty Management in GIS: Decision Support Tools for Effective Use of Spatial Data Resources.- IV Epilog.- 19 Epilog.