Implications for Remote Sensing and GIS Applications
Hunsaker, C.T., Goodchild, M.F., Friedl, M.A., Case, T.J. (Eds.)
Softcover reprint of the original 1st ed. 2001, XIII, 402 p.
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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.
Introduction and Overview * Use of Spatial Data in Ecological Analysis Spatial Ecological Models * Coastal Sage Scrub Case Study * Incorporating Uncertainties in Animal Location and Map Classification into Habitat Relationships Modeling * Generic Issues Regarding Uncertainty in Spatial Data * METHODS Mapping Ecological Uncertainty * A Cognitive View of Spatial Uncertainty * Spatial Analyses of Ecological Data * Geostatistical Models of Uncertainty for Spatial Data * Spatial Linear Models in Ecology * Characterizing Uncertainty in Digital Elevation Models * Uncertainty of Multinominal Spatial Data * An Overview of Uncertainty in Remote Sensing for Ecological Applications * Remote Sensing Classification of Forest Covertype and Estimation of Stand Leaf Area Index for Modeling Net Primary Production * Spatially Variable Thematic Accuracy: Beyond the Confusion Matrix * Modeling Spatial Variation of Classification Accuracy Under Fuzzy Logic * Set Theoretic Approaches to Uncertainty in Spatial Information * Roles of Meta-Information in Uncertainty Management * Making Decisions Under Uncertainty Using GIS * Epilogue