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Published: Monday, May 16, 2011 - 10:13 (CRC Press: Boca Raton, FL) -- Geospatial information modeling and mapping has become an important tool for investigating and managing natural resources at the landscape scale. Spatial Statistics: GeoSpatial Information Modeling and Thematic Mapping, by Mohammed A. Kalkhan, Ph.D. (CRC Press, 2011) reviews the types and applications of geospatial information data, such as remote sensing, geographic information systems (GIS), and the Global Positioning System (GPS) as well as their integration into landscape-scale geospatial statistical models and maps. The book explores how to extract information from remotely sensed imagery, GIS, and GPS, and how to combine this with field data—vegetation, soil, and environmental—to produce a spatial model that can be reconstructed and displayed using GIS software. Readers learn the requirements and limitations of each geospatial modeling and mapping tool. Case studies with real-life examples illustrate important applications of the models. Topics covered in this book include: The book includes practical examples and laboratory exercises using ArcInfo, ArcView, ArcGIS, and other popular software for geospatial modeling. It is accessible to readers from various fields, without requiring advanced knowledge of geospatial information sciences or quantitative methods. Mohammed A. Kalkhan, Ph.D., has more than 20 years’ experience in research and teaching at Colorado State University (CSU). As a member of the Natural Resource Ecology Laboratory (NREL) there, he has also served as an affiliate faculty in the Department of Forest, Rangeland, and Watershed Stewardship, and as an advisor for the Interdisciplinary Graduate Certificate in Geospatial Science Graduate Degree Program in Ecology (GDPE), the School of Global Environmental Sustainability (SOGES), and the Department of Earth Resources (currently the Department of Geosciences) at CSU. Kalkhan’s main interests are in the integration of field data, remote sensing, and GIS with geospatial statistics to understand landscape parameters through the use of a complex model with thematic mapping approaches, including sampling methods and designs, biometrics, determination of uncertainty and mapping accuracy assessment. Quality Digest does not charge readers for its content. We believe that industry news is important for you to do your job, and Quality Digest supports businesses of all types. However, someone has to pay for this content. And that’s where advertising comes in. Most people consider ads a nuisance, but they do serve a useful function besides allowing media companies to stay afloat. They keep you aware of new products and services relevant to your industry. All ads in Quality Digest apply directly to products and services that most of our readers need. You won’t see automobile or health supplement ads. So please consider turning off your ad blocker for our site. Thanks, CRC Press is a premier global publisher of science, technology, and medical resources. It offers unique, trusted content by expert authors, spreading knowledge and promoting discovery worldwide. Its aim is to broaden thinking and advance understanding in the sciences, providing researchers, academics, professionals, and students with the tools they need to share ideas and realize their potential. CRC Press is a member of Taylor & Francis Group, an informa business.Book: Spatial Statistics: GeoSpatial Information Modeling and Thematic Mapping
How to extract information from remotely sensed imagery, GIS, and GPS
• An overview of the geospatial information sciences, and technology and spatial statistics
• Sampling methods and applications, including probability sampling and nonrandom sampling, and issues to consider in sampling and plot design
• Fine- and coarse-scale variability
• Spatial sampling schemes and spatial pattern
• Linear and spatial correlation statistics, including Moran’s I, Geary’s C, cross-correlation statistics, and inverse distance weighting
• Geospatial statistics analysis using stepwise regression, ordinary least squares (OLS), variogram, kriging, spatial auto-regression, binary classification trees, cokriging, and geospatial models for presence and absence data
• How to use R statistical software to work on statistical analyses and case studies, and to develop a geospatial statistical modelAuthor biography
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