Geospatial information modeling and mapping has become an important tool for the investigation and management of natural resources at the landscape scale. Spatial Statistics: GeoSpatial Information Modeling and Thematic Mapping reviews the types and applications of geospatial information data, such as remote sensing, geographic information systems (GIS), and 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: * 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 Morans I, Gearys 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 model 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.
Geospatial Information Technology Remotely Sensed Data Instantaneous Field of View (IFOV) at Nadir (Resolution on the Ground) IKONOS ORBIMAGE (GeoEye) QuickBird The SPOT (System Probatori DObservation de la Terre) MODIS (Moderate Resolution Imaging Spectroradiometer) ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer) Active Remotely Sensed Data Radar Lidar Derived Remotely Sensed Data Vegetation Indices The Tasseled Cap Transformation Geographic Information Systems (GIS) Thematic Data Layers Geospatial Data Conversion Using ERDAS-IMAGINE Software Using ARCINFO Software Select Area of Interest (Study Site) Topographic Data Global Positioning System (GPS) GPS Services The GPS Satellite System and Fact GPS Applications References Data Sampling Methods and Applications Data Representation Data Collection and Source of Errors Data Types Sampling Methods and Applications Sampling Designs Simple Random Sampling Stratified Random Sampling Systematic Sampling Nonaligned Systematic Sample Cluster Sampling Multiphase (Double) Sampling Double Sampling and Mapping Accuracy Pixel Nested Plot (PNP): Case Study Plot Design Issues Characteristics of Different Plot Shapes Plot Size References Spatial Pattern and Correlation Statistics Scale Spatial Sampling Errors in Spatial Analysis Spatial Variability and Method of Prediction Spatial Pattern Spatial Point Pattern Linear Correlation Statistic Case Study Statistical Example Spatial Correlation Statistics Morans I and Gearys C Cross-Correlation Statistic Inverse Distance Weighting (IDW) Statistical Example References Geospatial Analysis and Modeling-Mapping Stepwise Regression Statistical Example Ordinary Least Squares (OLS) Variogram and Kriging Ordinary Kriging Simple Kriging Universal Kriging Developing Variogram Model and Kriging to Predict Plant Diversity at GSENM, Utah Spatial Autoregressive (SAR) Statistical Example Binary Classification Tree (BCTs) Cokriging Geospatial Models for Presence and Absence Data GARP Model Maxent Model Logistic Regression Classification and Regression Tree (CART) Envelope Model References R Statistical Package Overview of R Statistics (R) What Is R? Strengths of R/S The R Environment Scripts Working with R on Your COMPUTER Begin to Use R Statistical Analysis Examples Using R Common Statistics Common Graphics Common Programming Create and Examine a Logical Vector Working on Graphical Display of Data (Data distributions) Develop a Histogram Data Comparison between the Data and an Expected Normal Distribution More Statistical Analysis Reading New Variable (Enter new data set, WEIGHT) Plotting Weight and Height Test of Association Some Basic Regression Analysis Case Study Test for Spatial Autocorrelation Using Morans I Test for Spatial Autocorrelation Using Gearys C Test for Spatial Cross-Correlation Using Bi-Morans I Trend Surface Analysis Test for Spatial Autocorrelation of the Residuals Test for Morans I for Residuals Using Spatial AR Model without Regression Using Spatial AR with Regression (Using All Independent Variables as with OLS Model) Analysis of Residuals Develop Variogram Model (Modeling Fine Scale Variability) Plotting Variogram Model References Working with Geospatial Information Data Exercise 1: Working with Remotely Sensed Data Exercise 2: Derived Remote Sensing Data and Digital Elevation Model (DEM) Deriving Slope and Aspect from DEM Data Resample GRID Exercise 3: Geospatial Information Data Extraction Deriving SLOPE and ASPECT from DEM Data (ELEVATION) Resample GRID Select Area of Interest (Study Site) Data Extraction Steps for Converting the Geospatial Model to a Thematic Map Product Working with Vegetation Indices and Tasseled Cap Transformation Develop Thematic Layer in ARCVIEW or ARCMAP Map Layout References Index