Spatial statistics : geospatial information modeling and thematic mapping

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 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 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.

Author Notes

Dr. Mohammed A. Kalkhan has over 20 years experience in research and teaching at Colorado State University in Fort Collins, Colorado. 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 Department of Earth Resources (currently the Department of Geosciences) at Colorado State University (CSU).

Dr. Kalkhan received his BSc in Forestry (1973) and MSc in Forest Mensuration (1980) from the College of Agriculture and Forestry, the University of Mosul, Iraq. He received his PhD in forest biometrics- remote sensing applications from the Department of Forest Sciences at Colorado State University, USA, in 1994. From 1975 to 1982, he was a lecturer in the Department of Forestry, College of Agriculture and Forestry, University of Mosul. In 1994, he joined the Natural Resource Ecology Laboratory.

Dr. 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.

Preface	p. xi
About the Author	p. xiii
1 Geospatial Information Technology	p. 1
Remotely Sensed Data	p. 1
Instantaneous Field of View (IFOV) at Nadir (Resolution on the Ground)	p. 3
IkONOS	p. 4
Sensor Characteristics	p. 5
IKONOS Specifications	p. 5
ORBIMAGE (GeoEye)	p. 5
OrbView-2 Specifications	p. 6
OrbView-3 Specifications	p. 6
QuickBird	p. 6
QuickBird Satellite Sensor Characteristics	p. 7
The SPOT (System Probatori d'Observation de la Terre)	p. 7
SPOT-5 Satellite Sensor Characteristics	p. 8
MODIS (Moderate Resolution Imaging Spectroradiometer)	p. 9
MODIS Overview	p. 10
Technical Specifications of MODIS	p. 10
MODIS Land Products	p. 10
ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer)	p. 13
ASTER Uniqueness	p. 13
History of ASTER	p. 14
Organizational Framework of ASTER	p. 14
Active Remotely Sensed Data	p. 15
Radar	p. 15
Lidar	p. 17
Lidar System Differences	p. 18
How Does Lidar Work?	p. 18
Derived Remotely Sensed Data	p. 19
Vegetation Indices	p. 19
The Tasseled Cap Transformation	p. 22
Geographic Information Systems (GIS)	p. 25
Thematic Data Layers	p. 26
Geospatial Data Conversion	p. 27
sUsing ERDAS-IMAGINE Software	p. 27
sUsing ARCINFO Software	p. 29
Select Area of Interest (Study Site)	p. 31
Topographic Data	p. 31
Global Positioning System (GPS)	p. 32
GPS Services	p. 33
The GPS Satellite System and Facts	p. 33
GPS Applications	p. 34
References	p. 35
2 Data Sampling Methods and Applications	p. 39
Data Representation	p. 39
Data Collection and Source of Errors	p. 39
Data Types	p. 39
Sampling Methods and Applications	p. 40
Sampling Designs	p. 41
Simple Random Sampling	p. 41
Stratified Random Sampling	p. 42
Systematic Sampling	p. 42
Nonaligned Systematic Sample	p. 44
Cluster Sampling	p. 44
Multiphase (Double) Sampling	p. 44
Double Sampling and Mapping Accuracy	p. 45
Pixel Nested Plot (PNP): Case Study	p. 46
Plot Design	p. 49
Issues	p. 49
Characteristics of Different Plot Shapes	p. 49
Plot Size	p. 51
What to Record	p. 51
Issues	p. 51
References	p. 52
3 Spatial Pattern and Correlation Statistics	p. 57
Scale	p. 57
Spatial Sampling	p. 58
Errors in Spatial Analysis	p. 58
Spatial Variability and Method of Prediction	p. 58
Spatial Pattern	p. 59
Spatial Point Pattern	p. 59
Quadrant Count Method	p. 63
Linear Correlation Statistic	p. 63
Case Study	p. 64
Statistical Example	p. 65
Spatial Correlation Statistics	p. 65
Moran's I and Geary's C	p. 66
Cross-Correlation Statistic	p. 67
Inverse Distance Weighting (IDW)	p. 67
Statistical Example	p. 69
1 Develop Inverse Distance Weighting	p. 69
2 Develop Moran's I	p. 69
3 Develop Geary's C	p. 71
4 Develop Bi-Moran's I	p. 73
References	p. 75
4 Geospatial Analysis and Modeling-Mapping	p. 79
Stepwise Regression	p. 79
Statistical Example	p. 80
Ordinary Least Squares (OLS)	p. 81
Variogram and Kriging	p. 83
Ordinary Kriging	p. 85
Simple Kriging	p. 86
Universal Kriging	p. 87
Developing Variogram Model and Kriging to Predict Plant Diversity at GSENM, Utah	p. 87
Model Cross-Validation	p. 91
Spatial Autoregressive (SAR)	p. 91
Statistical Example	p. 92
Using Spatial AR Model (without Regression)	p. 94
Using Spatial AR Model (with Regression, OLS Model) Using R or S-Plus	p. 94
Example on How to Develop Plot of Standard Normal Distribution	p. 95
Analysis of Residuals for Plant Species Richness (gsenmplant) Data	p. 95
Weighted SAR Model	p. 96
Binary Classification Tree (BCTs)	p. 97
Cokriging	p. 100
Geospatial Models for Presence and Absence Data	p. 104
GARP Model	p. 105
Maxent Model	p. 106
Logistic Regression	p. 106
Classification and Regression Tree (CART)	p. 107
Envelope Model	p. 108
References	p. 108
5 R Statistical Package	p. 115
Overview of R Statistics (R)	p. 116
What Is R?	p. 116
Strengths of R/S	p. 116
The R Environment	p. 117
Scripts	p. 118
Working with R on Your COMPUTER	p. 118
Begin to Use R	p. 118
Statistical Analysis Examples Using R	p. 119
Common Statistics	p. 119
Common Graphics	p. 119
Common Programming	p. 120
Create and Examine a Logical Vector	p. 121
Working on Graphical Display of Data (Data Distributions)	p. 121
Develop a Histogram	p. 122
Data Comparison between the Data and an Expected Normal Distribution	p. 122
More Statistical Analysis	p. 124
Reading New Variable (Enter new data set, WEIGHT)	p. 124
Plotting Weight and Height	p. 126
Test of Association	p. 126
Some Basic Regression Analysis	p. 127
Case Study	p. 128
Test for Spatial Autocorrelation Using Moran's I	p. 131
Test for Spatial Autocorrelation Using Geary's C	p. 132
Test for Spatial Cross-Correlation Using Bi-Moran's I	p. 133
Trend Surface Analysis	p. 134
Test for Spatial Autocorrelation of the Residuals	p. 136
Test for Moran's I for Residuals	p. 137
Using Spatial AR Model without Regression	p. 138
Using Spatial AR with Regression (Using All Independent Variables as with OLS Model)	p. 138
Analysis of Residuals	p. 140
Develop Variogram Model (Modeling Fine Scale Variability)	p. 140
Plotting Variogram Model	p. 143
References	p. 143
6 Working with Geospatial Information Data	p. 145
Exercise 1 Working with Remotely Sensed Data	p. 145
Exercise 2 Derived Remote Sensing Data and Digital Elevation Model (DEM)	p. 145
Deriving Slope and Aspect from DEM Data	p. 147
ResampleGRID	p. 147
Exercise 3 Geospatial Information Data Extraction	p. 148
Deriving SLOPE and ASPECT from DEM Data (ELEVATION)	p. 149
Resample GRID	p. 149
Select Area of Interest (Study Site)	p. 150
Data Extraction	p. 150
Steps for Converting the Geospatial Model to a Thematic Map Product	p. 152
Working with Vegetation Indices and Tasseled Cap Transformation	p. 154
Vegetation Indices	p. 154
Tasseled Cap	p. 155
Develop Thematic Layer in ARCVIEW or ARCMAP	p. 157
VIEWS (Working Only with ARCVIEW)	p. 157
Map Layout	p. 159
References	p. 160
Index	p. 163

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Table of Contents