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E-raamat: Spatial Statistics: GeoSpatial Information Modeling and Thematic Mapping

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(Colorado State University, Fort Collins, USA)
  • Formaat: 184 pages
  • Ilmumisaeg: 09-May-2011
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781439891117
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Spatial Statistics: GeoSpatial Information Modeling and Thematic MappingSuurem pilt
  • Formaat: 184 pages
  • Ilmumisaeg: 09-May-2011
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781439891117
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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.

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 statistic, 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



Arvustused

"[ This book] covers many topics that are poorly treated by others. ... Chapter 2 on sampling is a true gem. It covers all the standard approaches, but in addition has an extensive discussion of multiphase or double sampling which Kalkhan has used extensively in his own research. There is also an extensive discussion of a case study in which a pixel nested plot (PNP) sampling design is used. This is useful material for researchers and course instructors alike. ... This reviewer enjoyed Chapter 4 immensely. It provides a stimulating discussion of geospatial analysis and modeling including the topics of variogram fitting and kriging. These are pitched at just the right level for most applied researchers who want to use these approaches as a tool to solve their spatial analysis problems. A particular treat is the explanation of spatial autoregressive approaches, binary classification trees and the GARP genetic algorithm. These are topics invariably neglected in many of the standard texts." Nigel Waters, Geomatica, Vol. 65, No. 4, 2011

Preface xi
About the Author xiii
1 Geospatial Information Technology 1(38)
Remotely Sensed Data
1(14)
Instantaneous Field of View (IFOV) at Nadir (Resolution on the Ground)
3(1)
IKONOS
4(1)
Sensor Characteristics
5(1)
IKONOS Specifications
5(1)
ORBIMAGE (GeoEye)
5(1)
OrbView-2 Specifications
6(1)
OrbView-3 Specifications
6(1)
QuickBird
6(1)
QuickBird Satellite Sensor Characteristics
7(1)
The SPOT (System Probatori d'Observation de la Terre)
7(2)
SPOT-5 Satellite Sensor Characteristics
8(1)
MODIS (Moderate Resolution Imaging Spectroradiometer)
9(4)
MODIS Overview
10(1)
Technical Specifications of MODIS
10(1)
MODIS Land Products
10(3)
ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer)
13(2)
ASTER Uniqueness
13(1)
History of ASTER
14(1)
Organizational Framework of ASTER
14(1)
Active Remotely Sensed Data
15(4)
Radar
15(2)
Lidar
17(2)
Lidar System Differences
18(1)
How Does Lidar Work?
18(1)
Derived Remotely Sensed Data
19(6)
Vegetation Indices
19(3)
The Tasseled Cap Transformation
22(3)
Geographic Information Systems (GIS)
25(2)
Thematic Data Layers
26(1)
Geospatial Data Conversion
27(5)
Using ERDAS-IMAGINE Software
27(2)
Using ARCINFO Software
29(2)
Select Area of Interest (Study Site)
31(1)
Topographic Data
31(1)
Global Positioning System (GPS)
32(3)
GPS Services
33(1)
The GPS Satellite System and Facts
33(1)
GPS Applications
34(1)
References
35(4)
2 Data Sampling Methods and Applications 39(18)
Data Representation
39(1)
Data Collection and Source of Errors
39(1)
Data Types
39(1)
Sampling Methods and Applications
40(1)
Sampling Designs
41(4)
Simple Random Sampling
41(1)
Stratified Random Sampling
42(1)
Systematic Sampling
42(2)
Nonaligned Systematic Sample
44(1)
Cluster Sampling
44(1)
Multiphase (Double) Sampling
44(1)
Double Sampling and Mapping Accuracy
45(4)
Pixel Nested Plot (PNP): Case Study
46(3)
Plot Design
49(3)
Issues
49(1)
Characteristics of Different Plot Shapes
49(2)
Plot Size
51(7)
What to Record
51(1)
Issues
51(1)
References
52(5)
3 Spatial Pattern and Correlation Statistics 57(22)
Scale
57(1)
Spatial Sampling
58(1)
Errors in Spatial Analysis
58(1)
Spatial Variability and Method of Prediction
58(1)
Spatial Pattern
59(4)
Spatial Point Pattern
59(4)
Quadrant Count Method
63(1)
Linear Correlation Statistic
63(2)
Case Study
64(1)
Statistical Example
65(1)
Spatial Correlation Statistics
65(10)
Moran's I and Geary's C
66(1)
Cross-Correlation Statistic
67(1)
Inverse Distance Weighting (IDW)
67(2)
Statistical Example
69(10)
1 Develop Inverse Distance Weighting
69(1)
2 Develop Moran's I
69(2)
3 Develop Geary's C
71(2)
4 Develop Bi-Moran's
73(2)
References
75(4)
4 Geospatial Analysis and Modeling–Mapping 79(36)
Stepwise Regression
79(2)
Statistical Example
80(1)
Ordinary Least Squares (OLS)
81(2)
Variogram and Kriging
83(8)
Ordinary Kriging
85(1)
Simple Kriging
86(1)
Universal Kriging
87(1)
Developing Variogram Model and Kriging to Predict Plant Diversity at GSENM, Utah
87(4)
Model Cross-Validation
91(1)
Spatial Autoregressive (SAR)
91(6)
Statistical Example
92(12)
Using Spatial AR Model (without Regression)
94(1)
Using Spatial AR Model (with Regression, OLS Model) Using R or S-Plus
94(1)
Example on How to Develop Plot of Standard Normal Distribution
95(1)
Analysis of Residuals for Plant Species Richness (gsenmplant) Data
95(1)
Weighted SAR Model
96(1)
Binary Classification Tree (BCTs)
97(3)
Cokriging
100(4)
Geospatial Models for Presence and Absence Data
104(4)
GARP Model
105(1)
Maxent Model
106(1)
Logistic Regression
106(1)
Classification and Regression Tree (CART)
107(1)
Envelope Model
108(1)
References
108(7)
5 R Statistical Package 115(30)
Overview of R Statistics (R)
116(3)
What Is R?
116(1)
Strengths of R/S
116(1)
The R Environment
117(1)
Scripts
118(1)
Working with R on Your COMPUTER
118(1)
Begin to Use R
118(1)
Statistical Analysis Examples Using R
119(9)
Common Statistics
119(1)
Common Graphics
119(1)
Common Programming
120(1)
Create and Examine a Logical Vector
121(1)
Working on Graphical Display of Data (Data Distributions)
121(1)
Develop a Histogram
122(1)
Data Comparison between the Data and an Expected Normal Distribution
122(2)
More Statistical Analysis
124(1)
Reading New Variable (Enter new data set, WEIGHT)
124(2)
Plotting Weight and Height
126(1)
Test of Association
126(1)
Some Basic Regression Analysis
127(1)
Case Study
128(6)
Test for Spatial Autocorrelation Using Moran's
131(1)
Test for Spatial Autocorrelation Using Geary's C
132(1)
Test for Spatial Cross-Correlation Using Bi-Moran's
133(1)
Trend Surface Analysis
134(9)
Test for Spatial Autocorrelation of the Residuals
136(1)
Test for Moran's I for Residuals
137(1)
Using Spatial AR Model without Regression
138(1)
Using Spatial AR with Regression (Using All Independent Variables as with OLS Model)
138(2)
Analysis of Residuals
140(1)
Develop Variogram Model (Modeling Fine Scale Variability)
140(3)
Plotting Variogram Model
143(1)
References
143(2)
6 Working with Geospatial Information Data 145(18)
Exercise 1: Working with Remotely Sensed Data
145(1)
Exercise 2: Derived Remote Sensing Data and Digital Elevation Model (DEM)
145(3)
Deriving Slope and Aspect from DEM Data
147(1)
Resample GRID
147(1)
Exercise 3: Geospatial Information Data Extraction
148(12)
Deriving SLOPE and ASPECT from DEM Data (ELEVATION)
149(1)
Resample GRID
149(1)
Select Area of Interest (Study Site)
150(1)
Data Extraction
150(2)
Steps for Converting the Geospatial Model to a Thematic Map Product
152(2)
Working with Vegetation Indices and Tasseled Cap Transformation
154(3)
Vegetation Indices
154(1)
Tasseled Cap
155(2)
Develop Thematic Layer in ARCVIEW or ARCMAP
157(2)
VIEWS (Working Only with ARCVIEW)
157(2)
Map Layout
159(1)
References
160(3)
Index 163
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. Kalkhans 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.
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