Practical handbook of remote sensing / Samantha Lavender, PhD, Andrew Lavender.

Format:

Book

Author/Creator:

Lavender, Samantha, author.

Lavender, Andrew, author.

Contributor:

Alumni and Friends Memorial Book Fund.

Language:

English

Subjects (All):

Remote sensing--Handbooks, manuals, etc.

Remote sensing.

Genre:

Handbooks and manuals.

Physical Description:

xxxii, 212 pages, 24 unnumbered pages of plates : illustrations (some color), maps (some color) ; 23 cm

Place of Publication:

Boca Raton : CRC Press, Taylor & Francis Group, [2016]

Contents:

1 What Is Remote Sensing? 1

1.1 Definition of Remote Sensing 1

1.2 History of Remote Sensing 2

1.3 Principles of Remote Sensing 3

1.4 Usefulness of Remote Sensing 4

1.5 Challenges of Remote Sensing 5

1.6 Summary and Scope of the Book 6

1.7 Key Terms 7

References 7

2 How Does Remote Sensing Work? 9

2.1 Principles of Satellite Remote Sensing 9

2.2 What the Sensor Measures in Remote Sensing 11

2.3 EM Spectrum 13

2.4 How Do Sensors Take Measurements? 15

2.5 Spatial, Spectral, and Temporal Resolutions 15

2.5.1 Spatial Resolution of Data 15

2.5.2 Spectral Resolution of Data 17

2.5.3 Temporal Resolution of Data 19

2.5.4 Resolution Compromises 19

2.6 Summary 20

2.7 Key Terms 20

References 20

3 Data Available from Remote Sensing 21

3.1 Optical Data 21

3.1.1 Passive: Visible and Infrared 21

3.1.2 Active: Lidar 22

3.2 Microwave Data 23

3.2.1 Passive: Radiometer 23

3.2.2 Active: Scatterometer 23

3.2.3 Active: Altimeter 23

3.2.4 Active: Synthetic Aperture Radar 24

3.3 Distinction between Freely Available Data and Commercial Data 25

3.4 Where to Find Data 27

3.5 Picking the Right Type of Data for a Particular Application 28

3.6 Summary 29

3.7 Key Terms 29

4 Basic Remote Sensing Using Landsat Data 31

4.1 Notation Used for Practical Exercises within the Book 31

4.2 History of Landsat 32

4.3 Summary of the Landsat Missions 32

4.4 Different Level? of Data Available 34

4.5 Accessing the Level 1 Landsat Data 35

4.6 Selecting the Level 1 Landsat Data to Download 35

4.7 Worldwide Reference System 38

4.8 Downloading the Level 1 Landsat Data 39

4.9 Basic Viewing and Using the Landsat Data 40

4.10 Landsat Calibration and Anomalies 40

4.10.1 Scan Line Corrector within Landsat 7 ETM+ 41

4.10.2 Bright Pixels 41

4.10.3 Cloud Cover Percentage 42

4.11 Practical Exercise: Finding, Downloading, and Viewing Landsat Data 42

4.12 Summary 44

4.13 Online Resources 44

4.14 Key Terms 45

References 45

5 Introduction to Image Processing 47

5.1 What Is an Image and How Are They Acquired? 47

5.2 Image Properties 49

5.3 Why Are Remotely Sensed Images Often Large in Size? 51

5.4 Image Processing Technique: Contrast Manipulation/ Histogram Stretching 52

5.5 Image Processing Technique: Filtering Pixels 54

5.6 Image Processing Technique: Applying Algorithms and Color Palettes 56

5.7 Summary 57

5.8 Key Terms 57

6 Practical Image Processing 59

6.1 Image Processing Software 59

6.2 Installing the SNAP 60

6.3 Introduction to the SNAP 61

6.4 The Geometry of Landsat Level 1 Data 62

6.5 Landsat Level 1 GeoTIFF Files 63

6.6 Downloading the Level 1 GeoTIFF Data 65

6.7 Importing Landsat Level 1 Data into SNAP 67

6.8 Practical Image Processing: Creating Simple Color Composites 67

6.9 Practical Image Processing: Creating a Subset 70

6.10 Practical Image Processing: Contrast Enhancement through Histogram Stretching 71

6.11 Practical Image Processing: Color Palettes 72

6.12 Practical Image Processing: Applying a Filter 74

6.13 Practical Image Processing: Applying the NDVI Algorithm 75

6.14 Summary 77

6.15 Online Resources 77

6.16 Key Terms 78

7 Geographic Information System and an Introduction to QGIS 79

7.1 Introduction to GIS 79

7.2 GIS Software Packages 82

7.3 Installing QGIS 82

7.4 Introduction to QGIS 83

7.5 Importing Remote Sensing Data into QGIS 84

7.6 GIS Data Handling Technique: Contrast Enhancement/Histogram Stretch 85

7.7 GIS Data Handling Technique: Combining Images 87

7.8 GIS Data Handling Techniques: Adding Cartographic Layers 89

7.9 CRS Adjustments within QGIS 91

7.10 Saving Images and Projects in QGIS 92

7.11 Summary 92

7.12 Online Resources 93

7.13 Key Terms 93

References 94

8 Urban Environments and Their Signatures 95

8.1 Introduction to Application Chapters of the Book 95

8.2 Urban Environments 95

8.3 Introduction to the Optical Signatures of Urban Surfaces 96

8.4 Introduction to the Thermal Signatures of Urban Surfaces 99

8.5 Urban Applications 100

8.5.1 Green Spaces and Urban Creep 100

8.5.2 Temperature Dynamics 102

8.5.3 Nighttime Imagery 103

8.5.4 Air Quality 105

8.5.5 Subsidence 106

8.6 Practical Exercise: Spectral and Thermal Signatures 108

8.6.1 Step One: Downloading, Importing, and Processing Landsat Optical Data to Determine Green Spaces 108

8.6.2 Step Two: Downloading and Importing MODIS Data to QGIS 110

8.6.3 Step Three: Combining MODIS Thermal Data with Optical Data from Landsat 111

8.6.4 Step Pour: Comparing Thermal Data from Landsat and MODIS 111

8.6.5 Step Five: Example of ASTER Data 112

8.7 Summary 113

8.8 Online Resources 113

8.9 Key Terms 113

References 114

9 Landscape Evolution 117

9.1 Principles of Using Time-Series Analysis for Monitoring Landscape Evolution 117

9.2 Landscape Evolution Techniques 119

9.3 Optical Vegetation Indices for Landscape Evolution 120

9.4 Microwave Data for Landscape Evolution 121

9.5 Landscape Evolution Applications 122

9.5.1 Mapping Land Cover 122

9.5.2 Agriculture 124

9.5.3 Forestry and Carbon Storage 125

9.5.4 Fire Detection 127

9.6 Practical Exercise: Supervised Land Cover Classification 128

9.6.1 First Stage: Creating the Data Set Ready for Land Classification 128

9.6.1.1 Step One: Installing Semi-Automatic Classification Plugin 128

9.6.1.2 Step Two: Importing and Preprocessing the Data 129

9.6.1.3 Step Three: Creating a False Color Composite 130

9.6.1.4 Step Four: Choosing Classification Wavebands 132

9.6.2 Second Stage: Performing a Supervised Land Classification Using Existing Training Sites 132

9.6.2.1 Step Five: Importing Spectral Signatures 132

9.6.2.2 Step Six: Importing ROI Shapefiles 134

9.6.2.3 Step Seven: Classification Algorithm and Preview 134

9.6.2.4 Step Eight: Whole Scene Classification 135

9.6.3 Third Stage: Performing a Supervised Land Classification with Your Own Training Sites 136

9.6.3.1 Step Nine: Creating a Pseudo-True Color Composite 136

9.6.3.2 Step Ten: Identifying and Selecting Your Own Training Sites 137

9.6.3.3 Step Eleven: Classification Algorithm and Preview 139

9.6.3.4 Step Twelve: Whole Scene Classification 140

9.7 Summary 140

9.8 Online Resources 141

9.9 Key Terms 141

References 142

10 Inland Waters and the Water Cycle 143

10.1 Optical and Thermal Data for Inland Waters 143

10.2 Microwave Data for Monitoring the Water Cycle 146

10.2.1 Altimetry 146

10.2.2 Passive Radiometry 147

10.3 Inland Water Applications 148

10.3.1 Water Cycle and Wetlands 148

10.3.2 Soil Moisture Monitoring 149

10.3.3 Lakes, Rivers, and Reservoirs 150

10.3.4 Flood Mapping 152

10.3.5 Groundwater Measurement 152

10.4 Practical Exercise: Analysis of the Aswan Dam 154

10.4.1 Step One: Obtaining the SAR Data 155

10.4.2 Step Two: Loading the SAR Data into QGIS 155

10.4.3 Step Three: Downloading the Landsat Data from Earth Explorer 155

10.4.4 Step Four: Importing Landsat Data into QGIS 158

10.4.5 Step Five: Creating an NDWI Using a Mathematical Function 158

10.4.6 Step Six: Creating a Pseudo-True Color Composite 159

10.4.7 Step Seven: Downloading the SRTM DEM Data 160

10.4.8 Step Eight Loading the SRTM DEM Data into QGIS 161

10.4.9 Step Nine: Merging the Four SRTM DEM Tiles into a Single Layer 161

10.4.10 Step Ten: Adding Contour Lines 162

10.5 Summary 163

10.6 Online Resources 163

10.7 Key Terms 164

References 164

11 Coastal Waters and Coastline Evolution 167

11.1 Optical Data 167

11.1.1 The Color of the Water 167

11.1.2 Bathymetric Data 171

11.2 Passive Microwave Signatures from the Ocean 172

11.3 Coastal Applications 173

11.3.1 Physical Oceanography That Includes

Temperature, Salinity, and Sea Ice 173

11.3.2 Water Quality, Including Algal Blooms 175

11.3.3 Mangroves and Coastal Protection 176

11.3.4 Coastal Evolution, Including Sediment Transport 178

11.4 Practical Exercise-New York Bight 180

11.4.1 Stage One: Importing and Processing MODIS L2 Data 180

11.4.1.1 Step One: Downloading MODIS 12 Data 180

11.4.1.2 Step Two: Importing the MODIS SST Data into SNAP 183

11.4.1.3 Step Three: Processing the MODIS-Aqua SST Data 183

11.4.1.4 Step Four: Importing and Processing the MODIS OC Data in SNAP 184

11.4.1.5 Step Five: Save the Products 186

11.4.2 Stage Two: Comparison of MODIS L2 and Landsat Data 186

11.4.2.1 Step Six: Restarting SNAP and Importing Landsat Data 186

11.4.2.2 Step Seven: Importing the Previous OC Product 187

11.4.2.3 Step Eight: Reprojection of the OC Image 187

11.4.3 Stage Three: MODIS L3 Data 189

11.4.3.1 Step Ten: Downloading MODIS L3 Data 189

11.5 Summary 190

11.6 Online Resources 190

11.7 Key Terms 191

References 191

12 Where to Next? 193

12.1 Developments in Satellite Hardware 193

12.1.1 Instruments 193

12.1.2 Satellite Developments 194

12.2 Developments in Data Processing 195

12.2.1 Accessing Online Data Sets 195

12.2.2 Cloud Processing 196

12.2.3 Integration 196

12.2.4 Object-Based Image Analysis 197

12.2.5 Open Source Software 197

12.3 Developments in Applications 198

12.3.1 Citizen Science 198

12.3.2 Climate Quality Data Sets 198

12.3.3 Repurposing 199

12.4 Long-Term Developments for Remote Sensing 199

12.5 Developing Your Knowledge Further 200

12.5.1 Examples of Further Reading 201

12.6 Summary 201

12.7 Online Resources 202

References 202.

Notes:

Includes bibliographical references and index.

Local Notes:

Acquired for the Penn Libraries with assistance from the Alumni and Friends Memorial Book Fund.

ISBN:

9781498704335

1498704336

OCLC:

932261922

Publisher Number:

99965603124