The Practical Handbook of Remote Sensing offers a complete understanding of the basic scientific principles needed to perform practical remote sensing at home or at work, using a personal computer. This book contains the information needed to effectively find, download, analyze, and view environmental data. Written by an expert with more than 15 years of experience along with the perspective of a non-expert navigating his way through remote sensing for the first time, it serves as a guidebook for anyone wanting to use remote sensing technology without becoming an expert on the subject.

Table of Contents

• List of Figures
• List of Tables
• Preface
• Acknowledgments
• Authors
• List of Symbols
• List of Acronyms and Abbreviations
• 1. What Is Remote Sensing?
• 1.1 Definition of Remote Sensing
• 1.2 History of Remote Sensing
• 1.3 Principles of Remote Sensing
• 1.4 Usefulness of Remote Sensing
• 1.5 Challenges of Remote Sensing
• 1.6 Summary and Scope of the Book
• 1.7 Key Terms
• References
• 2. How Does Remote Sensing Work?
• 2.1 Principles of Satellite Remote Sensing
• 2.2 What the Sensor Measures in Remote Sensing
• 2.3 EM Spectrum
• 2.4 How Do Sensors Take Measurements?
• 2.5 Spatial, Spectral, and Temporal Resolutions
• 2.5.1 Spatial Resolution of Data
• 2.5.2 Spectral Resolution of Data
• 2.5.3 Temporal Resolution of Data
• 2.5.4 Resolution Compromises
• 2.6 Summary
• 2.7 Key Terms
• References
• 3. Data Available from Remote Sensing
• 3.1 Optical Data
• 3.1.1 Passive: Visible and Infrared
• 3.1.2 Active: Lidar
• 3.2 Microwave Data
• 3.2.1 Passive: Radiometer
• 3.2.2 Active: Scatterometer
• 3.2.3 Active: Altimeter
• 3.2.4 Active: Synthetic Aperture Radar
• 3.3 Distinction between Freely Available Data and Commercial Data
• 3.4 Where to Find Data
• 3.5 Picking the Right Type of Data for a Particular Application …
• 3.6 Summary
• 3.7 Key Terms
• 4. Basic Remote Sensing Using Landsat Data
• 4.1 Notation Used for Practical Exercises within the Book
• 4.2 History of Landsat
• 4.3 Summary of the Landsat Missions
• 4.4 Different Levels of Data Available
• 4.5 Accessing the Level 1 Landsat Data
• 4.6 Selecting the Level 1 Landsat Data to Download
• 4.7 Worldwide Reference System
• 4.8 Downloading the Level 1 Landsat Data
• 4.9 Basic Viewing and Using the Landsat Data
• 4.10 Landsat Calibration and Anomalies
• 4.10.1 Scan Line Corrector within Landsat 7 ETM+
• 4.10.2 Bright Pixels
• 4.10.3 Cloud Cover Percentage
• 4.11 Practical Exercise: Finding, Downloading, and Viewing Landsat Data
• 4.12 Summary
• 4.13 Online Resources
• 4.14 Key Terms
• References
• 5. Introduction to Image Processing
• 5.1 What Is an Image and How Are They Acquired?
• 5.2 Image Properties
• 5.3 Why Are Remotely Sensed Images Often Large in Size?
• 5.4 Image Processing Technique: Contrast Manipulation/Histogram Stretching
• 5.5 Image Processing Technique: Filtering Pixels
• 5.6 Image Processing Technique: Applying Algorithms and Color Palettes
• 5.7 Summary
• 5.8 Key Terms
• 6. Practical Image Processing
• 6.1 Image Processing Software
• 6.2 Installing the SNAP
• 6.3 Introduction to the SNAP
• 6.4 The Geometry of Landsat Level 1 Data
• 6.5 Landsat Level 1 GeoTIFF Files
• 6.6 Downloading the Level 1 GeoTIFF Data
• 6.7 Importing Landsat Level 1 Data into SNAP
• 6.8 Practical Image Processing: Creating Simple Color Composites
• 6.9 Practical Image Processing: Creating a Subset
• 6.10 Practical Image Processing: Contrast Enhancement through Histogram Stretching
• 6.11 Practical Image Processing: Color Palettes
• 6.12 Practical Image Processing: Applying a Filter
• 6.13 Practical Image Processing: Applying the NDVI Algorithm … .
• 6.14 Summary
• 6.15 Online Resources
• 6.16 Key Terms
• 7. Geographic Information System and an Introduction to QGIS … .
• 7.1 Introduction to GIS
• 7.2 GIS Software Packages
• 7.3 Installing QGIS
• 7.4 Introduction to QGIS
• 7.5 Importing Remote Sensing Data into QGIS
• 7.6 GIS Data Handling Technique: Contrast Enhancement/Histogram Stretch
• 7.7 GIS Data Handling Technique: Combining Images
• 7.8 GIS Data Handling Techniques: Adding Cartographic Layers
• 7.9 CRS Adjustments within QGIS
• 7.10 Saving Images and Projects in QGIS
• 7.11 Summary
• 7.12 Online Resources
• 7.13 Key Terms
• References
• 8. Urban Environments and Their Signatures
• 8.1 Introduction to Application Chapters of the Book
• 8.2 Urban Environments
• 8.3 Introduction to the Optical Signatures of Urban Surfaces
• 8.4 Introduction to the Thermal Signatures of Urban Surfaces … .
• 8.5 Urban Applications
• 8.5.1 Green Spaces and Urban Creep
• 8.5.2 Temperature Dynamics
• 8.5.3 Nighttime Imagery
• 8.5.4 Air Quality
• 8.5.5 Subsidence
• 8.6 Practical Exercise: Spectral and Thermal Signatures
• 8.6.1 Step One: Downloading, Importing, and Processing Landsat Optical Data to Determine Green Spaces
• 8.6.2 Step Two: Downloading and Importing MODIS Data to QGIS
• 8.6.3 Step Three: Combining MODIS Thermal Data with Optical Data from Landsat
• 8.6.4 Step Four: Comparing Thermal Data from Landsat and MODIS
• 8.6.5 Step Five: Example of ASTER Data
• 8.7 Summary
• 8.8 Online Resources
• 8.9 Key Terms
• References
• 9. Landscape Evolution
• 9.1 Principles of Using Time-Series Analysis for Monitoring Landscape Evolution
• 9.2 Landscape Evolution Techniques
• 9.3 Optical Vegetation Indices for Landscape Evolution
• 9.4 Microwave Data for Landscape Evolution
• 9.5 Landscape Evolution Applications
• 9.5.1 Mapping Land Cover
• 9.5.2 Agriculture
• 9.5.3 Forestry and Carbon Storage
• 9.5.4 Fire Detection
• 9.6 Practical Exercise: Supervised Land Cover Classification
• 9.6.1 First Stage: Creating the Data Set Ready for Land Classification
• 9.6.1.1 Step One: Installing Semi-Automatic Classification Plugin
• 9.6.1.2 Step Two: Importing and Preprocessing the Data
• 9.6.1.3 Step Three: Creating a False Color Composite
• 9.6.1.4 Step Four: Choosing Classification Wavebands
• 9.6.2 Second Stage: Performing a Supervised Land Classification Using Existing Training Sites
• 9.6.2.1 Step Five: Importing Spectral Signatures … .
• 9.6.2.2 Step Six: Importing ROI Shapefiles
• 9.6.2.3 Step Seven: Classification Algorithm and Preview
• 9.6.2.4 Step Eight: Whole Scene Classification
• 9.6.3 Third Stage: Performing a Supervised Land Classification with Your Own Training Sites
• 9.6.3.1 Step Nine: Creating a Pseudo-True Color Composite
• 9.6.3.2 Step Ten: Identifying and Selecting Your Own Training Sites
• 9.6.3.3 Step Eleven: Classification Algorithm and Preview
• 9.6.3.4 Step Twelve: Whole Scene Classification
• 9.7 Summary
• 9.8 Online Resources
• 9.9 Key Terms
• References
• 10. Inland Waters and the Water Cycle
• 10.1 Optical and Thermal Data for Inland Waters
• 10.2 Microwave Data for Monitoring the Water Cycle
• 10.2.1 Altimetry
• 10.2.2 Passive Radiometry
• 10.3 Inland Water Applications
• 10.3.1 Water Cycle and Wetlands
• 10.3.2 Soil Moisture Monitoring
• 10.3.3 Lakes, Rivers, and Reservoirs
• 10.3.4 Flood Mapping
• 10.3.5 Groundwater Measurement
• 10.4 Practical Exercise: Analysis of the Aswan Dam
• 10.4.1 Step One: Obtaining the SAR Data
• 10.4.2 Step Two: Loading the SAR Data into QGIS
• 10.4.3 Step Three: Downloading the Landsat Data from EarthExplorer
• 10.4.4 Step Four: Importing Landsat Data into QGIS
• 10.4.5 Step Five: Creating an NDWI Using a Mathematical Function
• 10.4.6 Step Six: Creating a Pseudo-True Color Composite …
• 10.4.7 Step Seven: Downloading the SRTM DEM Data
• 10.4.8 Step Eight: Loading the SRTM DEM Data into QGIS …
• 10.4.9 Step Nine: Merging the Four SRTM DEM Tiles into a Single Layer
• 10.4.10 Step Ten: Adding Contour Lines
• 10.5 Summary
• 10.6 Online Resources
• 10.7 Key Terms
• References
• 11. Coastal Waters and Coastline Evolution
• 11.1 Optical Data
• 11.1.1 The Color of the Water
• 11.1.2 Bathymetric Data
• 11.2 Passive Microwave Signatures from the Ocean
• 11.3 Coastal Applications
• 11.3.1 Physical Oceanography That Includes Temperature, Salinity, and Sea Ice
• 11.3.2 Water Quality, Including Algal Blooms
• 11.3.3 Mangroves and Coastal Protection
• 11.3.4 Coastal Evolution, Including Sediment Transport
• 11.4 Practical Exercise–New York Bight
• 11.4.1 Stage One: Importing and Processing MODIS L2 Data
• 11.4.1.1 Step One: Downloading MODIS L2 Data … .
• 11.4.1.2 Step Two: Importing the MODIS SST Data into SNAP
• 11.4.1.3 Step Three: Processing the MODIS-Aqua SST Data
• 11.4.1.4 Step Four: Importing and Processing the MODIS OC Data in SNAP
• 11.4.1.5 Step Five: Save the Products
• 11.4.2 Stage Two: Comparison of MODIS L2 and Landsat Data
• 11.4.2.1 Step Six: Restarting SNAP and Importing Landsat Data
• 11.4.2.2 Step Seven: Importing the Previous OC Product
• 11.4.2.3 Step Eight: Reprojection of the OC Image …
• 11.4.3 Stage Three: MODIS L3 Data
• 11.4.3.1 Step Ten: Downloading MODIS L3 Data
• 11.5 Summary
• 11.6 Online Resources
• 11.7 Key Terms
• References
• 12. Where to Next?
• 12.1 Developments in Satellite Hardware
• 12.1.1 Instruments
• 12.1.2 Satellite Developments
• 12.2 Developments in Data Processing
• 12.2.1 Accessing Online Data Sets
• 12.2.2 Cloud Processing
• 12.2.3 Integration
• 12.2.4 Object-Based Image Analysis
• 12.2.5 Open Source Software
• 12.3 Developments in Applications
• 12.3.1 Citizen Science
• 12.3.2 Climate Quality Data Sets
• 12.3.3 Repurposing
• 12.4 Long-Term Developments for Remote Sensing
• 12.5 Developing Your Knowledge Further
• 12.5.1 Examples of Further Reading
• 12.6 Summary
• 12.7 Online Resources
• References
• Figures