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Practical Handbook of Remote Sensing, 2nd Edition

Name: Practical Handbook of Remote Sensing, 2nd Edition
ISBN: 9781000862225

by Samantha Lavender, Andrew Lavender

April 2023

Intermediate to advanced

319 pages

8h 49m

English

CRC Press

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Cover
Half Title
Title Page
Copyright Page
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 Sensing1.2 History of Remote Sensing1.3 Principles of Remote Sensing1.4 Usefulness of Remote Sensing1.5 Challenges of Remote Sensing1.6 Summary and Scope of the Book1.7 Key TermsReferences
2. How Does Remote Sensing Work?
2.1 Principles of Satellite Remote Sensing2.2 What Does the Sensor Measure in Remote Sensing?2.3 Electromagnetic Spectrum2.4 How Do Sensors Take Measurements?2.5 Spatial, Spectral, and Temporal Resolutions2.5.1 Spatial Resolution of Data2.5.2 Spectral Resolution of Data2.5.3 Temporal Resolution of Data2.5.4 Resolution Compromises2.6 Summary2.7 Key TermsReferences
3. Data Available from Remote Sensing
3.1 Optical Data3.1.1 Passive: Visible and Infrared3.1.2 Active: Lidar3.2 Microwave Data3.2.1 Passive: Radiometer3.2.2 Active: Scatterometer3.2.3 Active: Altimeter3.2.4 Active: Synthetic Aperture Radar3.3 Radio Data3.4 Distinction between Freely Available Data and Commercial Data3.5 Where to Find Data?3.6 Picking the Right Type of Data for a Particular Application3.7 Summary3.8 Key Terms
4. Basic Remote Sensing Using Landsat Data
4.1 Notation Used for Practical Exercises within the Book4.2 History of Landsat4.3 Summary of the Landsat Missions4.4 Different Levels of Data Available4.5 Accessing the Level 1 Landsat Data4.6 Selecting the Level 1 Landsat Data to Download4.7 Scene ID4.8 Worldwide Reference System4.9 Downloading the Level 1 Landsat Data4.10 Basic Viewing and Using the Landsat Data4.11 Landsat Known Issues4.11.1 Scan Line Corrector within Landsat-7 ETM+4.11.2 Bright Pixels4.12 Practical Exercise: Finding, Downloading, and Viewing Landsat Data4.13 Summary4.14 Online Resources4.15 Key TermsReferences
5. Introduction to Image Processing
5.1 What Is an Image and How Is It Acquired?5.2 Image Properties5.3 Why Are Remotely Sensed Images Often Large in Size?5.4 Image Processing Technique: Contrast Manipulation/Histogram Stretching5.5 Image Processing Technique: Filtering Pixels5.6 Image Processing Technique: Applying Algorithms and Color Palettes5.7 Summary5.8 Key Terms
6. Practical Image Processing
6.1 Image Processing Software6.2 Installing the SNAP6.3 Introduction to the SNAP6.4 The Geometry of Landsat Level-1 Data6.5 Landsat Level-1 GeoTIFF Files6.6 Downloading the Level-1 Product Bundle6.7 Importing Landsat Level-1 Data into SNAP6.8 Practical Image Processing: Creating Simple Color Composites6.9 Practical Image Processing: Creating a Subset6.10 Practical Image Processing: Contrast Enhancement through Histogram Stretching6.11 Practical Image Processing: Color Palettes6.12 Practical Image Processing: Applying a Filter6.13 Practical Image Processing: Applying the NDVI Algorithm6.14 History of the Copernicus Program6.14.1 Summary of Sentinel Missions6.14.1.1 Sentinel-1A and 1B6.14.1.2 Sentinel-2A and 2B6.14.1.3 Sentinel-3A and 3B6.14.1.4 Sentinel-5P6.14.1.5 Sentinel-66.15 Practical Exercise: Finding, Downloading, Processing, and Visualizing Sentinel-2 Data6.15.1 Downloading the Sentinel-2 Data6.15.2 Importing Sentinel-2 Level-1 Data into SNAP6.15.3 Practical Image Processing: Creating Simple Color Composites6.15.4 Practical Image Processing: Applying the NDVI Algorithm6.16 Summary6.17 Online Resources6.18 Key Terms
7. Geographic Information System and an Introduction to QGIS
7.1 Introduction to GIS7.2 GIS Software Packages7.3 Installing QGIS7.4 Introduction to QGIS7.5 Importing Remote Sensing Data into QGIS7.6 GIS Data Handling Technique: Contrast Enhancement/Histogram Stretch7.7 GIS Data Handling Technique: Combining Images7.7.1 GIS Data Handling Technique: Combining Data from Different Satellites7.8 GIS Data Handling Techniques: Adding Cartographic Layers7.9 Coordinate Reference System Adjustments within QGIS7.10 Saving Images and Projects in QGIS7.11 Summary7.12 Online Resources7.13 Key TermsReferences
8. Urban Environments and Their Signatures
8.1 Introduction to Application Chapters of the Book8.2 Urban Environments8.3 Introduction to the Optical Signatures of Urban Surfaces8.4 Introduction to the Thermal Signatures of Urban Surfaces8.5 Urban Applications8.5.1 Green Spaces and Urban Creep8.5.2 Temperature Dynamics8.5.3 Nighttime Imagery8.5.4 Subsidence8.6 Practical Exercise: Spectral and Thermal Signatures8.6.1 Step One: Downloading, Importing, and Processing Landsat Optical Data to Determine Green Spaces8.6.2 Step Two: Downloading and Importing MODIS Data to QGIS8.6.3 Step Three: Combining MODIS Thermal Data with Optical Data from Landsat8.6.4 Step Four: Comparing Thermal Data from Landsat and MODIS8.6.5 Step Five: Example of ASTER Data8.7 Summary8.8 Online Resources8.9 Key TermsReferences
9. Landscape Evolution
9.1 Principles of Using Time-Series Analysis for Monitoring Landscape Evolution9.2 Landscape Evolution Techniques9.3 Optical Vegetation Indices for Landscape Evolution9.4 Microwave Data for Landscape Evolution9.5 Landscape Evolution Applications9.5.1 Mapping Land Cover9.5.2 Agriculture9.5.3 Forestry and Carbon Storage9.5.4 Fire Detection9.6 Practical Exercise: Supervised Land Cover Classification9.6.1 First Stage: Creating the Data Set Ready for Land Classification9.6.1.1 Step One: Installing Semi-Automatic Classification Plugin into QGIS9.6.1.2 Step Two: Importing and Preprocessing the Data9.6.1.3 Step Three: Creating a False-Color Composite9.6.1.4 Step Four: Choosing Classification Wavebands9.6.2 Second Stage: Performing a Supervised Land Classification Using Existing Training Sites9.6.2.1 Step Five: Importing Spectral Signatures9.6.2.2 Step Six: Classification Algorithm and Preview9.6.2.3 Step Seven: Whole Scene Classification9.6.3 Third Stage: Performing a Supervised Land Classification with Your Own Training Sites9.6.3.1 Step Eight: Creating a Pseudo-True-Color Composite9.6.3.2 Step Nine: Identifying and Selecting Your Own Training Sites9.6.3.3 Step Eleven: Classification Algorithm and Preview9.6.3.4 Step Ten: Whole Scene Classification9.7 Summary9.8 Online Resources9.9 Key TermsReferences
10. Inland Waters and the Water Cycle
10.1 Optical and Thermal Data for Inland Waters10.2 Microwave Data for Monitoring the Water Cycle10.2.1 Altimetry10.2.2 Passive Radiometry10.3 Inland Water Applications10.3.1 Water Cycle and Wetlands10.3.2 Soil Moisture Monitoring10.3.3 Lakes, Rivers, and Reservoirs10.3.4 Flood Mapping10.3.5 Groundwater Measurement10.4 Practical Exercise: Analysis of the Aswan Dam10.4.1 Step One: Obtaining the TerraSAR-X SAR Data10.4.2 Step Two: Loading the SAR Data into QGIS10.4.3 Step Three: Downloading the Landsat Data from EarthExplorer10.4.4 Step Four: Importing Landsat Data into QGIS10.4.5 Step Five: Creating an NDWI Using a Mathematical Function10.4.6 Step Six: Creating a Pseudo-True-Color Composite10.4.7 Step Seven: Downloading the SRTM DEM Data10.4.8 Step Eight: Loading the SRTM DEM Data into QGIS10.4.9 Step Nine: Merging the Four SRTM DEM Tiles into a Single Layer10.4.10 Step Ten: Adding Contour Lines10.5 Summary10.6 Online Resources10.7 Key TermsReferences
11. Coastal Waters and Coastline Evolution
11.1 Optical Data11.1.1 The Color of the Water11.1.2 Bathymetric Data11.2 Passive Microwave Signatures from the Ocean11.3 Coastal Applications11.3.1 Physical Oceanography that Includes Temperature, Salinity, and Sea Ice11.3.2 Water Quality, Including Algal Blooms11.3.3 Mangroves and Coastal Protection11.3.4 Coastal Evolution, Including Sediment Transport11.4 Practical Exercise – New York Bight11.4.1 Stage One: Importing and Processing MODIS L2 Data11.4.1.1 Step One: Downloading MODIS L2 Data11.4.1.2 Step Two: Importing the MODIS SST Data into SNAP11.4.1.3 Step Three: Processing the MODIS-Aqua SST Data11.4.1.4 Step Four: Importing and Processing the MODIS OC Data in SNAP11.4.1.5 Step Five: Download and Import the OLCI L2 Product11.4.1.6 Step Six: Save the Products11.4.2 Stage Two: Comparison of MODIS L2, OLCI L2, and Landsat Data11.4.2.1 Step Seven: Restarting SNAP and Importing Landsat Data11.4.2.2 Step Eight: Importing the Previous OC Product11.4.2.3 Step Nine: Reprojection of the OC Image11.4.3 Stage Three: OLCI L3 Data11.4.3.1 Step Ten: Downloading OLCI L3 Data11.5 Summary11.6 Online Resources11.7 Key TermsReferences
12. Atmospheric Gases and Pollutants
12.1 An Understanding of the Atmosphere12.2 Detecting What Is in the Atmosphere12.3 Air Quality12.3.1 Real-Time and Forecasted Alerts12.3.2 The Impact of COVID-1912.4 Greenhouse Gas Emissions12.4.1 Observing Methane12.5 Practical – An Assessment of Air Quality and Temperature12.5.1 Stage One: Adding Cartographic Layers12.5.2 Stage Two: Adding CORINE Land Cover Data12.5.3 Stage Three: Downloading the CAMS Data set12.5.4 Stage Four: Visualizing the CAMS Time Series12.6 Summary12.7 Online Resources12.8 Key TermsReferences
13. Where to Next?
13.1 Developments in Satellite Hardware13.1.1 Instruments13.1.2 Satellite Developments13.1.2.1 Smaller and Smaller Satellites13.1.2.2 Constellations13.1.2.3 China13.1.2.4 Democratization of Space13.1.2.5 High-Altitude Pseudo-Satellite/High-Altitude Platform Station13.1.2.6 Uncrewed Aerial Vehicles13.1.2.7 Sustainability: Space Debris and Carbon Footprint13.2 Developments in Data Processing13.2.1 Accessing Online Data Sets13.2.2 Onboard Satellite Data Processing13.2.3 Integration13.2.4 Machine Learning and Artificial Intelligence13.2.5 Open Source and Open Science13.2.6 Data Standards13.3 Developments in Applications13.3.1 Citizen Science13.3.2 Climate Quality Data Sets13.3.3 Repurposing13.4 Developing Your Knowledge Further13.4.1 Examples of Further Reading13.5 Summary13.6 Online ResourcesReferences
Index

Content preview from Practical Handbook of Remote Sensing, 2nd Edition

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 Does the Sensor Measure in Remote Sensing?

2.3 Electromagnetic 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 ...

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ISBN: 9781000862225

Cloud Computing

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Soft Skills

Practical Handbook of Remote Sensing, 2nd Edition

by Samantha Lavender, Andrew Lavender

Contents

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.