rsmnl
Chapter 1 Introduction to Remote Sensing
Contents of this Manual-continue
Chapter 2 Principles Of Remote Sensing Systems
Component 1: Electromagnetic Energy Is Emitted From A Source
Summary of Electromagnetic Energy
Figure 2-3. Propagation of the electromagnetic and magnetic field
Measurement of Electromagnetic Wave Radiation
Figure 2-7. Electromagnetic spectrum displayed in meter and Hz units
Figure 2-8. Visible spectrum illustrated here in color
Regions of the Electromagnetic Spectrum
Table 2-2. Wavelengths of the primary colors of the visible spectrum
Table 2-3. Wavelengths of various bands in the microwave range
Implications for Remote Sensing
The Stefan-Boltzmann Law
Summary - rsmnl0023
Figure 2-11. The Sun and Earth both emit electromagnetic radiation
Figure 2-12. Various radiation obstacles and scatter paths
Nonselective Scattering
Atmospheric Absorption and Atmospheric Windows
The role of atmospheric compounds in the atmosphere
Geometric Effects
Component 3: Electromagnetic Energy Interacts with Surface and Near Surface Objects
Absorption - rsmnl0031
Specular and diffuse reflection
Figure 2-19. Diffuse reflection of radiation from a single target point
Spectral Reflectance Curves
Figure 2-21. Spectral reflectance of healthy vegetation
Spectral Reflectance of Water
Critical Spectral Regions
Real Life and Spectral Signatures
Component 4: Energy is Detected and Recorded by the Sensor
Table 2-5. Digital number value ranges for various bit data
Satellite Receiving Stations
Turning Digital Data into Images
Figure 2-28. Brightness levels at different radiometric resolutions
Figure 2-30. Landsat MSS band 5 data of San Francisco, California
Spectral Bands
Color in the Image
Figure 2-31. Individual DNs can be identified in each spectral band of an image
The true color image appears with these bands in the visible part of the spectrum
Interpreting the Image
Figure 2-33. Landsat 7 image of southern California
Spatial Resolution
Aerial Photography - rsmnl0052
Figure 2-34. Aerial photograph of a predominately agricultural area near Modesto, California
Brief History of Remote Sensing
NASA's First Weather Satellites
Future of Remote Sensing
Chapter 3 Sensors and Systems
Corps 9--Civil Works Business Practice Areas
Sensor Data Considerations (programmatic and technical)
Sensor Data Considerations (programmatic and technical)-continue - rsmnl0060
Sensor Data Considerations (programmatic and technical)-continue - rsmnl0061
Figure 3-1. In this CAMIS image a decrease in aircraft altitude
Value Added Products
Aerial Photography - rsmnl0064
Planning Airborne Acquisitions
Bathymetric and Hydrographic Sensors
Airborne Gamma
Satellite Orbits
Planning Satellite Acquisitions
Ground Penetrating Radar Sensors
Match to the Corps 9--Civil Works Business Practice Areas
Real Estate Needs
Research and Development
Chapter 4 Data Acquisition and Archives
Specifications for Image Acquisition
Satellite Image Licensing
Image Archive Search and Cost-continue - rsmnl0078
Image Archive Search and Cost-continue - rsmnl0079
Specifications for Airborne Acquisition
St. Louis District Air-Photo Contracting
Chapter 5 Processing Digital Imagery
Band/Color Composite
Image Projections
Latitude/Longitude Computer Entry
Map Projections
Rectification
Ground Control Points (GCPs)
The scene appearance of the GCP selection module
scene represents the original, unprojected data file
Unprojected data are then warped to the GCP positions
Project Image and Save
Image Enhancement
Figure 5-6. Contrast in an image before (left) and after (right) a linear contrast stretch
Data Analysis
Figure 5-7. Pixel population and distribution across the 0 to 255 digital number range
Enhancing Pixel Digital Number Values
Figure 5-9. Unenhanced satellite data on left
Histogram Equalization - rsmnl0100
Figure 5-10. Landsat image of Denver area
Figure 5-11. NASA Landsat images from top to bottom
Table 5-1. Effects of shadowing
Other Types of Ratios and Band Arithmetic
Figure 5-12. Top: True color CAMIS image
Convolution
The Convolution Method
Direction Filter: north-south component kernel
Principle Component Transformation
Transformation Series (PC1, PC2, PC3, PC4, PC5, etc.)
Image Classification - rsmnl0111
Supervised Classification
Figure 5-17. Landsat 7 ETM image of central Australia
Figure 5-18. Classification training data of 35 landscape classification features
Parallelepiped
Table 5-3. Omission and Commission Accuracy Assessment Matrix
Steps Required for Unsupervised Classification
Image Mosaics, Image subsets, and Multiple Image Analysis
Percent Calculation
Figure 5-21. Multiple Landsat TM images
Figure 5-22. Multi-image mosaic of Western United States centered on the state of Utah
Figure 5-23. Landsat 7 Image of the Boston, Massachusetts area
Digital Elevation Models (DEM)
Advanced Methods in Image Processing
Thermal Data
Figure 5-26. Close-up of the Atlantic Gulf Stream
Internal Programming
Assessing Project Needs
Visualization Interpretation
Figure 5-28. Forest fire assessment using Landsat imagery
Figure 5-29. Landsat scene bands 5, 4, 2 (RGB)
Water (Water, Clouds, Snow, and Ice)
Figure 5-31. AVIRIS image, centered on Arches National Park
Figure 5-32. MODIS image of a plankton bloom in the Gulf of St. Lawrence near Newfoundland, Canada
Figure 5-33. Orlando, Florida, imaged in 2000 by Landsat 7 ETM+ bands 4, 3, 2 (RGB)
Figure 5-34. Landsat image of Mt. Etna eruption of July 2001
Figure 5-37. July 2001 Saharan dust storm over the Mediterrenean
Figure 5-38. Oil trench fires and accompanying black smoke plumes over Baghdad Iraq (2003)
Statistical Analysis and Accuracy Assessment
Resolution and RMS (Root Mean Squared)
Figure 5-40. The final product may be displayed as a digital image or as a high quality hard copy
Chapter 6 Remote Sensing Applications in USACE
Description of Methods - rsmnl0144
Field Work - rsmnl0145
Study Results - rsmnl0146
Case Study 2: Evaluation of New Sensors for Emergency Management
Field Work - rsmnl0148
Table 6-1. Detection Matrix for Objects at Various GSDS
Case Study 3: River Ice Delineation with RADARSAT SAR
Sensor Selection and Image Post-Processing
Case Study 4: Tree Canopy Characterization for EO-1 Reflective and Thermal Infrared Validation Studies in Rochester, New York
Sensor System
Case Study 5: Blended Spectral Classification Techniques for Mapping Water Surface Transparency and Chlorophyll Concentration
Study Results - rsmnl0155
Case Study 6: A SPOT Survey of Wild Rice in Northern Minnesota
Case Study 7: Duration and Frequency of Ponded Water on Arid Southwestern Playas
Description of Methods - rsmnl0158
Case Study 8: An Integrated Approach for Assessment of Levees in the Lower Rio Grande Valley
Field Work - rsmnl0160
Case Study 9 : From Wright Flyers to Aerial Thermography
Field Work - rsmnl0162
Study Results - rsmnl0163
Case Study 10: Digital Terrain Modeling and Distributed Soil Erosion Simulation
Remotely Sensed DEM Data
Conclusions - rsmnl0166
Appendix A. References
Appendix A. References-continue - rsmnl0168
Appendix A. References-continue - rsmnl0169
Appendix B. Regions of the Electromagnetic Spectrum and Useful TM Band Combinations
Appendix C. Paper model of the color cube/space
Appendix D: Satellite Sensors
Appendix D: Satellite Sensors-continue - rsmnl0176
Appendix D: Satellite Sensors-continue - rsmnl0177
Appendix D: Satellite Sensors-continue - rsmnl0178
Appendix E. Satellite Platforms and sensors - rsmnl0179
Appendix E. Satellite Platforms and sensors - rsmnl0180
Appendix F. Airborne Sensors
Appendix G. TEC's Imagery Office (TIO)
Appendix G. TEC's Imagery Office (TIO)-continue
Appendix H. Example Contract - Statement of Work (SOW)
Appendix H. Example Contract - Statement of Work (SOW)-continue
Appendix I. Example Acquisition Memorandum of Understanding (MOU)
OBLIGATIONS OF SPONSOR
PAYMENT
POINTS OF CONTACT
WARRANTY AND INDEMNIFICATION
Governing Law
Binding Agreement-continue
Glossary - rsmnl0195
Glossary-continue - rsmnl0196
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Chapter 1 Introduction to Remote Sensing
Contents of this Manual-continue
Chapter 2 Principles Of Remote Sensing Systems
Component 1: Electromagnetic Energy Is Emitted From A Source
Summary of Electromagnetic Energy
Figure 2-3. Propagation of the electromagnetic and magnetic field
Measurement of Electromagnetic Wave Radiation
Figure 2-7. Electromagnetic spectrum displayed in meter and Hz units
Figure 2-8. Visible spectrum illustrated here in color
Regions of the Electromagnetic Spectrum
Table 2-2. Wavelengths of the primary colors of the visible spectrum
Table 2-3. Wavelengths of various bands in the microwave range
Implications for Remote Sensing
The Stefan-Boltzmann Law
Summary - rsmnl0023
Figure 2-11. The Sun and Earth both emit electromagnetic radiation
Figure 2-12. Various radiation obstacles and scatter paths
Nonselective Scattering
Atmospheric Absorption and Atmospheric Windows
The role of atmospheric compounds in the atmosphere
Geometric Effects
Component 3: Electromagnetic Energy Interacts with Surface and Near Surface Objects
Absorption - rsmnl0031
Specular and diffuse reflection
Figure 2-19. Diffuse reflection of radiation from a single target point
Spectral Reflectance Curves
Figure 2-21. Spectral reflectance of healthy vegetation
Spectral Reflectance of Water
Critical Spectral Regions
Real Life and Spectral Signatures
Component 4: Energy is Detected and Recorded by the Sensor
Table 2-5. Digital number value ranges for various bit data
Satellite Receiving Stations
Turning Digital Data into Images
Figure 2-28. Brightness levels at different radiometric resolutions
Figure 2-30. Landsat MSS band 5 data of San Francisco, California
Spectral Bands
Color in the Image
Figure 2-31. Individual DNs can be identified in each spectral band of an image
The true color image appears with these bands in the visible part of the spectrum
Interpreting the Image
Figure 2-33. Landsat 7 image of southern California
Spatial Resolution
Aerial Photography - rsmnl0052
Figure 2-34. Aerial photograph of a predominately agricultural area near Modesto, California
Brief History of Remote Sensing
NASA's First Weather Satellites
Future of Remote Sensing
Chapter 3 Sensors and Systems
Corps 9--Civil Works Business Practice Areas
Sensor Data Considerations (programmatic and technical)
Sensor Data Considerations (programmatic and technical)-continue - rsmnl0060
Sensor Data Considerations (programmatic and technical)-continue - rsmnl0061
Figure 3-1. In this CAMIS image a decrease in aircraft altitude
Value Added Products
Aerial Photography - rsmnl0064
Planning Airborne Acquisitions
Bathymetric and Hydrographic Sensors
Airborne Gamma
Satellite Orbits
Planning Satellite Acquisitions
Ground Penetrating Radar Sensors
Match to the Corps 9--Civil Works Business Practice Areas
Real Estate Needs
Research and Development
Chapter 4 Data Acquisition and Archives
Specifications for Image Acquisition
Satellite Image Licensing
Image Archive Search and Cost-continue - rsmnl0078
Image Archive Search and Cost-continue - rsmnl0079
Specifications for Airborne Acquisition
St. Louis District Air-Photo Contracting
Chapter 5 Processing Digital Imagery
Band/Color Composite
Image Projections
Latitude/Longitude Computer Entry
Map Projections
Rectification
Ground Control Points (GCPs)
The scene appearance of the GCP selection module
scene represents the original, unprojected data file
Unprojected data are then warped to the GCP positions
Project Image and Save
Image Enhancement
Figure 5-6. Contrast in an image before (left) and after (right) a linear contrast stretch
Data Analysis
Figure 5-7. Pixel population and distribution across the 0 to 255 digital number range
Enhancing Pixel Digital Number Values
Figure 5-9. Unenhanced satellite data on left
Histogram Equalization - rsmnl0100
Figure 5-10. Landsat image of Denver area
Figure 5-11. NASA Landsat images from top to bottom
Table 5-1. Effects of shadowing
Other Types of Ratios and Band Arithmetic
Figure 5-12. Top: True color CAMIS image
Convolution
The Convolution Method
Direction Filter: north-south component kernel
Principle Component Transformation
Transformation Series (PC1, PC2, PC3, PC4, PC5, etc.)
Image Classification - rsmnl0111
Supervised Classification
Figure 5-17. Landsat 7 ETM image of central Australia
Figure 5-18. Classification training data of 35 landscape classification features
Parallelepiped
Table 5-3. Omission and Commission Accuracy Assessment Matrix
Steps Required for Unsupervised Classification
Image Mosaics, Image subsets, and Multiple Image Analysis
Percent Calculation
Figure 5-21. Multiple Landsat TM images
Figure 5-22. Multi-image mosaic of Western United States centered on the state of Utah
Figure 5-23. Landsat 7 Image of the Boston, Massachusetts area
Digital Elevation Models (DEM)
Advanced Methods in Image Processing
Thermal Data
Figure 5-26. Close-up of the Atlantic Gulf Stream
Internal Programming
Assessing Project Needs
Visualization Interpretation
Figure 5-28. Forest fire assessment using Landsat imagery
Figure 5-29. Landsat scene bands 5, 4, 2 (RGB)
Water (Water, Clouds, Snow, and Ice)
Figure 5-31. AVIRIS image, centered on Arches National Park
Figure 5-32. MODIS image of a plankton bloom in the Gulf of St. Lawrence near Newfoundland, Canada
Figure 5-33. Orlando, Florida, imaged in 2000 by Landsat 7 ETM+ bands 4, 3, 2 (RGB)
Figure 5-34. Landsat image of Mt. Etna eruption of July 2001
Figure 5-37. July 2001 Saharan dust storm over the Mediterrenean
Figure 5-38. Oil trench fires and accompanying black smoke plumes over Baghdad Iraq (2003)
Statistical Analysis and Accuracy Assessment
Resolution and RMS (Root Mean Squared)
Figure 5-40. The final product may be displayed as a digital image or as a high quality hard copy
Chapter 6 Remote Sensing Applications in USACE
Description of Methods - rsmnl0144
Field Work - rsmnl0145
Study Results - rsmnl0146
Case Study 2: Evaluation of New Sensors for Emergency Management
Field Work - rsmnl0148
Table 6-1. Detection Matrix for Objects at Various GSDS
Case Study 3: River Ice Delineation with RADARSAT SAR
Sensor Selection and Image Post-Processing
Case Study 4: Tree Canopy Characterization for EO-1 Reflective and Thermal Infrared Validation Studies in Rochester, New York
Sensor System
Case Study 5: Blended Spectral Classification Techniques for Mapping Water Surface Transparency and Chlorophyll Concentration
Study Results - rsmnl0155
Case Study 6: A SPOT Survey of Wild Rice in Northern Minnesota
Case Study 7: Duration and Frequency of Ponded Water on Arid Southwestern Playas
Description of Methods - rsmnl0158
Case Study 8: An Integrated Approach for Assessment of Levees in the Lower Rio Grande Valley
Field Work - rsmnl0160
Case Study 9 : From Wright Flyers to Aerial Thermography
Field Work - rsmnl0162
Study Results - rsmnl0163
Case Study 10: Digital Terrain Modeling and Distributed Soil Erosion Simulation
Remotely Sensed DEM Data
Conclusions - rsmnl0166
Appendix A. References
Appendix A. References-continue - rsmnl0168
Appendix A. References-continue - rsmnl0169
Appendix B. Regions of the Electromagnetic Spectrum and Useful TM Band Combinations
Appendix C. Paper model of the color cube/space
Appendix D: Satellite Sensors
Appendix D: Satellite Sensors-continue - rsmnl0176
Appendix D: Satellite Sensors-continue - rsmnl0177
Appendix D: Satellite Sensors-continue - rsmnl0178
Appendix E. Satellite Platforms and sensors - rsmnl0179
Appendix E. Satellite Platforms and sensors - rsmnl0180
Appendix F. Airborne Sensors
Appendix G. TEC's Imagery Office (TIO)
Appendix G. TEC's Imagery Office (TIO)-continue
Appendix H. Example Contract - Statement of Work (SOW)
Appendix H. Example Contract - Statement of Work (SOW)-continue
Appendix I. Example Acquisition Memorandum of Understanding (MOU)
OBLIGATIONS OF SPONSOR
PAYMENT
POINTS OF CONTACT
WARRANTY AND INDEMNIFICATION
Governing Law
Binding Agreement-continue
Glossary - rsmnl0195
Glossary-continue - rsmnl0196
Glossary-continue - rsmnl0197
Glossary-continue - rsmnl0198
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