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Estimating Water Optical Properties, Water Depth and Bottom Albedo Using High Resolution Satellite Imagery for Coastal Habitat Mapping S. C. Liew # , P. Chen, B. Saengtuksin, C. W. Chang Centre for Remote Imaging, Sensing and Processing National University of Singapore # Corresponding Author (scliew@nus.edu.sg)
WorldView-2 High resolution with 8 spectral bands Launched: 8 October 2009 0.46 m panchromatic 1.84 m multispectral 8 spectral bands: Band 1:  429.3 nm (47.3)  “Coastal” Band 2:  478.8 nm (54.3) Blue Band 3:  547.5 nm (63.0) Green Band 4:  607.8 nm (37.4) Yellow Band 5:  658.5 nm (57.4) Red Band 6:  723.5 nm (39.3) “Red edge” Band 7:  825.0 nm (98.9) NIR1 Band 8:  919.4 nm (99.6) NIR2 Effective wavelength Bandwidth
WV2 Spectral Response  Tropical Atmosphere, 4 cm precipitable water Note the high water vapor absorption in band 6 (“red-edge” band), humid tropical atmosphere
 
WorldView-2 Image Semakau, 2010-03-24 Seagrass Submerged reefs
[object Object],[object Object],[object Object]
Classification Map Semi-automatic classification  Based on 8-bands WV-2 image and field survey. seagrass
Seagrass
Coral rubble with algae/seagrass/coral
Classification of submerged features ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object]
Pre-processing of WorldView-2 Image ,[object Object],[object Object],[object Object],[object Object],S.C. Liew, B. Saengtuksin, and L.K. Kwoh,  IEEE 2009 International Geoscience and Remote Sensing Symposium (IGARSS'09), 13 - 17 July 2009, Cape Town, South Africa. S.C. Liew and J. He,  IEEE Geoscience and Remote Sensing Letters 5(4), 701-704, 2008.
Band 8 (NIR2) Image Note the presence of various surface features
Band 7 (NIR1) Image Similar surface features are visible
Band 7 (NIR1) after subtracting Band 8 More homogeneous surface
Automatic Isodata clustering of submerged pixels into 50 classes Above-water land surface masked out by thresholding the NIR2 band Mean reflectance spectrum of each class is collected and matched with model reflectance
Shallow water reflectance Deep Water Shallow water reflectance Deep water reflectance
Model of Subsurface shallow water reflectance Reflection (scattering) from water column Reflection (scattering) from sea bottom
Deep water reflectance a(  ) =  Absorption coefficient b b (  ) =  Backscattering coefficient g 0 , g 1  = parameters dependent on scattering characteristics of suspended particles
Absorption and Backscattering Models
Sea bottom reflectance Sea bottom reflectance is modeled as a linear combination of typical sand and vegetation reflectance spectra. (Sea bottom NDVI, corrected for water column effects) vegetation sand
Example of spectral matching: Deep water Class 3: Deep water X = 0.25 m -1  ,  G = 0.096 m -1  P = 0  Water depth set to a large value H = 25 m during spectral fitting (actual value doesn’t matter)
Example of spectral matching: Reef edge Class 6: Fringe of coral reef X = 0.23 m -1  ,  G = 0.019 m -1  P = 0  Rb547 = 0.135, Rb659 =  0.154, Rb825 = 0.282, NDVI = 0.292 H = 1.30 m
Example of spectral matching: Submerged reef Class 41: shallow reef X = 0.26 m -1  ,  G = 0.0 m -1  P = 0.25 m -1 Rb547 = 0.226, Rb659 = 0.267 , Rb825 = 0.365, NDVI = 0.154 H = 0.31 m
Example of spectral matching: Submerged seagrass Class 25: submerged seagrass X = 3.21 m -1  ,  G = 0.0 m -1  P = 0 m -1 Rb547 = 0.024, Rb659 = 0.020, Rb825 = 0.155, NDVI = 0.776 H = 0.12 m
Water Depth 0 m 0.5  m 1.0  m > 1.5 m
Bottom Albedo (at 547 nm) 0 0.10 0.20 > 0.30
Vegetation Index (Water column corrected) 1.0 0.50 0.0 Detection of submerged aquatic vegetation
Concluding Remarks ,[object Object],[object Object],[object Object]
Concluding Remarks ,[object Object],[object Object],[object Object],[object Object]
Acknowledgment ,[object Object],[object Object],[object Object]
WV2 Spectral Response

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Estimating Water Optical Properties.ppt

  • 1. Estimating Water Optical Properties, Water Depth and Bottom Albedo Using High Resolution Satellite Imagery for Coastal Habitat Mapping S. C. Liew # , P. Chen, B. Saengtuksin, C. W. Chang Centre for Remote Imaging, Sensing and Processing National University of Singapore # Corresponding Author (scliew@nus.edu.sg)
  • 2. WorldView-2 High resolution with 8 spectral bands Launched: 8 October 2009 0.46 m panchromatic 1.84 m multispectral 8 spectral bands: Band 1: 429.3 nm (47.3) “Coastal” Band 2: 478.8 nm (54.3) Blue Band 3: 547.5 nm (63.0) Green Band 4: 607.8 nm (37.4) Yellow Band 5: 658.5 nm (57.4) Red Band 6: 723.5 nm (39.3) “Red edge” Band 7: 825.0 nm (98.9) NIR1 Band 8: 919.4 nm (99.6) NIR2 Effective wavelength Bandwidth
  • 3. WV2 Spectral Response Tropical Atmosphere, 4 cm precipitable water Note the high water vapor absorption in band 6 (“red-edge” band), humid tropical atmosphere
  • 4.  
  • 5. WorldView-2 Image Semakau, 2010-03-24 Seagrass Submerged reefs
  • 6.
  • 7. Classification Map Semi-automatic classification Based on 8-bands WV-2 image and field survey. seagrass
  • 9. Coral rubble with algae/seagrass/coral
  • 10.
  • 11.
  • 12.
  • 13. Band 8 (NIR2) Image Note the presence of various surface features
  • 14. Band 7 (NIR1) Image Similar surface features are visible
  • 15. Band 7 (NIR1) after subtracting Band 8 More homogeneous surface
  • 16. Automatic Isodata clustering of submerged pixels into 50 classes Above-water land surface masked out by thresholding the NIR2 band Mean reflectance spectrum of each class is collected and matched with model reflectance
  • 17. Shallow water reflectance Deep Water Shallow water reflectance Deep water reflectance
  • 18. Model of Subsurface shallow water reflectance Reflection (scattering) from water column Reflection (scattering) from sea bottom
  • 19. Deep water reflectance a(  ) = Absorption coefficient b b (  ) = Backscattering coefficient g 0 , g 1 = parameters dependent on scattering characteristics of suspended particles
  • 21. Sea bottom reflectance Sea bottom reflectance is modeled as a linear combination of typical sand and vegetation reflectance spectra. (Sea bottom NDVI, corrected for water column effects) vegetation sand
  • 22. Example of spectral matching: Deep water Class 3: Deep water X = 0.25 m -1 , G = 0.096 m -1 P = 0 Water depth set to a large value H = 25 m during spectral fitting (actual value doesn’t matter)
  • 23. Example of spectral matching: Reef edge Class 6: Fringe of coral reef X = 0.23 m -1 , G = 0.019 m -1 P = 0 Rb547 = 0.135, Rb659 = 0.154, Rb825 = 0.282, NDVI = 0.292 H = 1.30 m
  • 24. Example of spectral matching: Submerged reef Class 41: shallow reef X = 0.26 m -1 , G = 0.0 m -1 P = 0.25 m -1 Rb547 = 0.226, Rb659 = 0.267 , Rb825 = 0.365, NDVI = 0.154 H = 0.31 m
  • 25. Example of spectral matching: Submerged seagrass Class 25: submerged seagrass X = 3.21 m -1 , G = 0.0 m -1 P = 0 m -1 Rb547 = 0.024, Rb659 = 0.020, Rb825 = 0.155, NDVI = 0.776 H = 0.12 m
  • 26. Water Depth 0 m 0.5 m 1.0 m > 1.5 m
  • 27. Bottom Albedo (at 547 nm) 0 0.10 0.20 > 0.30
  • 28. Vegetation Index (Water column corrected) 1.0 0.50 0.0 Detection of submerged aquatic vegetation
  • 29.
  • 30.
  • 31.