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

5. WorldView-2 Image Semakau, 2010-03-24 Seagrass Submerged reefs

7. Classification Map Semi-automatic classification Based on 8-bands WV-2 image and field survey. seagrass

8. Seagrass

9. Coral rubble with algae/seagrass/coral

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

20. Absorption and Backscattering Models

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

32. WV2 Spectral Response