estimating water optical properties.ppt

Estimating Water Optical Properties, Water Depth and Bottom Albedo Using High Resolution Satellite

Imagery for Coastal Habitat Mapping

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. ChangCentre for Remote Imaging, Sensing and Processing

National University of Singapore

#Corresponding Author (scliew@nus.edu.sg)

WorldView-2High resolution with 8 spectral bands

Launched: 8 October 20090.46 m panchromatic1.84 m multispectral

8 spectral bands:

Band 1: 429.3 nm (47.3) “Coastal”Band 2: 478.8 nm (54.3) BlueBand 3: 547.5 nm (63.0) GreenBand 4: 607.8 nm (37.4) YellowBand 5: 658.5 nm (57.4) RedBand 6: 723.5 nm (39.3) “Red edge”Band 7: 825.0 nm (98.9) NIR1Band 8: 919.4 nm (99.6) NIR2

Effective wavelength Bandwidth

WV2 Spectral Response

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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

• The intertidal zone of Semakau has a rich seagrass habitat of several hundred meters in length.

• Such an extensive seagrass habitat is rare in Singapore coastal area. The seagrass habitats in other areas of Singapore mostly occur in patches.

• There are also live corals on the reefs near Semakau.

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

• The previous classification map shown was obtained by automatic clustering followed by manual editing guided by extensive ground truth observations.

• Time consuming, requiring visual interpretation• Visual interpretation complicated by effects of

water column– Scattering by suspended particles– Absorption by water and colored dissolved organic

matter– Different water depth

• We attempt to retrieve the water depth, bottom albedo and intrinsic optical properties of coastal sea water over submerged areas using a spectral matching algorithm.

Pre-processing of WorldView-2 Image

• Calibrate to radiance and top-of-atmosphere reflectance• Correct for Rayleigh scattering and gaseous absorptions,

integrated over sensor response functions.• Glint subtraction using band 8 (NIR2)• Convert to subsurface reflectance

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

wL ,L

H)(b

)( ),( bba

Deep Water

Shallow water reflectance Deep water

reflectance

sF

LR

cos

)(

s

ww F

LR

cos)( ,

F

s v

Model of Subsurface shallow water reflectance

)exp(1)( MKHrw Reflection (scattering) from water column

e)(subsurfac angleszenith solar andsensor ,

depth water

cos/1cos/1

tcoefficien extinction ,)()()(

ereflectanc water deep )(

vs

sv

b

w

H

M

baK

r

Reflection (scattering) from sea bottom )exp()(

MKHb

ereflectanc bottom )( b

)exp()(

)exp(1)()( MKHMKHrr bw

Deep water reflectance

)()(

)(

)( 210

b

b

w

ba

bu

ugugr

a() = Absorption coefficient

bb() = Backscattering coefficient

g0, g1 = parameters dependent on scattering characteristics of suspended particles

Absorption and Backscattering Models

)440( )];()ln()([)(

:lchlorophylby Absorption

)440( )];440(exp[)(

:detritus and CDOMby Absorption

)550( ;)/550()(

:matter eparticulatby ringBackscatte

)()()()( :tcoefficien Absorption

);()()( :tcoefficien ringBackscatte

10

aPaPaPa

aGSGa

bXXb

aaaa

bbb

gg

bpy

bp

gw

bpbwb

Sea bottom reflectance

)()()( ssvvb

vegetation

sand

Sea bottom reflectance is modeled as a linear combination of typical sand and vegetation reflectance spectra.

)659()659()659(

)825()825()825(

ssvvb

ssvvb

)659()825(

)659()825(

bb

bbNDVI

(Sea bottom NDVI, corrected for water column effects)

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 = 0.292H = 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 = 0.154H = 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 = 0.776H = 0.12 m

Water Depth

0 m

0.5 m

1.0 m

> 1.5 m

Bottom Albedo (at 547 nm)

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Vegetation Index (Water column corrected)

1.0

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Detection of submerged aquatic vegetation

Concluding Remarks

• We illustrated the application of a spectral matching algorithm in deriving the water depth, bottom albedo, vegetation index (for submerged aquatic vegetation) and water quality parameters from 8-bands high resolution WorldView-2 satellite images.

• The satellite derived reflectance spectra can be fitted quite well to the shallow water reflectance model.

• The 6th band (“red-edge” band centered at 723 nm) always has a high deviation from the best fit value for all the classes. This band happens to coincide with a water vapour absorption band.

Concluding Remarks

• Eight spectral bands of WorldView-2 enable the application of a spectral matching algorithm, but implementation on the full image is not time-efficient.

• Computational time efficiency is improved by clustering pixels with similar spectral values, and spectral matching is performed on the average spectrum of each class.

• The water column corrected NDVI can serve to detect submerged aquatic vegetation, and to quantify the abundance.

• Integrating with classification methods is on-going.

Acknowledgment

• Singapore Agency for Science, Technology and Research (A*STAR) for funding to CRISP

• Singapore National Parks Board (Nparks) for a grant supporting the project.

• S. C. Liew acknowledges support of Singapore-Delft Water Alliance (SDWA)

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