Remote sensing for habitat mapping and change

Remote sensing for habitat mapping and change detection in tropical Commonwealth marine protected areas – phase 2 E.J. Botha, A.G. Dekker, Y.J. Park, J.M. Anstee, N. Cherukuru, L. Clementson June 2010 Report prepared for: The Department of Environment, Water, Heritage and the Arts

Enquiries should be addressed to: Arnold Dekker [email protected]

Distribution list Christopher Cvitanovic

2

Copyright and Disclaimer © Commonwealth of Australia 2010 This work is copyright. Apart from any use as permitted under the Copyright Act 1968, no part may be reproduced by any process without prior written permission from the Commonwealth. Requests and inquiries concerning reproduction and rights should be addressed to the Commonwealth Copyright Administration, Attorney General’s Department, Robert Garran Offices, National Circuit, Barton ACT 2600 or posted at http://www.ag.gov.au/cca The views and opinions expressed in this publication are those of the authors and do not necessarily reflect those of the Australian Government or the Minister for the Environment, Heritage and the Arts or the Minister for Climate Change and Water. While reasonable efforts have been made to ensure that the contents of this publication are factually correct, the Commonwealth does not accept responsibility for the accuracy or completeness of the contents, and shall not be liable for any loss or damage that may be occasioned directly or indirectly through the use of, or reliance on, the contents of this publication.

Important Disclaimer CSIRO advises that the information contained in this publication comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law, CSIRO (including its employees and consultants) excludes all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it.

Contents 1.

2.

3.

Executive summary..........................................................................................xii 1.1

Background ......................................................................................................... xii

1.2

Aims.................................................................................................................... xii

1.3

Methodology ...................................................................................................... xiii

1.4

Key results ......................................................................................................... xiv

1.5

Key conclusions ................................................................................................. xvi

1.6

Key recommendations....................................................................................... xvii

Introduction ....................................................................................................... 1 2.1

Why we need marine habitat maps ...................................................................... 1

2.2

Earth observation ................................................................................................. 2

2.3

Aims..................................................................................................................... 3

2.4

Remote Tropical Marine Parks ............................................................................. 4

2.5

Links with strategic research and monitoring needs. ............................................. 5

Methodology: ..................................................................................................... 6 3.1

3.2

3.3

3.4

4.

5.

Field data collection ............................................................................................. 7 3.1.1

Field planning and preparation: Introduction ..................................................... 7

3.1.2

Field planning and preparation: Elizabeth Middleton Reef National Nature Reserve............................................................................................................ 8

3.1.3

Field campaign: Herald Cays (Coringa Herald National Nature Reserve) ........... 9

3.1.4

Field campaign: Lihou Reef National Nature Reserve...................................... 12

Field data processing ..........................................................................................15 3.2.1

Water quality analysis – Lihou Reef ................................................................ 15

3.2.2

Spectral library processing and benthic end-member spectra selection ........... 17

3.2.3

Benthic surveys (transects and polygons) ....................................................... 21

Image data acquisition ........................................................................................22 3.3.1

QuickBird system description ......................................................................... 22

3.3.2

Image Acquisition ........................................................................................... 25

Image data processing ........................................................................................30 3.4.1

Data pre-processing ....................................................................................... 30

3.4.2

Terrestrial image classification and cover mapping ......................................... 30

3.4.3

Aquatic image processing............................................................................... 31

3.4.4

Model accuracy estimates .............................................................................. 35

3.4.5

Validation ....................................................................................................... 36

Results: Terrestrial land cover maps............................................................. 37 4.1

North East Herald Cay (NE Herald Cay) ..............................................................37

4.2

South West Herald Cay .......................................................................................40

4.3

Georgina Cay ......................................................................................................43

Results: Aquatic benthic and bathymetry maps ........................................... 46

i

5.1

South West Herald Cay ....................................................................................... 47

5.2

Georgina Cay ...................................................................................................... 57

Elizabeth Reef (west) .................................................................................................... 66

6.

Conclusions and recommendations .............................................................. 75 6.1

Environmental baseline collection of high spatial resolution satellite data ............ 76

6.2

Change detection ................................................................................................ 76

6.3

Satellite image acquisition and QA/QC................................................................ 76

6.4

Field work ........................................................................................................... 77

6.5

Spectral library .................................................................................................... 77

6.6

Transfer of methodology to DEWHA/ERIN .......................................................... 78

6.7

Role of research providers: (CSIRO and UQ) ...................................................... 79

6.8

Links with strategic research and monitoring needs: Remote sensing capabilities in the context of MPA management. ....................................................................... 79

References ................................................................................................................ 83 Appendix A - Image acquisition ............................................................................... 87 Appendix B – Elizabeth and Middleton Reefs field plan for October 2008 ........... 96 6.9

Instrumentation ................................................................................................... 96 6.9.1

Equipment List ............................................................................................... 96

6.9.2

Data recording ............................................................................................... 96

6.10 Methodology ..................................................................................................... 103 6.10.1

Benthic substratum type sample site selection .............................................. 103

6.10.2

Benthic substrate polygons and boundaries .................................................. 104

6.11 Depth transects ................................................................................................. 105

Appendix C – Data pre-processing ....................................................................... 107 6.12 Sun glint removal from satellite images............................................................. 107 6.12.1

Spectral shape function of the sun glint ........................................................ 107

6.12.2

Sun glint magnitude estimation and correction for each pixel ........................ 108

6.13 Atmospheric and air-water interface correction .................................................. 108 6.13.1

Validation of atmospheric correction ............................................................. 111

6.13.2

Vicarious calibration ..................................................................................... 112

6.14 References........................................................................................................ 113

Appendix D – Aquatic data processing pathway ................................................. 114 6.15 Data volume reduction ...................................................................................... 114 6.15.1

Environmental dynamic range ...................................................................... 114

6.15.2

Data compression ........................................................................................ 115

6.16 Retrieval of bathymetry, substratum composition and the optically active constituents ....................................................................................................... 115 6.16.1

Principles of the physics based method ........................................................ 115

6.16.2

Semi Analytical Model for Bathymetry Unmixing and Concentration Assessment (SAMBUCA) ................................................................................................. 116

6.16.3

Optical properties of benthic substrates ........................................................ 118

6.16.4

Optical properties of Coral Sea waters .......................................................... 118

6.17 References........................................................................................................ 118

APPENDIX E - Analysis of bio-optical properties of particulate and dissolved substances in the water column measured during Lihou Reef field sampling survey in December 2008. ............................................................................ 120 6.18 Methods ............................................................................................................ 120 6.18.1

Sample collection ......................................................................................... 120

6.18.2

Measurement of concentration of particles in suspension .............................. 121

6.18.3

Collection of particulate matter for absorption measurements ....................... 121

6.18.4

Absorption due to particulate matter ............................................................. 121

6.18.5

Absorption due to coloured dissolved organic matter .................................... 121

6.18.6

Pigment analysis .......................................................................................... 122

6.18.7

Size structure of phytoplankton populations .................................................. 122

6.19 Results and discussion ...................................................................................... 123 6.19.1

Sampling locations ....................................................................................... 123

6.19.2

Distribution of suspended particulate matter ................................................. 124

6.19.3

Variations in pigment composition ................................................................ 125

6.19.4

Variations in size structure of algal populations............................................. 127

6.19.5

Light absorption budget in Lihou Reef ........................................................... 133

6.20 Summary .......................................................................................................... 135 6.21 References: ....................................................................................................... 136

iii

List of Figures Figure 1-1 Schematic diagram of the image processing pathways implemented in this project to produce the terrestrial and aquatic information products, reported in Chapter 4 (terrestrial) and Chapter 5 (aquatic). Sections detailing the methodology of each data processing step in the diagram are indicated in red text. .......................................................................... xiv Figure 1-2 Aerial photography interpretation (Batianoff and Naylor, 2006 based on data from 1995) and b) the seven main classes derived from unsupervised classification of QuickBird image. Similar colour codings have been used to demonstrate the ability to relate information in recent QuickBird data to historical maps. The photo interpretation class boundaries are shown in red on the QuickBird classification (Wettle et al. 2007). Note finer detail produced in the QuickBird-derived map. ................................................................. xv Figure 1-3 (a) True colour QuickBird image of Georgina Cay (31 July 2007) showing locations where benthic substratum-type observations (coloured dots) and depth soundings (red crosses) were collected for validation purposes during the December 2008 field campaign. (b) Detail of Georgina Cay dominant benthic cover type classification (location indicated by red square in (a)), illustrating the classification accuracy along a photo-transect. Locations of classified benthic photographs are indicated by dots color-coded to correspond to the dominant benthic cover. (c) Comparison of modelled bathymetry estimates with measured bottom depth (location indicated by red crosses in (a)). Black dots represent model output that was labelled statistically sound by the model while red dots represent model output that was flagged as either too deep to reliably model or statistically unsound (at depths of 15 to 18m and deeper, the substratum is too deep to be properly detected). Solid line represents the 1:1 line, dashed line represents the regression between reliable model output (represented by black dots) and in situ observations (R2 = 0.81, p = Pocillophora damicornis or Seritophora caliendrum or Seritophora hystrix or Seritophora pistillata Acroporidae -> Montipora aequituberculata or Montipora danae or Montipora efflorescens or Moiitpora incrassate or Montipora monasteriata etc. Next step Species Next step Dominant colour

Algae options (In divisions) Chlorophyta or Rhodophyta or Heterokontophyta

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Remote sensing in tropical Commonwealth marine protected areas – phase 2 • June 2010, Version # 3.0

APPENDIX B – ELIZABETH AND MIDDLETON REEFS FIELD PLAN FOR OCTOBER 2008

Next step Species

Caulerpa cuppressoides Coduim spongiosum Dictyosphaeria cavernosa Halimeda spp. Udotea argentea Colpomenia sinuosa Chlorodesmia sp. Dictyota sp. Lobophora sp. Padina australis Turbinaria ornate Amphiroa sp. Chondrococcus sp. Gracilaria sp. Jania sp. Laurencia brongniartii Stypopodium flabelliforme

Seagrass options Halophila ovalis

Bare substratum options


99


 Unconsolidated sand -> grey or white or beige  Fine coral rubble/shell fragments -> grey or white  Coarse coral rubble -> grey or white  Sand with microalgae film

Marine Pests Options: Crown-of-Thorns - > no of animals Drupella snail - > no of animals -> live or dead shells

Water Depth Options: Metres 0-100 Decimetres 0-10

Image capture Options Camera - > picture number -> 1-1000 -> time->HH 00 to 23-> MM 00 to 59 Video start time->HH 00 to 23-> MM 00 to 59

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Table 6-2 An example CSIRO Land and Water field data collection pro-forma.

Field work:

Operator(s):

Date

Time (local)

GPS & datum

Time (UTC)

Location site name

Time zone

Site no

Site name

Lat (dec. degrees)

S

Long

E

Site Description Location: (lagoon, reef crest, outside reef) Zonation: Dominant substrata

Density of cover

Other species

Density of cover Density of cover Density of cover Density of cover Density of cover

Epiphytic growth Conditions Bottom depth (m)

Measured (sounder or tape?)

Tide (in/out)

Water condition

Water colour

Wind direction

Cloud cover%

Wind speed

Pictures


101


Still or Video

time

description

Photo No.

Comments

Notes:

102



6.10 Methodology The three main objectives for this field work are: 1) Collecting a set of benthic substratum type photos with GPS location 2) Collecting GPS locations around polygons of homogeneous or systematically heterogeneous features larger than 10m2, 3) Recording depth transects over various parts of the reef as well as preferably over a homogeneous feature such as sand. The CSIRO team was able to acquire QuickBird satellite images of the Elizabeth and Middleton Reefs prior to the field work. These images are useful in optimising the choice of locations where to carry out field work. Simple image analysis provides images that clearly indicated where spectral, pattern and texture on the submerged reef areas are sufficiently different to merit field measurements. As the QuickBird imagery is delivered geocorrected it is possible to geolocate each image pixel and to use that location to find that pixel on the reef using a GPS with an approximate accuracy of about 15-20m. The QuickBird imagery can be used to identify spectrally different substratum and features which can be used to ensure the data collection is a true representation of all substrata.

6.10.1 Benthic substratum type sample site selection

Targets of the biota should include corals, macro-algae, seagrasses, sponges and soft corals, as well as the coral sands/muds/rubble and the benthic micro-algal layers on this substratum. In addition anything that contributes (or may in the past have contributed or may in the future contribute) colour to a remotely sensed pixel of 2.6 * 2.6 meter (QuickBird satellite imagery) for at least 1% - effectively meaning anything with an average total surface in a pixel of about 670 cm2). Suitable sites for substratum type recording are located on Figure 5-7 and Figure 5-8. Locations should be optimised for the current weather and sea state conditions. At each location, the following should be recorded: GPS position Date and time Water depth


103


Site description (eg bommie, lagoon, reef crest, etc) Species present and species density Photograph numbers Video time

This information can be stored both on the field proforma (Table 6-2) or the Trimble ArcPad if a suitable project file has been created to encompass this information. The photograph or video of the site should preferably be representative of the substrate features and be accompanied with a site description and site location, an example is shown in Figure 6-6.

Target 37 - porites bommie @ 3.6m Target 37; Waypoint 072 Date: 30/11/2006; Time 9:20am A large Porites spp. bommie Water depth 3.6m

Figure 6-6 An example of a useful field photo with a simple site description.

6.10.2 Benthic substrate polygons and boundaries GPS polygons around certain features at the field site can be used as validation data if collected with care. Traversing around homogeneous features (large enough to appear on the satellite imagery) while collecting GPS waypoints can provide excellent validation data. The following must be considered while collecting this data: Selection of a large (>10m2) homogenous feature such as a sand flat or bommie (using the satellite imagery as a guide) Comprehensive benthic substratum description with photos (including description of the substratum outside the polygon boundary). Potential homogeneous substratum features have been identified in Figure 6-7 and Figure 6-8 (green circles).

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6.11 Depth transects GPS transects across the field site are required to ‘calibrate’ the model applied to the imagery at CSIRO Land and Water. Although transects across homogeneous features are required, they are not essential but the substrate type should be recorded The ideal transect has:  waypoints collected every few (2-5) meters  a range of depths  homogeneous substratum  a length covered greater than 100m

Optimal depth transect locations are shown in the red circles in Figure 5-7 and Figure 5-8.

Figure 6-7 Middleton Reef (east) QuickBird image showing potential depth transect locations (red circles) and potential benthic substratum polygon sites (green circles).


105


Figure 6-8 Elizabeth Reef (east) QuickBird image showing potential depth transect locations (red circles) and potential benthic substratum polygon sites (green circles).

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APPENDIX C – DATA PRE-PROCESSING

APPENDIX C – DATA PRE-PROCESSING 6.12 Sun glint removal from satellite images Y-J. Park High spatial resolution satellite images over water bodies are often affected by specular (mirroring) surface reflection of incident sun light, called sun glint, as well as sky glint (the diffuse blue light) which impedes the accurate assessment of water leaving radiance thus affecting the accuracy and validity of the mapping of water depth, water column composition and benthic habitats. As a first step in the integrated physics-based mapping approach (Figure 3-13), glint is removed from the image as far as possible. All image processing methods benefit from this procedure. An approach to remove this sun glint has been developed by (Hochberg et al. 2003) and refined by (Hedley et al. 2005). This approach assumes negligible water reflectance at a near infrared (NIR) band. However, this assumption is not true if water column is shallow, where the water leaving reflectance is affected by the bottom reflectance. Consequently, this approach would overcorrect the glint for shallow water pixels. (Vahtmae and Kutser 2008) proposed another sun glint correction algorithm, which utilizes the absorption feature due to atmospheric oxygen at 760nm. Since this algorithm requires a fine spectral resolution around the oxygen absorption band, it can not be applied to satellite imagery from low spectral resolution satellites such as QuickBird and Landsat. Therefore, there is need to develop a glint correction algorithm, which maintains non-negligible NIR reflectance in shallow water pixels and is applicable for spectrally low (but spatially high resolution) imagery. For this purpose, we utilize the nature of spatial inhomogeneity of the sun glint patterns. There are two steps – 1) estimation of the sun glint spectral shape function and 2) sun glint estimation and correction for each pixel.

6.12.1 Spectral shape function of the sun glint The satellite measured reflectance,  t , consists of the atmospheric column reflectance,  atm and the glint reflectance,  g , and the water leaving reflectance,  w .

 t   atm   g   w

(1)

The glint reflectance,  g for pixel pi can be expressed:

 g ( pi )  rF ( pi )  T ( ) ,

(2)

where rF ( pi ) is Fresnel reflectance for the pixel pi and T ( ) is two-way atmospheric transmittance. The Fresnel reflectance due to air-water interface is almost spectrally constant in visible to near-infrared range.


107


In order to estimate the spectrum of sun glint, we select an area of the image where atmosphere and water optical properties are relatively homogenous. A good example of the homogenous area is a deep water part. Within the homogenous area, the reflectance difference between pixel pi and pixel p j is due to the difference of glint reflectance.

 t   g ( pi )   g ( p j )

(3)

Since the atmospheric transmittance is constant over the homogenous area, this can be written as:

 t  (rF ( pi )  rF ( p j ))  T ( )

(4)

From this equation, we get a spectrum that is proportional to the atmospheric transmittance, T ( ) . By taking an average of these spectra over pixels within the area and then normalizing at NIR band, we obtain the spectral shape function for the glint reflectance. This glint spectral shape function is applied for the entire image.

6.12.2 Sun glint magnitude estimation and correction for each pixel Within a window (with appropriate size of 3x3 pixels in this study), we assume that the pixel of minimum reflectance is free of the glint effects. We take a boxcar average for the minimum reflectance in 2-D space to get a smoothed glint-free reflectance. The spatial smoothing elevates the low values of the minimum reflectance which is often associated with wave shades. This computation is done for the NIR band reflectance. Following this, the glint reflectance at the NIR band for each pixel is estimated by subtracting the glint-free reflectance from the reflectance of the pixel. Finally, by multiplying the glint spectral shape function described above, the glint reflectance spectrum for each pixel is computed and is subtracted from the measured spectrum.

6.13 Atmospheric and air-water interface correction The terrestrial and aquatic information of satellite imagery is contaminated by the effects of atmospheric molecules and particles through absorption and scattering of the radiation from the sun and the earth surface and the effects of the Fresnel reflection at the air-sea interface. By removing the atmospheric and sea surface effects, the target reflectance, coming out of water, is retrieved from remotely sensed imagery and is used for further analysis such as classification of benthic substrate types and retrieval of the water constituents. In this section, we describe an approach for the atmospheric correction, which have been applied to imagery acquired in this project. The satellite measured radiance is converted to the top of the atmosphere (TOA) reflectance,

t

TOA

by normalisation to the downward solar irradiance at TOA. The TOA reflectance is

composed of the atmospheric path reflectance,  path , sun glint,  g reflectance,  w

t

TOA

108

TOA

  path   g

TOA

and water-leaving

(Gordon and Wang, 1994). TOA

 w

TOA


(5)


The atmospheric path reflectance  atm is divided into two terms-Rayleigh,  R and aerosol and,  a .

 path   R   a

(6)

The Rayleigh reflectance is the reflectance due to atmospheric molecules such as nitrogen and oxygen and the aerosol reflectance is due to aerosol particles and aerosol-Rayleigh interaction. The TOA water leaving reflectance  w

TOA

is related with the apparent reflectance (above-surface

water reflectance), Rapp as follows:

 wTOA  t E t L Rapp

(7)

Where t E and t L are the atmospheric diffuse transmittance for irradiance and radiance, respectively. Combining these equations, we get an expression for the surface water-leaving reflectance:

Rapp

TOA t   w  tE tL

TOA

  path   g

TOA

(8)

tE tL

This equation indicates that the apparent reflectance can be computed if the variables,  path (   R   a ) , t E and t L are known. Glint reflectance is corrected separately as described before. The water-leaving reflectance should be corrected for multiple reflections by atmosphere, which is not negligible in the case of high surface reflectance and high aerosol loading. This spherical albedo correction is not described here. Since the aerosol reflectance, the irradiance and radiance transmittances vary depends on aerosol optical properties (such as type and optical thickness) as well as sun-sensor geometry, they are sought using a series of radiative transfer simulations as described below. The simulations were made using Coupled Ocean and Atmosphere Radiative Transfer (Jin et al., 2006).

Simulation 1 for Rayleigh reflectance (  R ) It is known that the Rayleigh reflectance is accurately computed with sun-sensor geometry and atmospheric pressure using radiative transfer software. The Raylegh reflectance was computed assuming black surface (totally absorbing surface) and no aerosol loading.

Simulation 2 for aerosol reflectance (  a ) and the irradiance transmittance ( t E ) The aerosol reflectance is computed as the atmospheric path reflectance minus the Rayleigh reflectance using Eq. (6). The simulation is made assuming black surface with a given aerosol type and optical thickness. The irradiance transmittance, t E is estimated from this simulation as the ratio of the downwelling irradiance at TOA to at above the surface using

t E  Ed

surface

/ Ed

TOA

. The simulation also produce the atmospheric path radiance, Lu

path

, which

is used for transmittance computation. Such simulations repeat for several aerosol optical thickness. Optimum aerosol properties are sought by investigating the corrected reflectance at deep waters or known areas although a single


109


aerosol type (Modtran maritime) was sufficient for processing of satellite imagery around the remote marine parks, in most cases.

Simulation 3 for diffuse radiance transmittance In order to compute the transmittance for diffuse surface radiance, t L , the radiative simulation was made with same aerosol model and optical thickness as simulation 2 but with a Lambertian surface (albedo =0.2). The diffuse radiance transmittance is estimated using the relationship:

t L  ( Lu ,Lam

TOA

 Lu

Variables, Lu ,Lam

TOA

path

) / Lu ,Lam

and Lu , Lam

surface

surface

(Lambertian surface)

are obtained in this simulation while Lu

(9) path

is from the

simulation 2.

Simulation 4 for glint radiance transmittance In the high spatial resolution (2.6m for the QuickBird imagery), the glint reflectance varies pixel by pixel, since it depends on the surface slope of the pixel. Therefore, the correction of this glint effects is treated separately from the atmospheric correction as described before in section 5.4. However, the glint spectra can be estimated from radiative transfer simulation made with the Cox-Munk ocean surface model. The TOA glint reflectance  g is related with the surface glint (Fresnel) reflectance rF as follows:

 g TOA  t ETg rF ,

(10)

where Tg is glint radiance transmittance, which is computed from simulations similar to previous simulation 3 but with bottom boundary of ocean surface with a black water column. This black water column is required for avoiding any water-leaving radiance. The glint radiance transmittance is computed as:

Tg  ( Lu , gl int

TOA

 Lu

path

TOA

and Lu , gl int

Variables, Lu , gl int

) / Lu , gl int

surface

surface

(ocean surface)

are obtained in this simulation while Lu

(11) path

is from the

simulation 2. An air/water interface correction, consistent with the c-WOMBAT-c model (Brando and Dekker 2003), was applied to the Rapp data to retrieve subsurface irradiance reflectance (R0-).

R (0  ) 

d 1  d 2 Rapp d 3  d 4 Rapp

(12)

where d1, d2, d3 and d4 are the interface correction parameters which will differ for each spectral band. Example of atmospheric correction parameters for the QB image of 31 July 2008 over Georgina Cay - Solar zenith: 42.1 - Satellite zenith: 10.3

110



- Relative azimuth: 177.9 - Atmospheric model: Mid-Latitude Summer - Aerosol model: Modtran maritime with no stratospheric aerosol - Aerosol optical thickness: 0. (simulation 1) or 0.05 at 500nm (simulation 2,3,4) - Ozone: 0.35 atm-cm - Precitable water vapour: 4.2 g/cm2 - Windspeed: 5m/s - Boundary condition: black (simulation 1 and 2) or Lambertian (simulation 3) or wind-blown sea surface (simulation 4)

6.13.1 Validation of atmospheric correction In general, the validation of atmospheric correction is performed by comparison of water reflectance spectra between in situ and satellite image, concurrently measured for the same targets. However, collecting such in situ data (called matchup data) was not feasible due to very limited field work availability for remote marine parks. Therefore, simulated spectra were compared to the satellite-derived water reflectance. The simulated spectra were computed using Hydrolight (Mobley and Sundman, 2008) with water depth and substrate spectral library measured during the field experiments. Actually a success in this comparison is required for a success of the SAMBUCA retrieval, since SAMBUCA inverts a subsurface reflectance model, which was formulated based on Hydrolight simulations. The image based reflectance is obtained from the TOA reflectance through the atmospheric correction and interface correction procedures as described above. The simulated reflectance is computed using Hydrolight-Ecolight version 5 (Mobley and Sundman 2008) with a substrate spectrum representative for the target and in situ water column depth. Sand substrates were selected for this comparison since the sand reflectance is less variable than other targets and the sand patches are easily identified in the satellite imagery. An example of the comparison is shown in Figure 6-9 for two locations for water depths of 5.1m and 16.5m in the QuickBird image of 31-Jul-2008 around Georgina Cay. Ecolight simulations were made with water constituent contributions of chlorophyll =0.15 mg/m3, CDOM=0.005 m-1 and tripton=0.5 g/m3. The QuickBird image derived reflectaces well match the simulated spectra.


111


0.45

subsurface reflectance

0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 300

400

500

600

700

800

wavelength (nm)

HE - 5.1m sand

HE_band - 5.1m sand

QuickBird_ave - 5.1m sand

HE_16.5m sand

HE_band - 16.5m sand

QuickBird_ave - 16.5m sand

Figure 6-9 Comparison of subsurface irradiance reflectance between the Hydrolight-Ecolight (HE) simulation and the QuickBird image. The targets are located at (17.5967S, 151.4959E) and (17.5940S, 151.4930E) with water depth, 5.1m and 16.5m respectively. The QuickBird image was captured on 31 July 2008 around Georgina cay. The HE simulated spectra are indicated in red coloured curves (full spectra) and symbols (QB band weighted).

6.13.2 Vicarious calibration A quantitative inversion algorithm requires high radiometric accuracy of satellite measurements. This is challenging for aquatic applications since water reflectance is low due to high absorption. The radiometric calibration of the QuickBird sensor is not very precise. A few reflectance percentage differences were reported across four spectral bands between pre-launch (absolute) and after-launch (vicarious) calibration coefficients (Holekamp et al, 2006). Since a small reflectance differences can makes significant difference in atmospheric correction retrieval, it is necessary to investigate the optimum calibration numbers to be used. Vicarious calibration coefficients are obtained by comparison of the top-of-atmosphere reflectance between satellite and simulation for well known targets. (Werdell et al., 2007) The approach we adopted for the vicarious calibration is summarized as follows: 1. To select an image with negligible glint effects and clear atmosphere (low aerosol optical thickness). 2. To determine the atmospheric correction parameters (aerosol model, aerosol optical thickness) using deep water reflectance. Hence, the variables such as atmospheric path reflectance and transmittances are determined. 3. To simulate the apparent reflectance (Rapp) for a known target. A bright target (sand bottom in shallow area) with water column depth is chosen to get high reflectance for calibration. A homogenous target away from the exposed area is preferred to minimize the adjacency effects. At-surface reflectance for this target is simulated using the Hydrolight. Default water constituent concentrations (e.g. from water sample analyses) are used.

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4. To compute the simulation top of atmosphere (TOA) reflectance - The TOA water reflectance is computed by multiplying transmittances using Eq. (7) and then the total reflectance at TOA is obtained by adding the path reflectance according to Eq. (5) 5. To compute calibration coefficients - ratio of the TOA reflectance from image and simulation. It is noted that the vicarious calibration may be necessary when a systematic difference between HE simulation and the QB image is observed. Since the sensor calibration does not change much over short time, new calibration should be taken carefully, with checking with existing calibration.

6.14 References Hedley, J.D., Harborne, A.R., & Mumby, P.J. (2005). Technical note: Simple and robust removal of sun glint for mapping shallowwater benthos. International Journal of Remote Sensing, 26, 6 Hochberg, E.J., Andréfouët, S., & Tyler, M.R. (2003). Sea surface correction of high spatial resolution Ikonos images to iprove bottom mapping in near-shore environments. IEEE Transactions in Geoscience and Remote Sensing, 41, 1724-172 Gordon, H. and M. Wang, Retreival of water-leaving radiance and aerosol optical thickness over the ocean with SeaWiFS: a preliminary algorithm. Applied Optics. 33, 443-452, 1994. Holekamp, K., K. Ross and S. Blonski, System Characterization Results for the QuickBird Sensor (http://calval.cr.usgs.gov/JACIE_files/JACIE07/Files/38Holeka.pdf). Jin, Z., T.P. Charlock, K. Rutledge, K. Stamnes, and Y. Wang (2006), Analytical solution of radiative transfer in the coupled atmosphere-ocean system with a rough surface. Applied Optics. 45, 7443-7455. Mobley, C.D., and L.K. Sundman (2008), Hydrolight 5 Ecolight 5 Technical Documentation (Sequoia Scientific, Inc., Redmond, Wash., USA. Vahtmae, E., & Kutser, T. (2008). Sun glint correction of airborne AISA images for mapping shallow-water benthos. In, IEEE/OES Us/EU-Baltic International Symposium (pp. 239-246). Tallinn, ESTONIA: IEEE Werdell, P. J., S.W. Bailey, B.A. Franz, A. Morel and C.R. McClain (2007), On-orbit vicarious calibration of ocean color sensors using an ocean surface reflectance model, Applied Optics 46, 5649-5666.


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APPENDIX D – AQUATIC DATA PROCESSING PATHWAY

APPENDIX D – AQUATIC DATA PROCESSING PATHWAY E.J. Botha

6.15 Data volume reduction 6.15.1 Environmental dynamic range In order to understand the precision and accuracy that can be achieved in the estimate of an environmental variable derived from reflectance with satellite imagery, it is necessary to estimate the overall sensitivity of the entire sensor-atmosphere-air-water interface system for detecting changes in reflectance. This is achieved by estimating the environmental noise equivalent subsurface reflectance difference (NE∆R0-) of the scene. The NE∆R0- provides an integrated measure of sensor signal–to-noise ratio and scene-specific characteristics such as the atmospheric variability and effects from the air-water interface (Brando and Dekker 2003). NE∆R0- is estimated in the deepest waters in the imagery at the location identified as being the most homogenous using the methodology described by (Wettle et al. 2004). Figure 6-10 shows NE∆R0- for each of the images involved in the analysis.

Reflectance

0.006

0.004

0.002

0.000 450

500

550

600

650

700

Wavelength Georgina Cay

SW Coringa-Herald Cay

Elizabeth Reef (west)

Figure 6-10 The environmental noise equivalent spectra for each band in the Georgina Cay, SW Herald Cay and Elizabeth Reef (west) QuickBird images..

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6.15.2 Data compression Due to its fine spatial resolution, QuickBird images have a large number of spectrally similar pixels which can lead to unacceptably long processing times. In order to reduce the processing time of the inverted physics-based radiative transfer model, a data volume reduction protocol was implemented. In essence, it groups the image data into a set of spectrally distinct classes. The strength of this approach lies in the use of the in NE∆R0- characteristics inherent to the data. The rationale behind this is straightforward: if two spectra differ by less than the noise levels in the data, they can be grouped as one class with a relatively minimal loss of information. The advantage of grouping pixels into distinct classes is that each class (which can contain a large number of pixels) is represented by one spectrum, which can be ingested by SAMBUCA. The SAMBUCA output (e.g. water column depth) for each class can then be mapped back to every pixel labelled as pertaining to that class.

6.16 Retrieval of bathymetry, substratum composition and the optically active constituents The final step of the integrated physics based mapping approach is a physics based retrieval of bathymetry, substratum composition (i.e. fractional cover of sand, coral and algae) from the R0imagery. To this aim, the inversion/optimization method by Lee et al. (1999; 2001; 1998) was enhanced, as published in Brando et al. (2009) in order to: 1) retrieve the concentrations of optically active constituents in the water column (chlorophyll-a, CDOM and NAP), 2) account for more than one substratum cover type and 3)

to estimate the contribution of the substratum to the remote sensing signal. This implementation, called SAMBUCA (the Semi-Analytical Model for Bathymetry, Unmixing, and Concentration Assessment), is available from the authors upon request.

6.16.1 Principles of the physics based method At the core of the inversion/optimization method by Lee et al. (1999; 2001; 1998) lies an analytical expression for subsurface remote sensing reflectance ( rrs ) for an optical shallow water body (Maritorena et al. 1994):

rrs  rrsdp  exp( K d H )[ A exp( B H )  rrsdp exp( C H )]

(6)

where, rrsdp is subsurface remote-sensing reflectance over a hypothetical optically deep water column; H is the water depth; A the bottom albedo (substratum reflectance); K d the vertical attenuation coefficient for diffuse downwelling light,  B the vertical attenuation coefficient for diffuse upwelling light originating from the bottom; and  C the vertical attenuation coefficient for diffuse upwelling light originating from each layer in the water column. Note that the


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attenuation of the upward flux is not equivalent to the attenuation of the downward flux ( K d ). Attenuation of the upward flux must further be separated into a component originating from the water column (  C ) and that originating from the bottom (  B ) (Maritorena et al. 1994). By relating the four quantities rrsdp , K d ,  B and  C to absorption and backscattering via a series of semi-analytical relationships, Lee et al. (1999; 2001; 1998) modelled the R(0-) spectrum as a function of five independent variables (representing properties of water column and bottom):

rrs  f  P, G , X , B, H 

(7)

where P, G, X, and B are scalar values and represent absorption coefficients of phytoplankton and gelbstoff (coloured dissolved organic matter plus detritus), backscattering coefficient of suspended particles, and bottom reflectance at a reference wavelength, respectively; and H is the bottom depth.

6.16.2 Semi Analytical Model for Bathymetry Unmixing and Concentration Assessment (SAMBUCA) In the inversion-optimization scheme in SAMBUCA the modelled subsurface remote-sensing reflectance (R0- model) is compared to the measured subsurface remote-sensing reflectance (R0input ) which was obtained from each pixel in the remote sensing image. The set of variables that minimises the difference between these two spectra is used to estimate the environmental variables being sought, e.g. water column depth, substratum composition or the concentrations of the optically active constituents of the water column. The extraction of environmental information from measured reflectance spectra constitutes a radiative transfer inverse problem. Inverse problems are notoriously difficult because of potential non-uniqueness issues (Mobley et al. 2005). It is often necessary to constrain inverse problems so as to guide the inversion to the correct solution. Such constraints often take the form of simplifying assumptions about the underlying physical or mathematical problem, or of added environmental information. For the inversion-optimization in SAMBUCA, the Downhill Simplex method was adopted, whilst ranges for variables to be optimized were constrained to reduce the occurrence of spectral ambiguities (Wettle and Brando 2006; Wettle et al. 2005). In SAMBUCA, the algorithm by Lee et al. (1999; 2001; 1998) was modified (Brando et al. 2009) to retrieve the concentrations of optically active constituents in the water column (chlorophyll-a, CDOM and NAP). The absorption and backscattering coefficients are described as the sum of the contributions of N constituents and a constant coefficient for pure water: N

N

a  aw   a j C j ; bb  bbw   bbjC j j1

(8)

j1

Where aw and bw are the absorption and backscattering of pure water (Morel 1974; Pope and Fry 1997), aj* and bbj* are the specific inherent optical properties (SIOPs) of jth constituent with concentration Cj. In the formulation of equation (8) CDOM has no backscattering term associated with it, and a*CDOM(440nm)represents the concentration of CDOM.

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The non-water absorption terms are parameterized as a known shape with an unknown magnitude:

a phy ( )  CCHL  a *phy ( )

(9)

 aCDOM     CCDOM  aCDOM  0  exp   SCDOM    0 

(10)

a NAP     C NAP  a NAP  0  exp   S NAP    0 

(11)

Where CCHL is the concentration of chlorophyll-a and a*phy(λ)is the chlorophyll-a specific absorption spectrum. As the concentration of CDOM (CCDOM) is represented by a*CDOM(440nm), the reference wavelength λ0 was set at 440 nm, SCDOM is the spectral decay constant for CDOM absorption coefficient and a*CDOM(λ0) is set to 1. CNAP is the concentration of NAP; a*NAP(λ0) is the specific absorption of NAP at the reference wavelength, and SNAP is the spectral slope constant for NAP absorption coefficient; and the reference wavelength λ0 was set at 440 nm for NAP absorption coefficient. The non-water backscattering terms are parameterized as follows:

bbp  bbphy  bbNAP

(12) Y phy

* bphy

bbphy ( )  CCHL  b

  (0 )  0   

(13) YNAP

  bbNAP ( )  C NAP  bb*NAP (0 )  0   

(14)

where b*bphy(λ0) is the specific backscattering of algal particles at the reference wavelength, Yphy the power law exponent for the algal particles coefficient; b*NAP(λ0) is the specific backscattering of NAP at the reference wavelength, YNAP the power law exponent for NAP backscattering coefficient. The reference wavelength λ0 was set at 542 nm for both algal and non algal particle backscattering coefficient. In SAMBUCA, the algorithm by Lee et al. (1999; 2001; 1998) was modified (Brando et al. 2009) to account for more than one substratum cover type in a pixel or spectrum by expressing the bottom albedo A(λ) as linear combination of two substrata:

A     qij Ai     (1  qij ) A   

(15)

Where qij represents the fractional cover of substratum i and substratum j within each pixel, Ai(λ) and Aj(λ) are the albedos of substratum i and j, respectively. When solving for more that two cover types, SAMBUCA cycles through a given spectral library, retaining those two substrata and their estimated fractional cover qij which achieve the best spectral fit. In summary, the complete model parameterization of equation (8) for SAMBUCA is:


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 CCHL , CCDOM , C NAP , H , qij , Ai    , A j    , SCDOM , S NAP ,  rrsmodel  f    Y , Y , a*    , a*    , b* ( ), b* ( )  NAP 0 bPHY 0 bNAP 0  PHY NAP PHY 

(16)

6.16.3 Optical properties of benthic substrates The benthic substrate parameterization used in this project was based on three substrate types: sand, brown coral and turf algae. The reflectance spectra used to represent these substrate types (Figure 2-7) are taken from a representative substrate spectral library collected at Lihou Reef during the December 2008 field campaign. This limited set was used as QuickBird images have only three spectral bands in the visible portion of the spectrum. Multispectral imagery with higher spectral resolution in the visible spectrum, such as the six spectral band of WorldView-2, is likely to allow more benthic variable to be assessed.

6.16.4 Optical properties of Coral Sea waters The parameterisation of the semi-analytical model (eq. 16) relies on field sampling of the optical properties of the water body of interest. When this is not possible, the semi-analytical model (eq. 16) can be parameterized with sites of similar characteristics from the literature. For this project, the inherent and apparent optical properties of not all the sites were properly described. Although some optical data was collected during the Coringa-Herald field campaign and a full dataset was collected during the Lihou Reef field campaign, the optical properties of Elizabeth Middleton Reef waters were not described at all. The optical parameterization of water defined for SAMBUCA in this project was based on a comprehensive dataset collected at Heron Reef which was deemed representative of the range of optical properties expected at the remote tropical marine parks under investigation (Table 3-4 and Figure 3-14). Based on the parameterization, SAMBUCA was configured to estimate the concentrations of optically active constituents in the water column (chlorophyll-a, CDOM and NAP), water column depth, and benthic substratum composition that produces the best fit between modelled and measured R(0-) on a pixel-by-pixel basis. As the bathymetry results were good, this approach is acceptable for this project. However, collecting a comprehensive optical characterization is recommended improve future applications.

6.17 References Brando, V.E., & Dekker, A.G. (2003). Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality. IEEE Transactions in Geoscience and Remote Sensing, 41, 1378-1387 Brando, V.E., Anstee, J.M., Wettle, M., Dekker, A.G., Phinn, S.R., and Roelfsema, C. (2009). A physics based retrieval and quality assessment of bathymetry from suboptimal hyperspectral data. Remote Sensing of Environment, 113, 755-770.

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Lee, Z., Carder, K.L., Mobley, C.D., Steward, R.G., & Patch, J.F. (1999). Hyperspectral remote sensing for shallow waters: 2. deriving bottom depths and water properties by optimization. Applied Optics, 38, 3831-3843 Lee, Z., Ivey, J.E., Carder, K.L., & Steward, R.G. (2000). Pure water absorption coefficient around 400nm: lab measured versus field observed. In proceedings of OceanOptics IX, Monaco (p. 9) Lee, Z., Kendall, L.C., Chen, R.F., & Peacock, T.G. (2001). Properties of the water column and bottom derived from Airborne Visible Imaging Spectrometer (AVIRIS) data. JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 106 11639-11651 Lee, Z.P., Carder, K.L., Mobley, C.D., Steward, R.G., & Patch, J.S. (1998). Hyperspectral remote sensing for shallow waters. I. A semianalytical model. Applied Optics, 37, 6329-6338 Maritorena, S., Morel, A., & Gentili, B. (1994). Diffuse reflectance of oceanic shallow waters: influence of water depth and bottom albedo. Limnology and Oceanography, 39, 1689-1703 Mobley, C.D., Sundman, L., Davis, C.O., Bowles, J.H., Downes, T.V., Leathers, R.A., Montes, M.J., Bisset, W.P., Kohler, D.D.R., Reid, R.P., Louchard, E.M., & Gleason, A. (2005). Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables. Applied Optics, 44, 3576-3592 Morel, A. (1974). Optical properties of pure water and pure sea water. In N.G. Jerlov & E.S. Nielsen (Eds.), Optical Aspects of Oceanography (pp. 1-24). London: Academic Press Pope, R.M., & Fry, E.S. (1997). Absorption spectrum (380-700 nm) of pure water. II. Integrating cavity measurements. Applied Optics, 36, 8710-872 Wettle, M., Brando, V.E., & Dekker, A.G. (2004). A methodology for retrieval of environmental noise equivalent spectra applied to four Hyperion scenes of the same tropical coral reef. Remote Sensing of Environment, 93, 188-197 Wettle, M., Dekker, A., & Brando, V.E. (2005). Monitoring bleaching of tropical coral reefs from space. A feasibility study using a physics-based remote sensing approach. In: CSIRO Wealth from Oceans Flagship Program. Wettle, M., & Brando, V.E. (2006). SAMBUCA - Semi-analytical model for Bathymetry, unmixing and concentration assessment. . In, CSIRO Land and Water Science Report Canberra: CSIRO Land and Water


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APPENDIX E - ANALYSIS OF BIO-OPTICAL PROPERTIES OF PARTICULATE AND DISSOLVED SUBSTANCES IN THE WATER COLUMN MEASURED DURING LIHOU REEF FIELD SAMPLING SURVEY IN DECEMBER 2008.

APPENDIX E - ANALYSIS OF BIO-OPTICAL PROPERTIES OF PARTICULATE AND DISSOLVED SUBSTANCES IN THE WATER COLUMN MEASURED DURING LIHOU REEF FIELD SAMPLING SURVEY IN DECEMBER 2008. Nagur Cherukuru

6.18 Methods

6.18.1 Sample collection During the field campaign in the Lihou Reef National Nature Reserve and surrounding open waters a total of 19 stations were sampled during December, 2008 (Figure 6-11). Upon arrival at the sampling site water samples were collected following protocols suitable for complex waters (Clementson et al., 2004; Tilstone et al., 2004; Fargion and Muller, 2000). Surface water was collected using a jerry can and immediately onboard, was filtered and stored appropriately for biogeophysical measurements to be made in the laboratory at a later time.

Figure 6-11 A Map of Lihou Reef region where field survey was conducted during December 2008. Stations are marked on the map where water samples are collected.

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APPENDIX E - ANALYSIS OF BIO-OPTICAL PROPERTIES OF PARTICULATE AND DISSOLVED SUBSTANCES IN THE WATER COLUMN MEASURED DURING LIHOU REEF FIELD SAMPLING SURVEY IN DECEMBER 2008.

6.18.2 Measurement of concentration of particles in suspension Particles in suspension were separated from sea water by filtration using a glass fibre filter, with the net weight of material giving suspended particulate material (SPM) in mgl-1(Tilstone and Moore, 2002). Pre-washed, pre-ashed and pre-weighed Whatman GF/F filters (0.7 µm) were used as explained by Van der Linde (1998). The filters collecting the particulate material were dried in an oven at 75 °C for 24 hours, after which they were stored in a dessicator before weighing. The value thus obtained gives the mass of particulate material collected on the filter. Using the volume of the seawater filtered, it was possible to convert this mass into a concentration of SPM (mgl-1). These filters were further ashed at 500 °C for 5 hours to remove the suspended particulate organic matter content (SPOM) and to calculate the suspended particulate inorganic matter component (SPIM) concentration present in the total SPM.

6.18.3 Collection of particulate matter for absorption measurements Between 0.5 to 1.5 litres of surface water was filtered through a 25 mm glass fibre filter (Whatman GF/F, approx. 0.7 µm pore size) under subdued light and low vacuum pressure (