bathymetry from satellites for hydrographic purposes survey/presentations/26.pdf · bathymetry from...

Bathymetry from satellites for hydrographic purposes A.G. Dekker1, S. Sagar2, V.E. Brando1, D.Hudson2

1 CSIRO - Commonwealth Scientific & Industrial Research Organisation 2 GA – GeoScience Australia

Shallow Survey 2012

Wellington, New Zealand , 22 February 2012

CSIRO

Introduction

Contents:

• Introduction to method

• Case studies of airborne and satellite derived bathymetry

interpreted by IHO bathymetric standards

• Conclusions and Recommendations

CSIRO

The conceptual model for optically deep and shallow waters

CSIRO. V. Brando - A physics based retrieval and quality assessment of bathymetry from suboptimal hyperspectral data.

physics based approach

Integrated physics

based mapping

approach that

includes atmospheric

correction and an

objective process of

quality control:

1. Atmospheric correction

2. Noise estimate

3. Inversion optimization

(SAMBUCA)

4. Quality control

at sensorL

rsr

QA variables:

SDI

Raw output:

bathymetry

substratum

concentrations

( , ,CHL CDOM NAPC C C )

radiosonde data

wind speed

ozone

horizontal visibility

SIOPs

concentration ranges

substrate spectra library

ENE rsr

QC’ed output:

bathymetry

substratum

concentrations

( , ,CHL CDOM NAPC C C )

SDI

Step 3:

inversion optimization

(SAMBUCA)

Step 1:

atmospheric correction

(WOMBAT)

Step 4:

quality control

(SAMBUCA)

ENE rsr

imagery

or spectra

process

Input parameters

Step 2:

noise estimate

Remote sensing of remote tropical Commonwealth marine protected areas

water

optical

properties

Substratum

reflectance

spectra

Physics-based

inversion model

(SAMBUCA Model)

Information Products

Substrate maps

Bathymetry maps

Accuracy maps

Satellite Remote Sensing Processing Pathway-

Physics-based inversion approach

satellite image

Pre-processing:

• De-glinting

• Atmospheric correction

Empirical vs physics-based methods

Empirical methods work when

• substratum and water column are homogeneous

• in situ bathymetry data is available

Empirical methods have no capability of

• assessing model based uncertainty

• transferring relationships across sensors

Physics based models:

• can deal with variable substratum and variable water

column properties

• can determine model uncertainty

• use all spectral information available –and therefore can

switch across sensors types

• outperform empirical methods (See Dekker et al., 2011)

• also performs without in situ data- by parameterising

with “best guess” bio-optical data CSIRO

CSIRO Overview Estuarine and Coastal Remote Sensing in Australia

Variables that can be measured directly using

physics-based earth observation methods (1)

• Water Column Properties:

• Chlorophyll-a, Phaeophytin (all photosynthesizing orgs)

• Cyano-phycocyanin & -erythrin=>Cyanobacteria

• Total Suspended Matter

• Coloured Dissolved Organic Matter

• Transparency/Turbidity/Vertical Attenuation of Light

• 3-D Information (if the bottom is visible)

• Bathymetry (depth of substrate)

• Bottom Relief (topography)

CSIRO Overview Estuarine and Coastal Remote Sensing in Australia

• Benthic (& Intertidal) substratum • Coastal: Seagrasses, macro-algae and associated

substrates & freshwater: macrophytes and associated substrates

• Extent

• Main species differentiation: if spectrally & spatially discriminable!

• Density of cover; biomass

• Coral Reef and associated substrates

• Extent

• Bleaching

• Main substratum types (Live coral ,dead coral , seagrasses, macro-algae)-main species : if spectrally & spatially discriminable!

Variables that can be measured directly using

physics-based earth observation methods (1)(2)

(now also approaches using object-oriented image processing methods)

IHO Standards for Hydrographic Surveys-

at 10m survey depth

For satellite bathymetry the following criteria affect the meeting of IHO standards :

• Data quality (satellite imagery)

• Pre-processing (atmospheric & sun glint correction)

• Existing site information (bathymetry, substrate maps, GCPs..)

•„Tide reduction (post-processing) Note: a satellite image is a snapshot!

Total Vertical Uncertainty IHO definition

CSIRO

For Multispectral Satellite derived bathymetry “b” increases with depth: faster

than most other bathymetric methods:

• LADS has a similar depth dependency-but is not the same as for satellite

imagery as LADS is an active system generating its own pulses of light and

LADS timegates the response ping.

• Satellite imaging is based on measuring reflected solar and sky light from the

benthos

ALOS AVNIR-2 Bathymetry in the Great Barrier Reef – 15ARSPC

Example of using 10 m spatial resolution

ALOS data (low cost )

DARLEY AND DINGO REEF

Located approx 90km north off

Bowen on the Queensland Coast


Validation using LADS (I or II) data Surface Comparison

LADS – Inverse Distance

Weighting Interpolated

ALSOS Bathymetry Map

ALOS Bathymetry Map

Median 3x3 Filter


Validation of ALOS derived bathymetry using

LADS (I or II) data

300 Points randomly selected in low gradient homogenous areas

R2 = 0.94

RMSE = 0.5 m (note :Not TVU!)

North West Gizo

(Solomon Islands)

Quickbird v ALOS

Quickbird

2.4m pixels ALOS

AVNIR2

10m pixels

IHO Standards vs Satellite Derived Bathymetry @ 10m depth

(improves to shallower depths!):

ALOS-AVNIR 10 m pixels

CSIRO

•Based on ALOS 10 m pixel resolution data

•Applied to GBR and to Cocos Islands

•Validated using LADS (I or II) and nautical bathymetry charts resp.

Order 1a 1b

Total Vertical Uncertainty (TVU) 0.517m 0.517m

Total Horizontal Uncertainty (THU) 5.5m 5.5m

Feature Detection 2m object up to 40m depth n/a

Full Sea Floor Search at 10 m pixel size n/a

Maximum Line Spacing n/a 25m (or 3 times depth)

LiDAR 5m x 5m

Deriving Shallow Water Bathymetry from

Multi-spectral Data

SAMBUCA applied to multi-spectral

satellite data - shallow water

bathymetry up to 25m depth.

Spectral

measurements of

substrates made on-

site improve the

bathymetry

estimation process

Seagrass

Sand

Coral

Lord Howe Island, NSW - Quickbird 2.4m

resolution multi-spectral image acquired

9th Feb 2009

Lord Howe Island Bathymetry

- Northern Extents & Lagoon

Note: RAN LADS I - collected by RAN1997 & QuickBird data from Sep 2009

CSIRO



QuickBird 2.6 m pixels

CSIRO

•Based on QuickBird 2.6 m pixels

•Applied to Lord Howe Island

•Validated using LADS I and nautical bathymetry charts

Order 1a 1b




Full Sea Floor Search Yes(at 2.6 m pixels) n/a


LiDAR 5m x 5m


Hyperspectral airborne and acoustic bathymetric

survey for the Moreton Bay Rous Channel area

A number of underwater acoustical

and video sensors were deployed in

Moreton Bay between 29 August

and 5 September 2004.

Aim: to obtain an integrated data

set of airborne CASI, multibeam,

drop-down video and other sensors,

platforms and ground-truth

information to enable the

comparison of bathymetry

retrieval.


Acoustic bathymetric survey for the

Rous Channel area

• Bathymetric surface derived from the acoustic bathymetric survey

• Bathymetry vectors supplied by Port Authority- Queensland

Department of Transport.


Pseudo true colour composite of the CASI-2

imagery for the Rous Channel area

• inadequate quality of portions of the CASI-2 image:

• Some very shallow portions of the Eastern banks are as bright a as the

thick clouds.

• A cloud masking based on cloud spectral properties was attempted but it

was not adopted as it showed a low accuracy

•


Quality control map based on model agreement

and substratum visibility

Quality control of CASI image bathymetry

retrieval using SAMBUCA


Accuracy of bathymetry retrieval

To our knowledge, the quantitative

identification and screening of the

“optically deep waters” and the

“quasi-optically deep waters” was

never attempted prior to this

research, leading to a degraded

accuracy in the depth retrievals for

previous studies as the waters

became deeper or more turbid

Lee et al., 2007

Goodman & Ustin, 2007

Stumpf et al., 2003

0

3

6

9

12

15

18

0 3 6 9 12 15

Depth (Acoustics) [m]

Dep

th (

CA

SI-

SA

MB

UC

A)

[m]

sambuca (good closure,opt.shallow) sambuca (bad closure,opt.deep) sambuca (bad closure,opt. shallow) sambuca (bad closure,opt.deep)


(improves to shallower depths!)

CASI Airborne Imaging Spectrometry 4 m pixels

CSIRO

•Based on CASI airborne data 4m m pixels

•Applied to Moreton Bay

•Validated using acoustical and port authority bathymetry data.

Order 1a 1b




Full Sea Floor Search Yes (at 4 m pixels) n/a


LiDAR 5m x 5m

CSIRO

0

2

4

6

8

10

12

14

16

18

0 2 4 6 8 10 12 14 16

Re

trie

ved

Bat

hym

etr

y (m

)

True Bathymetry (m)

Sat +10%

1 a+b S44

Sat-10%

1 a+b S44

True Bathymetry

Comparison of Satellite Bathymetry uncertainty (10% of depth) to IHO Standardsbased on simulated data

Satellite Bathymetry uncertainty at Order 1 a & b from surface to 5m depth

CSIRO

0

1

2

3

4

5

6

7

8

0 1 2 3 4 5 6 7 8

Re

trie

ved

Bat

hym

etr

y (m

)

True Bathymetry (m)

Sat +10%

1 a+b S44

Sat-10%

1 a+b S44

True Bathymetry

Comparison of Satellite Bathymetry uncertainty (10% of depth) to IHO Standardsbased on simulated data

Satellite Bathymetry uncertainty at Order 1 a & b from surface to 5m depth

CSIRO

Recommendations

Perform a dedicated comparison project with remote sensing and hydrographic bathymetry experts:

• LADS and/or multibeam calibrated and validated data

• Multispectral satellite image(s) e.g. WorldView-2, Quick Bird, RapidEye, IKONOS etc.

• Preferably (near) synchronous collection

Scenario testing to assess applicability under various realistic conditions: process satellite images with

• No ancillary data (remote or denied area scenario)

• Limited ancillary data (remote or denied area scenario where a priori information can be obtained)

• Complete ancillary data

Assess utility of

• analysing multiple images in surf zones for surf zone bathymetry

• bottom topography and roughness mapping

• simultaneous benthic composition and water quality mapping capabilities

Rigorous uncertainty estimation

CSIRO

Recommendations

The physics-based inversion methods for satellite bathymetry, benthic mapping and water quality can be applied successfully to archival satellite images.

Retrospective mapping can be done: back to 1984 for Landsat (30 m resolution) and back to 1973 for Landsat

MSS images (80 m resolution)

Back to 1999 for IKONOS; 2002 Quickbird etc.,and then later on RapidEye, WorldView-2, GeoEye etc..

Note: for physics-based inversion at a minimum three visible spectral bands (Blue, Green and Red) are required and (for less then 2 m depth) one nearby Infrared band.

CSIRO

Should an additional column be added to IHO standards for high

spatial resolution multispectral satellite derived bathymetry?

Depending on

• quality of satellite data

• access to a priori site information

• Water column turbidity

• And the decrease of accuracy with depth inherent to a passive optical detection system

…bathymetry assessment performance varies across current IHO bathymetry standards……..!

Bathymetry from multispectral satellite data is:

• more accurate at very shallow depths (0 m to 5 m)

• sufficiently accurate to 5 or 10 m depth

• Capable of bathymetry from 15 to 35m depth at lower accuracy

• Spatially comprehensive

Coral Sea Millenium mapping zones overlaid

with bathymetry contours (100 m) from

National Marine Bioregionalisation of Australia

(DEWHA)

Satellite-derived bathymetry map

(CSIRO) at 2.6 m spatial resolution

(depth scale in m) down to 35 m

Atmospherically

and sun-glint

corrected

satellite image

Bathymetry map

40 to zero m



ALOS-AVNIR 10 m pixels

CSIRO

•Based on ALOS 10 m pixel resolution data

•Applied to GBR and to Cocos Islands

•Validated using LADS and nautical bathymetry charts resp.

Order 1a 1b




Full Sea Floor Search at 10 m pixel size n/a


LiDAR 5m x 5m



QuickBird 2.6 m pixels

CSIRO

•Based on QuickBird 2.6 mm pixels

•Applied to Lord Howe Island

•Validated using LADS and nautical bathymetry charts

Order 1a 1b




Full Sea Floor Search Yes (at 2.6 m pixels) n/a


LiDAR 5m x 5m


(improves to shallower depths!)

CASI Airborne Imaging Spectrometry 4 m pixels

CSIRO

•Based on CASI airborne data 4m pixels

•Applied to Moreton Bay

•Validated using acoustical and port authority bathymetry data.

Order 1a 1b




Full Sea Floor Search Yes(at 4 m pixels) n/a


LiDAR 5m x 5m

New satellite IHO Standards for Satellite Derived

Bathymetry @ 5 m depth ?

Order 1 Sat

Total Vertical Uncertainty (TVU) 10% of depth m)

Total Horizontal Uncertainty (THU) Pixelsize (2 – 10 m)

Note that with no GCPs THU may be 3 to 5 pixels High within image coregistration accuracy

Feature Detection A pixel size object up to 15 m depth

Full Sea Floor Search Yes – to visibility depth-at pixel size

Maximum Line Spacing Pixel size (2 to 10 m)

CSIRO

Thank you!

[email protected]

[email protected]

[email protected]

[email protected]

Acknowledgement s:

CSIRO: Janet Anstee and Simon Allen

James Cook University: Rob Beaman

Most relevant publications:

Dekker A.G., Phinn S.R., Anstee J.M., Bissett P. Brando V.E., Casey B. Fearns P., Hedley

J., Klonowski, W., Lee Z.P., Lynch M., Lyons M., Mobley C. and Roelfsema C. (2011)

Intercomparison of shallow water bathymetry, hydro-optics and benthos mapping

techniques in Australian and Caribbean coastal environments; Limnology &

Oceanography Methods. 9:pp 396-425. | DOI: 10.4319/lom.2011.9.396.

Brando, V.E., Anstee, J.M., Wettle, 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 (2009), pp. 755-770,

10.1016/j.rse.2008.12.003

CSIRO

mailto:[email protected]




Some related publications

• Dekker A.G., Phinn S.R., Anstee J.M., Bissett P. Brando V.E., Casey B. Fearns P., Hedley J., Klonowski, W., Lee

Z.P., Lynch M., Lyons M., Mobley C. and Roelfsema C. (2011) Intercomparison of shallow water bathymetry, hydro-

optics and benthos mapping techniques in Australian and Caribbean coastal environments; Limnology &

Oceanography Methods. 9:pp 396-425. | DOI: 10.4319/lom.2011.9.396.

• Brando, V.E., Anstee, J.M., Wettle, 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

(2009), pp. 755-770, 10.1016/j.rse.2008.12.003

• Kutser, T., Dekker, A. G., Skirving, W. (2003) Modeling spectral discrimination of Great Barrier Reef benthic

communities by remote sensing instruments. Limn.& Oceanogr. 48: p 497-510.

• Dekker, A. G. , Jordan, A. and Mount, R. (2008). Satellite and airborne imagery including aerial photography.

Chapter in “Marine benthic habitat mapping special publication” : p 11-28. Geological Association of Canada: 327

p. ISBN-13: 978-1-897095-33-1; ISSN: 0072-1042.

• Dekker, A. G., Brando, V.E., Anstee, J.M., Fyfe, S., Malthus, T.J.M. & Karpouzli, E. (2006) Remote sensing of

seagrass ecosystems: use of spaceborne and airborne sensors, Chapter 15 in : Larkum, A,., Orth, B and Duarte, C.

(eds) Seagrass Biology, Ecology and Conservation , Springer Verlag, Germany: pp 630.

• Zimmerman, R.C. and Dekker, A. G (2006) Aquatic Optics: Basic concepts for understanding how light affects

seagrasses and makes them measurable from space, Chapter 12 in : Larkum, A,., Orth, B and Duarte, C. (eds)

Seagrass Biology, Ecology and Conservation , Springer Verlag, Germany: pp 630.

• Dekker, A.G., Brodie, J. and Steven, A (2006) Cattle, crops and coral: Flood plumes and the Great Barrier Reef (ed:

Stephanie Renfrow), NASA Distributed Archive Center Annual Report 2006, NASA National Snow and Ice Data

Center DAAC; Boulder , Co, USA.

• Dekker, A. G., V. E. Brando, J. M. Anstee, N. Pinnel, T. Kutser, H. J. Hoogenboom, R. Pasterkamp, S. W. M. Peters,

R. J. Vos, C. Olbert, and T. J. Malthus, 2001, Imaging spectrometry of water, Ch. 11 in: Imaging Spectrometry: Basic

principles and prospective applications: Remote Sensing and Digital Image Processing, v. IV: Dordrecht, Kluwer

Academic Publishers, p. 307 - 359.

S. Sagar and M. Wettle (2010) Mapping the fine-scale shallow water bathymetry of the Great Barrier Reef using

ALOS AVNIR-2 data; presented at Oceans 2010 Conference.

CSIRO

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