2010E
arth
Observ
atio
n &
Mappin
g
© ESA
Photo NASA
EO in the coastal environment
Multi-sensor and shallow water observations
Thomas Heege
EOMAP GmbH & Co. KG
Airport Oberpfaffenhofen / Germany
www.eomap.de
2010
Oil- and gas industry
Dredging
Offshore construction
impact monitoring
Water ways
Pipeline routing
Biodiversity baseline
Environmental impact monitoring
for offshore applications
Water quality monitoring
Turbidity monitoring
Water depth mapping
Sea floor monitoring
EO based service linesEO based service lines
2010
EO products for non-space markets
Requirements:
� Fast, sustainable availability of standardized EO products
� Flexible and adequate resolution in space in time
Solutions:
� Multi sensor approaches
� Standardized data processing, worldwide applicable
2010
Data and product processing methods
Generic physics based data processing systemGeneric physics based data processing system
� Effective atmospheric and surface correction procedures
� e.g. all bidirectional relationships accounted for
(atmosphere, water surface, underwater)
� Robust data inversion techniques
� Sensor independent data processing technology
� Fast product generation, worldwide applicable
2010Mapping littoral bottom coverage
atmospheric & water surface
corrections
spectrometer
data
subsurface
reflection R-
maps of littoral bottom coverage
water column correction
R- -> R-z (bottom)
spectral unmixing /„classification“spec. spectral reflectance
of littoral surfaces
spec. optical propertiesof water constituents
S,P,Y
Sea floor product generation
2010
Bathymetry from space
QuickBirdQuickBird satellite scene, satellite scene, RottnestRottnest Island / AustraliaIsland / Australia
2010RottnestRottnest Island (West Australia)Island (West Australia), , QuickBirdQuickBird satellite data July 17, 2005satellite data July 17, 2005
Subsurface reflectance (atmospheric and water surface corrrected)
1km
NN
Bathymetry from space
2010RottnestRottnest Island (West Australia)Island (West Australia), , QuickBirdQuickBird satellite data July 17, 2005satellite data July 17, 2005
Modular Inversion and Processing System MIP
1km
Areas deeper than 22m: white
- 0.1- 0.2
- 0.5 - 1- 2- 5 - 10- 20
Water depth [m]
Bathymetry from space
2010RottnestRottnest Island (West Australia)Island (West Australia), , QuickBirdQuickBird satellite data July 17, 2005satellite data July 17, 2005
Echo sounding depth
- 0.1- 0.2
- 0.5 - 1- 2- 5 - 10- 20
Water depth [m]
1km
Areas deeper than 22m: white
Bathymetry from space
2010RottnestRottnest Island (West Australia), Island (West Australia), QuickBirdQuickBird satellite data July 17, 2005satellite data July 17, 2005
- 0.1- 0.2
- 0.5 - 1- 2- 5 - 10- 20
Depth [m]
Satellite retrieved by MIP
Echo sounding map
White mask > 22m VALIDATION:
0-18m
R=0.94
SD=20%
N=50439 points
Areas deeper than 22m: white
Ellipsoid: Existing reef/shelf not detected by echo sounding measurement
Bathymetry from space: validation
2010
SAR satellite based retrieval of water depth
© S. Lehner, A. Pleskachevsky, DLR
2010
BathymetryBathymetry fromfrom spacespace: : ProductProduct fusionfusion opticoptic//radarradar
Synergethic water depth product using QuickBird and TerraSAR-X data
2010
Conclusion water depth mapping from space
� ACCURACY:
In comparison to validation data sets: RMS 8 – 30 %
Low error in validation data results in RMS 5 – 20 %
� DEPTH RANGE:
Optical sensors: Clear water conditions: 0 – 18 m
Intermediate conditions: 0 – 5 m
SAR / Optical: Incomming wave fields: 12 – 80 m
� RESTRICTIONS:
� Increased turbidity results in underestimation of water depth
� No retrieval possible under optical deep conditions (any depth)
� Incomming wave fields needed for SAR technology
� ADVANTAGE:
� Shallow water areas inaccessibly by echo-sounding are covered
� Usually better spatial resolution than echo sounding data
� Wide coastal areas accessibly
� Multi-sensor approach possible: Many optical sensors, SAR-sensors
BathymetryBathymetry fromfrom spacespace
2010BathymetryBathymetry fromfrom spacespace
State of the technology: Map products generated ..
� for various satellite and airborne sensors (IKONOS, QuickBird, RapidEye, HYMAP, ..)
WordView-2: Most promising if radiometric calibration stable (under investigation)
� for various areas in Australia, Indonesia, Armenia, Germany, Mexico, …
� for industrial, governmental and research applications
� At more than 10 000 sqkm of shallow coastal and inland waters
Outlook in expanding the technology in synergy with radar sensors:
� Advantage: Expand depth range down to 80m. Technology independent on weather and in-water optical conditions
� Precondition: Existence of incoming wave field for water depth retrieval using the dispersion relation
� Market entrance: Expected for 2011
Conclusion water depth mapping from space
2010
Seagrass Mixed algae
Coral
Seagrass Turf algae Ecklonia
O C E A N V I S I O Nenvironmental research
M. Harvey, H. Kobryn
Benthic habitats
2010
Spectral maps
Initial input image
spectral libraryclustering
Image BImage BImage BImage BImage B, C, ...
spectral classes
Spectral
classification
Training
Data
processing
SeaSea floorfloor classificationclassification: : DataData processingprocessing procedureprocedure
Initial input image
Sea floor albedo
after AC and water
column correction.
Can be replaced by
spectral measure-
ments if available
Image B, C, D …
Sea floor albedo
after AC and water
column correction
Habitat maps
Spectral map
Map of spectral
classes, based on
spectral library or
clustering
Habitat map
Fusion of ground
truth with EO product
Transformation to habitat classes
Linking spectral classes, water depth,
and object oriented features with habitats
2010
World View 2, Puerto World View 2, Puerto MorelosMorelos, Mexico 11.04.2010, Mexico 11.04.2010Raw data
2010
Subsurface reflectance. Channels 4, 3, 2 (RGB)Subsurface reflectance. Channels 4, 3, 2 (RGB)World View 2. MIP data processing. Product after atmospheric and sea surface correction
2010
Sea floor Sea floor albedoalbedo. Channels 3, 2, 1 (RGB). Channels 3, 2, 1 (RGB)World View 2. MIP data processing. Processing: water column correction & depth retrieval
2010
Class 1Class 1
Class Class 22
Class Class 33
Class 4Class 4
Class 5Class 5
Class 6Class 6
Class 7Class 7
Class 8Class 8
Class 9Class 9
Class 10Class 10
Class 11Class 11
Class 12Class 12
Class 13Class 13
Class 14Class 14
Class 15Class 15
Class 16Class 16
Spectral ClassesSpectral Classes
Seafloor Classification, Puerto Seafloor Classification, Puerto MorelosMorelos / Mexico 2010/ Mexico 2010World View 2. MIP data processing. Spectral classes (Processing: clustering, classification)
2010
BARE SUBSTRATE
BIO-SUBSTRATE
MACROALGAESEAGRASS
CANOPY ALGAE
TURF ALGAESAND
CORAL
Classifying the marine benthic habitats of Rottnest Island
O C E A N V I S I O Nenvironmental research
M. Harvey, H. Kobryn
2010
Survey of ~3400km2 of
• reef and lagoons (to ~20m)
• using hyperspectral airborne instrument (HyMap)
Focus is on:
• Baseline mapping
• Long term monitoring tool for large areas
• Definition of sanctuary zones
NingalooNingaloo Marine Park: Marine Park:
Habitat and biodiversity mapping Habitat and biodiversity mapping
2010
NingalooNingaloo Reef, Reef, YardieYardie CreekCreek AustraliaAustralia
Raw dataRaw data
Atmospheric & Atmospheric & sunglittersunglitter corrected data: subsurface reflectancecorrected data: subsurface reflectance
2010
24
Cover forming substrate components; Habitat maps at different thematic levels (3, 12, 46) and maps
Products: raster at 3.5m
Halina T. Kobryn, Kristin Wouters, Lynnath E. Beckley, Thomas Heege
Major
substrate type
Area
(ha)
Sand and limestone
28600
Macro- and turfing algae
41702
Hard coral 5268
Soft coral 591
Habitat mapping with hyperspectral remote sensing
2010
Overall accuracy:
70,5 % on level 4a (highest number of fuzzy classes)
2010
densedensemediummediumsparsesparsemediumsparsesparsesparsePrimary Biota
Density
87.67%
5
0
199
7
9
4
0
3
Cor3
Hard
coral
89.92%88.68%87.23%85.71%94.87%96.28%96.24%79.13%88.5%63.46%User
Accuracy
NA020102231Massive Coral
NA010000001Coralline Algae
95.38%23520623007109Hardcoral
Tabulate
Branching
79.56%1949089674814214Macroalgae/Sargassum
63.95%51102011413112Deadcoral
Rubble
91.33%52851271144235882Limestone
Pavement
78.57%0001001111Microalgae
Sand
99.4%1327246104384317122Sand
Producer
Accuracy
Cor2Cor1Bio4Bio3Bio2Bio1Sed3Sed2Sed1Class
Hard
coral
Sand
Hardcoral
Macroalgae
Dead Coral
Sand
Pavement
Sand
Pavement
Macroalga
Dead Coral
Sand
Pavement
Macroalga
Sand
Pavement
Macroalgae
Sand
Pavement
Macroalgae
Sand
Pavement
Deadcoral
Sand
MicroalgaeLabel
Hyperspectral image classification
Gro
un
d T
ruth
data
ValidationValidationN. Pinnel, Murdoch Univ.
2010
Multi-sensor water quality monitoring
River Elbe, Germany. MODIS 500m 23. Juni 2010, 10:15 UTC
TurbidityTurbidity
[NTU][NTU]
Land
Cloud
- 1
- 5
- 9
- 12
- 15
- 20
- 24
MODIS 500m: 10:15 UTC
2010River Elbe, Germany. MODIS 250m, MERIS 300m, 23. Juni 2010
MERIS 300m: 10:24 UTC
TurbidityTurbidity
[NTU][NTU]
Land
Cloud
- 1
- 5
- 9
- 12
- 15
- 20
- 24
Multi-sensor water quality monitoring
MODIS 250m: 10:15 UTC
2010River Elbe, Germany. 23. Juni 2010
TurbidityTurbidity
[NTU][NTU]
Land
Cloud
- 1
- 5
- 9
- 12
- 15
- 20
- 24
Rapideye
11:19 UTC
MODIS 250m: 10:15 UTC
Rapideye 5m
11:19 UTC
Multi-sensor water quality monitoring
2010River Elbe, Germany. MODIS 250m 23. Juni 2010, 12:05 UTC
TurbidityTurbidity
[NTU][NTU]
Land
Cloud
- 1
- 5
- 9
- 12
- 15
- 20
- 24
MODIS 250m: 12:05 UTC
Multi-sensor water quality monitoring
2010River Elbe, Germany.
TurbidityTurbidity
[NTU][NTU]
Land
Cloud
- 1
- 5
- 9
- 12
- 15
- 20
- 24
Rapideye 5m
11:19 UTC
Multi-sensor water quality monitoring
2010
Conclusion
� Increasing spatial/temporal resolution using multi-sensor approaches
� Methods word-wide applicable to different aquatic systems
Applicability of procedures depend on:
� Spectral and spatial resolution of sensor, calibration
� Environmental parameters
� RS can be a powerfull and cost effective mapping tool
� Fully automized for water quality products
� Establishment as additional monitoring instrument not yet exploited in integration with traditional methods
2010
THANK YOU FOR YOUR ATTENTION
Contact:
Info (at) eomap.com
