Urban Mapping, Lagoon EO-1 Validation in Venice, Italy · Michael Abrams, Jet Propulsion Laboratory...
Transcript of Urban Mapping, Lagoon EO-1 Validation in Venice, Italy · Michael Abrams, Jet Propulsion Laboratory...
Consiglio Nazionale delle RicercheMichael Abrams, Jet Propulsion Laboratory
Michael AbramsMichael Abrams, JPL, Pasadena CA, JPL, Pasadena CA
Luigi Luigi AlberotanzaAlberotanza, CNR, Venice, Italy, CNR, Venice, Italy
Stefano Stefano PignattiPignatti, Rosa , Rosa CavalliCavalli, CNR-, CNR-LARA, Rome, ItalyLARA, Rome, Italy
Valerio TramutoliValerio Tramutoli, U. , U. PotenzaPotenza, Italy, Italy
EO-1 Validation in Venice, Italy:EO-1 Validation in Venice, Italy:Urban Mapping, LagoonUrban Mapping, LagoonEnvironment, and BathymetryEnvironment, and Bathymetry
Applications to:
• Urban/Industrial Mapping: Venice and Porto Marghera
• Wetlands Vegetation: Venice Lagoon
• Bathymetry: Adriatic Sea and Lake Tahoe
Supplemental data:
• ASTER
• Landsat ETM+
• MIVIS Airborne Imaging Spectrometer
• IKONOS
ETM+ & LAC
ASTER
HYPERION & MIVIS
ALI
"Applications and Validation of EO-1 Data forOceanography, Pollution and Urban Mapping"
The project aims to evaluate the spaceborne high resolution Hyperion andALI data, by integrating them with hyperspectral airborne data for:
The city of Venice is characterized by an urban structure typical ofa secular town, whose buildings are very close to one another andthe road plan is irregular with narrow streets intersecting eachother.
1 Minute
Landsat ETM+MultispectralSwath Coverage(185 km @ 30 m)
705 kmAltitude
ALI MultispectralSwath Coverage(37 km @ 30 m)
Atmospheric CorrectorHyperspectral Coverage(185 km @ 125 / 250 m)
Landsat-7EO-1
Satellite and Airborne Synergy
HyperionHyperspectral SwathCoverage(7.5 km @ 30 m)
MIVIS Underflight
(4 km @ 8 m)
Resolution,m
Swath, Km # Bands Wavelength,µmEO-1 satellite
ALI 10-30 37 10 0.4-2.4
HYPERION 30 7.5 220 0.4-2.5
LAC 250 185 256 0.89-1.6
TM satellite 15 ,30, 60 185 8 0.42-12.5
ASTER satellite
VNIR 15 60 3 0.52-0.86
SWIR 30 60 6 1.6-2.43
TIR 90 60 5 8.1-11.7
MIVIS airborne
@ 4000m a.s.l. 8 5.6
@ 1500m a.s.l. 3 2.1
VIS 20 0.43-0.83
VNIR 8 1.15-1.55
SWIR 64 2-2.5
TIR 10 8.2-12.7
Sensors’ characteristics
Aerosol optical thickness, Bouguer-Langley method
A1 & A2
Atmospheric Characterization
A1
0
0,05
0,1
0,15
0,2
0,25
0,3
1 1,2 1,4 1,6 1,8 2 2,2
m
AO
T
λ=530nm
Ground Spectral Measurements
Sea/Lagoon Measurements
OCR-200 ⇑⇑⇑⇑300-1000nm 7 ch
WET Labs ac-9Dual Path SpectralAbsorption/Attenuation Meter410-850nm 10nm/ch
OCR-200 ⇓⇓⇓⇓300-1000nm 7 ch
Optical measurements along water andatmospheric column, water sampling
Aeronet site
PR-650 SPECTRASCAN380-780nm
Acqua Alta Ocean PlatformBoat Spectral Measurements
Consiglio Nazionale delleRicerche
Michael Abrams, Jet Propulsion Laboratory
April 4 April 20March 19
June 7 July 9 July 25
2001 2001 Landsat Landsat ETM+ ImagesETM+ Images
XXXXXGPS/INS
Registration toIKONOS with NN
XXXXModtran
ACORN
XXXXXConversion to
radiance atsensor
XAdjustment
VNIR & SWIRgeometry
ETM+HyperionALIASTERMIVISIKONOS
Venice Data Pre-processingVenice Data Pre-processing
Hyperion 30m
ALI 30m
TM 30m
MIVIS 8m
ALIALI30m30m
9 bands9 bands
HyperionHyperion30m30m
242 bands242 bands
TMTM30m30m
6 bands6 bands
ASTERASTER
15m VNIR,15m VNIR,30m SWIR30m SWIR
9 bands9 bands
MIVISMIVIS8m8m
102 bands102 bands
IKONOSIKONOS4m4m
4 bands4 bands
IKONOS 4mIKONOS 4m MIVIS 8mMIVIS 8m ASTER 15mASTER 15m
ETM+ 30mETM+ 30m ALI 30mALI 30m HyperionHyperion 30m 30m
Image processing techniques applied to data
•ISODATA
•LINEAR MIXING MODEL (LMM)
•MINIMUM NOISE FRACTION (MNF)
•MIXTURE TUNED MATCHED FILTER (MTMF)
•PIXEL PURITY INDEX (PPI)
•SPECTRAL ANGLE MAPPER (SAM)
•SUBPIXEL SPECTRAL ANALYTICAL PROCESS (SSAP)
•NEURAL NETWORK
ALI
ETM+
ASTER
MIVIS
IKONOSHyperion
Vegetation
Paving
Tile roof
4.37.32.9IKONOS
7.56.74.7MIVIS
14.68.05.1ASTER
9.512.83.0Hyperion
2.313.33.7ETM+
2.310.66.1ALI
PavementTile RoofVegetation
Urban Material 3-class MappingUrban Material 3-class Mapping(% pixels)(% pixels)
New tiles
Old tiles
The entire roof data set were spectrally investigated by using a PPI procedures on MIVIS scene.Two different spectra were recognized as corresponding to brick coverings of new and oldbuildings.
The spatial recognition of such covering materialswas obtained by means of SAM classificationprocedure. This procedure also stressed theorthogonality of the two selected spectral classes.
Metallic coverings, Asphalt and Trachyte spectralclasses instead were classified in a MTMF procedureto derive the abundance of each input spectralclasses.
MIVIS MIVIS MMappingapping with 8m with 8m PPixelsixels, 102 , 102 BBandsands
New tileNew tilessOld tileOld tiless
Metallic roofMetallic roofinging
Trachyte
Asphalt
MIVIS MIVIS MMappingapping with 8m with 8m PPixelsixels, 102 , 102 BBandsands
Spectral Angle Spectral Angle MapperMapper Classifications Classifications
ETM+ALI
Hyperion
Asphalt
Trachyte
Old tile
New tile
Metal roof
Trees
Grass
Spectral Angle Spectral Angle MapperMapper Classifications Classifications
Ikonos MIVIS
ASTER
Asphalt
Trachyte
Old tile
New tile
Metal roof
Trees
Grass
Metal roof
New tiles
Old tiles
Trachyte
Grass
Trees
Asphalt
% p
ixels
Trachyte – blue
Zinc roof – red
Limestone - green
MIVIS MIVIS Sub-Pixel MSub-Pixel Mappingapping with 8m with 8m PPixelsixels, 92 , 92 BBandsands
Detectable only at 8m pixel resolution
MIVIS spectra used for Hyperion ummixing
• MIVIS and Hyperion converted to radiance at sensor
• Atmospheric correction applied
• Hyperion resampled to MIVIS spectral bands
• Data sets were co-registered
• Empirical line method used to normalize Hyperion to MIVIS,
using MIVIS image spectra
• Hyperion data unmixed using end-members derived from
MIVIS data
Hyperion unmixingby using MIVIS derived purespectra
Hyperion unmixing by usingpure derived spectra
MIVIS TIR composite:bands 7-5-2 in RGB
Consiglio Nazionale delleRicerche
Michael Abrams, Jet Propulsion Laboratory
Comparison of water penetration ofComparison of water penetration of
ALI, ETM+ and ASTERALI, ETM+ and ASTER
!! Data acquired September 1, 2002 by all 3 Data acquired September 1, 2002 by all 3satellitessatellites
!! Instrument characteristics: Instrument characteristics:
ALI: 30m, push-broom, line arraysALI: 30m, push-broom, line arrays
TM: 30m, whisk-broom, discrete detectorsTM: 30m, whisk-broom, discrete detectors
ASTER: 15m, push-broom line arraysASTER: 15m, push-broom line arrays
!! Test site is Lake Tahoe, CA/NV Test site is Lake Tahoe, CA/NV
Consiglio Nazionale delleRicerche
Michael Abrams, Jet Propulsion Laboratory
Lake Tahoe, CA/NVLake Tahoe, CA/NV
Lake Tahoe is atLake Tahoe is at1900 m.a.s.l. It is1900 m.a.s.l. It is18 x 33 km in size.18 x 33 km in size.The lake hasThe lake hasextremely clearextremely clearwater: however,water: however,visibility hasvisibility hasdeclined from 37mdeclined from 37mto 22m in the lastto 22m in the last35 years.35 years.
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Michael Abrams, Jet Propulsion Laboratory
Secchi Secchi Depth MeasurementsDepth Measurements
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Michael Abrams, Jet Propulsion Laboratory
MethodologyMethodology
!! Co-register ALI, ETM+, ASTER to high Co-register ALI, ETM+, ASTER to highresolution DEM using nearest-neighborresolution DEM using nearest-neighborresamplingresampling
!! Convert 3 data sets to radiance at sensorConvert 3 data sets to radiance at sensor(W/m2*(W/m2*srsr*um)*um)
!! Plot depth vs. radiance profiles Plot depth vs. radiance profiles
Consiglio Nazionale delleRicerche
Michael Abrams, Jet Propulsion Laboratory
0.4 0.5 0.6 0.7 0.8 0.90.4 0.5 0.6 0.7 0.8 0.9
ALIALI
ETM+ETM+
ASTERASTER
11 22 33
11
11
22
22
33
33
11’’ 44
44
Bandpasses Bandpasses of Instrumentsof Instruments
Wavelength, umWavelength, um
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Michael Abrams, Jet Propulsion Laboratory
Lake Tahoe, CA/NV: Sept. 1, 2002Lake Tahoe, CA/NV: Sept. 1, 2002False Color IR CompositesFalse Color IR Composites
ALI: 5-4-3ALI: 5-4-3 TM: 4-3-2TM: 4-3-2 ASTER: 3-2-1ASTER: 3-2-1
Consiglio Nazionale delleRicerche
Michael Abrams, Jet Propulsion Laboratory
Lake Tahoe, CA/NV: Sept. 1, 2002Lake Tahoe, CA/NV: Sept. 1, 2002
ALI: 5-4-3ALI: 5-4-3
TM: 4-3-2
ASTER: 3-2-1ASTER: 3-2-1
Consiglio Nazionale delleRicerche
Michael Abrams, Jet Propulsion Laboratory
ALI: 3-2-1ALI: 3-2-1 TM: 3-2-1TM: 3-2-1 ASTER: 3-2-1ASTER: 3-2-1
Composites of 3 lowest wavelength channelsComposites of 3 lowest wavelength channels
Consiglio Nazionale delleRicerche
Michael Abrams, Jet Propulsion Laboratory
High Resolution DEMHigh Resolution DEM
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Michael Abrams, Jet Propulsion Laboratory
Profile for depth analysisProfile for depth analysis
Consiglio Nazionale delleRicerche
Michael Abrams, Jet Propulsion Laboratory
ALI-3 &ALI-3 &AST-1AST-1at ~30at ~30’’
ALI-2ALI-2at ~50at ~50’’
TM2minus40TM2minus40at~20at~20’’?? TM-1TM-1
at ~?at ~?
Consiglio Nazionale delleRicerche
Michael Abrams, Jet Propulsion Laboratory
ALI-1ALI-1
Low signal rangeLow signal range
Suggestion ofSuggestion of~60~60’’
-6
-5
-4
-3
-2
-1
0
1 2 3 4 5 6 7 8 9 10 11
depth
[m
]
bathymetry case A
Adriatic Sea
Coastal water bathymetry
ALI
0.720.400.350.08Case D
0.620.260.200.04Case C
0.740.280.350.07Case B
0,510.220.300.09Case A
ALI 0.660ALI 0.443ALI 0.482TM 0.485
Profilo A
(RadMAX-RadMIN)
(RadMAX+RadMIN)
8 0
130
180
230
280
330
380
430
- 0 , 5 - 1 , 2 - 1 , 1 - 2 , 2 - 2 , 8 - 3 , 2 - 3 , 5 - 1 , 2 - 4 , 3 - 4 , 5 - 5 , 1
depth [m]
TM 485
ALI 482
ALI 443
ALI 660
Subpixel Processing Approach
SSAP method assumes that every pixel contains a fraction of thematerial of interest (e.g. submerged vegetation species), and theremainder contains other background materials.
SSAP method detects the material of interest, of the pixel underinvestigation, by subtracting fractions of candidate background spectra.
The output is presented in form of fraction planes (fraction maps) foreach material of interest.
The submerged vegetation species mixing generates, for each pixel,composite spectral signature of different species, in which lagoonbottom and atmospheric effects are also included.
Aerial strip (July 26, 2001) covers theareas with macro-algae located in theNorthern and Central Basins of thelagoon and a wide part of the area withsea phanerogams located in theSouthern Basin of the lagoon.
Lagoon EnvironmentMarco Polo Airport
City of Chioggia
Northen Basin
Central Basin
Southern Basin
Tidal conditions were in inflow conditions(4cm P.Salute)
Water thickness overhanging thesubmerged vegetation in the SouthernBasin was estimated of about 50 cm.
P. Salute
Submerged Vegetation Species Object of Investigation
From sea truth data, the species with a wide bottom cover, at aerial andsatellites survey dates, were the following:
Benthic macro-algae: Ulva rigida,
Gracilaria confervoides,
Chaetomorpha aerea.
Sea phanerogams: Zostera marina,
Zostera noltii,
Cymodocea nodosa.
submerged vegetationin situ measurements performed from april to sept. 2001 by CNR - ISDGM
0,000
0,005
0,010
0,015
0,020
0,025
0,030
400 450 500 550 600 650 700 750
wavelength (nm)
spectr
al ra
dia
nce (
W/m
2*s
r*nm
)
Ulva1
Ulva2
Cymodocea
Zostera Noltii
Chaetomorpha1
Chaetomorpha2
Chaetomorpha3
Chaetomorpha4
Cl-a Cl-a
phc
phe
fuc
In the Venice lagoon, the submerged aquatic vegetation maps are usedto plan:
! a selective harvesting of benthic macro-algae,
! all activities of sea phanerogam plantation, for the lagoon bottommorphological control.
Mapping techniques currently used are based on in situ observationsand aerial photo-interpretations.
Airborne and satellite-borne multispectral images were used for a longtime.
Results obtained with multispectral data were limited to the detection ofentire submerged vegetation cover, and in 1988 and 1989 to thedetection of Ulva rigida that was extremely diffused in the lagoon.
MIVIS MIVIS SubpixelSubpixel C Classificationlassification
Chaetomorpha - Gracilaria
Ulva - Gracilaria
Ulva - Chaetomorpha
Ulva
Chaetomorpha - Gracilaria - Ulva
Sea truth data map of July 18, 2001 Benthic macro-alga distribution.
MIVIS MIVIS SubpixelSubpixel C Classificationlassification
Chaetomorpha - Gracilaria
Ulva - Gracilaria
Ulva - Chaetomorpha
Ulva
Chaetomorpha - Gracilaria - Ulva
Sea truth data map of July 18, 2001 Benthic macro-alga distribution.
MIVIS MIVIS SubpixelSubpixel C Classificationlassification
Lagoon EnvironmentLagoon Environment
ETM+ dataETM+ data
0
20
40
60
80
100
120
0,4 0,5 0,6 0,7 0,8 0,9 1
wavelenght
radi
ance
Ulva
Ulva Gracilaria Chaetomorpha
Ulva Gracilaria
high torb Ulva-Gracilaria
Landsat/ETM data allow discrimination only of vegetationcover. There are poor distinctions between macroalgae’sand phanerogams’ signatures measured in the same EO-1/ALI test areas
ALIALI data data
0
100
200
300
400
500
600
700
800
0,4 0,5 0,6 0,7 0,8 0,9 1
wavelenght
radia
nce
Ulva
Ulva Gracilaria Chaetomorpha
Gracilaria Chaetomorpha
high torb Ulva-Gracilaria
Lagoon EnvironmentLagoon Environment
ALI data have higher SNR than ETM+, and“bluer” band. Signatures are distinct andwell separated.
SSAP classification using ALI bands 1-5. Band 9 used to mask outland. Benthic macro-algae, sea phanerograms, and mixture of 2
types were mapped.
Intensive crop area
Airport Marco Polo
MIVIS (8m) isodata
MIVIS (8m) neural net. TM (30m) neural net. HYPERION (30m) neural net
Airport
Marco Polo
Airport Marco PoloFraction map abundance: soya
Hyperion SSP
50-59
20-29 %
30-39
40-49
60-69
70-79
80-89
90-99
TM SSP
Intensive crop area
The characterization in frequency of noise present in channel 3 (0.565mm) of the ALIimage has been realized by means of the Fourier trasform series. The vectors analysed infrequency correspond to a vertical and horizontal section of a subset of the firstspectrometer (image left size) extracted from the mosaic of the whole ALI scene
ALI noise frequency characterization
74pixel
87pixel
The development in Fourier series is given by
∑−
=
−=
1
0
2
)(1
)(N
x
N
uxj
exfN
uFπ
Where: N is the number of elements (pixel) of the vector,x is the variable in the start domain (pixel), u is the variable of the arrival domain (1/pixel).
Vertical Fourier series coefficients
Horizontal Fourier series coefficients
Continuing WorkContinuing Work in Italyin Italy
ESA (SPECTRA) and ASI (HYPSEO) are planningfuture hyperspectral missions
EO1 Hyperion data are a critical source ofinformation for testing applications
Research funding is committed by ASI for continuedresearch
Consiglio Nazionale delle RicercheMichael Abrams, Jet Propulsion Laboratory
Summary of ResultsSummary of Results
Urban Mapping:
" ALI was similar to ETM+ to map surface materials
" The most important factor is spatial resolution: 30m istoo coarse to isolate pure end members for unmixing
" Hyperion’s spectral bands allowed sub-pixel unmixingusing end members from MIVIS
Lagoon Mapping
" ETM+ unable to separate types of submerged vegetationdue to low SNR
" ALI’s higher SNR and bluer band allowed spectralseparation and sub-pixel unmixing of vegetation types
Consiglio Nazionale delle RicercheMichael Abrams, Jet Propulsion Laboratory
Summary of Results (contSummary of Results (cont’’d)d)
Bathymetry:
" Case I water study suggested that “bluer” band providedgreater depth penetration
" Case II water study did not show a significant increase indepth penetration with ALI-1’, due to high water scattering
" In both cases, ETM+ data are inferior to ALI data due tolower SNR
Consiglio Nazionale delle RicercheMichael Abrams, Jet Propulsion Laboratory
TTTTHHHHAAAANNNNKKKK YYYYOOOOUUUUTTTTHHHHAAAANNNNKKKK YYYYOOOOUUUU to: to:
EO-1 Project for heroic data collection activityEO-1 Project for heroic data collection activity
Italian National Research Council (CNR) forItalian National Research Council (CNR) forsubstantialsubstantial support for Co- support for Co-II’’ss
My wife and daughter for allowing me to go toMy wife and daughter for allowing me to go toVenice without themVenice without them