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



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



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.



Secchi Secchi Depth MeasurementsDepth Measurements



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



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



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



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



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



High Resolution DEMHigh Resolution DEM



Profile for depth analysisProfile for depth analysis



ALI-3 &ALI-3 &AST-1AST-1at ~30at ~30’’

ALI-2ALI-2at ~50at ~50’’

TM2minus40TM2minus40at~20at~20’’?? TM-1TM-1

at ~?at ~?



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

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


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


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


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

urban mapping, lagoon eo-1 validation in venice, italy · michael abrams, jet propulsion laboratory...

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