Multi- and Hyperspectral Geologic Remote Sensing: a Review

Freek van der Meer, UT-ITC, Netherlandsf.d.vandermeer@utwente.nl

Workshop Geological Remote Sensing, 17 April 2013

2

23 May 1991

THE ROSETTA STONE OF REMOTE SENSINGJOHN SALISBURY AND GRAHAM HUNT, 1970-1980

3

Pioneering work in laboratory spectroscopy byGraham Hunt and JohnSalisbury in 1970’s and1980’s. ->this led to the Development of hyper-Spectral sensors

The Optimum Index Factor (OIF) Select 3 bands for FCChighest amount of 'information' (= highest sum of standard deviations)least amount of duplication (=lowest correlation among band pairs)

MULTISPECTRAL: LANDSAT ERA OPTIMUM INDEX FACTOR (OIF)

4

MULTISPECTRAL: LANDSAT ERARATIO, DÉCOR STRETCH, SATURATION EHNANCEMENT

5

TM ratio 3/1 - iron

TM 5/7 - hydroxyl

Different ages of lava flows

MULTISPECTRAL: THE LANDSAT ERAPCA BASED CROSTA COMPOSITES

6

Geologic mapping (Schetselaar et al., 2000, Fraser et al., 1997)Lithologic mapping (Gad and Kusky, 2006)Structural mapping (Boccaletti et al., 1998, Yesou et al., 1993) Volcano monitoring (Oppenheimer et al., 1993)Coral reef mapping (Mumby et al., 1997)Natural oil seep detection (Macdonald et al., 1993)Landslide mapping (Singhroy et al., 1998, Lee and Talib, 2005) Mineral exploration (Abdelsalam et al., 2000, Sabins, 1999, Ferrier et al., 2002)Gneissic terrains (De Souza, 1998)

MULTISPECTRAL: LANDSAT ERAGEOLOGIC USE OF LANDSAT TM

7

ASTER

8

ASTER VERSUS LANDSAT TMGLOBAL EARTH OBSERVATION DATA!

9

‘Silica’‘Advanced Argillic’

‘Iron’ ‘Argillic/Phyllic’‘Propyllitic’ ‘Garnet’

DEMs

Hydroxyl Minerals ‘Clay’‘Red Iron’‘Yellow Iron’

ASTERRGB = 4,6,8

Graphite schists

Carboneras fault

Volcanic crater- Gabo de Gata volcanics -

ITC course GRS

3D surface view of color composite: RGB = bands 4, 6, 8

Dark oval units -> graphite schists units; Linear range of hills -> Carboneras fault; Oval crater near hill top: volcano crater; etc.

Ratio band 4 / band 6 FCC 4,6,8

Alteration centers

ASTER band ratio’ show alteration centers

ITC course GRS

Band ratio 4/6

Field-based alteration map (Arribas, 1995)

ITC course GRS

ASTER MEASURING GROUND DISPLACEMENTCO-REGISTRATION OF OPTICALLY SENSED IMAGES - COSICORR

14http://www.tectonics.caltech.edu/slip_history/spot_coseis/index.htmlCosicorr = Sebastien Leprince

ASTER MEASURING GROUND DISPLACEMENTCO-REGISTRATION OF OPTICALLY SENSED IMAGES - COSICORR

15http://www.tectonics.caltech.edu/slip_history/spot_coseis/index.htmlMSc. M. Yaseen, ITC 2009

ASTER MEASURING GROUND DISPLACEMENTCO-REGISTRATION OF OPTICALLY SENSED IMAGES - COSICORR

16

http://www.tectonics.caltech.edu/slip_history/spot_coseis/index.htmlMSc. M. Yaseen, ITC 2009

100 ASTER scenes (Rockwell and Hofstra, 2008, Hewson et al., 2005).

Lithologic mapping (Li et al., 2007, Qiu et al., 2006, Rowan and Mars, 2003, Gomez et al., 2005, Khan et al., 2007).

Granites (Massironi et al., 2008, Watts et al., 2005),

Ophiolite sequences (Qiu et al., 2006, Khan et al., 2007)

Basement rocks (Qari et al., 2008, Gad and Kusky, 2007, Vaughan et al., 2005).

Sedimentary terrains (Pena and Abdelsalam, 2006).

Mineral exploration - geothermal (Vaughan et al., 2005)

Hydrothermal (Zhang et al., 2007, Hubbard et al., 2003, Yamaguchi and Naito, 2003, Carranza et al., 2008, Mars and Rowan, 2006, Mars and Rowan, 2010)

Evaporate systems (Kavak, 2005).

Offshore hydrocarbons (Lammoglia et al 2012)

MULTISPECTRAL ERA: ASTER

17

THE HYPERSPECTRAL ERA

181818888888181818888188881888188881818888881111111

Surface composition mapping

AVIRIS spectradistance 22km.

Surface composition mapping

HYPERSPECTRAL RSMY FIRST ENCOUNTER

21Fred Kruse: Third JPL Imaging spectroscopy WS

"There is no reason anyone would want a computer in their home.“ Ken Olson, president, chairman and founder of DEC

Now look at Hymap…NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNoooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooowwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwww lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooookkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaattttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttt HHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyymmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaappppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp………………………………………………………………………………………………………………………………………………………………………………… ASTER

HyMAp

MSc. Bedini, ITC

0 5 10 15 Km.

Image fusion product of Landsat TM and gravityData courtesy CSIRO

Intrusives = granite

Extrusives = basalt

Sediments = ocean sediments

granite

granite

Cudahy, 1999, Mapping the Panorama...

24

White mica, Al Al- -poor

Van Ruitenbeek, 2006

?

Case study Cu-Zn deposits PilbaraIntegrated interpretation of the results

Reconstruction of physico-chemical conditions

Van Ruitenbeek et al. RSE

Case study Cu-Zn deposits PilbaraIntegrated interpretation of the results

Note: Position of mineralization relative to fluid pathways. Impact on target generation!

Reconstruction of fluid pathways

Van Ruitenbeek et al. RSE

PILBARA, AUSTRALIA

Greenschist facies

Amphibolite facies

Hornblende IntChlorite+*Hornblende+*Actinolite

IntChlorite

MSc, M. Abweny, ITC 2011

28

SMECTITE-ILLITE CRYSTALLINITY AND COMPOSITION

SWIR vs. XRD• XRD: interlayer space• SWIR crystallinity

MSc,Guatame Garcia, ITC 2012

Epithermal gold systems (Crosta et al., 1998, Kruse et al., 2006, Chen et al., 2007, Rowan et al., 2000, Gersman et al., 2008, Bedini et al., 2009, Van der Meer, 2006b) Geothermal systems(Vaughan et al., 2005, Yang et al., 2001, Yang et al., 2000, Kratt et al., 2010, Hellman and Ramsey, 2004)Carlin-type systems (Rockwell and Hofstra, 2008)Archean lode (Bierwirth et al., 2002)Skarns (Windeler, 1993)Calcic skarn (Kozak et al., 2004, Rowan and Mars, 2003, Bedini, 2009)VMS (Berger et al., 2003)Dolomitization (Gaffey, 1986, Van der Meer, 1996, Windeler and Lyon, 1991)

HYPERSPECTRAL ERAGEORESOURCE EXPLORATION

29

Lithologic mapping in Arctic conditions (Harris et al., 2005)Granitic terrain (Rivard et al., 2009) Ophiolite sequence (Roy et al., 2009, Chabrillat et al., 2000, Launeau et al., 2004)Mine tailings (Choe et al., 2008, Shang et al., 2009, Riaza and Muller, 2010, Richter et al., 2008, Mars and Crowley, 2003).Oil seeps onshore (Horig et al., 2001, Kuhn et al., 2004) Oil seeps offshore (Lammoglia et al. 2012)Gas seeps (van der Meer et al., 2002, Van der Werff et al., 2006) Tar sands (Lyder et al., 2010, Rivard et al. 2010). Drill core (Gallie et al., 2002, Bolin and Moon, 2003, Brown et al., 2008). Outcrops (Ragona et al., 2006).SEBASS TIR system (Vaughan et al., 2003, Vaughan et al., 2005).

HYPERSPECTRAL ERAGEORESOURCE EXPLORATION

30

Source: Mustard, 2010

OVERVIEW OF RELEVANT MISSIONS

ESA Mars Pilot Project, 2011

What is the origin of these water bearing clay minerals on Mars: hydrothermal or weathering

FURTHER INTERPRETATION IN ‘CAVE’

ESA Mars Pilot Project, 2011

Mars general (Bibring et al., 2005, Poulet et al., 2005, Mustard et al., 2005)Mars sulfates (Wang et al., 2006, Mangold et al., 2008, Aubrey et al., 2006)Mars hydrated silicates (Mustard et al., 2008, Ehlmann et al., 2009) Mars phyllosilicates (Loizeau et al., 2007)

HYPERSPECTRAL ERAMARTIAN GEOLOGY

35

(Ashley and Abrams, 1980)

PhD Hecker, ITC 2012SEBASS data interpretation

HYPERSPECTRAL & OIL/GASLOOK WARM

37

Satellite finds oil from space!

Courtesy H. Yang, ITC PhD 1999

Geothermal Energy Industry

Photo source: Coolbaugh et al., 2002 (Brady System, Nevada)

Developing methods to characterize geothermal system dynamics, fluid pathways, link surface to subsurface expressions thus assessing heat-renewable energy potential in a context of electricity pricing and climate debate.

SPECTROSCOPY OR X-RAY DIFFRACTIONA MATTER OF WHICH STANDARD TO ACCEPT

39

Emissive

Reflective

Can be directly assessed withRemote sensing instruments

PhysicalChemical

‘My’ professor

Need for more accurate measurementsEducation of studentsAdvance of computer and sensor technologyAn hyperspectral ‘of the

quality of AVIRIS’ imager in orbit

ALEXANDER GOETZ, 2009 REVIEWRAISED 4 MAIN ISSUES OR OBSERVED 4 TRENDS

40

QUANTIFIABLE, VERIFIABLE, REPEATABLESURFACE COMPOSITIONAL MAPPING/CLASSIFICATION

41

UN

M

Spectral libraryIS data cube

Process model

Binary encoding Minimum distance

Spectral angle SID

R=aluniteB=kaoliniteG=illite

QUANTIFIABLE, VERIFIABLE, REPEATABLESURFACE COMPOSITIONAL MAPPING/CLASSIFICATION

42

UN

M

Spectral libraryIS data cube

Process model

Binary encoding Minimum distance

Spectral angle SID

R=aluniteB=kaoliniteG=illite

TRL URLSURFACE COMPOSITIONAL MAPPING

43

UN

M

Spectral libraryIS data cube

Process model43

EMPIRICAL MODELSQUANTIFIABLE (YES), REPRODUCIBLE (?)

44

Soil spectral signatures

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

300 800 1300 1800 2300

Wavelength (nm)

Rel

ativ

e re

flect

ance

Courtesy: Keith Shepherd, ICRAF

Soil organic carbon calibration

r2 = 0.940

50

100

150

0 50 100 150

Predicted value

Act

ual v

alue

Strength: mimicking field reflectanceCross comparison to field dataCater for many applications

Weakness:Engineering challenge to produce sufficient NERComputing powerCalibration complexityData redundancy per channel and per application

SWOT ‘TOO MANY BANDS’

45Picture source: Karl Staenz, CCRS

DATA CONTINUITY AND TIME SERIESTHE IMAGING SPECTROMETRY PLAN IN 1984

46Source: Goetz, 2009, RSE 119, fig 17

Hyperion •220 bands •0.4 to 2.5 μm•30 m. •7.5km strips

US/Japan ASTER Interface Meeting August 2012 Goddard, Courtesy: Mike Abrams, NASA JPL

Landsat Data Continuity Mission

Michael Abrams, JPL, 2012 ITC, Enschede

IFOV, meters

# Band

s

HySI

ASTERLandsat

CHRIS

HICO HJ-1A

HyperionHyspIRI*Hypxim

HISUIMSMI

EnMAPPRISMA

CURRENTREADYPLANNED

IFOV vs Number Bands

*Proposed instrument –Pre-decisional for planning and discussion purposes only

Shalom

IFOV

Num

ber of bands

Spectral Coverage and Swath Width VNIR SWIR TIR Swath

Landsat 4 2 1 180ASTER 3 6 5 60CHRIS 37 13HYSI 64 130Hyperion 85 135 7HJ-1A 110 50HICO 128 42MSMI 80 120 15EnMAP 85 135 30PRISMA 90 145 30HISUI 85 100 15HYPXIM-P 65 135 40 30HyspIRI* 85 135 8 145/600Shalom 90 145 30

*Proposed instrument –Pre-decisional for planning and discussion purposes onlyMichael Abrams, JPL, 2012 ITC, Enschede

CURRENTREADYPLANNED

Instrument Lifetimes

Michael Abrams, JPL, 2012 ITC, Enschede

CURRENTREADYPLANNED

hyperion

EnMAP

HyspIRI

ASTERlandsat

chris

HySI

HS-1AHICO

PRISMA

MSMI

HISUI

HypXIM

Note that lifetimes estimated are 5 years, luckily in reality instruments by far outlive their lifetime - > ASTER, 1999 onward

GMES dedicated missions: Sentinels

2011

Sentinel 1 – SAR imagingAll weather, day/night applications, interferometry

2012

Sentinel 2 – Multispectral imagingLand applications: urban, forest, agriculture,..Continuity of Landsat, SPOT

2012

Sentinel 3 – Ocean and global land monitoringWide-swath ocean color, vegetation, sea/land surface temperature, altimetry

2017+

Sentinel 4 – Geostationary atmosphericAtmospheric composition monitoring, trans-boundary pollution

2019+

Sentinel 5 – Low-orbit atmosphericAtmospheric composition monitoring(S5 Precursor launch in 2014)

GOCE17 March 2009

ADMAEOLUS

EARTH CARESWARM

November 2012

SMOS2 Nov. 2009

Cryosat8 April 2010

ESA Earth Explorer Satellites

54

Difference 3D-Feng model 3D model FengWater Surface Velocity

Ocean Dynamic Topography

Next in line: SWARM – ESA‘s magnetic fieldmission

– Swarm will provide the best-ever survey of the Earth’sgeomagnetic field and its variation in time

– Swarm will allow to gain new insights into the Earth’sinterior and climate

– Launch scheduled for October 2012

Swarm in February 2012 at ABG Ottobrunn, Germany.

Both images © ESA

Swarm: earth magnetic field

Understanding “Earth’s dynamo”in the outer core

Looking intothe compositionof the mantle

Mapping “magneticfingerprints” inEarth’s crust

A unique view inside Earth

Sensing the weak signature of the ocean currents

57

GEO = Group on Earth Observation88 Nations European Commission67 Participating Organizations

GEOSS = One VisionThe global earth observation system of systems

How is the global earth system changing? What are the primary forces of the Earth system? How does the earth system respond to natural and human-induced changes? What are the consequences of change in the earth system for human civilization? What are the consequences of processes such as urbanization and global economic development for system Earth?…………………………

THE BIG SCIENCE QUESTIONSNASA DECADAL SURVEY

58

Two remarks:Global coverage from earth observation needed with frequent revisit!Geologists unite and voice your contribution to answer these questions!!

Landsat AND mineral = 233Hyperspectral AND mineral = 472

Landsat AND vegetation = 3896Hyperspectral AND vegetation = 1723Landsat AND water = 2626Hyperspectral AND water = 1540

HOW BIG IS OUR WORLD?

59

CHALLENGESCORE LOGGING, HYPERSPECTRAL ROCK, THERMAL EMISSIVITY SPECTROSCOPY

60

SulfideTourmalineMuscoviteIlliteKaolinite

=+

= Mineralogy + texture

Terrestrial hyperspectral scanning‘virtual outcrop models’ – PhD Tobias Kurz, Uni Bergen

Hyperspectral scanning of rocksA. Agus MSc -ITC

Hyperspectral RS is accepted technology by the mining industryQuantification, verification, repeatability; in search for standards and protocolsEmpirical versus physical models

Monitoring means time series to contribute to the Big q.’s‘no single analytical technique can be used to fully deconvolvehyperspectral data in the absence of ancillary data’ - Cloutis, 1996GRS = evolution not revolution (cf InSAR)

Lord Kelvin - "There is nothing new to be discovered in physics now. All that remains is more and more precise measurement” – fewyears later Albert Einstein proposed relativity theory.

CONCLUDING THOUGHTS

61

ACKNOWLEDGEMENT

62

Michael Berger ESAMike Abrams JPLTom Cudahy CSIROGEO secretariatITC geological remote sensing groupITC MSc graduates: Abweny, Bedini, Guatame Garcia, YaseenITC PhD graduates: Van Ruitenbeek, Hecker

ENSCHEDETHE NETHERLANDSWWW.ITC.NL