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1
Sensoriamento Remoto Hiperespectral e
Multiespectral de Alta Resolução:
Princípios, Processamento de Dados e
Aplicações
Prof. Carlos Roberto de Souza Filho (IG-UNICAMP)
Perfil do curso:Princípios físicos de sensoriamento remoto; interferências atmosféricas; propriedades espectrais demateriais naturais. Noções sobre medições espectrorradiométricas com sensores portáteis e interpretaçãode dados. Sensores (orbitais e aeroportados) da região do visível, infravermelho próximo, infravermelho deondas curtas, infravermelho médio e infravermelho termal - os programas AVIRIS, HyMAP, SEBASS, HSS,HYPERION e ASTER,. Processamento de imagens multiespectrais (ASTER) e hiperespectrais (AVIRIS,
HyMAP, SEBASS, HYPERION). Exemplos de aplicação da tecnologia de S.R. e PDI em mapeamento geológicoe exploração mineral.
Bibliografia básica:
•Andrew Rencz and Robert A. Ryerson (Editors), 1999, Remote Sensing for the Earth Sciences (Manual of Remote Sensing,Vol 3), John Wiley & Sons; 3rd Edition.•Gary L. Prost, 2002, Remote Sensing for Geologists, Taylor and Francis, 2nd Edition•John A. Richards, Xiuping Jia, 2005, Remote sensing digital image analysis, Springer-Verlag, 2nd Edition.•John R Jensen, 2004, Introductory Digital Image Processing, Prentice Hall 3rd Edition.•Paulo Roberto Meneses & Joséda Silva Madeira Netto, 2002, Sensoriamento Remoto – Reflectânciados Alvos Naturais,Brasília – DF; Editora UnB, 1a Edição.•Steven Drury, 2001, Image Interpretation in Geology, Stanley Thornes Pub Ltd, 3rd Edition.•Steven M. de Jonge Freek D. van der Meer (Editors), 2004, Remote Sensing Image Analysis: Including the Spatial Domain(Remote Sensing and Digital Image Processing), Springer.•Thomas M. Lillesand, Ralph W. Kiefer, Jonathan W. Chipman, (2004), Remote Sensing and Image Interpretation, John Wiley& Sons, 5th Edition.•Freek Van der Meer, 2001, Imaging Spectroscopy: Basic Principles and Prospective Applications, KluwerAcademics.•Volume Especial da Revista Remote Sensing of Environment sobre o ASTER (Vol. 99, No. 1-2), de Dezembro de 2005.•Material bibliográfico do ASTER disponível em: http://asterweb.jpl.nasa.gov/bibliography.asp
Introduction to Spectral Remote Sensing
and Principles of Spectroscopy
• Electromagnetic Radiation
• Atmospheric windows available VNIR, SWIR, TIR
• EMR interactions with matter
• Spectroscopy
Sources: Sources: University of Campinas - Profs. Carlos Roberto de Souza Filho Lecture Notes on Remote SensingCSIRO Exploration and Mining (Australia) - Robert Hewson,Tom Cudahy and Jon HuntingtonJPL/NASA - Mike Abrams, Simon Hook - ASTER DocumentationDrury, S.A., 2001, Image Interpretation in Geology, 3rd Edition.Pontual, S., Merry, N. and Gamson, P., 1997, Spectral Interpretation Field Manual (G-MEX).
REMOTE SENSING OF E ARTH RESOURCES
(i) Source
(iii)Interaction with
surface materials
(ii) Atmos phere
(iv)Retransmission
to the atmosphere
(v)Sensors
USERS
(vi) Data (viii) Finalproducts
(vii) Interpretationand analysis
Analog ic
Digital
Visual
Digital
DATA ACQUISION DATA ANALYSIS
The Electromagnetic Spectrum
Radiowaves
Microvave
Thermal
Infrared (>3μm; <1mm)
Infrared
Near (0.78-1.5μm)
Short (1.5-3 μm)
e Mid (3-5μm)
Ultraviolet(0.28-0,38μm)
X Rays
Rays
Wavelength (μm)
UV
Visible
IV
10-6
0.38 0.780.5 0.6
RedGreenBlue
10-410-5 10-3 10-2 10-1 1 10 10210-7 103 107104 106105 108
1mm 1m
c = f * λ (wavetheory)
E = h * f (quantum theory)
E = h. c /
Electromagnetic EnergyElectromagnetic SpectrumElectromagnetic EnergyElectromagnetic Spectrum
Electromagnetic SpectrumElectromagnetic Spectrum
WavelengthWavelength(nm)(nm)
CosmicCosmicRaysRays
GammaGammaRaysRays
XXRaysRays
MicrowavesMicrowaves(Radar)(Radar)
Radio & TelevisionRadio & TelevisionWavesWavesUVUV
101055 101066 101077 101088 101099 10101010 10101111 1010121210101110101010--111010--221010--331010--441010--55
ShorterWavelengthsHigh Energy
ShorterWavelengthsHigh Energy
LongerWavelengthsLow Energy
LongerWavelengthsLow Energy
V / NIR / SWIR /V / NIR / SWIR /MWIR / LWIRMWIR / LWIR
Optical RegionOptical Region
400400 1400014000
400
0.4
400
0.4
14000
14.0
14000
14.0
1500
1.5
1500
1.5
3000
3.0
3000
3.0
5000
5.0
5000
5.0
700
0.7
700
0.7
NIRNIR MWIRMWIRSWIRSWIRRRGG LWIRLWIRBB LWIRLWIRWavelength
(nm)(μm)
Wavelength(nm)(μm)
EmittedEmittedEnergyEnergy
ReflectedReflectedEnergyEnergy
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c = f * λ (wavetheory) E = h * f (quantum theory) E = h. c /
Reflected vs. Emitted EnergyReflected vs. Emitted Energy
1
104
1000
100
10
0.1 1 1053 7
I r r a d i a n c e
( W - m - 2 - u m - 1 )
Wavelength (µm)
EarthEmission
(100%)
EarthReflectance
(100%)
r a d i an t ex i t an c e ( W-m-2 - um-1 )
MWIR
Ass umes noatmosphere
.4 .7
Sampling the SpectrumSampling the Spectrum
NIR SWIR MWIR LWIR
400 nm400 nm 700700 15001500 30003000
RRBB
50005000 14000 nm
GG
Panchromatic: one very wide bandPanchromatic: one very wide bandLOWLOW
Multispectral: several to tens of bandsMultispectral: several to tens of bandsMEDMED
Hyperspectral : hundreds of narrow bandsHyperspectral : hundreds of narrow bandsHIGHHIGH
Blackbody radiation curve atthe sun’s temperature
Blackbody radiation curve atincadescentlamp temperature
Blackbody radiation curveat the Earth’s temperature
(373°K = H2O bo iling)
WAVELENGTH (μ
m)
0.1 0.2 10.5 2 105 20 50 100
101
1
102
103
104
106
105
107
108
109
visble radiante energy band
S p e c
t r a l r a
d i a n
t e x c
i t a n c e
( W m - 2
m - 1 )
1000°K
6000°K
200°K
300°K
2000°K
3000°K
500°K
4000°K
ENERGY DISTRIBUTION CURVES FOR BLACKBODIES AT
TEMPERATURES BETWEEN 200°K AND 6000°K
Areas under curves correspond to
the total amount of emitted energy
in all wavelengths
Stefan-Boltzmann´s Law.
Higher T => >> amount of emittedenergy
M = τ . T4
τ = Stefan Boltzmann’scte. = 5,67 . 10-8 W.m2
Wien´s Displacement Law
λmax = A/T
A = 2898μm . °K
600 K-1000K - fires
ALVO
TRANSMITIDA,REFLETIDA
TRANSMITIDA,REFLETIDA ABSORVIDA ABSORVIDA
TRANSMITIDATRANSMITIDA
ABSORVIDA ABSORVIDAESPALHADAESPALHADA
ESPALHADAESPALHADAESPALHADAESPALHADA
TODA ENERGIA TRANSMITIDA ATRAVESSA A ATMOSFERA E ALCANÇA O SENSOR SEM
SOFRER ALTERAÇÃO
TODA ENERGIA TRANSMITIDA ATRAVESSA A ATMOSFERA E ALCANÇA O SENSOR SEM
SOFRER ALTERAÇÃO
ENERGIA ABSORVIDAESQUENTA A ATMOSFERA
OU É RE-EMITIDA COM SUASCARACTERÍSTICAS
ESPECTRAIS ALTERADAS
ENERGIA ABSORVIDAESQUENTA A ATMOSFERA
OU É RE-EMITIDA COM SUASCARACTERÍSTICAS
ESPECTRAIS ALTERADAS
ScatteredScattered
Scattered
Absorbed
Absorbed
Transmitted
Transmitted
TargetReflected Image from the NASA Langley Research Center, Atmospheric Sciences Division.
http://asd-www.larc.nasa.gov/erbe/ASDerbe.html
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Electromagnetic Energy Atmospher ic Absorp tion
Electromagnetic Energy Atmospher ic Abs orpt ion
Transmitância Atmosférica
UV NIRVIS SWIR MWIR LWIR
T r a n s m
i t â n c
i a T o
t a l ( % )
BAIXA
ALTA
Comprimento de Onda (μm)
1.0
0.4
0.6
0.8
0.2
0.00.4 0.5 0.6 0.8 1 432 5 10
Absor ção Atmos férica
Energia (luz) Transmitida
WAVELENGTH (μm)
T o t a l T r a n s m i s s i o n ( % )
Atmosp heric Absor ption
Energy(lig ht) transmitted
LOW
HIGH
Atmospheric transmittance (windows)
Atmospheric Windows
“Reflected Wavelengths” “Emitted Wavelengths”
A t m o s p
h e r i c T r a n s m
i s s
i o n
TypesTypes ofof EnergyEnergy RecordedRecorded byby SensorsSensorsandand RespectiveRespective RegionsRegions ofof the Spectrumthe Spectrum
Sensor Sun
Incoming Solar EnergyReflectedReflected SolarSolar EnergyEnergy
Sensor
EmittedEmitted thermalthermalenergyenergy
Atmosp here
SAR Antenna
BackscatteredBackscattered energyenergypulsepulse
Surface
MicrowavesMicrowaves
VISVIS – – NIRNIR -- SWIRSWIR TIRTIR
InformationInformation aboutabout chemicalchemical compositioncomposition
ofof surfacesurface materialsmaterials
InformationInformation aboutabout physicalphysical characteristicscharacteristics
((geometrygeometry andand shapeshape) of) of surfacesurface materialsmaterials
Atmosp here
U.V VIS IR
Energy
0.3μm 1μm 10μm 100μm 1mm 1m
Wavelength
Sun’s Energy ( 6000°K)
Earth’s Energy (300°K)
0.3μm 1μm 10μm 100μm 1mm 1m
Transmittance
SOURCES
ENERGY
ATMOSPHERICTRANSMITANCE
0 %
100 %
U.V VIS IR
HumanEye
PhotographyThermalScanners
Mult ispect ral Scanners
Radar andPassive
Microwave
0.3μm 1μm 10μm 100μm 1mm 1m
BlockedEnergy
Wavelength
Wavelength
Interaction of energy and objectsInteraction of energy and objects
Transmitted EnergyTransmitted Energy
Abso rbed Ener gy Abso rbed Energ y
Reflected EnergyReflected EnergyVV--MWIRMWIR
Emitted EnergyEmitted EnergyMWMW--LWIRLWIR
Energy Balance Equation: EI (λ) = ER(λ
) + E A(λ
) + ET(λ
)Energy Balance Equation: EI (λ) = ER(λ) + E A(λ) + ET(λ)
Incident EnergyIncident Energy
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Reflected EnergyReflected Energy
• The manner in which a material reflects energy is primarily afunction of the optical properties and surface roughness of thefeature.
• Most objects are diffuse reflectors
• The manner in which a material reflects energy is primarily afunction of the optical properties and surface roughness of thefeature.
• Most objects are diffuse reflectors
Specular Reflectance
Specular Reflectance
DiffuseReflectance
DiffuseReflectance
Angl e of Inc iden ce = Ang le of Ref lectan ce Angl e of Inc iden ce = Ang le of Reflect ance
SmoothSurface
Rough
Surface(Microscopic)
EnergyScattered in
All Direct ions
Reflectance: Is the ratio of reflected energy to incident energy. Varies with wavelength
Function of the molecular properties of the material.
Reflectance Signature: A plot of the reflectance of a material as a
function of wavelength.
Reflectance: Is the ratio of reflected energy to incident energy. Varies with wavelength
Function of the molecular properties of the material.
Reflectance Signature: A plot of the reflectance of a material as a
function of wavelength.
Reflected EnergyReflected Energy
Red brickKaoliniteSandy loamConcreteGrass
All sol ids and li qui ds
have reflectancesignatures that
potentially can be
used to identify them.
All sol ids and li qui dshave reflectance
signatures that
potentially can be
used to identify them.
Emissive EnergyEmissive Energy• Emissiv ity- is a measure of how efficiently an object radiates energy compared to a
blackbody at the same temperature.
Varies with wavelength
Function of the molecular properties of the material.
• EmissivitySignature - A plot of emissivity as a function of wavelength. All
materials have emissivity signatures that potentially can be used to identify them.
• Emissiv ity- is a measure of how efficiently an object radiates energy compared to a
blackbody at the same temperature.
Varies with wavelength
Function of the molecular properties of the material.
• EmissivitySignature - A plot of emissivity as a function of wavelength. All
materials have emissivity signatures that potentially can be used to identify them.
Blackbody
Graybody
Selectiveemitter (emissivitysignature)
Selectiveemitter (emissivitysignature)
E m i s s i v i t y
0
0.5
1.0
Wavelength
Red brick Kaolinite
Grass Water
Black paint Concrete
ABS.
Fe
2+
Fe3+ REDPEAK
ABSORPTION ALTERED ROCKS SHOWHIGH REFLECTANCE
IN THIS REGION
(OH-) HYDROXYLSMINERALS (Clays),-
CARBONATES, MICAS,CHLORITE, AMPHIBOLES
DOMINANTMINERALOGICAL
EFFECTS
VEGETATIONREFLECTANCE
LEAF WATER CONTENTCELL STRUCTURELEAFPIGMENTS
TM7
SWIR ~ 2.2mm
REFLEC.PEAK
SWIR~ 1.6mm
TM5
H2O
1.4mm
WATER ABSORPTION
1.93mm
H2O
FRACA ABSORÇÃO
DE ÁGUA
REFLECTANCEPEAKVIS ~ 0.54mm
50
30
20
10
00.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.62.4
400 600 800 1000 1200 1400 1600 1800 2000 2200 26002400
WATER ABSORPTION
BANDS
2.6mm-2.73mm
BL-GREEN- RED.
1.30.72
R E F L E C T A N C E ( % )
CHLOROPHYL ABSORPTIONS
0.45μm 0.65μm
0.38 3.0
nmmm l
H2O
H2O
1.1μmTM4
0.96μm
0.7mm
( “ re d e dg e” )40
VISIBLE NEAR INFRARED SHORTWAVE INFRARED
Al-OH Mg-OH CO3=
Fe3+Fe2+
A T M O S P H E R I C
A B S O R P T I O N
VEGETATIONVEGETATION SOILSOIL WATERWATER
1 2 3
MSS74 5 6
A T M O S P H E R I C
A B S O R P T I O N
REFLEC.PEAK
WATER ABSORPTION
Vegetation SpectroscopyVegetation Spectroscopy
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outrospigmentos
R e
f l e c
t â n c
i a águaestrutura
do dossel eestrutura foliar
água
componen tes bioquím icos:proteínalignina
celulose
chlorophyland otherpigments
water dossel
structureleafcell structure
water
biochemicalcomponents :proteinlignin
cellulose
Wavelength ( m)
Green
Vegetation Altered
Vegetation
Soil
R e
f l e c
t a n c e
( % )
Wavelength ( m)
R e
f l e c
t a n c e
( % )
SPECTRAL BEHAVIOUR OF VEGETATION
R e f l e c t â n c i a
ALTA
BAIXA
Pine
Abet o Asp en
GramaGrass
Comprimento de Onda (μm)
Curvasde Reflectância Espectralde Alguns Tipos de Vegetação
Visível Infraverm.Próximo0.4 0.9μm0.6 0.70.5
Reflectance Spectra of different types of vegetation
Wavelength
Visible Near Infrared
R e
f l e c
t a n c e
90
80
70
60
50
40
30
20
10
0
0.4 0.5 0.6 0.7 0.8 0.9
Pine
Oak
Asp en
R e
f l e c
t a n c e
( % )
Wavelength (μm)
Grass
VISIBLE NIR
TM1 TM2 TM3 TM4
Influência (i) do conteúdo de
clorofila; (ii) da forma, área e
número de folhas e, (iii) da
estrutura geral (celulose),nas
propriedades espectrais de
plantas. Embora exibam
propriedades similares no
espectro visível, as plantas
podem ser facilmente
distinguidas pela sua
reflectância no infravermelho
próximo (NIR).
Effects of (i) chlorophyll content;
(ii) of the shape, area and
number of leaves and; (iii) cell
structure of celulose on the
spectral properties of plants.
Although plants show similarsignatures in the visible
spectrum, they may be
distinguished in the NIR.Reflectance curves 1-5 show pro gressive stages of colo r changes
in leaves, previously to the Autumn. The colors varies from green,
to yellow-green, to red-green, to maroon, until they dry completely.
60
0
20
40
0.4 0.6 0.8 1.0 1.2
Wavelength (microns)
R e
f l e c
t a n c e
( % )
TM2 TM3 TM4TM1
Visible Near Infrared
0.7 1.10.90.5
Mineral SpectroscopyMineral Spectroscopy
InteractionInteraction BetweenBetween ElectromagneticElectromagneticEnergyEnergy andand SurfaceSurface MaterialsMaterials
RocksRocks // MineralsMinerals
Energy Source(Sun) Sensor
Photons reflected/emitedtowards thesensor
after interact ingwithsurfacematerials
((mineralsminerals andand rocksrocks))
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Spectral Regions Relevant to GeologySpectral Regions Relevant to Geology
• Visible and near infrared (VNIR) – 400 - 1000 nm
– Iron oxides (Hematite, Goethite, Jarosite) – REEs
– Vegetation
• Shortwave Infrared (SWIR) – 1000 - 2500 nm
– (OH) bearing minerals
• Clays, phyllosilicates, amphiboles, sulphates
– Carbonates
• Thermal Infrared (TIR) – 8000 - 12000 nm
– Silicates: quartz, feldspars, garnets, pyroxenes
– carbonates
PhenomenaPhenomena ResponsibleResponsible ForFor SpectralSpectral BehaviorBehaviorofof MineralsMinerals andand RocksRocks
10-6 10-410-5 10-3 10-2 10-1 1 10 10210-7 103 107104 106105 108
1mm 1m
VisibleVisible
Electronic
Transitions
ElectronicElectronic
TransitionsTransitions
VibracionalVibracional
TransitionsTransitions
Nuclear Nuclear
TransitionsTransitions
SpinSpin
OrientationOrientation
TV / RadioTV / Radio
MicrowavesMicrowaves
Thermal InfraredThermal Infrared
(>3µm - <1mm)
InfraredInfraredNear (0,78-1,5 µm)
Shortwave (1,5-3 µm)UltravioletUltraviolet
(0,28-0,38 µm)
XX--RaysRays
--RaysRays
Wavelength (µm)
Causes of SpectralCauses of Spectral Behaviour Behaviour
• Absorption coefficient – wavelength-dependent
– compositional
– electronic and/or vibrational processes
• Scattering effects – Diffuse and/or specular reflection
– Volume and/or surface scattering – Single and/or multiple scattering
– wavelength-dependent
Spectral Regions Relevant to GeologySpectral Regions Relevant to Geology
• Visible and near infrared (VNIR) – 400 - 1000 nm
– Iron oxides (Hematite, Goethite, Jarosite)
– REEs
– Vegetation
• Shortwave Infrared (SWIR) – 1000 - 2500 nm
– (OH) bearing minerals
• Clays, phyllosilicates, amphiboles, sulphates
– Carbonates
• Thermal Infrared (TIR) – 8000 - 12000 nm
– Silicates: quartz, feldspars, garnets, pyroxenes
– carbonates
SPECTRAL SIGNATURESSPECTRAL SIGNATURES
1. Mineralogy
2. Cation Composition
3. Crystallinity (disorder)
4. Water (free, absorbed, structural)
5. Particle size
6. Orientation
7. Mixtures
8. Organic matter
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Physical BasisPhysical Basis
• Minerals exhibit diagnostic features at various
wavelengths which provide a means for their
remote identification.
• These features are produced by electronic or
vibrational processes resulting from the
interaction of electromagnetic energy with the
atoms and molecules which comprise the
minerals that make up a rock.
• The different processes require different amounts of
energy to proceed, and therefore are manifest in
different wavelength regions.
• Electronic processes require the most energy and
results in spectral features at visible to near infrared
wavelengths.
• Fundamental vibrational processes require less
energy, and occur beyond 2.5 um. Between 0.5 and 2.5
um there is an overlap of features due to both
processes.
Physical BasisPhysical Basis
• Electronic Processes
– Crystal field effects, Charge Transfer
Bands, Conduction Bands, Color Centers
• Vibration Processes
– Fundamentals, Water and Hydroxl,Carbonates Other groups, e.g phosphates
Physical BasisPhysical Basis Crystal Field EffectsCrystal Field Effects
• Most common electronic process, seen in
spectra of transition elements.
• Electron moves from lower to higher energy
state by photon absorption.
• Crystal field varies with crystal structure
allowing mineral identification.
Reflectance spectra of two olivines, showing change in band shape and position
with composition. 1 um band due to crystal field absorption of Fe 2+.
Fo - forsterite (Mg2SiO4) in the forsterite-fayalite (Fe22+ SiO4) solid solution
series. Fo29 has an FeO content of 54% while Fo91 has an FeO content of 8%.
The 1 um band position varies from 1.08 um at Fo 10 to 1.05 um at Fo 90.
From Clark (1999).
Charge Transfer AbsorptionsCharge Transfer Absorptions
• Inter-element transitions where the
absorption of a photon causes an electron to
move between ions or between ions and
ligands.
• Very strong compared with crystal field
effects. Typically centered in the ultra violet
with wings extending into the visible.
• Cause of red color in iron oxide.
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Reflectance spectra of iron oxides: hematite (Fe2O3) and goethite (FeOOH). The intense absorption
around 0.4 um is due to charge transfer. The 0.9 and 0.86 u m absorption features are due to transitions
(crystal field absorption).
From Clark (1999).
Vibrational Vibrational ProcessesProcesses
• The bonds in a molecule or crystal lattice act like
springs with attached weights. The frequency of
the vibration depends on the strength of the bond
and mass of the elements.
• For a molecule with N atoms, there are 3N -6
normal modes of vibration called fundamentals.
• The fundamental vibration modes of silicate
minerals occur near 10 um.
Silicate MineralsSilicate Minerals
0.8
1
1.2
1.4
1.6
1.8
E
m i s s i v i t y ( O f f s e t f o r c l a r i t y )
8 10 12 14Wavelength (micrometers)
Quartz
Olivine
Hornblende
Augite
Muscovite
Albite
0
0.2
Vibrational Vibrational ProcessesProcesses
• Also vibrations can occur at multiples of
the fundamental frequency.
• The additional vibrations are called
overtones when they involve multiplesof a single fundamental mode, and
combinations when they involve
different modes of vibration.
Reflectance spectra showing vibrational bands
due to OH, and H2O (from Clark 1999).Reflectance spectra showing vibrational
bands due to OH, CO3 and H2O (from Clark
1999).
Radiance andRadiance and
EmissivityEmissivity
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PlancksPlancks FormulaFormula
⎥⎦
⎤⎢⎣
⎡−⎟⎟
⎠
⎞⎜⎜⎝
⎛ =
1exp 25
1
T
C C
M
λ λ
λ
where:
blackbody spectral exitance.
= wavelength.
absolute temperature.
first radiation constant.
second radiation constant.
λ
λ
M
T
C
C
=
=
=
=1
2
0
20
40
60
80
100
R a d i a n c e ( W / m * m * m ) / 1 . 0 e 6
4 6 8 10 12 14 16 18 20Wavelength (micrometers)
450K
350K
273.15K
SpectralSpectral EmissivityEmissivity
Materials are not perfect blackbodies, but instead emit radiation in
acordance with their own characteristics. The ability of a material
to emit radiation can be expressed as the ratio of the spectral
radiance of a material to that of a blackbody at the same
temperature. This ratio is termed the spectral emissivity:
λ λ λ ε = L L( (Material) / B lackbody)
SpectralSpectral EmissivityEmissivity (cont.)(cont.)
The most intense absorption features in the
spectral of all silicates occur near 10 µm in theregion referred to as the Si-O stretching region or
reststrahlen band.
The emissivity minimum occurs at relatively shortwavelengths (8.5 µm) for framework silicates (quartz,feldspar) and progressively longer wavelengths for
silicates having sheet, chain and isolated SiO4
tetrahedra.
Gypsum
Quartz
Sinter
Alunite
Kaolinite
Montmorillonite
Jarosite
Dolomite
Calcite
E m i s s i v i t y ( o f f s e t f o r c l a r i t y )
Spectral Analysis
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Sources of Spectral FeaturesSources of Spectral Features -- VNIR VNIR
• Electronic processes which involve the transfer
of electrons from lower to higher energy states
within electron orbits (crystal field) or from the
ligand to the cation (charge transfer)
• Minerals included in this group:
– Hematite, Goethite, Jarosite
Hematite
Jarosite
Goethite
Wavelength (μ
m)
R e
f l e c
t a n c e
( % )
VNIR spectra of key Fe-bearing minerals
0.4 1.4
Crystal Field
Charge transfer adsorption
0,90-0,92um
0,86-0,92um0,65um
REE-bearing VNIR SpectraREE-bearing VNIR Spectra
• crystal fieldeffects
VibrationalVibrational TransitionsTransitions (SWIR)(SWIR)
• Phenomena that occur at molecular levels, caused by molecular vibrations and their effects over the bonds
between their atoms (stretch & bend). The vibrations that matter to SWIR remote sensing are not the main
ones (called fundamentalsfundamentals), but the secondary ones, called overtonesovertones and combination tonescombination tones.
• They occur generally between 1,2 a 5,0 µm and are typical of materials that contain:
•• OO--HH-- , H2O, CO32-, PO4
3- and BO33-
• The most common absorption feature of this type in geologic materials is due to OO--HH-- (hydroxylhydroxyl), common in
several minerals. The exactλ
of the feature depends on the place within the molecule where thehydroxilis located and also on the strength of the bond . These characteristics may be associated with specificfeatures, used to identify the presence of several types of minerals through the analysis of theirreflectance spectra.
• Minerals that have spectral absorption features due to vibrational transitions:
– kaolinite/dickite/haloysite - chlorites (Mg – Fe)
– pyrophyllite - biotite/phlogopite
– illite/sericite/muscovite - amphiboles (actinolite/tremolite/hornblende)
– smectites - carbonates (calcite/dolomite/siderite/magnesite/ankerite)
– Mg-clays - sulphates (alunite, gipsum, jarosite)
– chalco-silicates (epidote) - Mg-phylosilicates(talc, serpentine)
– zeolites (natrolite) - Ammonium-bearing minerals (buddingtonite, NH4-illite/sericite)
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Sources of Spectral FeaturesSources of Spectral Features -- SWIR SWIR
• Vibrational processes related to OH – Di-octahedral and Tri-octahedral
• Fundamental stretches (2.7 - 3.0 um region)
• Fundamental bends (9.0 - 11.0 um region)
• Overtone and combination tones (SWIR active)
• 1400 nm feature (1st overtone of OH
fundamental stretching vibration)
• 2000-2500 nm features (combination of
stretching and bending vibrations)
SWIR Spectra of Minerals
Wavelength (μm)
R e f l e c t a n c e ( % )
Silicates -> ions (OH-)
+
(Mg-OH e Al-OH,...)
vibrational process
bond-bending
combinations
Absorption features at
2.2 - 2.3 m.
Micas e Clay Minerals
Kaolinite
Muscovite
Montmorillonite
SWIR Spectra of Minerals
Wavelength (μm)
R e f l e c t a n c e ( % )
vibrational process
(OH-) and H2O
in the mineral
crystalline structure
Absorption features
at 0.94 , 1.14 , 1.4 and
1.9 μm.Minerals with OH- or H2O molecules
Gipsum
Montmorillonite
HYDROXYL MINERAL GROUPS- Mineral Absorption Bands in the2000 - 2500 nm region
Al(OH) 2160 - 2170 nm – Pyrophyllite, Alunite
Al(OH) 2180 - 2228 nm – Halloysite, Kaolin ite, Dickite, Nacrite (doublets at ~1.4 and ~2.2μm) – Muscovit e, Illite, NH4-Illite (single, symmetric absorption features at~1.4 and ~2.2μm) – Montmorillonite, Palygorskite (asymmetric absorption feature at~1.4μm)
“Fe(OH)” 2230 -2260 nm – FeOH Chlorite, FeOH Biotite
“ Fe(OH)” & Al (OH) 2260 - 2298 nm – Jarosite, Nontronite & Gibbsite
• “ Fe-Mg(OH)” 2300 - 2330 nm – Phlogop ite I and II, Mg-Chlorite, Hornblende (edenite), Actinol ite/Tremolite, Talc, Serpentine (antigorite), Saponite
• “Fe-Mg(OH)” 2330 - 2360 nm – Epidote, Mg-Fe Chlorite, Fe-Chlorite, Biotite, Fe-Biotite, Hornblende
• Si (OH) 2240 nm (b ro ad ) – Opaline silica
HYDROXYL MINERAL GROUPS- Mineral Absorption Bands in the
2000 - 2500 nm region Significance of (OH)Significance of (OH)--Bearing MineralsBearing Minerals
• Mineralised environments: ie. alteration zonation (mica,chlorite, pyrophyllite, sulphates etc)
• Primary rock types (mica, amphiboles, chlorite, serpentineetc.)
• Weathering regimes and processes (kaolinite and illitechemistry, smectites, gibbsite, sulphates etc)
• Fluid composition, temperature/pressure
– e.g. Al/Mg-Fe substitution
– e.g. High temperature species: Pyrophyllite, Topaz, Dickite, etc.
– e.g. Crystallinity: Illite and kaolinite
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Al Al --(OH) Mineral Spectra(OH) Mineral Spectra
• Most have their major combination bandabsorptions between 2160 and 2228 nm
• Some (kaolinites) have doublets in this range
• Many also have smaller secondary featuresbetween 2300 and 2400 nm
• Minerals include in this group:
– Pyrophyllite Alunite Halloysite
– Kaolinite Dickite Nacrite
– Muscovite Illite Montmorillonite
– Palygorskite
Al(OH) Mineral Spectra Al(OH) Mineral Spectra2160-2228 nm
Montmorillonite
Illite
Kaolinite
Pyrophyllite
R e f l e c t a n c e ( S p e c t r a O f f s e t f o r C l a
r i t y )
Al(OH) Mineral Spectra (zoom) Al(OH) Mineral Spectra (zoom)
Montmorillonite
Illite
Kaolinite
Pyrophyllite
R e f l e c t a n c
e ( S p e c t r a O f f s e t f o r C l a r i t y )
Kaolinite Group SpectraKaolinite Group Spectra
• Kaolinite (Kandite) Group: – Kaolinite
– Dickite
– Halloysite
– Nacrite
• All tend to havediagnostic doubletstructures near 1400
and 2200 nm
Kaolinite
Halloysite
Dickite
R e f l e c t a n c e
• Different 1400 nm doubletspacings
• Different absorption geometry between2160 and 2210 nm
Al-OH (Kaolin) Spectra Al Al--OH (Kaolin) SpectraOH (Kaolin) Spectra Hydroxyl Mineral Absorption Bands inHydroxyl Mineral Absorption Bands inthe 2000the 2000 -- 2500 nm region2500 nm region
• Al(OH) 2170 - 2210 nm – Topaz, Pyrophyllite, Kaolinite, Montmorillonite, Muscovite,
Illite
•• Fe(OH)Fe(OH) 22402240 -- 2320 nm2320 nm – – JarositeJarosite,, NontroniteNontronite,, SaponiteSaponite,, HectoriteHectorite
• Mg(OH) 2300 - 2400 nm – Chlorite, Talc, Epidote, Amphibole, Antigorite, Biotite,
Phlogopite
• Si(OH) 2240 nm (broad) – Opaline silica
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Fe(OH) and Fe/Mg(OH) MineralFe(OH) and Fe/Mg(OH) MineralSpectraSpectra -- II
• Have their major combination band absorptions between2240 and 2320 nm, between the Al(OH) and Mg(OH) regions
• Minerals included in this group:
– Saponite (Mg,Fe) Nontronite (Fe) Hectorite (Li,Mg)
– Jarosite (Fe) Fe-rich illite
• The first three are smectites and have deep water bandsnear 1900 nm
Fe(OH) Mineral Spectra (2240 - 2320 nm)Fe(OH) Mineral Spectra (2240 - 2320 nm)
Hydroxyl Mineral Absorption Bands inHydroxyl Mineral Absorption Bands inthe 2000the 2000 -- 2500 nm region2500 nm region
• Al(OH) 2170 - 2210 nm – Topaz, Pyrophyllite, Kaolinite, Montmorillonite, Muscovite,
Illite
• Fe(OH) 2250 - 2300 nm – Jarosite, Nontronite, Saponite, Hectorite
•• Mg(OH)Mg(OH) 23002300 -- 2400 nm2400 nm – – Chlorite, Talc,Chlorite, Talc, EpidoteEpidote, Amphibole,, Amphibole, Antigorite Antigorite,, BiotiteBiotite,, PhlogopitePhlogopite
• Si(OH) 2240 nm (broad) – Opaline silica
MgMg--(OH) Geol. Significance(OH) Geol. Significance
• Weathering and alteration products of mafic rocks
• Components of propyllitic alteration zones
• Weathering components of kimberlitic rocks
• Secondary biotite in porphyry alteration systems
MgMg--(OH) Mineral Spectra(OH) Mineral Spectra -- II
• Have their major combination bandabsorptions between 2300 and 2400 nm
• Many have two features in this range
• Some have strong secondary featuresnear 2260 nm (believed to be related toiron)
• Minerals included in this group:
– Amphiboles Talc Chlorites
– Epidote Phlogopite Biotite
– Anthophyllite Antigorite
Mg(OH) Mineral SpectraMg(OH) Mineral Spectra2300-2400 nm
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Mg(OH) Mineral SpectraMg(OH) Mineral Spectra Chlorite SWIR SpectraChlorite SWIR Spectra
Wavelengths of Chlorite features vary with Mg / Fe composition.
Note in this plot the 1900 nm water band does not change but
all the other chlorite features vary as these two chlorites havedifferent Mg / Fe ratios.
Fe absorption near1100 nm causes variable
gradients in this region
Hydroxyl Mineral Absorption Bands in the2000 - 2500 nm region
• Al(OH) 2170 - 2210 nm – Topaz, Pyrophyllite, Kaolinite, Montmorillonite, Muscovite,
Illite
• Fe(OH) 2250 - 2300 nm – Jarosite, Nontronite, Saponite, Hectorite
•• Mg(OH)Mg(OH) 23002300 -- 2400 nm2400 nm – – Chlorite, Talc,Chlorite, Talc, EpidoteEpidote, Amphibole,, Amphibole, Antigorite Antigorite,, BiotiteBiotite,, PhlogopitePhlogopite
• Si(OH) 2240 nm (broad) – Opaline silica
Si(OH) Mineral Spectra
• Have a very broad feature near 2240-2250 nm
• Sometimes also associated with deepwater bands near 1430 and 1930 nm
• Minerals included in this group
– Opaline silica
Si(OH) Mineral SpectraSi(OH) Mineral Spectra2240 nm
Hydrothermal opaline silicaspectra from Cuprite Nevada
Other Minerals active in the SWIROther Minerals active in the SWIR
• Carbonates
• Sulphates
• NH4 minerals
–buddingtonite
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Carbonates – Geol. Significance
• Zonation possibly indicative of proximityto base metal carbonate-gold systems – Distal to porphyry source & cool
• (Fe) Siderite
• (Mn) Rhodochrosite
• (MnMg) Kutnahorite
• (MgCaFe) Ankerite
• (MgCa) Dolomite
• (CaMg) Mg-Calcite
• (Ca) Calcite
– Proximal to porphyry & hot
Carbonate SWIR Spectra
• Major 2300 - 2400 nm feature• Minerals included in this group
– Calcite, Dolomite, Magnesite, Siderite, Ankerite
• Ferroan species show broad absorptionnear 1000 nm
• Many carbonates also have a smallabsorption feature near 2000 nm
Carbonate SWIR SpectraCarbonate SWIR Spectra2300-2400 n m
Carbonate SWIR SpectraCarbonate SWIR Spectra
Mg Ca
Sulphate Mineral SpectraSulphate Mineral SpectraSulphates have their (OH) bands inrelatively unique positions and are
easily interpreted
Ammonium Mineral SWIR Spectra Ammonium Mineral SWIR Spectra
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• VNIR spectral effects ⇒ changes in iron oxide unit cell
• Al3+ substitution in Hematite and Goethite – Al3+ smaller cation than Fe - distorts unit cell
– Up to 33% Al substitution for Fe in Goethite
– Broadens and shifts absorption to longer wavelengths
– 900 nm crystal field absorption - 39 nm shift
• Mn2+ and Fe2+ substitution for Fe3+ in Maghemite – Broad conduction band - reduces albedo
– Ref: Morris et. al. (1985)
Mineral ChemistryMineral Chemistry -- Spectra (1)Spectra (1) Mineral ChemistryMineral Chemistry -- Spectra (2)Spectra (2)
• SWIR Absorptions related to vibration of
octahedrally coordinated atoms (Al3+, Fe2+,Fe3+, Ca2+, Mg2+, Cr3+, Ti4+, vacancies)
• Tschermak substitution (e.g. white micas)
– Tetrahedral Si4+ for Al3+
– Octahedral Mg2+ or Fe2+ for Al3+
– Increase interlayer cations K, Na, Ca
• Cation substitution (Al, Fe and Mg) in chlorite,
biotite, phlogopite
– Mg number (Fe:Mg ratio)
White MicaChemistry
Muscovite
PhengitePhengite
(Mg,Fe)(Mg,Fe)octoct SiSitettet == AlAloctoct AlAl tettet
Wavelength (nm)
4.004.00
3.403.40
3.603.60
3.803.80
3.203.20
3.003.00
21902190 22052205 22102210 2215221521952195 22002200
RRIIIIII == AlAloctoct + V + Cr+ V + Cr
muscovitemuscovite
phengitephengite
R I I I ( m o l )
From Scott and Yang, 1997
• Tschermak substitution
• Longer λ => less Al, more Si anddivalent cations (Mg and Fe++)
Chlorite Spectral CharacteristicsChlorite Spectral Characteristics
Compositional Effects - IIICompositional EffectsCompositional Effects -- IIIIIIChlorites
Wavelength of FeOH absorption versus Mg number
y = -16.243x + 2261.6
R 2 = 0.9011
2242
2244
2246
2248
2250
2252
2254
2256
2258
2260
2262
0 0.2 0.4 0.6 0.8 1
M g N umbe r
W a v e l e n g t h ( n m )
Compositional Effects - IVCompositional EffectsCompositional Effects
--
IVIV
Chlorites
Wavelength of M gOH absorption versus Mg number
y = -41.2x + 2365.5
R 2 = 0.9192
2320
2325
2330
2335
2340
2345
2350
2355
2360
2365
0 0.2 0.4 0.6 0.8 1
M g N umbe r
W a v e l e n g t h ( n m )
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Compositional Effects -VCompositional EffectsCompositional Effects --VV
Biotites and Phlogopites
Wavelength of MgOH absorption versus Mg number
R 2 = 0.9071
y = -41.184x + 2361.4
2315
2320
2325
2330
2335
2340
2345
2350
2355
2360
2365
2370
0 0.2 0.4 0.6 0.8 1
M g N u mb er
W a v e l e n g t h ( n m )
CrystallinityCrystallinity
• Ordered versus disordered kaolinites
– More ordered samples produce stronger, sharper
absorptions: doublets are more sharply defined
– The Hinckley Index – usually clear correlation
– Other Kaolin family variations
• (Halloysite, Kaolinite, Dickite)
• Ordered versus disordered illite / muscovites
– 1M, 2M & 2T illites
Kaolinite Hinckley Index SeriesKaolinite Hinckley Index Series Water EffectsWater Effects
• Water is SWIR active
– reduces overall brightness
– reduces spectral contrast of mineral features
– may even completely obscure features
• Water is a blackbody in TIR
• Major water absorptions at 1450, 1920, 2700 and 7000 nm
• Major ramp down from 1000 - 2700 nm
• Samples should therefore be “dry”
• SWIR can identify different types of water
• Hydrous species (eg. Smectites, Halloysite)
SWIR Water SpectrumSWIR Water SpectrumWater has its own SWIR spectrum
that mixes with mineral spectra
Dry and Wet Cracow KaoliniteDry and Wet Cracow KaoliniteWet samples can compromise
spectral interpretation
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Particle Size Effects - 1
• Rock versus Powder measurements – Generally smaller particle size (powders) yields
brighter spectra
– Powders produce relatively weaker absorptionsdue to greater surface scattering and less volume
scattering
• Not all minerals react to grain size effects in the
same way or to the same degree
– volume (multiple/single) scattering
– absorption coefficient (opaque/transparent)
Particle Size EffectsParticle Size Effects
Powders yield brighter
spectra than rocks
Particle Size EffectsParticle Size Effects
These three calcite grain sizes illustrate thatsmaller particle sizes yield brighter spectra
with reduced absorption depths
Mineral Mixture EffectsMineral Mixture Effects
• Linear mixtures – magnitude of spectral features correlated with abundance
– single scattering (e.g. many clays)
• Non-linear mixtures – Magnitude of spectral features correlated non-linearly with
abundance (e.g. carbonates)
– multiple scattering
– contrasting absorption coefficients (opaques andtransparent materials like sulphides mixed withcarbonates/quartz/mica/chlorite).
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Kaolinite / Muscovite MixturesKaolinite / Muscovite Mixtures
Computed additive mixtures
Muscovite / Chlorite MixturesMuscovite / Chlorite Mixtures
Computed additive mixtures
QuartzMicroclineOrthoclaseAlbite
QuartzMicroclineOrthoclaseAlbite
Visible -SWIR
thermal IR
Library DHR - Quartzites
R e f l e c t a n c e
SilicaCrystallinity
0.45
0.55
0.65
0.75
0.85
0.95
7.5 8.5 9.5 10.5 11.5 12.5 13.5
wavelength (um)
e m i s s i v i t y
CUP001A
CUP001B
0
1000
2000
3000
4000
5000
6000
7000
8000
15 25 35 45 55 65
XRD 2theta
i n t e n s i t y
Cup1A
Cup1B
Opaline
Chalcedony
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Feldspars
• Ubiquitous
• 11 types
(structure-
chemistry)
• K-Na-Ca ternary
• Alkali vs
plagioclase
• Chemistry, temp
• Igneous rock
classification
• Alteration
Alkali
feldspars
Plagioclase feldspars
Feldspar TIR Reflectance Spectra
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
7.5 8.5 9.5 10.5 11.5 12.5 13.5
wavelength (μ
m)
r e f l e c
t a n c e
microcline (K)
orthoclase (K/Na)sanidine (K/Na)
albite (Na)
labradorite (Na/Ca)
anorthite (Ca)
9.62
9.48
10.0
9.0
10.5
9.3
alkali
plagioclase
Field TIR
spectra
y = 12.7x - 46.8
R2
= 0.61
0.95
1
1.05
1.1
1.15
1.2
1.25
3.765 3.77 3.775 3.78
XRD d-spacing
r a t i o
o f m F T I R e
m i s s i v i t i e s
( 9 . 6 μ m / 9 . 9 μ m )
albite-rich
0
0.05
0.1
0.15
0.2
0.25
0.3
8 8.5 9 9.5 10 10.5 11
wavelength (μ
m)
e m
i s s
i v i t y
Y100i.txt
Y101ii(B)txt
Y107b.txt
Y107c.txt
Y107l.txt
Y115ii.txt
Y121ii.txt
Y122i.txt
Y123iii.txt
Y123iv.txt
Y124i.txt
Y124iv.txt
Ca albite-rich
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
8 8.5 9 9.5 10 10.5 11
wavelength (μ
m)
e m
i s s
i v i t y
Y100ii.txt
Y101viii.txt
Y109i.txt
Y111iii.txt
Y112i.txt
Y112iv.txt
Y112iii.txt
Y113ii.txt
Y113i.txt
Y116i.txt
Y118iii.txt
Y119i.txt
Y119iv.txt
Y127i.txt
Na anorthite-rich0
0.05
0.1
0.15
0.2
0.25
8 8.5 9 9.5 10 10.5 11
wavelength (mm)
e m
i s s
i v i t y
Y101ix.txt
Y101vii(A)txt
Y108i.txt
Y110i.txt
Y110ii.txt
Y114i.txt
Y114ii.txt
Y120ii.txt
Albitised monzodiorites
Feldspar ChemistryTIR (all l) and XRD (3.7 d-spacing)
y = 0.7267x + 1.0317
R2 = 0.6705
3.768
3.77
3.772
3.774
3.776
3.778
3.78
3.782
3 .7 66 3 .7 68 3 .7 7 3 .7 72 3 .7 74 3 .7 76 3 .7 78 3 .7 8 3 .7 82
actual XRD d-spacing
p r e d i c t e d X R D d
- s p a c i n g
-0.012
0
0.012
7.5 8.5 9.5 10.5 11.5 12.5 13.5
wavelength (μ
m)
F R C " w e
i g h t e d " e m
i s s
i v i t y
9.6
10.59.0
8.213
Alb iti sati on
Na-rich
Ca-rich
PLS Final Regression Coeffs.
Garnets
• structural andchemical variations
– X3Y2Z3O12 where Z is
Si4+ ; X and Y vary
VNIR-SWIR
0
0.5
1
1.5
2
2.5
3
3.5
7.5 8.5 9.5 10.5 11.5 12.5 13.5
wavelength ( m)
m e a n - n o r m l a s i e d r e f l e c t a n c e
spessartine
andradite
grosssularite
pyrope
almandine
Mg-Al
Mn-Al
Al-Ca
Fe-Ca
TIR
2 groups - isomorphous
substitiuion(1) ugrandite (uvarite, grossular,
andradite) – Ca-richY site - (Al3+,
Fe3+, Ti3+ and Cr3+)
(2) pyralspite (pyrope-almandine-
spessartine) – Ca-poor
Pyroxenes
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TIR JHU Library - Olivines
NAME SOURCE COST RANGE (nm) INSTRUMENT SOFTWARE CHARACTERISED
C OM PA TI BI LI TY A NC ILLA RY D ATA
MINLIB/3000.REF CSIRO/ DEM Nil 400-2500 IRIS XSPECTRA Samples
JPL JPL Free 400-2500 Beckman XSPECTRA Yes
USGS USGS Free 400-2500 Beckman XSPECTRA Yes
VICM CSIRO/DEM Nil 1300-2500 PIMA-II XSPECTRA/PIMAVIE
W
Some
AUSM CSIRO/DEM Nil 1300-2500 PIMA-II XSPECTRA/PIMAVIE
W
Some
SPECMIN Spe ctral Interna tio nal $US2 000 1300 -250 0 PIMA-II XSPE CT RA/ PIMAVIE
W
Yes
ISPL/USGS ISPL ? 1300-2500 PIMA-II XSPECTRA/PIMAVIE
W
Yes
SALISBURY Jack Salisbury Free MID IR FTIR XSPECTRA ?
CO2
Laser CSIRO/DEM Free 9.2-11.5 CO2Laser XSPECTRA Some + Samples
F ree= P ub licd omain N il= No co st t o p ro jec t
Available Spectral Libraries Available Spectral Libraries
JHU John HopkinsUniv. Free 2000-25.000 Yes
SWIR Spectral Libraries
• USGS & JPL libraries
– available in ENVI
– 0.4 to 15 μ m reflectance
– Minerals
– Vegetation
TIR Spectral LibrariesTIR Spectral Libraries• Johns Hopkins Univ. TIR library (available in ENVI)
– 0.4 to 15 μ m, hemispherical reflectance
– Minerals• Igneous (coarse and fine grained)
• Sedimentary (coarse and fine grained)
• Metamorphic (coarse and fine grained)
– Environmental• Soi ls
• Vegetation
• Water and snow
• Lunar
• Meteor
• Man made
FieldField--LaboratoryLaboratory--Mine SpectrometersMine Spectrometers•• IRIS Mk IV and Mk VIRIS Mk IV and Mk V VNIRVNIR--SWIRSWIR
•• PIMAPIMA--II and PIMAII and PIMA--SPSP SWIRSWIR
•• Ocean OpticsOcean Optics VNIRVNIR
•• ASD ASD FieldSpecFieldSpec ProPro VNIRVNIR--SWIRSWIR
•• COCO22 LaserLaser TIRTIR
•• MicroFTIRMicroFTIR SWIRSWIR--MIRMIR --TIRTIR
•• OARSOARS--TIPS (TIPS (HyLogger HyLogger )) VNIRVNIR--SWIRSWIR--MIRMIR-- TIRTIR
•• ASD ( ASD (FaceMapper FaceMapper )) VNIRVNIR--SWIRSWIR--MIRMIR --TIRTIR
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Analytical
Spectral
Devices
• Fieldspec Pro
– Various configurations
– 350-2500 nm
– 10 nm spectral resulotion @ SWIR
• TERRASPEC
– More robust fibre
– contact
http://www.http://www.asdiasdi.com/.com/
TERRASPEC
Fieldspec Pro
FieldSpec FR
(CARY - 5G)
FieldSpec FR
Field Spectrometer –
FIELDSPEC FR
Field Spectrometer –
FIELDSPEC FR
FIELDSPEC FR Field Portable Spectrometer FIELDSPEC FR Field Portable Spectrometer FIELDSPEC FR Field Portable Spectrometer
• 1512 channels (spectral bands)
between 350-2500nm
• 10 measurements per second
• IFOV 25º but adaptabledown to 1º
• allows simulation of any remote sensing
system based on reflection of sun light
Field Spectrometer - PIMAField Spectrometer - PIMA PIMA Field Portable Spectrometer PIMA Field Portable Spectrometer
• SWIR only (1300 - 2500 nm)
• Suitable for phyllosilicates, sulphates, carbonates
• Internal light source (no atmospheric effects)
• “Contact” mode of measurement
• 2 nm sampling, 4 nm resolution
• 1 cm FOV
• 8 second to X minute measurement (integration)
• Australian developed & supported
• Superior SNR and spectral resolution
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Core LoggingContinuous coreContinuous core
/chip scanning/chip scanning
0.50.5--1 cm1 cmresolutionresolution
10,000’s of10,000’s ofobservationsobservations
~ 1 m / minute~ 1 m / minuteat presentat present
1000’s1000’s metresmetres ofofcore/chipscore/chips
Field μFTIR Thermal Infrared
Spectrometer SpectralRa nge
5000-666 cm-1 (2.0-15.0 µm) (Nominalrange, but
fullrange with hot source is 6410-415 cm-1, or 1.56-24 µm.)
S pe ct ra l Sa mpling N/A
Data Interval 3 cm-1
SpectralRe solution 6 cm-1
Field of View 4.6°
S ca n Tim e 1 se c ( us ua lly ave ra ge d fo r 1 6 s ec on ds )
Power Source2.5 kg 12 V battery in sling pack for spectrometer and blackbody (4 hr. operation). Computer independently powered.
Size Optical head 25 x 25 x 20 cmElectronics/computer case 33 x 46 x 5 cm
WeightOpticalhead 4.4 kgElectronics/computer case 7.55 kg (with computer)Battery 2.5 kgLight duty tripod 1.5 kg
Field μFTIR Thermal Infrared
Spectrometer Raw Data
Wavelength (micrometers)
7.00 8.00 9.00 10.00 11.00 12.00 13.00 14.00
R a w
O u t p u t ( I n s t r u m e n t U n i t s )
0.0
6.00
12.00
18.00
24.00
30.00
Cold Blackbody Warm Blackbody Sample
Calibrated Data
Wavelength (micrometers)
7.00 8.00 9.00 10.00 11.00 12.00 13.00 14.00
R a d i a n c e ( W / m 2 u m s r )
5.00
6.00
7.00
8.00
9.00
10.00
Calibrated quartz
Apparent Emissivity
Wavelength (micrometers)
7.00 8.00 9.00 10.00 11.00 12.00 13.00 14.00
A p p a r e n t E m i s s i v i t y
0.60
0.70
0.80
0.90
1.00
1.10
Apparent quartz emissivity
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Field vs Lab.
Wavelength (micrometers)
7.00 8.00 9.00 10.00 11.00 12.00 13.00 14.00
0.50
0.62
0.74
0.86
0.98
1.10
Fie ld measurement (uFTIR) Laboratory measurement (Nicolet)
Spectral Analysis Methods
• Spectral enhancement
– Hull quotient spectra• Background, continuum or hull removed
– Derivative spectra
• 1st or 2nd derivatives
• Feature extraction
– Gaussian decomposition - wavelengths, depths, widths andasymmetries
• Automatic Mineral Identification
– Tetracorder
– Spectral Assistant
• Similarity measures
– Partial Unmixing
– Spectral Angle Mapper
• Quantification
– Partial Least Squares
SoftwareSoftware• SIMIS (Spectrometer-Independent Mineral Identification Software)
– Field/lab spectra
• TSG (The Spectral Geologist) – Field/lab and core spectra
• TSA (The Spectral Assistant)• TSG-Core
• PIMAVIEW-111 – Field/lab spectra
• ASD – Field/lab spectra
• ENVI (Environment for Visualising Images) – Hyperspectral images
– Field/lab spectra
• Tetracorder (USGS SpecLab) – Hyperspectral images
– Field/lab spectra
• Nei l PendockSuite – ASTER and hyperspectralimages
• CSIRO/HyVista Suite – ASTER and hyperspectralimages
• ERMapper – ASTER
Reflectance, Hull and HullQuotient Spectra
Reflectance, Hull and Hull
Quotient Spectra
.... Hull or continuum
.... Hull quotient spectrum
Reflectance spectrum ....
Continuum Slope Effect
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Spectral Analysisand its application to
Exploration and Mining
DrDr SashaSasha PontualPontual , , AusSpec AusSpec InternationalInternational
Main Approaches to SpectralMain Approaches to Spectral
Data AnalysisData Analysis
• Manual Interpretation
• Mineral identification
software
– Automated
– User defined training libraries
• Spectral parameters / digital
mineralogy
Mineral Identification SoftwareMineral Identification Software
• Rely totally upon training library
• Some have fixed libraries (e.g. TSA)
• Some have user defined libraries (i.e. built into TSG)
Mineral Identification SoftwareMineral Identification Software
• Fast, consistent, digital output
• Still misses many subtle
variations
• But very useful with large
datasets
(if used intelligently)
Mineral Identification SoftwareMineral Identification Software
• Questionable accuracy (i.e. if more than 2 minerals)
• Danger of “black box” mentality
• User must carry out visual checks
• User must use other knowledge (geology, target etc.)
ExampleExample
• Reconnaissance phase
• Ridge and Spur sampling
• ~5 x 5 km area
• over 2000 samples collected for
geochemical analysis
• Measured on-site using PIMA II
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Dominant Mineral Map: TSA resultsDominant Mineral Map: TSA results
IlliteParagoniteKaoliniteHalloysiteGibbsite
~ 5 x 5 kmridge and
spur samples
Single Mineral GroupSingle Mineral Group
TSA results - classextraction scalars
Illite Group
Distribution
Colours relate to
signature strength
(spectral
weightings)
Spectral ParametersSpectral Parameters
• They are measurements of:
– Wavelength
– Depth
– Width
– Areas of absorption features
– Or combinations of these values i.e. commonly asratios of depths of features
• Influenced by mineralogical factors such as
composition and crystallinity
A Few Common ParametersA Few Common Parameters
• Wavelength AlOH – illite composition
• Depth AlOH/water – illite-smectitecrystallinity
• Kaolinite crystallinity
• Wavelength MgOH/CO3
• Depth of AlOH, MgOH or FeOH
Used in over 90% of projects –
calculated automatically
Advantages of Spectral ParametersAdvantages of Spectral Parameters
• Highly specific controlled by only one or twovariations in mineralogy
• Consistent
• Very fast
• Produce digital results that can be integratedwith other data
• Allow results to be presented in a familiarformat(i.e. to the non-spectral expert geologists)
Understanding Spectral ParametersUnderstanding Spectral Parameters
• Can be influenced by several factors
• Can mean different things with different
datasets
By themselves spectral parameters arenot intelligent!
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Calculating Spectral ParametersCalculating Spectral Parameters
• TSG calculates spectral parameters using
any of these methods:
– The spectral profile (i.e. directly from the
spectra)
– Arithmetic expressions (i.e. ratios)
– Feature parameters (i.e. based on
deconvolution of the spectra)
– Class Extraction Scalars (i.e. data filtered by
Class, eg. TSA Mineral 1, lithology etc.)
Provide information on:
• Alteration – Zoning, lithology and structure
• Weathering – Weathering profiles
– Weathered altered signatures
Patterns of Mineral Distribution
Applications to Mineral Exploration Applications to Mineral Exploration
IlliteIllite CrystallinityCrystallinity MapMap
Class extraction of
Spectral Parameters
- Hot coloursindicate increasedillite crystallinity
- Greys are samples with noillite
Scatterplot screen in TSG
Data Integration
• Geochemical data - elemental distribution, and
mineralisation
• Geophysical data - changing physical
characteristics
• Spectral Data - associated mineralogical
variation
Understanding alterationUnderstanding alteration--
mineralisationmineralisation relationshipsrelationships
10m
50m
55m
60m
100m
150m
200m
L i t h o l o g y
T a r g
e t
M g O
K O
2 A l O 2
3
M g O
/ K O
2
ChloriteMg Fe C
h l o r i t e
S e r i c i t e
m u s c o v i t e
p h e n g i t e
Depth
Geochemical Data Mineralogical Data(Spectral Data)
Data IntegrationData Integration
0 9 1 0 1 1 1 2 0 1
2 0 2 10 50 4
0 6 0 7 0 8 1 4 1 5 1 6 1 7
1 0 0 m E 2 0 0 m E 3 0 0 m E 4 0 0 m E 5 0 0 m E 6 0 0 m E 7 0 0 m E 8 0 0 m E 9 0 0 m E 1 0 0 0 m E11 0 0 m E 1 2 0 0 m E1 3 0 0 m E1 4 0 0m E
3 0 0 R L
3 5 0 R L
4 0 0 R L
4 5 0 R L
0 9 1 0 1 1 1 2 0 1
2 0 2 10 50 4
0 6 0 7 0 8 1 4 1 5 1 6 1 7
1 0 0 m E 2 0 0 m E 3 0 0 m E 4 0 0 m E 5 0 0 m E 6 0 0 m E 7 0 0 m E 8 0 0 m E 9 0 0 m E 1 0 0 0 m E11 0 0 m E 1 2 0 0 m E1 3 0 0 m E1 4 0 0m E
3 0 0 R L
3 5 0 R L
4 0 0 R L
4 5 0 R L
As ppm
Sericite Composition
Fe Carbonate
Basalt
Basalt
Basalt
Geochemistry
Mineralogy(Spectral data)
Mineralisation
Specifically mappingthe alteration envelope
Muscovite & kaolinitecharacterise mineralised structures
Case Study: Allendale, VictoriaCase Study: Allendale, Victoria