1. technology and computing

Utility of ASTER derived emissivity for
mapping greenstone rocks and associated
granitoids- a case study in Hutti Maski
Schist Belt, Karnataka
Arindam Guha, K. Vinod Kumar
Geosciences Division,
National Remote Sensing Centre,
Indian Space Research Organisation,
Balanagar, Hyderabad
Presented by
Arindam Guha
NRSC,ISRO
ISPRS Technical Commission VIII Mid
Term Symposium 2014
Contents
•Introduction
•Objectives
•Study area
•Geology
•Methodology
•Thermal data analysis and results
•ASTER –SWIR band data analysis and results
•Conclusion
• Important references
ISPRS Technical Commission VIII Mid
Term Symposium 2014
Introduction
•The emissivity of an isothermal, homogeneous emitter is
defined as the ratio of the actual emitted radiance to the
radiance emitted from a black body at the same
thermodynamic temperature(Norman, 1995).
• Emissivity can be used to identify individual minerals, and has
been also related to silica content of rocks (Lyon 1972).
• Importantly, igneous rocks are characterised with varying
silica( i.e. silicon-di-oxide) content.
• Spectral emissivity of quartz is lower in the 8–9 μm region (
ASTER bands 10 to 12) than in the 10–12 μm region( ASTER
band 13 and 14). Feldspar also has absorption in band 11.
• Ninomiya Index( band 10*band 11)/band 12* band 12) used
to detect silica rich rock(Ninomiya,2005).
ISPRS Technical Commission VIII Mid
Term Symposium 2014
Alkali
Spectral feature in TIR region shifts
towards shorter wavelength as bond
strength within the lattice increases.
Emissivity spectra of different rocks
( JHU lab) with respective ASTER spectra
ISPRS Technical Commission VIII Mid
Term Symposium 2014
Objectives
• Processing of ASTER thermal infrared(TIR) bands for
delineating variants of
Archaean greenstone rocks and
associated granitoids.
• Evaluation of the potential of thermal emissivity images
derived using different emissivity extraction methods for
delineating rock types.
• Comparison of the results of ASTER thermal bands processing
with the results of ASTER
visible near infrared(VNIR)
shortwave infrared(SWIR) band processing
in terms of
delineation of major rock types.
Collaborator: Geological Survey of India; CHQ, Kolkata
ISPRS Technical Commission VIII Mid
Term Symposium 2014
Study area
a
d
b
c
e
a) Black soil
d) Pink
developed over
Granite(Yelgalli
metabasalt.
e) Mixites
b) Isolated exposures f) Kavital
of amphibolite
Granite(Granodiori
c) Altered rock
te)
ISPRS Technical Commission VIII Mid
Term Symposium 2014
f
Geology
• Older
Gneiss: Peninsular Gneiss.
• Green stone rocks
• Metabasalt dominant
• Amphibolite dominant
•Granodiorite
•Kavital: Older phase with
recognizable gneissosity
and granodiortic composition.
Yelgatti granite: Yelgatii granite
is silica rich granite and KFeldspar rich.
At places it has facies
Few acid intrusive specially at
Chinchergi are younger than the
granite and greenstone rocks as
it is intrusive on both these
rocks.
The greenstone belts all over the rock has problem for its relation
with associated granite-gneiss. Palkanmardi mixites contain clast of
gneiss indicating the contact although remobilized but gneiss is the
basement of the greenstone belt. Other hand; there are two distinct
phase of granitoids present in the area.
ISPRS Technical Commission VIII Mid
Term Symposium 2014
ASTER data specification
Subsyste
ms
VNIR
SWIR
TIR
Band
Numbers
1
2
3
3B
4
5
6
7
8
9
10
11
12
13
14
Spectral
Spatial
domain(Micromete Resolution
r)
(Meter)
52–0.60
63–0.69
78–0.86
78–0.86
1.60- 1.70
2.145- 2.185
2.185-2.225
2.235-2.285
2.965-2.360
2.360- 2430
8.125-8.475
8.8475-8.825
8.925-9.275
10.25-10.95
10.95-11.65
15
15
15
15
30
30
30
30
30
30
90
90
90
90
90
Radiometric
Resolution
8 bits
8 bits
8 bits
8 bits
8 bits
8 bits
8 bits
8 bits
8 bits
8 bits
12 bits
12 bits
12 bits
12 bits
12 bits
ISPRS Technical Commission VIII Mid
Term Symposium 2014
Methodology(ASTER Thermal data Processing)
ASTER Level 1B thermal bands are processed to derive emissivity.
Steps followed to calibrate thermal bands are:
Atmospheric Correction: Image based thermal atmosphere correction. This
algorithm assumes that the atmosphere is uniform over the data scene and that a
near-blackbody surface exists within the scene.
Emissivity Estimation: Estimation of emissivity is done either by considering
emissivity of a band constant or by deriving the radiant temperature of all the
pixels of each band using a fixed emissivity value and using the highest radiant
temperature of each pixels to derived emissivity based on inverse Planck’s
equation. In alpha residual method, error derived due to linear approximation of
Planck’s equation have been used as proxy to emissivity.
Processing of Emissivity images: Noise removal, Minimum noise fraction
image derivation and false colour composite derivation after applying inverse
MNF.
ISPRS Technical Commission VIII Mid
Term Symposium 2014
(Methodology -ASTER VNIR-SWIR data Processing)
ASTER data calibration: VNIR bands are processed using IARR method; whereas
SWIR bands are processed using Log residual methods.
ASTER data processing: Derivation of false colour composites, MNF composites
etc.
Identification of
lithology
FCC/Band ratio
derivation
ASTER VNIR
Bands
Calibration
ASTER SWIR
Bands
ASTER TIR
Bands
In scene
thermal
correction
ASTER Relative
Reflection Image
Emissivity
derivation
MNF Composite
MNF
Calibratio
n
Inverse MNF
Composite and
ratio
image
Inverse MNF
(Decorrelation
stretched)
Emissivity and Radiant
temperature image
Radiance composite
and albedo image
derivation
ASTER data processing is supplemented with field survey along selected traverses
followed by spectral data analysis of rock-samples
ISPRS Technical Commission VIII Mid
Term Symposium 2014
Thermal data analysis
GN
AMP
MBL
GR
GR
a
Radiance composite
14, 12, 11
C
b
Albedo Image (Broadband
albedo)
Radiant temperature
AMP:
Amphibolite
GR:
Granodiorite
•In the false color composite of thermal radiance image; GN:
granite/granodiorite appears with red colour as silica and feldspar rich Granodiorite
rocks have absorption at band 12,and Band 11.
MBL:
•Black soil developed over granodiorite has reddish tint whereas black Metabasalt
soil developed over metabasalt Is bright without the reddish tint.
•Amphibolite and metabasalt appear similar in the radiance composite and
also in the albedo image( Son et al. 2014).
•Radiant temperature image has lower contrast than albedo image and
distinction between amphibolite and granite is difficult.
ISPRS Technical Commission VIII Mid
Term Symposium 2014
Minimum noise fraction image derivation
MNF1
MNF2
MNF 3
MNF 4
MNF 5
Emissivity Normalisation
MNF 1
MNF 2
MNF 3
MNF 4
MNF 5
Reference Channel
MNF 1
MNF 2
MNF 3
MNF 4
MNF 5
Alpha Residual
Emissivity images derived using emissivity-temperature derivation
algorithms have low signal to noise ratio (SNR)and high band vs band
correlation. Minimum noise fraction( noise segregated MNF bands have
been examined to under stand the dimensionality of the data
ISPRS Technical Commission VIII Mid
Term Symposium 2014
Thermal data analysis
16-17/76-36
14, 12, 11
14, 12, 11
14, 12, 11
MB
GN
GR
MB
Reference Channel
Emissivity normalisation
16-00/76-51
Alpha Residual
•False colour composites of emissivity bands derived using different
emissivity extraction algorithms (e.g. emissivity normalisation,
reference channel and alpha residual methods) are subdued in terms
of colour contrast of different rock types.
•Distinction between granite and granodiorite is also not
pronounced. However granite is broadly represented with red colour
whereas granodiorite with magenta colour..
ISPRS Technical Commission VIII Mid
Term Symposium 2014
Thermal data analysis
AMP
GN
GR
MB
14, 12, 11
Inverse MNF Composite(Decorrelation
stretched)- Emissivity normalization
method
AMP: Amphibolite
GR: Granodiorite
GN: Granodiorite
MBL: Metabasalt
In decorrelation stretched inverse MNF
Image; distinction between mafic and felsic
rocks have been enhanced .Further granite
and granodiorite have better contrast in
comparison
to
simple
emissivityISPRS Technical Commission VIII Mid
Term Symposium 2014
composites.
B1
GN
B2 B3
B4
B5
GR
GN
Amphibolite
Granodiorite
Granite
GR
MBL
a
14,/12 10/12
12
AMP: Amphibolite
GR: Granodiorite
GN: Granodiorite
MBL: Metabasalt
Silicic rocks are
delineated.
Granophyres are also
delineated.
Absorption feature have
subdued depth than
corresponding
laboratory spectra
b
c
ISPRS Technical Commission VIII
MiTerm Symposium 2014
ASTER SWIR band data analysis
AMP GN
GN
AMP
GR
GR
MBL
MBL
6, 4, 2
8,6,4
Correlation
Band41
Band
Band52
Band
Band63
Band
Band 4
Band
7
Band 5
Band 8
Band 6
Band
9
Band 1
Band 2
14, 12, 11
Band 3
Band 4
Band 5
Band 6
1
0.98524
0.977419
0.970718
0.976372
0.984385
0.98524
1
0.98898
0.967348
0.973339
0.9821
0.977419
0.98898
1
0.961085
0.966955
0.973114
0.970718 0.967348
0.961085
1
0.990846
0.981729
0.976372 0.973339
0.966955
0.990846
1
0.988684
0.984385
0.973114
0.981729
0.988684
1
0.9821
Correlation Matrix of SWIR bands
ISPRS Technical Commission VIII Mid
Term Symposium 2014
ASTER SWIR band data analysis
GN
AMP
1
GR
2
Amphibolite: (6+9)/(7+8),Chlorite
rich metabasat:5/8,Granite: 4/5
1
3
4
5
78 9
4
3
ISPRS Technical Commission VIII Mid
Term Symposium 2014
ASTER analysis for gold mineralization
A
R=14/12*12/10,G=CEM-Carbonate,
B=Calcite
Alteration Zone
Calcite
Talc
Clinochlore
Quartz
Chromite
Calcite
CEM
Major
Good amount
Good amount
Trace
Trace likely
Major
ISPRS Technical Commission VIII Mid
Term Symposium 2014
Conclusions
•Multiband emissivity information of ASTER sensor derived using
different emissivity extraction methods are highly correlated.
• Emissivity images are characterised with low SNR and affected with
striping noise.
• Emissivity composite, radiance composite and albedo image composite
derive comparable geological information.
• Ratio
image
derived
after
Inverse-MNF
of
emissivity
bands(decorrelation stretched) provide better contrast between granite/
granodiorite)
• Different emissivity information derive similar/comparable geological
information from ASTER data.
•Delineation of metabasalt and amphibolite is better in ASTER SWIR
band and ratio composites. Moreover; granitoids of varying silica content
are also delineated in SWIR bands based on the variations in mafic
mineral contents in these granitoids as these minerals have their
diagnostic absorption feature ASTER SWIR bands.
• ASTER emissivity spectra can be used to indicate the variation in silica
content but holistic mapping of greenstone rocks and associated
granitoids is better ASTER VNIR-SWIR bandS.
ISPRS Technical Commission VIII Mid Term Symposium 2014
Important references
Lyon, R.J.P., 1972. Infrared spectral emittance in geological mapping:
airborne spectrometer data from Pisgah Crater. Science, 7, pp 983–986.
Norman, J.M., & Becker, F.,1995. Terminology in thermal infrared remotesensing of natural surfaces. Agricultural and Forest Meteorology 77, pp
153−166.
Ninomiya, Y., Fu, B., Cudahy, T.J., 2005. Detecting lithology with Advanced
Spaceborne Thermal Emission and Reflection Radiometer (ASTER)
multispectral thermal infrared radiance-at-sensor data. Remote Sensing
of Environment, 99, pp 127-139.
Yajima, T., Yamaguchi, Y., 2013. Geological mapping of the Francistown
area in north-eastern Botswana by surface temperature and spectral
emissivity information derived from Advanced Spaceborne Thermal
Emission and Reflection Radiometer (ASTER) thermal infrared data. Ore
Geology Reviews, 53, pp 134-144.
Son,Y.-S., Kang, M.-K., Yoon, W.-J., 2014. Lithological and mineralogical
survey of the Oyu Tolgoi region, South-eastern Gobi, Mongolia using
ASTER reflectance and emissivity data. International Journal of Applied
Earth Observation and Geoinformation, 26, pp
205-216.
ISPRS
Technical Commission VIII Mid
Term Symposium 2014
Thank You
ISPRS Technical Commission VIII Mid
Term Symposium 2014