using aster image processing for geothermal energy
TRANSCRIPT
USING ASTER IMAGE PROCESSING FOR GEOTHERMAL ENERGY POTENTIAL AREAS
MIGUEL ANGEL ROJAS APARICIO
Geosciences Graduate Project
Directed by
FABIO IWASHITA
Ph. D in Geology and Natural Resources, Campinas University
Geosciences Department
Science Faculty
Universidad de los Andes, Bogotá DC.
2017
Universidad de los Andes-Departamento de Geociencias
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Content USING ASTER IMAGE PROCESSING FOR GEOTHERMAL ENERGY POTENTIAL AREAS ............................................... 1
General Objective .......................................................................................................................................................................... 3
Specific Objectives ........................................................................................................................................................................ 3
Figures ........................................................................................................................................................................................... 3
I. Introduction ............................................................................................................................................................................... 5
II. Question .................................................................................................................................................................................... 5
III. Objectives ............................................................................................................................................................................. 5
IV. Study Areas .......................................................................................................................................................................... 6
A. El Tatio Volcanic Complex .................................................................................................................................................. 6
B. Azufral Volcano ................................................................................................................................................................... 6
C. Nevado del Ruiz ................................................................................................................................................................... 7
V. ASTER Imagery ....................................................................................................................................................................... 7
VI. State of Art ........................................................................................................................................................................... 8
A. Hydrothermal Alterations ..................................................................................................................................................... 8
B. Atmospheric Corrections ...................................................................................................................................................... 8
C. Color Composites ................................................................................................................................................................. 8
D. Band Ratios .......................................................................................................................................................................... 9
E. Principal Components Analysis ............................................................................................................................................ 9
VII. Methodology ........................................................................................................................................................................ 9
A. Images .................................................................................................................................................................................. 9
B. Software.............................................................................................................................................................................. 10
C. Color Composites ............................................................................................................................................................... 10
D. Band Ratio .......................................................................................................................................................................... 10
E. Principal Components ........................................................................................................................................................ 11
VIII. Results ................................................................................................................................................................................ 11
A. El Tatio ............................................................................................................................................................................... 11
B. Azufral ................................................................................................................................................................................ 16
C. Nevado del Ruiz ................................................................................................................................................................. 20
IX. Conclusions ........................................................................................................................................................................ 24
X. Acknowledgements ................................................................................................................................................................. 24
XI. Bibliography ....................................................................................................................................................................... 24
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General Objective
Analyze ASTER satellite images to obtain a preliminary study of geothermal resources in active volcanic areas
across Colombia and Latin-America.
Specific Objectives
• Apply color compositions to ASTER satellite images to identify discriminating characteristics.
• Use band ratios to compare main bands of hydrothermal altered minerals and enhance them in the
image.
• Use Principal Components analysis to develop a mineral mapping of areas.
Figures
Figure 1. Geothermal reservoir and energy facilities model [1] ........................................................................................................ 5 Figure 2. El Tatio volcanic complex .................................................................................................................................................. 6 Figure 3. Azufral Geographic area .................................................................................................................................................... 6 Figure 4. Nevado del Ruiz geographic area ....................................................................................................................................... 7 Figure 5. Comparison of Spectral Bands between ASTER and Landsat-7 Thematic Mapper [10] ................................................... 7 Figure 6. Color compositions of El Tatio volcano (Scale: 1:629000) A: left upper position a False Color Composition of bands
(3,2,1), B: at left lower position bands (6,3,1), C: at right upper position bands (4,6,8) and finally D at lower position (7,2,1) .... 12 Figure 7. El Tatio Volcano at 4,6,8 RGB color composite at 1:200000 scale ................................................................................. 13 Figure 8. Band Ratio 4/7 of el Tatio volcano at 1:200000 scale ...................................................................................................... 13 Figure 9.Band Ratio False Color Composite (2/1,4/9,3/2) of el Tatio volcano at 1:200000 scale .................................................. 13 Figure 10. Band Ratio False Color Composite (4/5,4/6,4/7) of el Tatio volcano at 1:200000 scale ............................................... 13 Figure 11. Band Ratio of a relative band-depth RBD (4+7)/6 of el Tatio volcano at 1:200000 scale ............................................. 13 Figure 12. PC2 of ASTER bands 4 y 6 of el Tatio volcano at 1:200000 scale ................................................................................ 13 Figure 13. PC4 of ASTER bands 1,3, 5,7 of el Tatio. Brightness pixels indicates presence of Alunite .......................................... 14 Figure 14. PC4 of ASTER bands 1,3,5,6 of El Tatio. Brightness pixels indicates presence of Illite .............................................. 14 Figure 15. PC4 of ASTER bands 1,4,6,7 of El Tatio. Brightness pixels indicates presence of Kaolinite and Smectite .................. 14 Figure 16. PC4 of ASTER bands 1,4,6,9 of El Tatio. Brightness pixels indicates presence of Kaolinite ....................................... 14 Figure 17. Color compositions of Azufral volcano area (Scale: 1:620000): A at left upper position a False Color Composition of
bands (3,2,1), B: at left lower position bands (6,3,1), C: at right upper position bands (4,6,8) and finally D:at lower position
(7,2,1) .............................................................................................................................................................................................. 16 Figure 18. Azufral Volcano at 4,6,8 RGB color composite at 1:200000 scale ................................................................................ 17 Figure 19. Band Ratio 4/7 of Azufral volcano at 1:200000 scale .................................................................................................... 17 Figure 20. Band Ratio False Color composite (2/1,4/9,3/2) of Azufral volcano at 1:200000 scale ................................................ 17 Figure 21. Band Ratio False Color composite (4/5,4/6,4/7) of Azufral volcano at 1:200000 scale ................................................ 17 Figure 22. Band Ratio of a relative band-depth RBD (4+7)/6 of Azufral volcano at 1:200000 scale ............................................. 17 Figure 23. PC2 of ASTER bands 4 y 6 of Azufral volcano at 1:200000 scale ................................................................................ 17 Figure 24. PC4 of ASTER bands 1,4,6,7 of Azufral. Brightness pixels indicates presence of Kaolinite ........................................ 18 Figure 25. PC4 of ASTER bands 1,4,6,9 of Azufral. Brightness pixels indicates presence of Kaolinite and Smectite .................. 18 Figure 26. PC4 of ASTER bands 1,3,5,7 of Azufral. Brightness pixels indicates presence of Alunite ........................................... 18 Figure 27. Color compositions of Nevado del Ruiz volcano area (Scale: 1:619238): A at left upper position a False Color
Composition of bands (3,2,1), B: at left lower position bands (6,3,1), C: at right upper position bands (4,6,8) and finally D:at
lower position (7,2,1) ....................................................................................................................................................................... 20 Figure 28. Nevado del Ruiz Volcano at 4,6,8 RGB color composite at 1:200000 scale ................................................................. 21 Figure 29. Band Ratio 4/7 of Nevado del Ruiz volcano at 1:100000 scale ..................................................................................... 21 Figure 30. Band Ratio False Color composite (2/1,4/9,3/2) of Nevado del Ruiz volcano at 1:100000 scale .................................. 21 Figure 31. Band Ratio False Color composite (4/5,4/6,4/7) of Nevado del Ruiz volcano at 1:100000 scale .................................. 21 Figure 32. Band Ratio of a relative band-depth RBD (4+7)/6 of Nevado del Ruiz volcano at 1:100000 scale............................... 21 Figure 33. PC2 of ASTER bands 4 y 6 of Nevado del Ruiz volcano at 1:200000 scale................................................................. 21 Figure 34. PC4 of ASTER bands 1,3,5,6 of Nevado del Ruiz. Brightness pixels indicates presence of Illite ................................. 22 Figure 35. PC4 of ASTER bands 1,4,6,7 of Nevado del Ruiz. Brightness pixels indicates presence of Kaolinite ......................... 22 Figure 36. PC4 of ASTER bands 1,3,5,7 of Nevado del Ruiz. Brightness pixels indicates presence of Alunite ............................ 22 Figure 37. PC4 of ASTER bands 1,4,6,9 of Nevado del Ruiz. Brightness pixels indicates presence of Kaolinite-Smectite .......... 22
Universidad de los Andes-Departamento de Geociencias
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USING ASTER IMAGE PROCESSING FOR GEOTHERMAL ENERGY POTENTIAL AREAS
Rojas-Aparicio, Miguel Angel
December 2017
Director: Iwashita, Fabio
Abstract
Geothermal systems are an important source of energy commonly available in active volcanic areas across Colombia
and Latin-America. To build geothermal plants and generate electricity it is necessary to explore potential areas with
meaningful temperature gradients and reservoirs. Geologic and feasibility studies are necessary to determine the potentials
of such areas. This process requires a great amount of time and resources, so satellite imagery analysis is a widely used
approach for a first survey, especially in remote areas that present logistic challenges. Freely available ASTER images
allow the study of potential areas for geothermal energy, through the detection of certain minerals present in the surface
generated by geothermal alteration. In this work, digital image processing techniques are applied across three study areas,
El Tatio in Chile, Azufral and Nevado del Ruiz in Colombia. First, in order to carry out the image analysis, an atmospheric
correction was performed to obtain reflectance values. Second, false color images were generated for an exploratory
analysis. Third, band ratios were calculated to enhance absorption features for different clay minerals. Fourth, principal
component analysis was applied, and component images were used to generate compositions to highlight areas with
hydrothermal alterations. It was determined that 468 RGB color composition enhanced alterations, 4/7 band ratio
eliminates errors of color composites and PCA discriminates between alteration minerals such as Kaolinite, Smectite, Illite
and Alunite.
Key Words
Hydrothermal Alterations, ASTER, PCA, Color Composition, Remote Sensing, Band Ratios
Resumen
Los sistemas geotérmicos son una importante fuente de energía comúnmente disponible en áreas con vulcanismo activo
a lo largo de Colombia y Latinoamérica. Para construir plantas geotérmicas y generar electricidad es necesario explorar
áreas potenciales con significativos gradientes de temperatura y reservorios. Estudios geológicos y de factibilidad son
necesarios para determinar los potenciales en tales áreas. Este proceso requiere de una gran cantidad de tiempo y recursos.
Análisis de imágenes satelitales son ampliamente usados como una aproximación a un primer estudio, especialmente en
áreas remotas que presenta retos logísticos. Datos libremente disponibles como las imágenes ASTER, permiten el estudio
de áreas potenciales para energía geotérmica, a través de la detección de ciertos minerales presentes en la superficie
generados por alteración geotérmica. En este trabajo, algunas técnicas de procesamiento digital de imágenes son aplicados
sobre tres áreas de estudio, El Tatio en Chile, Azufral y Nevado del Ruiz en Colombia. Primero, para llevar a cabo los
análisis de imagen, correcciones atmosféricas fueron desarrolladas para obtener valores de reflectancia. Segundo, imágenes
de color falso fueron generadas para un análisis exploratorio. Tercero, razones de bandas fueron calculados para resaltar
características de absorción para diferentes minerales de la arcilla. Cuarto, análisis de componentes principales fueron
aplicados, e imágenes de composición fueron usadas para generar un resaltado de las áreas con alteraciones hidrotermales.
Se determinó que la composición de color RGB 468 resalta áreas con alteraciones, razón de bandas 4/7 elimina errores de
composiciones de color y PCA discrimina entre minerales de alteración tales como Caolinita, Smectita, Illita y Alunita.
Palabras Clave
Alteración Hidrotermal, ASTER, PCA, Composición de Color, Sensores Remotos, Razón de Bandas
Universidad de los Andes-Departamento de Geociencias
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I. INTRODUCTION
The increase of human activities is consuming the non-
renewable energy resources leading to the depletion of
fossil fuels. The need for an alternative initiated research
to identify a cheap renewable source of energy with great
potential for production. A sustainable society will
require reduction of dependency on fossil fuels and
lowering the amount of pollution. Because, some of the
existing methods to produce energy are not sustainable.
Also, they are harmful towards the climate, contribute
to global warming, and therefore there needs to be a
change of perspective [1]. In other words, it is necessary
to develop a renewable energy project that is cleaner and
friendlier to the environment. The use of geothermal
energy has been presented as a solution to this problem,
because it gathers heat from the inside of the earth, them
providing a continuous energy source.
Geothermal energy originates from the temperature
gradient that exists between the surface and the center of
the earth, where every 100m from the surface in the crust
there is an increase of approximately 2 ° C to 4 ° C. To be
able to use geothermal energy, it is necessary to have a
reservoir with 3 elements: a heat source, an aquifer and a
seal. The heat source corresponds to magma intrusion or
plate boundary friction which is transferred to the upper
layers through conduction.
Figure 1. Geothermal reservoir and energy facilities model [1]
The aquifer refers to a rock formation of permeable
character such sands, which indicates that fluids such as
water can pass through it and increase in temperature.
Some temperatures of waters from geothermal source
exceed 150 °C at depth. When water reaches the vapor
state, this produces characteristic pressures and
temperatures that drive a turbine, which is linked to a
generator to produce energy.
II. QUESTION
Is it possible to find areas and develop a preliminary
study of feasible geothermal projects through the use of
ASTER Imagery in Colombia? ASTER imagery has been
used to determine the Gold Bar orebodies in Eureka
Country, Nevada [2] and key alteration minerals on
Siyahrud area [3].
III. OBJECTIVES
The present work applies a geothermal prospecting tool
to arid areas in Colombia and Chile. This tool is an
analysis of ASTER satellite images to obtain a
preliminary study of geothermal resources and to compare
with other areas in the Andes mountain range.
This paper explains the following activities: First, to
process ASTER images in Envi Software with various
color-infrared compositions of True Color Composition,
False Color Composition and Principal Components
Analysis. Second, to detect different mineral anomalies
and relate with the presence of Geothermal sources
evidences of hydrothermal alteration minerals, Kaolinite,
Illite, Smectite and Alunite. Third, application of
principal component analysis and generation colored
composites of images. Finally, to analyze the potential of
each area and determine feasibility to implement a project
for Geothermal Energy production.
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IV. STUDY AREAS
Three areas with potential for geothermal energy were
chosen based on their area, low percentage of vegetation
and cloud coverage just like places near to volcanos. So,
the areas chosen were: El Tatio, Azufral y Nevado del
Ruiz.
A. El Tatio Volcanic Complex
El Tatio volcanic area is located in Calama City, Chile
with an altitude of 4300 m. El Tatio is a complex of
stratovolcanoes and lava domes with several thermal
springs, fumaroles, geysers, and boiling mud pools. The
occurrence of significant inputs of magmatic fluids in the
El Tatio geothermal field, presents relatively young
emplacement of Co. Additionally, La Torta rhyolitic lava
dome and active surface deformation, accounts for the
active magmatism in the study area, with potential for
volcanic hazard [4].
Hydrogeological models indicate that meteoric waters
infiltrate in recharge areas at 15 km East of the field. The
potential geothermal reservoir is hosted in the Puripicar
Formation and Salado Member, with temperature of
approximately 170 °C was recorded in the permeable
levels hosted in the Tukle volcanic group [5] in western
part of Figure 4. El Tatio contains mainly hydrothermal
reservoirs that predominantly consist of andesitic lava and
pyroclastic flows, conglomerates, breccias, sandstones,
siltstones, limestones, marls and evaporites. Several
thermal springs, fumaroles, geysers and boiling mud
pools are present.
Figure 2. El Tatio volcanic complex
Hydrogeological models indicate that meteoric waters
infiltrate in recharge areas 15 km E of the field [5]. These
alterations are present in rim craters of Vn. Tocorpurí and
Vn. Tatio formation with occurrence of Kaoline and
Sulphur deposits in wide sectors [4].
B. Azufral Volcano
Azufral volcano hosts a geothermal system, which has
a surface manifestation of hydrothermal alteration zones,
hot springs, fumaroles and craters of hydrothermal
eruptions. This area is of great interest for the geothermal
community since the publication of the national
geothermal reconnaissance study, in 1982. The volcano is
located at southwest of Colombia in Nariño department
[6]. Previous studies describe the geological and
structural map, magnetic and gravimetric studies,
geoelectric surveys (VES), surface hydrothermal
alteration and fluid geochemical analyses.
Azufral Volcano is a structure with exogenous domes
inside, and was built above the volcanic edifices. This
volcano has active surface hydrothermal manifestation
like hot springs, fumaroles and domes and the caldera lake
(Laguna Verde). The basement rock of the volcano
consists of metasedimentary rocks of Lower Cretaceous
(Dagua Group) and volcanic-sedimentary rocks of Upper
Cretaceous (Diabasic Group). Also, the geothermal
system includes young domes of rhyodacites with a
higher magma differentiation compared to andesitic
volcanoes.
Figure 3. Azufral Geographic area
Azufral
Chimangual
Cumbal
Co La Torta
Vn. Tocorpuri
Vn. El Tatio
Tukle
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Pyroclastic surge deposits indicate the abundance of
geothermal reservoir water. The thick lava and welded
ignimbrites, related to ancient volcanic edifices, could
host secondary permeability in the geothermal reservoir.
Altered pyroclastic flows and surge deposits could
become the cap rock of the reservoir [6]. The geothermal
system of Azufral volcano has four main discharge areas:
Crater, Tercan (Quebrada El Baño), Chimangual
(Quebrada Blanca) and Sapuyes (Malaber) and the
individual springs in San Ramón and La Cabaña [6].
C. Nevado del Ruiz
In Colombia, ISAGEN has developed exploration
studies in an area of 200 𝑘𝑚2 around the Nevado del Ruiz
Volcano since 2010. The activities include a cartographic
restitution, a detailed structural geology, hydrothermal
alteration, fluid inclusion analysis, geochemistry of
thermal waters, hydrogeology and geophysics. Overlay
anomalies of magnetometry and gravimetric surveys, and
the structural lineaments, allowed them to identify areas
with potentially anomalous thermal gradients near the
surface [7].
The stud of El Nevado del Ruiz at the north part of
volcanic chain is the result of the convergence of the
Caribbean, Nazca and the Suramerican plates. It is
dominated by the metamorphic and igneous units like
Cajamarca and Quebradagrande complex. These rocks are
discordantly covered by pyroclastic deposits and lava
flows produced mainly by the volcanoes activity.
Figure 4. Nevado del Ruiz geographic area
The hydrothermal alteration at the surface is an
extensive argillic zone that affects most ancient andesitic
lava flows (augite-hypersthene-andesites). This alteration
is observed at the Rio Azufrado valley that follows a
north-northeast trend controlled by the dextral strike-slip
of the Palestina fault system. Advanced argillic alteration
is also observed along the Aguacaliente creek and in a 59
°C hot spring in the Marcada creek; in this area the
alteration is covered by pyroclastic, lahars deposits, and
by the ice cap at the highest elevations of the volcano [8].
V. ASTER IMAGERY
The Advanced Spaceborne Thermal Emission and
Reflection Radiometer (ASTER) sensor measures
reflected radiation in visible/near-infrared (VNIR; 0.5–
0.8 μm), short-wave infrared (SWIR; 1.6–2.4 μm) and
thermal infrared (TIR) electromagnetic energies [9].
ASTER is an instrument on the Terra multi-instrument
spacecraft of Earth Observing System (EOS) program by
NASA. It provides images in 14 wavelength channels
from the optical to thermal infrared. The VNIR subsystem
obtains optical images, with a spatial resolution of 15 m.
The SWIR subsystem also scans optical images of six
bands, with a spatial resolution of 30 m. The TIR
subsystem obtains optical images of five bands with a
spatial resolution of 90 m.
Figure 5. Comparison of Spectral Bands between ASTER and
Landsat-7 Thematic Mapper [10]
Advantages of using ASTER images are elimination of
blue band at visible spectra with respect to Landsat
images and a more specific band to SWIR spectra. The
absent blue band reduces the interference from scattered
rays in the atmosphere. ASTER is freely available at the
Rio Azufrado
Parque los Nevados
Nevado del Ruiz
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server Earth Explorer of the U.S. Geological Survey
(USGS). ASTER imagery can be especially useful for
geosciences applications when comparing with Landsat
due to its higher spectral and spatial resolution. ASTER
bands can be combined to obtain all Landsat spectra and
can emulate its characteristics [11].
ASTER Level 1A data are reconstructed and
unprocessed instrument data at full resolution. They
consist of the image data, the radiometric coefficients, the
geometric coefficients and other auxiliary data without
applying the coefficients to the image data, thus
maintaining original data values. The ASTER Level 1B
has the radiometric and geometric coefficients applied.
These data are stored together with metadata in one HDF
file. For SWIR, data are corrected the parallax errors due
to the spatial locations of all its bands [10]. A data that
contains calibrated at-sensor radiance with the ASTER
Level 1B, that has been geometrically corrected, and
rotated to a north-up UTM projection correspond to a
ASTER Level 1T [12]. This is defined as Precision
Terrain Corrected Registered At-Sensor Radiance, and is
useful to analyze geothermal resources.
Due to an Aster User Advisory, SWIR data acquired
since April 2008 are not useable, and show saturation of
values and severe striping. This is because ASTER
presented anomalously high SWIR detector temperatures.
Then, the images used in this work are before this date
[13].
VI. STATE OF ART
Geothermal exploration is made by characterization of
thermal gradients in boreholes and through geological
mapping. But spectral identification of potential areas of
hydrothermal alteration minerals is a common application
of remote sensing to mineral exploration. Different image
processing techniques such as Crosta methods, band ratio
and false Color composites methods have been used to
analyze the ASTER data.
A. Hydrothermal Alterations
Common types of evidence for the presence of
geothermal resources are presented below. Sulfate-rich
waters upwelling in a fault zone leave behind gypsum
crust following evaporation, indicating geothermal heat
sources. Tuff outcrops include both shoreline deposits
from past higher levels and structurally controlled
deposits that may indicate geothermal Potential.
Argillic alteration minerals, such as kaolinite and
alunite commonly form near the surface above upwelling
plumes of geothermal waters. The presence of kaolinite
and alunite indicates acidic conditions which are made
possible by boiling geothermal groundwater at depth.
Steam generated by boiling tends to be acidic because of
preferential partitioning of volatile solutes such as H2S
and HCl into the vapor phase [9]. Finally, detection of
evaporates and chemical precipitates with remote sensing
can help complete a model of possible geothermal fluid
flow in the subsurface [9].
B. Atmospheric Corrections
ASTER images contain surface reflectance data set
information for each of the nine VNIR and SWIR bands
at 15-m and 30-m resolutions. A correct image analysis
can be obtained by applying an atmospheric correction to
radiances reported by the ASTER sensor. The
atmospheric correction removes effects due to changes in
satellite-sun geometry and atmospheric conditions. Also,
an accurate atmospheric correction improves surface type
classification and Earth's radiation budget estimations.
The atmospheric correction algorithm is applied to
clear-sky pixels only and the results are reported as a
number between 0 and 1. The atmospheric correction
algorithm used to retrieve the surface reflectance relies on
a look-up table (LUT) approach [10].
C. Color Composites
A color-infrared composite is used while manually
examining the full-range reflectance spectra of extreme
pixels and are related with anomalous presence of
minerals. In this way, there are distinct color
compositions to determine minerals that may be evidence
of a specific phenomenon. By example, detected gypsum
can be concentrated at the surface by diffuse capillary
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evaporation of geothermal waters that are inherently rich
in sulfur.
Color composite ortho-images and pan-sharpened color
composites of ASTER like 468 (RGB) correspond to a
false color image for distinguishing hydrated minerals by
hydrothermal alteration products. Other false color
images like 721 and 631 (RGB) permits a maximum
discrimination of geological information (solid and drift
lithologies) in semi-arid and arid regions
D. Band Ratios
Band Ratio images are known for improving spectral
contrasts between the different bands considered in the
ratio and have successfully been used in mapping
alteration zones [14]. Relative band-depth and band-ratio
images are designed to detect the presence of diagnostic
mineral absorption features. Also, is possible minimize
reflectance variations related to topographic slope and
albedo differences [15]
To evaluate the accuracy of the resultant argillic
alteration map, a Relative Band-Depth (RBD) image was
produced to display the extent of Al-OH clays in the study
area. This ratio was calculated as adding band 4 and band
7 and dividing by band 6: (b4+b7)/(b6). In the resultant
image, argillically altered rocks are enhanced with bright
pixels [16]. The ASTER satellite has 14 spectral bands
offering many permutations of Band Ratios, and spectral
matching analyses were applied to the SWIR and TIR
datasets [17]. Some of these permutations applied to
lithologic and mineralogical indices are Alunite-Kaolinite
(b4+b6)/b5 and Illite-Smectite (b5+b7)/b6.
E. Principal Components Analysis
Crosta and McM-Moore [18] describe a methodology
called Feature Oriented Principal Components Selection
(FPCS) [2]. Crosta technique is also known as feature
oriented principal component selection. The principal
component (PC) transformation is a multivariate
statistical technique that selects uncorrelated linear
combinations of variables to extracted linear combination
with a smaller variance [3].
Principal component analysis (PCA) is a multivariate
technique that analyzes a dataset where observations are
described by several inter-correlated quantitative
dependent variables. This analysis has the objective of
extracting the important information from the data, to
represent it as a set of new orthogonal variables called
principal components, and to display the pattern of
similarity of the observations and the variables as points
in maps. The quality of the PCA model can be evaluated
using cross-validation techniques [19].
Principal components analysis of four bands generates
four components (PC1, PC2, PC3 and PC4). Generally,
the forth component PC4 can be used as a good material
for discriminating phyllosilicates which are the main
features of alteration. Some of bands used for identifying
minerals are Alunite (bands 1, 3, 5, 7), Illite (bands 1, 3,
5, 6), Kaolinite and Smectite (bands 1, 4, 6, 9), Kaolinite
(bands 1, 4, 6, and 7) [13].
Additionally, analysis of two and three components can
be obtained. PC2 image of selective principal components
analysis on bands 4 and 6 of ASTER show clay minerals
of argillic alteration enhanced as bright pixels [22].
Alternatively, PC3 ASTER SWIR bands for mapping the
alteration zones. Some of these are phyllic alterations with
minerals muscovite/Illite (bands 5,6,7), argillic alterations
with Kaolinite/dickite/montmorillonite (bands 4,5,6), and
propylitic alterations chlorite/epidote (bands 7,8,9). The
bands were chosen based on the spectral characteristics of
the alteration minerals [20].
VII. METHODOLOGY
A. Images
Earth Explorer is a tool where users search catalogs of
satellite and aerial imagery, providing basic information
for on-line access to remotely-sensed data from the U.S.
Geological Survey Earth Resources Observation and
Science (EROS) Center archive. It is a new and improved
version that allows the download of data over
chronological timelines, a wide range of specifying search
criteria, aerial photography, satellite data, elevation data,
land-cover products, and digitized maps [21].
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To select the work images an area of interest with low
vegetation coverage was introduced and arid or semiarid
zones were defined. The image set consists of Level 1T,
calibrated at sensor radiance, and corresponds with the
ASTER Level 1B, but has been geometrically corrected,
and rotated to north-up UTM projection. Additional
criteria for image selection was low cloud coverage (30%-
20%), and date (before SWIR sensor damage, in April
2008).
B. Software
The standard product provides by Earth Explorer
consist of a Hierarchical Data Format file (HDF) that
comprehend all data of each band. HDF was developed
by the National Center for Supercomputing Applications
(NCSA) at the University of Illinois. It was originally
developed for handling massive arrays to support
supercomputing applications. HDF-EOS is a specific
implementation of HDF to handle lines, grids, and points
[10].
During the study, data analyses were carried out using
ENVI 5 image analysis software. It is used to extract
meaningful information from imagery. This is a modern
system of image processing that analyze multispectral
data of airborne and satellite remote sensing. It works
with stereo images, individual bands and sets of bands.
ENVI is written in interactive data language IDL matrix
based [22]. It is user friendly and very practical for image
treatment. The ASTER images are opened like EOS
optical sensor.
When the HDF file is opened, it is divided in three parts
of the spectra: 3 layers for VNIR, 6 layers for SWIR and
5 layers for TIR. The Data Manager tool allow choosing
the RGB bands to obtain color composites. A stack
operation is required to generate color composites
between layers in different parts of the spectra.
Histograms of digital numbers analysis are important to
process the image because these values are between 0
(black color) to 255 (white color) and can be converted
into reflectance values. Equalization of histograms
allows optimization of the dynamic range of the bands by
dividing gray levels in an equal number of pixels to each
color. An equalization of histograms for each color is
necessary to provide a better perception of the objects in
the image [23].
C. Color Composites
The first stage of image analysis is to develop a Color
composite of ASTER images in ENVI software using the
tool Band Selection in Data Manager. Bands selected to
obtain false color images to distinguish hydrated minerals
by hydrothermal alteration products are 4, 6, 8
corresponding to red, blue and green colors (RGB). Other
false color images like 7, 2, 1 maximizes discrimination
of geological information in semi-arid and arid regions
similar to bands 6, 3, 1.
D. Band Ratio
A second stage corresponds to obtaining a more
detailed information about the alteration in area which is
done using a band ratio composite. Along those lines, it is
necessary to apply an atmospheric correction in bands to
eliminate errors in reflectance values. Envi tool QUAC
(Quick Atmospheric Correction) provides a better quality
of each images at different range of bands. Because
corrected images are separated into three parts of
spectrum (VNIR, SWIR, TIR) a stacking of these parts
generates only one image. In this way, it is possible to
build band ratio between bands of VNIR and SWIR.
Then, bands of corrected images are divided by others
through ENVI tool Band Ratio. For example, the 4/7 band
ratio shows the contrast between reflectance in these
bands. Also, combinations of bands ratio like 4/5, 4/6, 4/7
in a RGB composition image generates a resulting image
with alteration zones due to high responses in reflectance
quotients. Other compound band ratios of RGB (2/1, 4/9,
3/2) generates an image in which yellow color represents
the presence of hydrothermal alteration by the presence of
hydroxyl bearing minerals [3].
Other operations with bands are related to Relative
Band-Depth (RBD), for example, band operation
(b4+b7)/(b6) was chosen because it displays the extent of
Al-OH clays in the study area. This ratio was calculated
by adding bands with ENVI tool Band Math of band 4 and
band 7, obtaining an image. This image is divided by band
6 with the Band Ratio tool. In the resultant image,
argillically altered rocks are enhanced with bright pixels.
In that order, we try various color composites related with
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mineral contents through ENVI software applying
previous filters.
E. Principal Components
A third step consist of applying Crosta Method to
identify areas with anomalous content by analysis of
principal components. ENVI software has the Forward
PCA Rotation New Statistics and Rotate tool to obtain a
principal component calculation with a specific number
of bands. These bands can be selected by the spectral
subset generated by stacked atmospheric corrections.
Principal components analysis of two and four
components can be obtained. Two bands PCA generates
two components (PC1 and PC2). PC2 monochromatic
image of bands 4 and 6 of ASTER was selected because
this image shows clay minerals of argillic alterations
enhanced as bright pixels [16]. Four-band PCA generates
four components (PC1, PC2, PC3 and PC4). Generally,
the monochromatic image of the fourth component PC4
was chosen because it is useful for discriminating
phyllosilicates. Minerals that can be extracted are Alunite
(bands 1, 3, 5, 7), Illite (bands 1, 3, 5, 6), Kaolinite and
Smectite (bands 1, 4, 6, 9) and Kaolinite (bands 1, 4, 6,
and 7) [13].
PCA statistics can be analyzed to explain the main
features of the obtained images. It is possible to know
values of alteration minerals in the fourth component,
which can be high (bright pixels) or low (dark pixels). If
low pixels are obtained, it is necessary to apply an inverse
of the image to produce bright pixels and enhance them.
This analysis takes into account the spectral response of
the mineral to be studied, then it is possible determine the
characteristic band and a comparison with responses of
other bands with the image treatment.
Finally, the images obtained will be compared with the
geology of the zone, reports in the literature and previous
geothermal studies to determine areas with specific
alteration minerals. It is possible to identify known areas,
stratigraphic formations and its implications in the
geologic framework related to hydrothermal systems. A
comparison between processed images allows to
determine if some areas correspond to hydrothermal
alteration when they are not present in all image
processing
VIII. RESULTS
Digital image processing techniques were applied to
ASTER images in volcanic areas across the Andes that do
not have studies of satellite surface and prospective maps.
Geothermal studies are mainly based on geology and
temperature gradient borehole surveys. By the study of
one area with a potential geothermal field, El Tatio
volcanic complex, and identification of alteration
minerals and hydrothermal sources, it is possible to
develop the study of some Colombian volcanos such as
Azufral and Nevado del Ruiz.
A. El Tatio
The first area studied is the volcano called El Tatio
(Chile). An ASTER image of December 13th, 2006, was
selected without cloud cover in the volcano area. By false
color composites, it is possible to obtain images like
Figure 6. With bands 321 (Figure 6 A) it's also possible to
observe blue and dark predominant colors near the
principal lava dome with a peak of absorption at 0.55
micrometers. There are a few spots of red color because
agriculture lands and vegetation are not present.
Predominant yellow colors are observed in flat areas with
juvenile fragments of pyroclastic rocks. At La Torta
formation there is a rhyolitic lava dome which is white to
cyan colors, however there is no evidence of alteration.
With bands 468 (Figure 6C) a peak of absorption is
located at the red band and present in some lava domes
near the Vn. El Tatio and Tocorpurí corresponds to a
possible hydrothermal alteration. Dark colors represent
principally volcanic areas with the presence of local
volcanoclastic products. High brightness areas
correspond to flat areas with ignimbrite deposits of
external provenance. La Torta formation have a white
color and do not present hydrothermal alterations.
With bands 631 (Figure 6B) a peak of absorption at 2.2
micrometers is associated to blue color present in crater
rims related to hydrothermal alterations. Red colors are
presented in flat areas and in some mountain borders.
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Green colors are presented in river zones near Tukle
volcanic group and geothermal fields of El Tatio. Dark
colors are mountain areas.
Finally, with band 721 (Figure 6D) it is important to
show that cyan and red colors are predominant in the
image. The spectral profile was generated to a minimum
of 2 micrometers, related to the seventh band. This band
occurs mainly in the surroundings of the volcanic zone.
The colors in the last two images are very similar and
correspond to geologic discrimination pattern. Green
colors in river zones at the previous images changes to
dark red colors in this image. La Torta formation have
bright colors, but not blue colors due to recent apparition
and low alteration.
According to the above, the hydrothermal alteration
was enhanced with a 468-band combination, it shows a
zoomed image (Figure 7) over principal volcanic areas.
This image will be compared with the band ratio analysis
and the PCA. When the band ratio analysis was applied to
SWIR bands, it is possible to differentiate the altered zone
of other responses generated by the false color
composites. Large values give a more visible color of the
thermal alterations, demonstrating the presence of
specific minerals.
In view of the good results generated by the band ratio
4/7, a green scale it was established with principal areas
of hydrothermal alterations in Figure 8. These areas
around Tocorpurí, El Tatio and Tukle presents mineral
alteration with high values in the band ratio. If compared
with 4,6,8 RGB composites, red colors correspond to
areas that were found with hydrothermal alterations.
When a color composite of band ratios (2/1, 4/9, 3/2) is
produced in Figure 9, it is possible to observe yellow
colors with alteration zones corresponding to previous
areas. Green colors are predominant in the image and are
present in some areas of alterations, but also covers La
Torta formation.
Figure 6. Color compositions of El Tatio volcano (Scale: 1:629000) A: left upper position a False Color Composition of bands (3,2,1),
B: at left lower position bands (6,3,1), C: at right upper position bands (4,6,8) and finally D at lower position (7,2,1)
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Figure 7. El Tatio Volcano at 4,6,8 RGB color composite at
1:200000 scale
Figure 8. Band Ratio 4/7 of el Tatio volcano at 1:200000 scale
Figure 9.Band Ratio False Color Composite (2/1,4/9,3/2) of el Tatio
volcano at 1:200000 scale
Figure 10. Band Ratio False Color Composite (4/5,4/6,4/7) of el Tatio
volcano at 1:200000 scale
Figure 11. Band Ratio of a relative band-depth RBD (4+7)/6 of el Tatio
volcano at 1:200000 scale
Figure 12. PC2 of ASTER bands 4 y 6 of el Tatio volcano at 1:200000
scale
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For that reason, these do not correspond completely to
alteration zones, instead, it shows bright areas in the band
ratio in visible parts of the spectra. Blue colors are present
on the western part of Figure 9 in flat areas and Red colors
in the eastern part of the image, mainly in volcanic areas of
Tocorpurí.
A similar analysis for band ratio composition (4/5, 4/6,
4/7), in Figure 10, generates high intensity values in
hydrothermal alteration zones, excluding La Torta
formation. This band ratio only takes into account bands of
SWIR part of the spectra, it eliminates natural color effect
from visible colors. A blue predominant color in the image
corresponds to high values in 4/7 band ratio.
Figure 13. PC4 of ASTER bands 1,3, 5,7 of el Tatio. Brightness
pixels indicates presence of Alunite
Figure 14. PC4 of ASTER bands 1,3,5,6 of El Tatio. Brightness
pixels indicates presence of Illite
Relative Band Depth display the distribution of clays in
the study area corresponding to the areas mentioned in the
earlier discussion. As a result of these areas, it is possible to
show that hydrothermal alterations are enhanced using the
ratio of bands 4 and 7 between band 6 in Figure 11 as can
be observed in the last band ratio composition.
Principal Components analysis of two bands 4 and 6,
generates a two PCA (PC1 and PC2). When we observed
the second component PC2 is possible to enhance argillic
minerals with red colors as in the Figure 12. A better
mineral extraction is possible if the procedure is done with
four bands of SWIR part of spectra.
Figure 15. PC4 of ASTER bands 1,4,6,7 of El Tatio. Brightness
pixels indicates presence of Kaolinite and Smectite
Figure 16. PC4 of ASTER bands 1,4,6,9 of El Tatio. Brightness
pixels indicates presence of Kaolinite
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PC4 generates, for example, a mapping of Alunite with
bands 1, 3, 5, 7 and describes a gray scale with high values
of pixels (bright) related to this mineral in surface in Figure
13. Alunite is located principally in eastern part of El Tatio,
Tukle formation, and some points over Tocorpurí.
Alunite has high reflectance values in ASTER bands 3
and 7 and absorbs strongly in bands 1 and 5; the PCA
eigenvector statistics of these bands shows that PC4 has a
high and negative loading from band 3 (-0.534) and high
and positive loading from band 1 (0.822), indicating that
pixels likely to contain this mineral will be represented by
low (dark) DN values in PC4. Values for bands 1 and 7 are
lower values (0.198 and 0.011), indicating that the relevant
spectral contrast is between two initial values.
Table 1. Eigenvector statistics for ASTER bands 1, 3, 5, 7 (Alunite).
Eigenvector Band 1 Band 3 Band 5 Band 7
PC1 0.314 0.642 0.395 0.578
PC2 0.410 0.548 -0.187 -0.704
PC3 0.239 0.052 -0.878 0.412
PC4 0.822 -0.534 0.198 0.011
PC4 of Illite with bands 1, 3, 5, 6 in Figure 14 describes
a gray scale with more dispersed values, where white values
of pixels (bright) are related to this mineral in surface and
dark values do not. Illite is located principally in eastern
part of El Tatio and over Tocorpurí volcano but also in flat
areas and other places. Illite has high reflectance values in
ASTER bands 3 and 6 and absorbs strongly in bands 1 and
5.
PCA eigenvector statistics of these bands shows that PC4
has a high and negative loading from band 5 and high and
positive loading from band 6, indicating that pixels likely to
contain this mineral will be represented by low (dark)
digital numbers DN values in PC4. Values for bands 1 and
7 are lower values, indicating that the relevant spectral
contrast is between two initial values. This contrast of bands
is low then generates dispersed values in the image.
Table 2. Eigenvector statistics for ASTER bands 1, 3, 5, 6. (Kaolinite)
Eigenvector Band 1 Band 3 Band 5 Band 6
PC1 -0.350 -0.713 -0.435 -0.423
PC2 -0.428 -0.454 0.525 0.579
PC3 -0.832 0.534 -0.120 -0.089
PC4 0.037 0.012 -0.722 0.691
PC4 of Kaolinite with bands 1, 4, 6, 7 in Figure 16 and
the PC4 of Kaolinite -Smectite with bands 1, 4, 6, 9 in
Figure 15 describe a gray scale with enhanced values,
where bright pixels are related to this mineral at the surface
and dark values do not. Kaolinite is located principally in
the eastern part of El Tatio and Tocorpurí volcano. This
mineral has high reflectance values in ASTER bands 4, 7
and 9 absorbs strongly in bands 1 and 6. PCA eigenvector
statistics of these bands shows that PC4 has a high and
negative loading from band 4, additionally, high and
positive loading from band 6, indicating that pixels likely to
contain this mineral will be represented by low (dark) DN
values in PC4. This bands have a great spectral contrast and
generate a better enhanced of values.
Table 3. Eigenvector statistics for ASTER bands 1, 4, 6, 7.
(Smectite -Kaolinite)
Eigenvector Band 1 Band 4 Band 6 Band 7
PC1 0.348 0.493 0.439 0.666
PC2 0.840 0.064 0.074 -0.534
PC3 -0.406 0.371 0.667 -0.502
PC4 0.095 -0.785 0.597 0.137
Table 4. Eigenvector statistics for ASTER bands 1, 4, 6, 9.
(Kaolinite)
Eigenvector Band 1 Band 4 Band 6 Band 9
PC1 0.354 0.499 0.445 0.654
PC2 0.924 -0.092 -0.112 -0.354
PC3 -0.122 0.229 0.745 -0.615
PC4 0.077 -0.831 0.485 0.262
With this in mind, it is possible to affirm that
hydrothermal alterations are present in the Tocorpurí and
Geothermal Field of el Tatio. These minerals are a good
indicator of geothermal fluids and temperature gradients.
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B. Azufral
At the second area, Azufral volcano, an ASTER image
of January 28th, 2004, was selected with low cloud cover
in western part and good center of volcanic cone. By false
color composites, it is possible obtain images like Figure
17. With bands 321 in the Figure 17A it is possible to
observe a green predominant color over volcano cone,
surroundings and arid areas with a peak of absorption in
0.66 micrometers. The red color represents agriculture
lands and vegetation. A low area is covered by blue
pixels.
With bands 468 (Figure 17C) a peak of absorption is
located at 2.4 micrometers that correspond to blue band
is presented in mountain peaks and arid areas near to
Azufral like Chimangual and Cumbal. Small blue points
around the volcanic cone are possible to observe that
could correspond to hydrothermal alterations. Red and
brown colors are present in vegetation and agriculture
lands. Also, dark pixels are present in the surroundings of
the volcanic cone.
With bands 631 (Figure 17B) a peak of absorption at
2.2 micrometers is related to red and purple color
presented in volcano borders. A predominant green color
in the image is caused by presence of vegetation. The
Darkest green pixels are replacing dark colors presented
in the last images.
Finally, with bands 721 (Figure 17D) it shows a
predominant green and blue color in the image. The
spectral profile generated to minimum of 2 micrometers,
related to a seventh band and red colors. Green colors are
present mainly on surroundings of volcano zone. Orange
and red pixels exist over the volcanic cone and at the
peaks of Cumbal and Chimangual mountain arm, and
could be evidence of hydrothermal alterations. Blue
colors are located in vegetation zones.
Figure 17. Color compositions of Azufral volcano area (Scale: 1:620000): A at left upper position a False Color Composition of
bands (3,2,1), B: at left lower position bands (6,3,1), C: at right upper position bands (4,6,8) and finally D:at lower position (7,2,1)
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Figure 18. Azufral Volcano at 4,6,8 RGB color composite at
1:200000 scale
Figure 19. Band Ratio 4/7 of Azufral volcano at 1:200000 scale
Figure 20. Band Ratio False Color composite (2/1,4/9,3/2) of
Azufral volcano at 1:200000 scale
Figure 21. Band Ratio False Color composite (4/5,4/6,4/7) of
Azufral volcano at 1:200000 scale
Figure 22. Band Ratio of a relative band-depth RBD (4+7)/6 of
Azufral volcano at 1:200000 scale
Figure 23. PC2 of ASTER bands 4 y 6 of Azufral volcano at
1:200000 scale
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If it is compared with last images, it is possible to
establish that hydrothermal alterations were better
highlighted in the 468-band color composite. We obtain a
zoomed image at 1:200000 scale (Figure 18) over principal
volcanic areas and compare band ratio analysis with PCA.
When band ratio analysis was applied it is possible to
differentiate the altered zone of other responses generated
by a false color composite. A more visible color of the
thermal alterations is generated by high values of band
ratio, demonstrating the presence of specific alteration
minerals.
When the band ratio 4/7 was developed, we get a green
scale with principal areas of hydrothermal alterations in
Figure 19. These areas around the northeast and western
part of Azufral volcano, peak of Chimangual mountain and
Cumbal present alteration minerals with high values in the
band ratio. If this image is compared with color composites,
some dark and blue colors correspond to areas that were
found.
On the other hand, it is possible to observe that in Figure
20, yellow colors appear in Azufral volcano in the color
composite RGB of band ratio (2/1, 4/9, 3/2). But, those
yellow colors do not correspond to aforementioned areas in
last analysis. Purple colors are predominant in the image
and are present in some areas of alterations as described
before. For that reason, yellow colors could not correspond
totally to alteration zones, instead, show brightness areas by
the band ratio in visible part of spectra as volcano lake. Red
colors are presented mainly in Cumbal lake and mountain,
Chimangual peak and some areas around Laguna Verde in
Azufral peak.
A similar analysis for band ratio composition (4/5, 4/6,
4/7) generates green values in hydrothermal alteration
zones in Figure 21, related with dark areas in (468) RGB
false color composite. This band ratio only takes into
account bands of SWIR part of the spectra, it eliminates the
natural color effect in visible colors such as red in the last
band ratio. A blue predominant color in the image
corresponds to brightness values in 4/7 band ratio related to
the volcanic cone. White pixels correspond to vegetation
and flat areas. Relative Band Depth of bands 4 and 7
between band 6 in Figure 22, display the distribution of
clays in the study area corresponding to the areas mentioned
in the yellow colors at band ratios (2/1, 4/9, 3/2).
Figure 24. PC4 of ASTER bands 1,4,6,7 of Azufral. Brightness pixels
indicates presence of Kaolinite
Figure 25. PC4 of ASTER bands 1,4,6,9 of Azufral. Brightness pixels
indicates presence of Kaolinite and Smectite
Figure 26. PC4 of ASTER bands 1,3,5,7 of Azufral. Brightness pixels
indicates presence of Alunite
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As result of these areas, is possible to affirm that
hydrothermal alteration are enhanced by the ratio of like
could be observed in the last band ratio composition.
Principal Components analysis of bands 4 and 6,
generates a second component PC2 showing enhanced
argillic minerals with red colors in the Figure 23. A better
mineral extraction is possible if we work with four bands of
SWIR part of spectra.
PC4 generates a mapping of Alunite with bands 1357 in
Figure 24 describing a gray scale with high values of pixels
(bright) related to this mineral in surface. Alunite is located
principally in the surroundings of the volcanic cone,
Chimangual formation, and some points over Cumbal
volcano. Bright values over flat areas can be affected by
vegetation coverage in the area and do not correspond to
alteration minerals.
Alunite has high reflectance values in ASTER bands 3
and 7 and absorbs strongly in bands 1 and 5; the PCA
eigenvector statistics of these bands shows that PC4 has a
high and negative loading from band 1 (-0.625) and high
and positive loading from band 7 (0.660), indicating that
pixels likely to contain this mineral will be represented by
low (dark) DN values in PC4. Values for bands 3 are lower
values (0.020), indicating that the relevant spectral contrast
is between two initial values but band 5 have a great value
(-0.147) that could generate noise in dataset.
Table 5. Eigenvector statistics for ASTER bands 1, 3, 5, 7 (Alunite).
Eigenvector Band 1 Band 3 Band 5 Band 7
PC1 0.352 0.626 0.402 0.568
PC2 0.240 -0.779 0.343 0.468
PC3 -0.654 0.032 0.740 -0.153
PC4 -0.625 0.020 -0.417 0.660
PC4 of Illite with bands 1, 3, 5, 6 describes a gray scale
with very dispersed values, where as white values of pixels
(bright) are related to this mineral in surface and dark values
do not. It was not possible generate a mapping of Illite
because the image is not clear and could be noise.
Illite has high reflectance values in ASTER bands 3 and
6 and absorbs strongly in bands 1 and 5; the PCA
eigenvector statistics of these bands shows that PC4 has a
high and negative loading from band 6 and high and
positive loading from band 5, indicating that pixels likely to
contain this mineral will be represented by low (white) DN
values in PC4. Values for bands 1 and 7 are lower values,
indicating that the relevant spectral contrast is between two
initial values. This contrast of bands is low then generates
dispersed values in the image.
Table 6. Eigenvector statistics for ASTER bands 1, 3, 5, 6. (Kaolinite)
Eigenvector Band 1 Band 3 Band 5 Band 6
PC1 0.384 0.696 0.443 0.415
PC2 0.240 -0.709 0.461 0.476
PC3 0.891 -0.109 -0.294 -0.327
PC4 0.026 0.018 -0.710 0.703
PC4 of both Kaolinite and Smectite with bands 1469 in
Figure 25 and PC4 Kaolinite with bands 1467 in Figure 26
and the describe a gray scale with enhanced values, where
bright pixels are related to this mineral in surface and dark
values do not. Kaolinite is located principally at the
volcanic cone of Azufral but Smectite has a smaller
coverage area over volcano. Smectite is principally found
in southern part of Volcano.
These minerals have high reflectance values in ASTER
bands 4, 7 and 9 absorbs strongly in bands 1 and 6. PCA
eigenvector statistics of these bands shows that PC4 has a
high and negative loading from band 7, additionally, high
and positive loading from band 6, indicating that pixels
likely to contain this mineral will be represented by low
(dark) DN values in PC4. This bands have a great spectral
contrast and generate a better enhanced of values.
Table 7. Eigenvector statistics for ASTER bands 1, 4, 6, 7.
(Smectite- Kaolinite)
Eigenvector Band 1 Band 4 Band 6 Band 7
PC1 0.381 0.541 0.418 0.622
PC2 0.546 -0.692 -0.223 0.417
PC3 0.532 0.458 -0.653 -0.286
PC4 0.524 -0.138 0.591 -0.598
Table 8. Eigenvector statistics for ASTER bands 1, 4, 6, 9. (Kaolinite)
Eigenvector Band 1 Band 4 Band 6 Band 9
PC1 0.421 0.607 0.468 0.484
PC2 0.801 -0.582 -0.084 0.113
PC3 -0.406 -0.537 0.534 0.511
PC4 -0.123 0.066 -0.699 0.701
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It was possible to establish that hydrothermal
alterations are mainly located between southern part of
Azufral volcano and Chimangual.
C. Nevado del Ruiz
The third area, Nevado del Ruiz volcano, has an
ASTER image of May 07th, 2003, and was selected with
cloud cover of 30% out of volcano area in the Southeast
and Northwest of image. It is possible obtain images like
Figure 27 by false color composites. With bands 321 of
visible part of spectra in the figure 27A it is possible to
observe a green and blue predominant color into volcano
cone and arid areas with a peak of absorption in 0.66
micrometers. The red color represents agriculture lands or
vegetation and white points are principally snow and
clouds. Nevado del Ruiz area is covered by blue pixels
around the snowy part of volcanic cone. Green pixels are
referred to arid zones in the surroundings of volcanic
cones. Dark pixels could be related with the presence of
hydrothermal alterations.
With bands 4,6,8 (Figure 27C) a peak of absorption is
located at 2.4 micrometers that corresponds to the blue
band is presented in mountain peaks and arid areas. Blue
pixels are related with band 8 that could indicate the
presence of arid areas in the volcanic cone. Snow appears
in black colors in Nevado del Ruiz and Parque los
Nevados. High bright pixels in volcanic areas could
correspond with dark areas described to 321 RGB color
composite. Green colors are present in arid areas near to
volcanic cones.
With bands 6, 3, 1 (Figure 27B) a peak of absorption
corresponds to 2.2 micrometers which is related with the
red color present in volcano borders indicating arid zones.
Purple values in pixels show similar areas of blue colors
in the previous bands analysis. Blue values indicate the
presence of snow in volcanic peaks. White values inside
the volcanic cone can be related with alteration areas.
Green values are present because of vegetation coverage.
Finally, with a band 7,2,1 (Figure 27D) it is important to
show a predominant green and yellow color in the image
around volcanic cones.
Figure 27. Color compositions of Nevado del Ruiz volcano area (Scale: 1:619238): A at left upper position a False Color Composition
of bands (3,2,1), B: at left lower position bands (6,3,1), C: at right upper position bands (4,6,8) and finally D:at lower position (7,2,1)
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Figure 28. Nevado del Ruiz Volcano at 4,6,8 RGB color composite
at 1:200000 scale
Figure 29. Band Ratio 4/7 of Nevado del Ruiz volcano at 1:100000
scale
Figure 30. Band Ratio False Color composite (2/1,4/9,3/2) of
Nevado del Ruiz volcano at 1:100000 scale
Figure 31. Band Ratio False Color composite (4/5,4/6,4/7) of
Nevado del Ruiz volcano at 1:100000 scale
Figure 32. Band Ratio of a relative band-depth RBD (4+7)/6 of
Nevado del Ruiz volcano at 1:100000 scale
Figure 33. PC2 of ASTER bands 4 y 6 of Nevado del Ruiz
volcano at 1:200000 scale
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The spectral profile generated to minimum of 2
micrometers, is related to a seventh band. Blue values are
present principally in the volcanic zone, and gray pixels in
the volcanic peaks.
Hydrothermal alteration was better enhanced in 4,6 and 8
bands. We obtain a zoomed image at 1:100000 scale
(Figure 28) due principal volcanic area is smaller than
others two areas. When I apply band ratio analysis of bands,
it is possible differentiate the altered zone of other
responses generated by the false color composites, for
example snow in volcanic cone and in Parque los Nevados.
Large values of band ratio generated in the image a more
visible color of the thermal alterations demonstrating the
presence of specific alteration minerals in volcano borders.
Figure 34. PC4 of ASTER bands 1,3,5,6 of Nevado del Ruiz.
Brightness pixels indicates presence of Illite
Figure 35. PC4 of ASTER bands 1,4,6,7 of Nevado del Ruiz.
Brightness pixels indicates presence of Kaolinite
When band ratio 4/7 is generated (Figure 29), a green
scale shows principal areas of hydrothermal alterations.
These areas around south and western part of Nevado del
Ruiz volcano and near to rivers and arid areas present
alteration minerals with high values in the band ratio. If it
is compared with 4,6 and 8 compositions, some white colors
correspond to areas that were found over volcanic cone
related with rivers and geysers, principal sources of
geothermic alterations.
When a color composite of band ratios (2/1, 4/9, 3/2) is
created in Figure 30, it is possible to observe green colors
in Nevado del Ruiz volcano corresponding to previous
areas. Purple and dark blue colors are predominant in the
image and are present in volcanic areas and the dome.
Figure 36. PC4 of ASTER bands 1,3,5,7 of Nevado del Ruiz.
Brightness pixels indicates presence of Alunite
Figure 37. PC4 of ASTER bands 1,4,6,9 of Nevado del Ruiz.
Brightness pixels indicates presence of Kaolinite-Smectite
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Brightness areas and cyan colors by the band ratio in
visible part of spectra correspond to vegetation coverage
and flat areas. Red colors are not presented mainly by low
values of band ratio 2/1 compared to the other bands.
A similar analysis for band ratio composition (4/5, 4/6,
4/7) generates green and yellow values in hydrothermal
alteration zones, related with dark areas in (468) RGB false
color composite. This band ratio only takes into account
bands of SWIR part of the spectra, then it eliminates natural
color effect in visible colors like red colors in the previous
band ratio. A blue and purple predominant color in the
image corresponds to vegetated areas in Figure 31.
Dark pixels correspond to volcano body and snow.
Relative Band Depth in a red scale display the distribution
of clays in the study area corresponding to the areas
mentioned in the yellow colors at band ratios (2/1, 4/9, 3/2).
As result of these areas, it is possible to affirm that
hydrothermal alteration are enhanced by the ratio of bands
4 and 7 between band 6, as can be observed in the last band
ratio composition (Figure 32).
Principal Components analysis of two bands 4 and 6,
generates a two PCA (PC1 and PC2). When it was observed
the second component PC2, it is possible to enhance argillic
minerals with red colors in the Figure 36. A better mineral
extraction is possible if we work with four bands of SWIR
part of spectra. PC4 generates, for example, a mapping of
Alunite with bands 1, 3, 5, 7 and describes a gray scale with
high values of pixels (bright) related to this mineral in
surface. Alunite is located principally in eastern part of
Nevado del Ruiz volcano, in surroundings of the rivers.
Table 9. Eigenvector statistics for ASTER bands 1, 3, 5, 7 (Alunite).
Eigenvector Band 1 Band 3 Band 5 Band 7
PC1 0.331 0.859 0.286 0.267
PC2 0.598 -0.512 0.446 0.426
PC3 -0.715 -0.030 0.624 0.316
PC4 -0.146 -0.002 -0.575 0.805
Alunite has high reflectance values in ASTER bands 3
and 7 and absorbs strongly in bands 1 and 5; the PCA
eigenvector statistics of these bands shows that PC4 has a
high and negative loading from band 7 (-0.575) and high
and positive loading from band 5 (0.805), indicating that
pixels likely to contain this mineral will be represented by
low (dark) DN values in PC4. Values for bands 1 and 3are
lower values (-0.146 and -0.002), indicating that the
relevant spectral contrast is between two initial values.
PC4 of Illite with bands 1, 3, 5, 6 and describes a gray
scale with more dispersed values, where white values of
pixels (bright) are related to this mineral in surface and dark
values do not. Illite is located principally in surroundings of
volcanic area but also in flat areas and other places. Illite
has high reflectance values in ASTER bands 3 and 6 and
absorbs strongly in bands 1 and 5; the PCA eigenvector
statistics of these bands shows that PC4 has a high and
negative loading from band 5 and high and positive loading
from band 6, indicating that pixels likely to contain this
mineral will be represented by high (bright) DN values in
PC4. Values for bands 1 and 7 are lower, indicating that the
relevant spectral contrast is between two initial values. This
contrast of bands is low then generates dispersed values in
the image.
Table 10. Eigenvector statistics for ASTER bands 1, 3, 5, 6. (Kaolinite)
Eigenvector Band 1 Band 3 Band 5 Band 6
PC1 0.329 0.851 0.285 0.293
PC2 0.490 -0.524 0.458 0.525
PC3 0.806 -0.028 -0.433 -0.403
PC4 -0.042 0.020 -0.722 0.690
PC4 of Kaolinite with bands 1, 4, 6, 7 and the PC4 of
Kaolinite -Smectite with bands 1, 4, 6, 9 describe a gray
scale with enhanced values, where bright pixels are related
to this mineral at the surface and dark values do not.
Kaolinite is located mainly in the eastern part of Nevado del
Ruiz volcano.
Table 11. Eigenvector statistics for ASTER bands 1, 4, 6, 7.
(Smectite-Kaolinite)
Eigenvector Band 1 Band 4 Band 6 Band 7
PC1 0.478 0.651 0.440 0.392
PC2 0.812 -0.572 -0.097 0.068
PC3 -0.298 -0.498 0.716 0.388
PC4 -0.153 -0.028 -0.534 0.831
These minerals have high reflectance values in ASTER
bands 4, 7 and 9 absorbs strongly in bands 1 and 6. PCA
Universidad de los Andes-Departamento de Geociencias
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eigenvector statistics of these bands shows that PC4 has a
high and negative loading from band 6, additionally, high
and positive loading from band 7 and 9, indicating that
pixels likely to contain this mineral will be represented by
high (bright) DN values in PC4. These bands have a great
spectral contrast and generate a better enhanced of values.
Table 12. Eigenvector statistics for ASTER bands 1, 4, 6, 9.
(Kaolinite)
Eigenvector Band 1 Band 4 Band 6 Band 9
PC1 -0.486 -0.663 -0.449 -0.350
PC2 -0.830 0.550 0.086 0.000
PC3 0.268 0.469 -0.409 -0.735
PC4 0.047 0.194 -0.790 0.580
With this in mind, it is possible to affirm that
hydrothermal alterations are present in eastern part of
volcano near to Azufrado river. These minerals are a good
indicator of geothermal fluids and temperature gradients.
IX. CONCLUSIONS
The present work applied three image processing
techniques to ASTER imagery with the software ENVI.
These images reveal a great amount of information across
arid zones, and provide a first study of mineralogy of the
soil. It was found that the images have a similar pattern with
the spectrum profile and absorption wavelength that could
be an indicator of the presence of minerals related to
geothermal alteration, such as gypsum with absorption
features of 2.4 micrometers.
It was determined that the better color composite to
enhance hydrothermal minerals is with bands within SWIR
spectra RGB 468 bands. It was possible to observe in detail
alteration areas related to geological features and spots near
to the volcanoes. Band Ratio processing enhanced different
areas and allowed the discrimination of area with high
values due to line of sight, sun inclination and shades. Color
composites of three band ratios generates a better response
that monochromatic band ratio because differentiate
alteration minerals of possible errors generated by
calculations, clouds, snow, water, etc.
Principal Components Analysis generates an
enhancement of areas with alteration minerals and a
discrimination of four minerals identification areas with
superficial content. The best PCA technique is the fourth
component monochromatic composite of Kaolinite because
generates a good separation of the mineral on the surface.
As result of the presented evidences is possible to sustain
that a combination of digital image processing techniques,
(SWIR false color composite, bands ratios and PCA) are
adequate set of tools for a first survey across volcanic zones,
prospection and exploration programs in geothermal
energies.
X. ACKNOWLEDGEMENTS
The present work was developed, primarily thanks to
God, with the support and accompaniment of my parents,
the unconditional help and strong corrections of some of my
friends (David, Silvia/girlfriend), the attention presented
and recommendation of my Geodesy classmates, the
collaboration of some of my professors from the
department; Natalia Pardo and Jillian Pearse. Finally, I want
to recognize the special commitment, interest and great
disposition of my director Fabio Iwashita, that cheer me up,
and gave me the strength to start and finish this project.
Thank you.
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