Using aster image processing for geothermal energy

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

2

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

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4

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


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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


7

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


9

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].


10

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


11

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.


12

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)


13

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


Figure 11. Band Ratio of a relative band-depth RBD (4+7)/6 of el Tatio


Figure 12. PC2 of ASTER bands 4 y 6 of el Tatio volcano at 1:200000

scale


14

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.


pixels indicates presence of Kaolinite and Smectite


pixels indicates presence of Kaolinite


15

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)


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


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)


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


(Kaolinite)


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.


16

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)


17

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 22. Band Ratio of a relative band-depth RBD (4+7)/6 of


Figure 23. PC2 of ASTER bands 4 y 6 of Azufral volcano at

1:200000 scale


18

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.


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


indicates presence of Kaolinite and Smectite


indicates presence of Alunite


19

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.







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.



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


high and negative loading from band 6 and high and


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.



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


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.


(Smectite- Kaolinite)


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



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


20

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)


21

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


Nevado del Ruiz volcano at 1:100000 scale



Figure 32. Band Ratio of a relative band-depth RBD (4+7)/6 of


Figure 33. PC2 of ASTER bands 4 y 6 of Nevado del Ruiz



22

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


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.


Brightness pixels indicates presence of Alunite


Brightness pixels indicates presence of Kaolinite-Smectite


23

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.


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.



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







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.



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.


(Smectite-Kaolinite)


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


24


high and negative loading from band 6, additionally, high

and positive loading from band 7 and 9, indicating that


high (bright) DN values in PC4. These bands have a great

spectral contrast and generate a better enhanced of values.


(Kaolinite)


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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