Evaluation of the use of hyperspectral imagery for

identification of microseeps near Santa Barbara, California

Project 2 Report for Master of Science in Geology

West Virginia University

By

Heather Freeman

September 26, 2003

Committee:

Dr. Timothy Warner, Chair Dr. Dorothy Vesper

Eberhard Werner

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Abstract

AVIRIS data of the coastline near Santa Barbara, CA, was used to evaluate the

potential of hyperspectral data to identify mineral alteration associated with petroleum

hydrocarbon microseeps. Two spectral matching techniques were used to classify

minerals in the images: Spectral Angle Mapper and Spectral Feature Fitting. The

minerals mapped were alunite, calcite, jarosite, kaolinite, and siderite, all of which have

been identified as being potentially associated with hydrocarbon seeps. In addition, two

vegetation classes were included in the analysis. The Spectral Feature Fitting analysis

was found to be complex, and the results were not regarded as satisfactory. The Spectral

Angle Mapper results were more promising. Although vegetation dominated the

classification, large areas of siderite were identified, as well as smaller areas of jarosite

and kaolinite.

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Table of Contents Abstract ............................................................................................................................ ii Table of Contents ............................................................................................................iii Table of Figures .............................................................................................................. iv 1. Introduction .................................................................................................................. 1 2. Background: Hydrocarbons ........................................................................................ 1 2.1 Remote Sensing of Geologic Structures .............................................................. 3 2.2 Remote Sensing of Vegetation Stress Associated with Seeps ............................. 4 2.3 Remote Sensing of Minerals Associated with Seeps ........................................... 4 2.4 Spectra of Petroleum Hydrocarbons .................................................................... 5 3. Objectives .................................................................................................................... 6 4. Geology of the Santa Barbara Region ......................................................................... 6 5. Data .............................................................................................................................. 7 6. Methods ........................................................................................................................ 9 6.1 FLAASH .............................................................................................................. 9 6.2 The Spectral Angel Mapper ............................................................................... 10 6.3 Spectral Feature Fitting ...................................................................................... 10 6.4 Selection of Data ................................................................................................ 13 7. Results and Discussion .............................................................................................. 13 7.1 SAM Results ...................................................................................................... 13 7.2 SFF Results ........................................................................................................ 17 7.3 Comparison of SAM and SFF Results ............................................................... 18 7.4 Limitations ......................................................................................................... 18 8. Conclusions ................................................................................................................ 19 9. References .................................................................................................................. 20

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Table of Figures Figure 1. Study Area. ...................................................................................................... 7 Figure 2. Spectra of USGS Minerals Selected for Analysis. .......................................... 9 Figure 3. SAM Classified Images. ................................................................................ 15 Figure 4. SFF Classified Image. ................................................................................... 17

1. Introduction

Remote sensing of the earth can potentially provide a wide array of information

not easily acquired from surface observations. For example, remote sensing can be used

to investigate vegetation for leaf water, chlorophyll, cellulose, and leaf structure (Green

et al., 1998). Geologic applications are particularly wide-ranging, reflecting the long

history of remote sensing and air photo interpretation for hydrology, lithologic mapping,

and economic deposit exploration. Hydrological applications of remote sensing include

monitoring suspended sediments in streams (Halbouty, 1976). In recent years,

hyperspectral imagery has opened new opportunities to identify minerals remotely.

Hyperspectral imagery, also known as imaging spectrometry, is the acquisition of many

narrow, contiguous spectral bands (Goetz et al., 1985). Hyperspectral imagery has been

particularly effective for mapping the alteration minerals associated with hydrothermal

economic deposits (Hunt, 1979; Crosta et al., 1998; Buckingham and Sommer, 1983).

Since oil and gas seeps have been documented to alter surface minerals, it may also be

possible to identify macro- and microseepages of oil and gas by mapping mineral

assemblages associated with such alterations.

2. Background: Hydrocarbons

Petroleum exploration began with the search for oil that flowed from surface

rocks. Petroleum seeps from leaking subsurface reservoirs have been recorded as far

back as 3000 B.C. (Tedesco, 1995). Macroseeps are visible oil and gas seeps, such as the

La Brea tar pit in Los Angeles, California. The largest known concentration of seeps

occurs in the Ventura oil-rich basin in the Santa Barbara Channel, California. In 1973,

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the total area of seeps in the Santa Barbara Channel was estimated to be 0.9 km2, but by

1995, the total area of seeps had decreased to 0.4 km2 due to offshore pumping (Quigley,

1999). The tar from these seeps forms masses that float on the ocean and are deposited

on beaches. Another seep example is the km2 Trinidad Asphalt Lake on the Caribbean

island of Trinidad, where a stream of asphalt 5 – 6 meters deep has been documented

floating into the Gulf of Paria (Landes, 1973). By the 1920’s nearly all the visible oil and

gas seeps had been drilled (Tedesco, 1995). Onshore and offshore macroseeps are a form

of natural pollution for beaches and oceans (Landes, 1973). Microseeps are seeps that are

not directly visible, but may produce visible conditions that suggest the occurrence of

seeps.

The vertical migration of oil and gas along fractures is called the chimney effect

(Tedesco, 1995; Donovan, 1974; Berger, 1994). Hydrocarbons are composed of chains

of hydrogen and carbon that comprise gases such as methane, ethane, and propane, as

well as liquid petroleum and semi-solid asphalt and tar (Tedesco, 1995). Petroleum

seeping to the surface encounters geochemical environments different from that of the

source rock or reservoir. Consequently, the petroleum undergoes an alteration, including

the evaporation of volatile hydrocarbons; microbial degradation of petroleum by

oxidation; polymerization of petroleum by eliminating water, carbon dioxide, and

hydrogen, thus producing bitumen; and leaching of water-soluble sulfur and oxygen

compounds through oxidation.

The vertical migration of seeps also causes chemical alterations to the

surrounding rock, and modification of the near-surface weathering processes. Several

mineralogic and chemical alterations produced by the reactions of petroleum

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hydrocarbons can be determined through soil testing: changes in Eh and pH; increase in

trace and major metals; increase in iodine and brines; cementation of clastic sediments;

changes in the concentrations of iron- and magnesium-rich clay minerals due to

complexing by organic acids, and increases in carbonate (Tedesco, 1995). It is important

to note that neither the presence of seeps, nor of alteration minerals associated with seeps,

necessarily indicate that the reservoir is economic. Furthermore, the presence of a seep

implies a poor reservoir seal.

For environmental and economic reasons, future petroleum exploration needs to

be non-invasive and cost effective. Remote sensing is therefore increasingly being used

for hydrocarbon exploration. Remote sensing for hydrocarbon exploration generally

focuses on the identification of indirect evidence of hydrocarbons, including mapping

favorable reservoir structures (Halbouty, 1976). In addition, it is possible to map indirect

evidence of seeps, using the spectra of stressed vegetation (Everett et al., 2002; Yang et

al., 1998), or through the identification of alteration minerals associated with seeps. In

rare cases, it may even be possible to identify hydrocarbons directly, if they accumulate

on the surface in sufficient quantities.

2.1 Remote Sensing of Geologic Structures

Aerial photography and multispectral images have been used to map the location

of lineaments and other geologic features as an aid to hydrocarbon exploration.

Lineaments may in places represent geologic structures related to basement weakness

zones that can cross hydrocarbon-bearing sedimentary basins. Some oil fields have been

documented to cluster along lineaments because the hydrocarbon migration pathways are

controlled by fault/fracture systems (Everett et al., 2002; Halbouty, 1976; Bailey and

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Anderson, 1982). Landsat has been found to be effective for lineament analysis and

mapping subtle spectral features associated with changes in lithology (Halbouty, 1976;

Bailey and Anderson, 1982).

2.2 Remote Sensing of Vegetation Stress Associated with Seeps

The spectra of stressed vegetation can potentially indicate hydrocarbon seeps, but

certain complications can limit the effectiveness of this method. Hyperspectral data have

been used to identify a shift in the “red edge”, the boundary between chlorophyll

absorption of red wavelength energy and the scattering of near-infrared energy by the

mesophyll structures of leaves (Everett et al., 2002; Yang et al., 1998). However, the

plant structural and physiological changes that cause the red edge to shift tend to be

subtle because microseeps occur over longer periods of time than the vegetation life span.

Also, the changes in vegetation are location-specific, depending on climate, drainage, and

soil type (Everett et al., 2002).

2.3 Remote Sensing of Minerals Associated with Seeps

Multispectral remote sensing can be used to detect changes in lithology, while

hyperspectral imagery can potentially be used to identify minerals and differentiate

alteration products. Theoretically, it is possible to identify minerals using hyperspectral

data and a generic library of laboratory mineral spectra, without a priori knowledge of

the area (Crosta et al., 1998; Buckingham and Sommer, 1983; Hunt, 1979; Green et al.,

1998).

Some of the mineralogic changes associated with oil and gas seeps have been

identified in remotely sensed imagery. Bleaching and iron loss in sandstones caused by

seeps have been mapped along the crests of anticlines (Yang et al., 1998; Everett et al.,

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2002; Donovan, 1974; McCoy et al., 2001; Berger, 1994). Seeps may alter gypsum to

carbonate (Donovan, 1974) and also, through the reaction of hydrocarbons with carbon

dioxide, produce secondary carbonate or delta carbonate (Yang et al., 1998). Another

potential indicator of hydrocarbons is enrichment of kaolinite due to the alteration and

depletion of other clays (Yang et al., 1998; Berger, 1994; Buckingham and Sommer,

1983; Donovan, 1974). Finally, certain minerals found predominantly in altered areas,

including jarosite, siderite and alunite, (Buckingham and Sommer, 1983; Everett et al.,

2002), have been used to identify subsurface reservoirs.

2.4 Spectra of Petroleum Hydrocarbons

A more direct method of exploration is to detect hydrocarbon seeps by identifying

the distinct absorption features of hydrocarbons themselves. Hydrocarbons are

characterized by absorption features at 1.72 µm, 1.73 µm, 2.31µm and 2.33 µm (Yang et

al., 1998; Cloutis, 1989; Horig et al., 2001; Hunt, 1979; Ellis et al., 2001; McCoy et al.,

2001; Buckingham and Sommer, 1983). Horig et al. (2001) tested the hyperspectral

sensor HyMap for discrimination of sandy soil, oil-contaminated soil, grass, and plastic

tarpaulin. The visible and near-infrared portions of the spectra were found to be effective

for distinguishing among hydrocarbon-bearing materials such as plastic, roof material,

and soil (Horig et al., 2001). Calcite has absorption features similar to hydrocarbons at

2.335 µm, but the absorption features shapes differ, and calcite does not have

corresponding features at 1.720-1.73 µm (McCoy, et al., 2001; Ellis et al, 2001).

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3. Objectives

The objective of this project was to investigate the potential for identifying

petroleum hydrocarbon seeps using hyperspectral imagery. The study area was the

coastal area near Santa Barbara, California, because this region has many documented

seeps (Figure 1). Seeps were identified in the hyperspectral imagery based on the

classification of mineral spectra. In particular, I mapped the minerals kaolinite, jarosite,

siderite, calcite and alunite, which may indicate hydrocarbon seeps. The mapped

distribution of these minerals was compared to the pattern of known seeps (Minor et al.,

2002; USGS, 1999) in order to determine if the classified mineral patterns appear to be

influenced by the seeps.

4. Geology of the Santa Barbara Region

Southern California is in the Transverse Range physiographic province. Santa

Barbara is bounded by the Santa Ynez Mountains to the north, and the Santa Barbara

Channel to the south (Minor et al., 2002). The topography varies from relatively smooth

at the coast, to rugged in the mountains. The Santa Ynez Mountains begin less than eight

miles from the coast, and the elevation quickly rises to over 750 meters. A common

geographic term used to describe the area is Rincon, which is Spanish for an inside corner

of a cove defined by a point projecting into the sea (Sharp, 1978). Geologically, the area

is unstable and prone to earthquakes and landslides because of the underlying east-west-

trending Santa Barbara fold and fault belt. The marine and non-marine sedimentary

rocks, formed on a coastal-margin, range in age from Quaternary to Tertiary (Sharp,

1978; Minor, 2002). Many of the surficial formations are of shale and sandstone with

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Approximate location of Image #: 13 | 12 | 11 | 10 | 9 | 8 | 7 | 6 | 5 | 4 | 3 | 2 | 1

Figure 1. Study Area. The false-color AVIRIS image of Santa Barbara with the

approximate boundaries of individual images marked, and the USGS map of hydrocarbon seeps (green triangles).

some conglomerate and colluvium deposits. Several surficial asphalt-filled fractures are

associated with a Pleistocene sandstone and Pleistocene and Pliocene siltstone.

5. Data

The primary data for this project was Airborne Visible/Infrared Imaging

Spectrometer (AVIRIS) hyperspectral imagery. AVIRIS is the premier imaging

spectrometer. The AVIRIS sensor is radiometrically and optically calibrated for each

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flight. First flown in 1987, AVIRIS measures radiance from 400 nm to 2500 nm in 224

spectral bands (Green et al., 1998). The AVIRIS sensor is usually flown at 20 km above

the ground surface, producing 20m pixels.

The AVIRIS imagery of the Santa Barbara coast was acquired on September 19,

1998. The flight line was provided by the NASA Jet Propulsion Laboratory in thirteen

individual frames, also referred to as images (Figure 1). The remote sensing software

package ENVI (Research Systems, 2002) was used to pre-process and classify the

images.

A second important data set for this study was the digital geologic and seeps maps

of Santa Barbara (Figure 1) produced by Minor et al. (2002) and the USGS (1999).

These maps were the reference data.

Reference spectra libraries were used to classify the minerals in the images.

Spectra libraries are primarily used with hyperspectral data because the key to

hyperspectral data is to compare image spectra to the generic spectra of minerals

themselves. Spectra for alunite, calcite, jarosite, kaolinite, siderite, sagebrush, and

saltbrush, from the USGS spectra libraries, were used to classify the images (Figure 2).

Classification was attempted with tar and asphalt from the Johns Hopkins University

spectra library, but those spectra were not included in the final classifications. The

library spectra used for classification are referred to as endmembers.

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Key: Siderite Kaolinite Jarosite

Calcite Alunite Figure 2. Spectra of USGS Library Minerals Selected for Analysis.

6. Methods

6.1 FLAASH

The first step in pre-processing was to convert the image radiance data to apparent

reflectance to facilitate comparison with the library reflectance spectra. This process

normalizes for solar illumination and suppresses the effect of the atmosphere, including

spectral absorption and scattering by diffuse gases and particles. Each image was

processed with FLAASH (Research Systems, 2001), an add-on program for ENVI that

uses MODTRAN radiation transfer code and the image spectra themselves to estimate the

spectral reflectance conversion factors. To begin, a scale factor was required for the

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input radiance data. The AVIRIS metadata provided this information in an ASCII

radiance scale factor file. The individual image navigation files provided the image and

sensor information for each image. Because no local radiosonde data were available, the

Mid-Latitude Summer model atmosphere was chosen. This model was selected despite

the User’s Guide (Research Systems, 2001) recommendation to use the tropical model for

the location at the latitude of Santa Barbara (34oN) in September because Santa Barbara

has a Mediterranean climate and is relatively dry in the late summer. The maritime

aerosol model was used. The navigation file listed the weather condition at the time of

flight as clear, so the scene visibility was set to the default value of 40 km. Aerosol

Retrieval and Spectral Polishing were performed with the width of spectral polishing kept

at the default of 9 bands (approximately 900 nm).

Once the radiance was converted to reflectance, minerals were mapped using two

spectral matching methods, described in more detail below.

6.2 The Spectral Angle Mapper (SAM)

The Spectral Angle Mapper (SAM, Kruse et al., 1993) was used to classify the

minerals in the AVIRIS images. SAM is a supervised classification method for

comparing image spectra to library spectra.

Underlying the SAM analysis approach is the conceptualization of the n-band

image spectrum as an n-dimensional vector. The magnitude of this vector can be related

to the illumination of the pixel, and the angle of the vector to the spectrum shape. Thus

pixels with similar spectral shapes, but differing illumination, should have similar vector

angles. The vector angles have been successfully used for both supervised and

unsupervised classification (Sohn and Rebello, 2002).

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SAM (Research Systems, 2002) measures similarity by calculating the angle

between the unknown pixel spectrum and the library spectra. A smaller angle represents

higher similarity between the pixel and the reference spectra. Pixels with angles greater

than an arbitrary user-selected threshold remain unclassified. SAM produces two forms

of images. A grayscale Rule image is produced for each endmember, where the pixel

value represents the angular distance in radians between each pixel spectrum and the

selected library spectrum. Therefore, darker pixels in the rule image are more similar to

the library spectra. The other type of SAM classification is a color-coded classification,

with each endmember mapped as a distinct color in a single image.

As discussed above, SAM is particularly effective in compensating for variations

in illumination, which can be a problem in an area of steep terrain, such as Santa Barbara.

SAM is not well suited for pixel mixing or determining small spectral differences

between mineral species. SAM is also highly dependent on the threshold selected.

The SAM analysis in this study began with the selection of reference library

endmember spectra for the classification: alunite, calcite, jarosite, kaolinite, siderite.

Sagebrush and saltbrush spectra were analyzed to account for the vegetation cover.

SAM was performed using only bands 173-204 (wavelength 2.0-2.5µm) to focus the

analysis on the characteristic absorption features of alteration minerals. The threshold for

a pixel to be unclassified was set empirically to 0.2 radians. These are the only

parameters controlled by the user.

6.3 Spectral Feature Fitting

Spectral Feature Fitting (SFF) (Clark et al., 1990, Clark et al., 1991, Research

Systems, 2002) is another spectral library matching technique for classifying unknown

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image spectra. A particular strength of SFF is that it isolates individual absorption

features for comparison, and only the shapes of the features are compared, not the depth

of those features.

The first step in the SFF analysis is the removal of the overall shape of the

spectrum, known as the continuum, from the image and reference spectra. The

continuum is formed by connecting the local maxima of the spectrum with straight line

segments (Research Systems, 2002). Without removing the continuum, it is difficult to

define distinct absorption features because illumination and particle size differences tend

to dominate the spectra. The image and reference spectra are therefore normalized by

dividing the radiance or reflectance values by the estimated continuum values (Clark et

al., 1990, Research Systems, 2002).

A constant is added to the library continuum-removed spectrum to provide a

scaling factor in comparing the library and image data. This scaling is needed because

the absorption features in the library data typically have greater depth than in the image

spectra. Next, a least-squares-fit is calculated band-by-band between each reference

endmember and the unknown spectrum, using standard statistical methods. Three types

of images are produced with SFF: scale, RMS, and fit image. The scale image, produced

for each endmember, is the scaling factor used to fit the unknown spectra to the library

spectra. The total root-mean-square (RMS) error is a measure of the average difference

between the image spectrum and the library reference spectrum. Low RMS values are

equivalent to good spectral matches. The fit image is the ratio of the scale image to the

RMS image. The fit image can be used to provide an overall perspective of how well the

unknown spectrum matches the reference spectrum on a pixel-by-pixel basis.

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The same endmembers and 2.0-2.5µm spectral region were chosen for the SFF

classification as were used for the SAM classification. While running SFF, this was the

only parameter able to be modified. SFF does not produce a color-coded map, so post

classification was required to generalize the classes. The ENVI program Rule Classifier

(Research Systems, 2001) was used to create a new classified image based on thresholds

from the histograms for each endmember. The thresholds, chosen subjectively, represent

a scaling factor for comparing the fit values. These thresholds varied between

endmembers and even between images.

6.4 Selection of Data

Out of thirteen images from the Santa Barbara AVIRIS imagery, seven images

were pre-processed and classified (images 5, 6, 8, 9, 10, 11, and 12). The seven images

were selected to represent the major seeps in the area, as well as control areas, where no

seeps had been mapped. The interpretation of these results was purely qualitative; the

pattern of seeps in the reference maps were visually compared with the pattern of

classified alteration minerals in the images.

7. Results and Discussion

7.1 SAM Results

Not surprisingly, vegetation dominated the SAM classification of the seven

classified images (Figure 3). Siderite was the most common mineral mapped in the

classification, with jarosite identified in small isolated clumps, and kaolinite forming a

diffuse pattern. In most of the images, especially 5 and 6 (Figure 3), the pixels classified

as siderite were mapped in intersecting linear patterns, resembling city streets. A visual

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comparison between the images and a 1:24,000 topographic map of Santa Barbara

confirmed that some of the siderite followed street patterns. It may be that these are

concrete streets, and the carbonate in the concrete has absorption features similar to those

of siderite, which is an iron-carbonate. Notably, in other areas (images 8 and 10), the

pattern did not follow streets. Theses siderite zones could be of significance for

identifying seeps. The areas classified as jarosite are limited to a small number of

discrete clumps, notably in images 5 and 6 (Figure 3). The pixels classified as kaolinite

are found mostly along stream channels. The fact that the kaolinite pixels are distributed

along steam channels undermines their value as indicator minerals because they may

represent transported or alluvial, and not residual, material.

Of the mapped images, those with seeps identified by the USGS (1999), include

images 6, 8, and 10. Image 5 is likely positioned near a seep, though the mapped location

is likely just to the south of image 5 (right in Figure 3). No seeps were mapped by the

USGS in the region covered by images 9, 11, and 12. These images have the least

siderite in the SAM classifications (Figure 3), although image 9 does have some clumps

of this mineral, those areas appear to be bare soil.

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Image 5 Image 8

Image 6 Image 9 KEY: Unclassified Alunite Calcite Jarosite Kaolinite Siderite Sagebrush Saltbrush

Figure 3. SAM Classified Images. East is up, and each image covers approximately 10 x 12 km.

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Image 10 Image 12

Image 11 KEY: Unclassified Alunite Calcite Jarosite Kaolinite Siderite Sagebrush Saltbrush Figure 3 (Continued). SAM Classified Images. East is up, and each image covers

approximately 10 x 12 km.

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7.2 SFF Results

Spectra Feature Fitting is a more statistical absorption matching technique than

SAM. However, for this study, SFF was not found to be well suited. Without expert

knowledge of the distribution of the different minerals in the area, a satisfactory threshold

for each mineral could not be determined for the rule classifier by which the mineral

maps are combined to form a final classification. Consequently, the final maps appear to

be dominated by noise (Figure 4). An indication of the problem with the SFF

classification is that unlike the SAM classification, water was classified as various

minerals.

KEY: Unclassified Alunite Calcite Jarosite Kaolinite Siderite Sagebrush Saltbrush Figure 4. SFF Classified Image. Left: SFF image 8 after application of the Rule

Classifier, with no thresholds. Right: False-color composite of image 8 for comparison. East is up, and each image covers approximately 10 x 12 km.

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7.3 Comparison of Results Obtained by SAM and SFF

The extensive vegetation cover may have contributed to the poor classification

results for the SFF and the SAM classifications. Hyperspectral mineral classification is

generally applied to arid regions where rock exposure is excellent (Kruse et al., 1993).

While both the SAM and SFF spectral matching methods were set up to classify the same

endmembers using the same wavelength regions, the outcomes were very different. The

SAM results produced patterns that appear to be interpretable; the SFF classifications

have no discernable patterns, and appear to be dominated by noise.

7.4 Limitations

The extensive vegetation masked the minerals on the surface. Classifying an

image without vegetation endmembers produced a map dominated by just one

endmember, siderite. It is interesting that the vegetation classification was so successful

because vegetation has few absorption features at the wavelengths analyzed. Lawn grass

and dry grass spectra were also included in the analysis. However, no pixels were

classified as either of these reference classes, and therefore, these spectra were removed

from the final classifications.

Tar and asphalt spectra were also added as endmembers because of the many

known tar seeps along the coast. However, no pixels were classified as tar or asphalt in

the images, possibly because of the relatively flat spectra with almost no characteristic

absorption features. Therefore, these spectra were also excluded from the final

classification. It is recommended in order to further analyze for those spectra, dark pixels

should be analyzed separately from the original image.

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Because only limited minerals were mapped, and a similarity matching technique

was used for classification, some of the pixels may be misclassified. However, it is

possible that the misclassified pixels may still be in the same mineral group, such as

siderite an iron carbonate mapped in areas where cement occurs.

8. Conclusions

Seven AVIRIS images of Santa Barbara, CA, were classified in this project using

two standard spectral matching techniques: SAM and SFF (Kruse et al., 1993). Four of

the seven images had previously mapped seep locations. The minerals alunite, calcite,

jarosite, kaolinite, and siderite were mapped, as well as two vegetation classes. For this

study, jarosite was found to have the most potential as a hydrocarbon indicator because it

was rarely classified and classified pixels often corresponded to where hydrocarbon seeps

had been mapped. The pattern of siderite was interpreted in many cases to represent

streets, and kaolinite appeared along stream channels. Alunite and calcite were not

mapped in most of the images, and only as a few individual pixels in two images. The

SAM was found to be more successful in mapping minerals at Santa Barbara than the

SFF.

Perhaps a major drawback to this study was the presence of vegetation in each

image. Hyperspectral mineral mapping clearly requires only sparse vegetation cover.

Another drawback was the lack of detailed ground information, including both land cover

maps, and the precise location of the seeps.

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