Evaluation of the use of hyperspectral imagery for...
Transcript of Evaluation of the use of hyperspectral imagery for...
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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
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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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9. References Bailey, G.B. and Anderson, P.D., 1982, Applications of Landsat imagery to problems of petroleum exploration in Qaidam Basin, China: AAPG Bull, v. 66:9, pp. 1348-1354. Berger, Z., 1994, Satellite Hydrocarbon Exploration: Springer-Verlag, Berlin. Buckingham, W.F. and Sommer, S.E., 1983, Mineralogical characterization of rock surfaces formed by hydrothermal alteration and weathering – Application to remote sensing: Economic Geology, v. 78, pp. 664-674. Clark, R.N., Gallagher, A.J., and Swayze, G.A., 1990, Material absorption band depth mapping of imaging spectrometer data using the complete band shape least-squares algorithm simultaneously fit to multiple spectral features from multiple materials: in Proceedings of the Third Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop, JPL Publication 90-54, pp. 176-186. Clark, R.N., Swayze, G.A., 1991, Mapping with imaging spectrometer data simultaneously fit to multiple spectral features from multiple materials: in Proceedings of the Third Airborne Visible/Infrared Imaging Sepctrometer (AVIRIS) Workshop, JPL Publication 91-28, pp. 2-3. Cloutis, E.A., 1989, Spectral reflectance properties of hydrocarbons: remote-sensing implications: Science, v. 245, pp. 165-168. Crosta, A.P., Sabine, C. and Taranik, J.V., 1998, Hydrothermal Alteration Mapping at Bodie, California, Using AVIRIS Hyperspectral Data: Remote Sensing of Environment, v. 65:3, pp. 309-319. Donovan, T.J., 1974, Petroleum microseepage at Cement Oklahoma: Evidence and mechanism: AAPG Bull, v. 58:3, pp. 429-446. Ellis, J.M., Davis, H.H., Zamudio, J.A., 2001, Exploring for onshore oil seeps with hyperspectral imaging: Oil and Gas Journal, Sept. 10, pp. 49-56. Everett, J.R., Staskowski, R.J. and Jengo, C., 2002, Remote sensing and GIS enable future exploration success: World Oil, v. 223:11, pp. 59-60, 63-5. Goetz, A.F.H., Vane, G., Solomon, J.E. and Rock, B.N., 1985, Imaging spectrometry for earth remote sensing: Science, v. 211, pp. 1147-1153. Green, R. O., Eastwood, M. L., Sarture, C. M., Chrien, T. G., Aronsson, M., Chippendale, B. J., Faust, J. A., Pavri, B. E., Chovit, C. J., Solis, M., Olah, M. R. and Williams, O., 1998, Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS): Remote Sensing of Environment, v. 65:3, pp.227-248.
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