FEASIBILITY OF UTILIZING SPACEBORNE IMAGERY TO IDENTIFY LOST GAS
IN A NATURAL GAS GATHERING SYSTEM
by
Michael E. Burgess II
A thesis presented to the Department of Geography
and the Graduate School of the University of Central Arkansas in partial
fulfillment of the requirements for the degree of Master of Geographic Information
Systems
Master of Geographic Information Systems
Conway, Arkansas
December 2012
TO THE OFFICE OF GRADUATE STUDIES:
The members of the Committee approve the thesis of
Michael E. Burgess II presented on ???? X, 2012
____________________________________
Dr. Brooks Green, Committee Chairperson
____________________________________
Dr. Brooks Pearson
____________________________________
Dr. Balraj Menon
PERMISSION
Title
Feasibility of Utilizing Space-borne Imagery to Identify Lost Gas in a
Natural Gas Gathering System
Department
Geography
Degree
Master of Science
In presenting this thesis in partial fulfillment of the requirements for graduate degree
from the University of Central Arkansas, I agree that the Library of this University shall
make it freely available for inspections. I further agree that permission for extensive
copying for scholarly purposes may be granted by the professor who supervised my
thesis work, or, in the professor‟s absence, by the Chair of the Department or the Dean of
the Graduate School. It is understood that due recognition shall be given to me and to the
University of Central Arkansas in any scholarly use which may be made of any material
in my thesis.
_____________________
Michael E. Burgess II
September 4, 2012
Acknowledgement
iv
Abstract
Methane (CH4), a greenhouse gas, is released into the atmosphere by natural and
anthropogenic processes such as power plants, natural gas processing, industrial areas,
landfills, swamps, and rice patties. A low cost, accurate method for monitoring CH4
releases can be useful for identifying natural and anthropogenic sources, conserving CH4
as an energy source, and to assist energy production/processing/transportation companies
in maximizing revenues.
Numerous studies have been conducted regarding the indirect identification of terrestrial
CH4 plumes utilizing airborne and spaceborne multi-spectral/hyper-spectral sensors by
targeting the effects of CH4 on the spectral response of plants and ground materials. Less
research has been conducted on the direct identification of CH4 by targeting specific
absorption bands of CH4. This research aims to build on past studies to directly identify
terrestrial CH4 plumes utilizing the Hyperion 1 hyperspectral sensor aboard NASA‟s
Earth Observing (EO-1) satellite. Rather than targeting specific absorption bands of CH4
this thesis will identify pixels covering a landfill with known CH4 production and
identify other pixels that have a similar spectrum. The methodology will utilize a threestep process that incrementally increases in intensity. Any positives will be field
validated using flame pack equipment and the spectrum of any true positives will be
compared to the traditional 1.66µm and 2.3 µm CH4 absorption bands.
Keywords: methane, Hyperion, absorption, training data, remote sensing, near-infrared
v
Table of Contents
Acknowledgments………………………………………………………………………..iv
Abstract……………………………………………………………………………………v
List of Figures……………………………….....………………………………………..viii
List of Symbols and/or Abbreviations……………………………………………………ix
1.0 Introduction……………………………………………………………………….…..1
1.1 Background…………………………………………………………………...1
1.2 Literature Review…………………………………………………………..…3
1.2.1
Agricultural Emissions……………………………………………3
1.2.2
Wetland Emissions………………………………………….……4
1.2.3
Oil and Gas Applications…………………………………….…..5
1.2.4
Atmospheric Applications…………………………………….…7
1.2.5
Extraterrestrial Applications………………………………….….8
1.3 Methane Spectra and Properties…………………………………………..….9
1.4 Overview of Hyperspectral Platforms……………………………………....10
1.5 Discussion……………………………………………………………….…...12
2.0 Problem Statement……………………………………………………………….…..12
3.0 Research Questions…………………………………………………………………..14
4.0 Hypotheses………………………………………………………………..................14
5.0 Methodology………………………………………………………………………....14
5.1 Research Area………………………………………………………….….....14
5.2 Hyperion………………………………………………………………….….15
5.3 Software………………………………………………………………….......17
vi
5.4 Analysis………………………………………………………………………18
5.4.1
Imagery Pre-Processing………………………………………….18
5.4.1.1 Conversion to Radiance…………………………………18
5.4.1.2 Conversion to Reflectance (Atmospheric Correction) …...19
5.4.2
Anomaly Detection………………………………………………19
5.4.3
Target Detection ……..…………………………………………..20
5.4.3.1 Spectrum Derivation ...…………...……………………..20
5.4.4
Sub-pixel Analysis……………………………………………….22
5.4.5
Geographic Association………………………………………….23
5.5 Validation…………………………………………………………………….24
6.0 Expected Results…………………………………………………………………......25
7.0 Timeline……………………………………………………………………………...26
8.0 References……………………………………………………………………………27
9.0 Appendices…………………………………………………………………………...31
Appendix A…………………………………………………………………………..31
Appendix B………………………………………………………………………......31
Appendix C…………………………………………………………………………..32
Appendix D…………………………………………………………………………..35
vii
List of Figures
Figure 1 Equation Estimating Total Cost of Lost………………………………………..2
Gas to Production Companies Per Day
Figure 2 JAXA Calculated and Observed Absorption Near the ………………………..9
1.66μm Methane Absorption Band.
Figure 3 Fayetteville Shale Play in North Central Arkansas…………………………....17
Figure 4 ESRI‟s® Raster Calculator…………………………………………………….19
Figure 5 Location of Sample Spectra…………………………………………………....21
Figure 6 Photopac Micro FID Detector……………………………………………….…24
Figure 7 Research Flow Diagram………………………………………………………..24
viii
List of Symbols and/or Abbreviations
ADEQ-Arkansas Department of Environmental Quality
AOGC-Arkansas Oil and Gas Commission
BSCFD-Billion Standard Cubic Feet/Day
CH4-Methane
CHRIS-Compact High-Resolution Imaging Spectrometer
DAR-Data Acquisition Request
DN- Digital Number
EPA- Environmental Protection Agency
EROS-Earth Observation and Science Center
ESRI-Environmental Systems Research Institute
HDF- Hierarchical Data Format
JAXA- Japanese Aerospace Exploration Agency
MIR-Middle Infrared
NASA-National Aeronautics and Space Administration
NIR-Near Infrared
nm- Nanometer
NOAA-National Oceanic and Atmospheric Administration
GOSAT- Greenhouse gas Observing Satellite
OSP-Orthogonal Subspace Projection
PROBA- Project for OnBoard Autonomy
LIDAR- Light Detection And Ranging
MAS- MODIS Airborne Simulator
SWIR-Short Wave Infrared
Tg-Teragram
TIR-Thermal Infrared
TM-Thematic Mapper
μm-Micrometer
VIR-Visible Infrared
USDA-United States Department of Agriculture
USGS-United States Geological Survey
ix
1.0 Introduction
1.1 Background
Considering the growing concern of greenhouse emissions and the growing
popularity of natural gas as fuel for vehicles and energy generation, an efficient method
of identifying surface methane (CH4), the primary component of natural gas, plumes is
becoming increasingly important. A method that can routinely, at minimal cost, monitor
emission sources such as power plants, industrial areas, landfills, swamps, and rice
patties, is needed. More importantly such a methodology could also be used for natural
gas energy conservation and production, such as leak detection, and in natural gas
pipeline systems or detection of microseepages to identify gas reserves. Concerning
natural gas usage, the United States Energy Information Administration (2012) projects
the United States consumption of natural gas to be at 736 billion cubic meters (26.55
trillion cubic feet) per year by 2035, which is an increase of 19% since 2009. The
Environmental Protection Agency (EPA) estimated that the production, processing,
transmission/storage, and distribution of natural gas accounts for 19% (8.9 +- 2.9 billion
cubic meters) of the total United States anthropogenic emissions (as sited in
Kirchgessner, Lott, Cowgill, Harrison, & Shires, 1997). The EPA further estimated that
12% of the 19% was the result of processing. In this study, natural gas gathering was
included within processing. Based on the EPA‟s conclusions and assuming a $4.00
wellhead price per 28.3 cubic meters (1,000 cubic feet or 19.26 grams) of natural gas,
this is of potential lost revenue of near half a million dollars per day to natural gas
production companies operating in the United States (See Figure 1).
1
Figure 1. Equation estimating total cost of lost gas to production companies per day.
Considering these numbers a method that would allow for continuous monitoring of
gathering systems to decrease lost gas in the form of leaks could prove extremely
beneficial in reducing greenhouse emissions, maximizing revenue, and maximizing
natural gas production.
Several widely known methods for identifying terrestrial methane plumes are
currently in use or in an experimental stage. These include, but are not limited to: 1)
Light Detection and Ranging (LIDAR) 2) Thermography 3) Imaging Synthetic Aperture
Radar (SAR) 4) Hydraulic modeling software 4) Acoustic modeling 5) Gas detectors that
analyze the composition of the air 6) Trained scent dogs and 7) Air-borne spectroscopy.
Ground and air-borne methods can be laborious and expensive as compared to spaceborne identification. This is especially the case if CH4 surveys are needed on a periodic
basis. Because spaceborne imagery can be less expensive due to cost sharing, is periodic
(if the orbit is not geosynchronous), and can be gathered/analyzed at the desktop, it is
logical to research the feasibility of identifying methane emission sources using spaceborne imagery.
2
1.2 Literature Review
1.2.1 Agricultural Emissions
Through an intensive literature review one will find that scholarly research
conducted on direct remote sensing of CH4 is, while extensive, dispersed across several
types of applications.
Several studies have utilized indirect remote sensing of CH4 emissions such as
Melack, Hess, Gastil, Forsberg, Hamilton, Lima, and Novo (2004) used Scanning MultiChannel Microwave Radiometer and the Japanese Earth Resources Satellite-1 to calculate
total CH4 emissions from wetlands in the Amazon Basin. Their identification of CH4, as
is the case with many similar projects, is an indirect identification of CH4. Pre-tested
formulas were used that were created from results in laboratory wetland ecosystems and
from other studies. The satellite imagery was only used to identify wetland or aquatic
areas. Then the area totals were applied to the formula to reach the total CH4 emissions.
Thimsuwan, Eiumnoh, Honda, and Tingsanchali (2000) used both Landsat
Thematic Mapper (TM) and National Oceanic and Atmospheric Administration
Advanced Very High Resolution Radiometer (NOAA AVHRR) for a similar study.
However, this too was an indirect measure of methane. The remotely sensed imagery was
only used to calculate the total biomass, or age, of rice stands whereas the biomass of a
rice paddy changes as it ages, so does the electromagnetic absorption/reflectance. The
researchers had previously grown rice in an isolated environment and measured the
produced CH4. From this, a formula was created which was used once the area total and
biomass total was calculated based on the imagery. In addition to this study, Manjunath,
Panigrahy, Kundan, Adhya, and Parihar (2006) conducted a comparable study in India.
3
This methodology could be considered even more of an indirect calculation of CH4 than
Thimsuwan et al. (2000) because the authors utilized SPOT imagery to identify possible
rice farming areas based on elevation, irrigation, and rainfall. This is in contrast to
Manjunath et al. (2006) where the actual paddies were identified. Thimsuwan et al.
(2000) created a formula, similar to Manjunath et al. (2006), which was applied to the
potential paddy area totals to calculate total methane emissions.
The studies by Thimsuwan et al. (2000) and Manjunath et al. (2006) are not
applicable to studies where the presence of methane must be determined. The titles for
both of these works are somewhat misleading since space-borne imagery is not actually
used to calculate/identify methane emission, but used, rather, to identify areas that are
known to produce CH4.
This method is only useful when it is known that an entity is a methane source
and that it emits methane at a constant rate. Studies such as this are not applicable to this
work since the purpose is to identify sources rather than analyze known sources.
1.2.2 Wetland Emissions
Research regarding CH4 emissions of wetlands has similarly used indirect
methodologies. Melack, Hess, Gastil, Forsberg, Hamilton, Lima, and Novo (2004) used
microwave and radar sensors to establish inundation patterns in the Amazon Basin from
1979-1987 and 1995-1996. The two sensors, Scanning Multichannel Microwave
Radiometer (SMMR) and Japanese Earth Resources Satellite-1 (JERS-1), were used
because longer wavelengths were needed to penetrate predominating cloud cover, smoke,
and forest canopy. However, like the aforementioned research on CH4 emissions from
rice cultivation, this study also field measured the parts of methane per million parts of
4
air (ppm) then associated these values with the inundation levels to quantify total
emissions. This study actually builds from an earlier study by Rosenqvist, Forsburg,
Pimentel, Rauste, and Richey (2002). The studies can almost be considered identical
except that Rosenqvist et al. (2002) did not utilize any imagery other than JERS-1
imagery whereas Melack et al. (2004) used SMMR to widen the temporal coverage.
1.2.3 Oil and Gas Applications
Similar to the preponderance of indirect quantification of CH4 due to agriculture
and wetlands emissions, research has primarily focused on indirect identification of
hydrocarbons within oil and gas exploration and transmission research. One may realize
upon reading this work that spatial resolution of current sensors is limited regarding the
ability to directly identify point sources such as natural microseepages or pipeline leaks.
As a result, research has primarily focused on isolating the spectral signatures of
vegetation that is stressed by released hydrocarbons due to microseepages and pipeline
leaks. Conversely, the low spatial resolution of space-borne sensors as of 2000 lacked the
high spectral resolution needed to identify hydrocarbons directly (includes CH4) or the
signatures created in plants that have been stressed by hydrocarbons (Yang, Zhang, &
Van der Meer, 2000).
To assist in CH4 pipeline leak detection Smith, Steven, and Colls (2004) “gassed”
different species of grasses in a laboratory to graph how the spectral signatures would
change. They were unable to establish a gassing ratio that could be definitively used
without comparison to ungassed adjacent vegetation. Due to this, it is rational to research
direct CH4 identification. Doing so would remove the “indirect” variable and allow for a
more reliable identification that removes the need for intensive, manual interpretation.
5
This is in contrast to Van Der Meer, Van Dijk, Van Der Werff, and Yang (2002)
whom indicated that CH4 strongly absorbs in the 2.2-2.4μm range and that hydrocarbon
seepages can be identified with the use of additional geologic data. Conflicting
statements/results such as this could explain why a widely used protocol has not been
developed for identifying small CH4 point sources. Also possible is that much of the
research regarding microseepages is conducted by private energy firms and therefore
publishing results is obviously a competitive disadvantage in this situation. Because the
transportation of natural gas (CH4 is primary component) is not viewed as competitive as
exploration and production regarding identification of leaks, it is more logical to review
research conducted in this sector.
A more recent study by Khan and Jacobson (2008) did utilize a more advanced
hyperspectral sensor with better spatial resolution. This study utilized Hyperion to detect
hydrocarbon seepages in the Patrick Draw in South-central Wyoming. While they were
successful in identifying hydrocarbon seepages, they used indirect methods. In their
study, they collected training data utilizing a field spectrometer in areas of obvious
vegetation stress in an existing oil field. While this study shows that better spectral
resolution makes it possible to identify hydrocarbons on the Earth‟s surface, it relied on
training data. Obtaining training data for leaks is obviously not possible due to the
dynamics of gas concentration based on leak size and atmospheric conditions at the time
of scene collection.
Barnhouse (2005) is an example of one of the few works that has studied the
feasibility of directly detecting methane produced by oil and gas operations and landfills.
This study attempted to identify terrestrial methane plumes using the MODIS Airborne
6
Simulator (MAS) around 3.314μm. The study pointed to the need for increased spatial
and spectral resolution as MODIS is actually a multi-spectral scanner rather than hyperspectral. These implications will be further discussed as Barnhouse (2005) is this most
applicable research to this study.
1.2.4 Atmospheric Applications
Conversely, CH4 has been identified directly in Earth‟s atmosphere in a multitude
of ways. Frankenberg, Meirink, van Weele, and Platt (2005) used SCIAMACHY
(scanning imaging absorption spectrometer for atmospheric chartography) on board the
European Space Agency‟s ENVISAT satellite to detect CO2 (carbon dioxide) and CH4 in
the atmosphere. ENVISAT has a very high spectral resolution at 8000 channels between
240 and 2,390 nanometers (nm) (McCarthy, Pratum, Hedges, & Benner, 1997). To detect
CO2 and CH4 this study used differential optical absorption spectroscopy (DOAS) based
on the assumption that these gases absorb both Earth thermal radiation and solar nearinfrared radiation proven by McCarthy, Hedges, and Benner (1998). Retrieval windows
of 1562 nm to 1586 nm were used for CO2 while a retrieval window of 1630-1670 nm
was used for CH4. Also integrated into this methodology is the volume mixing ratio
(VMR) which is a ratio of the parts that make up a larger part. In this case, it refers to the
parts of CO2 and CH4 per million parts of air. It is unclear, but assumable, whether this
study utilized a sub-pixel analysis process to calculate the CO2 and CH4 volumes since
the results were actually the VMRs. While this direct identification method with a passive
sensor works well for atmospheric applications it is not applicable to identification of
specific surface sources due to the spatial resolution of SCIAMACHY. The spatial
7
resolution varies depending the spectrum and angle, but, for example, the spatial
resolution of channel 8 is 120km x 30km (Meirink, Eskes, & Goede, 2006).
1.2.5 Extraterrestrial Applications
Extensive research has been conducted on extraterrestrial methane identification
particularly on Mars and Titan (one of Saturn‟s moons). Even though the sensors used in
these studies are not applicable to terrestrial CH4 identification, a brief discussion is
needed in order to understand any strengths that may be applied or short comings that
may be avoided. Three groups believe to have identified methane plumes on Mars:
Krasnopolsky, Mailliard, and Owen (2004), Formisano, Atreya, Encrenaz, Ignatiev, and
Giuranna (2004) and Mumma, Novak, DiSanti, and Bonev (2003). Krasnopolsky et al.
(2004) and Mumma et al. (2003) utilized terrestrial telescope sepectroscopy to identify
methane while Formisano et al. (2004) utilized the Planetary Fourier Spectrometer (PFS)
aboard the Mars Express orbiter. To relate this to terrestrial identification of CH4, it is
worth noting the spectral range that each group used for methane identification.
Krasnopolsky et al. (2004) identified CH4 in the 3.31-3.52 μm wavelengths with the
strongest detection at 3.7 μm. Mumma, Novak, DiSanti, Bonev, and Dello Russo (2004)
observed methane in the 3030-3040 cm-1 spectral range. Using the PFS, Formisano et al.
(2004) used the PFS, covering 1.2-4.5μm spectral range, to identify CH4 in the 30003030 cm-1 window.
In addition to Mars, several teams have attempted to detect and quantify CH4 in
Titan‟s atmosphere. Using Hubble Space Telescope Imaging Spectrograph (HST)
Anderson, Young, Chanover, and McKay (2008) attempted to, not only quantify, but also
identify the elevation at which methane clouds exist on all of the moons. In order to
8
isolate CH4 signature in the lower levels of the atmosphere, multiple absorption bands
were used within the .6-1μm range. The authors were, however, unable to isolate surface
methane. To do so, the Anderson et al. (2008) believe that collection should be beyond
1μm, perhaps between 2.0-2.35μm. Extraterrestrial research is very useful since it is an
indicator of what ranges CH4 may be identified. However, as one can see, identifying
terrestrial CH4 based on a pre-determined range may not be feasible. Doing so may
require the researcher to test numerous ranges to determine what works best for the
analysis.
1.3 Methane Spectra and Properties
Understanding of the spectral response of the material which is being analyzed is
necessary even if utilizing training data, which will be discussed in the methodology
section of this project. If true positives of the target material are identified, knowledge of
the specific absorption bands are needed to validate the results and assist in future
research.
After reviewing the existing research on remote sensing of hydrocarbons covered
in this project, it is apparent these studies have mainly attempted to target absorption
bands in the regions of 1.66 μm, 2.3 μm, and 3.3 μm. The 3.3μm region is the
fundamental C-H molecular stretch such that this region shows the most intense
absorption. Weaker absorptions in the NIR region from 1-2.6µm, particularly around
1.66µ and 2.3µ, are the result of combination overtones (King & Clark 1988).
9
Figure 2. JAXA calculated and observed absorption near the 1.66μm
methane absorption band. Source: Modified from JAXA Press Release (2009).
Because the fundamental absorption band is outside of the sensor‟s spectral range
that will be utilized in this research, one of the weaker overtone combination absorption
bands will be vital. This will also be discussed in more detail later in this thesis.
1.4 Overview of Hyperspectral Platforms
There are many commercial and civilian airborne and space-borne multi-spectral
sensors available, including, but not limited to: MODIS Airborne Simulator (MAS),
Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER),
Enhanced
Thematic
Mapper
(ETM+),
and
Moderate
Resolution
Imaging
Spectroradiometer, and Satellite pour l‟Observation de la Terra (SPOT). However, multispectral imagery does not allow for finite separation of wavelengths for identifying
substances with similar, but slightly different spectral responses (Aspinall, Marcus, &
Boardman, 2002)
Several options are available regarding hyperspectral airborne imagery. Two
notable sensors are AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) and the
commercial HYMAPTM (Hyperspectral Mapper) owned by HyVista Corporation.
However, this type of data collection is difficult to acquire for routine monitoring. For
example, the NASA Jet Propulsion Laboratory only allows graduate students one free
10
flight-line of data per year regarding AVIRIS imagery. Furthermore, data is only made
available after data intended for NASA funded researchers have been processed and
delivered (Jet Propulsion Laboratory 2012).
Few options are available regarding hyperspectral space-borne imagery. Shippert
(2004) noted that only three space-borne sensors are currently available, including the
FTHSI sensor MightySat II, the Compact High Resolution Imaging Spectrometer
(CHRIS) aboard the European Space Agency‟s Project for On Board Autonomy
(PROBA-1) satellite, and the Hyperion 1 sensor aboard NASA‟s Earth Observing
(EO-1).
MightySat II, the first true space-borne hyperspectral sensor, was inserted into
orbit in 2000 (Yarbrough, Caudill, Kouba, Osweiler, & Arnold, 2002). However,
MightySat II re-entered the atmosphere in November 2002 and is therefore obviously not
applicable to this study since development of the study area (discussed later) did not
begin until 2004 (Earth Observing Portal 2010).
CHRIS may be considered a second option regarding hyperspectral sensors
regarding this study. While the spatial resolution of 20 meters is superior to other sensors,
it only collects within the 400-1050 nm spectral range (European Space Agency 2010).
This range is insufficient because this author feels that a significant amount of
atmospheric noise will be detected. In addition, aforementioned studies were only
successful in detecting methane at much higher wavelengths outside this range.
Since Shippert (2004), the Japanese Aerospace Exploration Agency (JAXA)
launched the IBUKI (GOSAT-Greenhouse Observing Satellite) in early 2008. The issue
with utilizing this sensor is the data format is vector point data with, minimally, 500
11
meter spacing. This far exceeds what is required to detect materials with varying
locations on Earth‟s surface since it is not continuous. This satellite is still in the test
phase, but JAXA claims that a leakage of .73 Tg CH4 per day has been detected from
compressor stations in Russia (Inoue & o‟Hashi, 2008). Furthermore, Inoue and o‟Hashi
(2008) state that detecting methane is dependent upon the wind carrying and dispersing
the methane from its point source. This aligns with the fact that GOSAT‟s point sampling
is extremely course. This author feels that it will be very difficult or impossible to
identify point sources such as pipeline leaks with this sensor. In addition, leaks as large as
.73 Tg are not likely to be found in gathering systems in the United States due to the
prudence of natural gas pipeline operators and the strong regulatory environment.
Building on Barnhouse‟s (2005) study, discussed in 1.2.3., it is apparent that a
hyper-spectral sensor with, at least, moderate spatial resolution should be examined. As
recommended for future research by Barnhouse (2005), a sensor such as Hyperion will be
used for this study. Hyperion is discussed in more detail in section 6.2. In addition to
targeting oil and gas operations, Barnhouse (2005) targeted sanitary landfills in Ohio with
limited success in identifying CH4 with high confidence. This thesis will further build on
Barnhouse (2005) by targeting a sanitary landfill, but, rather than targeting methane using
a laboratory collected CH4 spectrum, apply the spectrum from the landfill across an area
with extensive natural gas gathering infrastructure.
1.5 Discussion
After reviewing current research regarding methane identification using
spaceborne sensors, it is apparent many barriers exist to direct terrestrial identification of
surface methane point sources. More research is needed regarding direct identification of
12
terrestrial methane or more importantly what spectral and spatial resolution is required to
do so. Research regarding plume calculations and minimum mixture is needed to better
understand the minimal emissions needed in order to better understand the minimum
spatial resolution needed for full or sub-pixel identification of CH4. In relation to this, it
appears to be unpredictable what spectral range CH4 will register when identified
through Earth‟s atmosphere at, or just above ground level. To date, only CH4 signatures
within laboratory, ground-based, air-borne, and extraterrestrial have been tested to the
point that a spectral table could be created
2
Problem Statement
Regarding detection of CH4 utilizing a space-borne sensor, many researchers have
identified and/or quantified CH4 emissions from agricultural operations through indirect
identification of plant species. Researchers have been able to quantify or estimate CH4
atmospheric column abundance. However, while some researchers state that it is possible,
very little research exists regarding direct detection of terrestrial methane plume sources.
Based on past research, the reasons for this appear to stem from difficulties due to: 1)
sensor limitations regarding the inability of multi-spectral sensors to discern narrow
bands of electromagnetic radiation and 2) ability to isolate wavelengths that nullify the
effects of mixed atmospheric gases. Therefore, this study will investigate the detection of
CH4 terrestrial methane plume sources utilizing the Hyperion hyper-spectral sensor
aboard the Earth Observing-1 satellite.
13
3
Research Questions
1) Does the spectrum of training data collected from a landfill with known CH4
production have similar enough characteristics to be used to identity CH4 plumes
released by a natural gas gathering system?
2) Is the spatial resolution of a Hyperion scene fine enough to detect CH4 using
anomaly detection, target detection, and sub-pixel analysis methodologies?
3) Will the spectra of the training data and any detected true positives indicate
absorption near the 1.66µm or 2.3µm CH4 absorption bands?
4
Hypotheses
1) Spectrum of training data collected from a landfill may be used to identify other
methane plumes, but will be limited to areas with similar ground cover.
2) CH4 plume sources will not be identifiable with 30 meter spatial resolution hyperspectral Hyperion imagery. However, CH4 will be detected when the spectrum of
the training data is used with the target detection and sub-pixel analyses.
3) The spectrum of the training data and any true positives will show absorption near
1.66µm or 2.3µm CH4 absorption bands but may vary from the center frequency
of the utilized band due to the effects of the background signatures.
5
Methodology
5.1 Research Area
The Fayetteville Shale natural gas play in North Central Arkansas was chosen as
the study area. This area is one of the most active natural gas shale plays in the United
States. Based on gas sales data from the Arkansas Oil and Gas Commission, as of
14
November 1, 2010, over 3,500 wells have been completed in the Fayetteville Shale since
drilling began in 2004 with daily production at approximately 56.6 million cubic meters
per day (2 Bcfd) (AOGC 2010). In addition, based on the Arkansas Department of
Environmental Quality‟s (ADEQ) online Air Permitting Database, approximately 75
minor source permits for gas processing facilities have been submitted within the study
area since 2004 (ADEQ 2010). Also, this author estimates, based on cursory review of
aerial imagery, more than 3,000 kilometers (1,864 miles) of natural gas pipeline have
been installed since 2004 to gather this gas. This extraordinary amount of activity should
allow ample opportunities for targeting and validating CH4.
The physiology of the study area can be classified as mountainous in the North
and transitioning to rolling hills in the South. The southwestern area of the Fayetteville
Shale Play transitions to the Arkansas River Valley while the eastern edge can be
considered Mississippi River Alluvial Plain. The remainder of the study area is located
within the Boston Mountains on the Ozark Plateau in north central Arkansas. This area
may be further classified as having flat-topped mountains with steep slopes. The
hydrology may be described as a dendritic drainage pattern (Arkansas Geological Survey
2010). Elevation of this region ranges from 107 meters (350 feet) to 640 meters (2,100
feet) above mean sea level.
5.2 Hyperion
Vincent and Singleton (1995) estimated that airborne hyperspectral platforms are
theoretically capable of identifying onshore CH4 plumes with a thickness of 1 meter and
a concentration of at least 2000 ppm or .2% CH4 by volume, which can be considered a
very lean mixture. Hyperion is a space platform and will be more susceptible to the
15
effects of other atmospheric gases. Therefore, it will have more difficulty targeting
specific absorption bands of methane even if the mixture above a landfill may be up to
four times the minimum mixture needed for airborne platforms. However, this project‟s
aim is not to target a specific absorption band, but rather use pixels where the likelihood
of CH4 is high and apply that spectrum to the remainder of the scene to identify similar
responses.
Scenes collected by the Hyperion hyperspectral sensor aboard the USGS‟s EO-1
will be utilized in this study. As previously mentioned in section 1.2.7, Hyperion is the
best available hyperspectral space-borne sensor for this study when one considers its
spectral resolution, spatial resolution, wavelength range, cost, and availability. This
sensor detects 220 channels ranging from .357 to 2.576 μm (Beck 2003). Appendix A
represents a cross-comparison of NASA‟s current sensors, including Hyperion, that are
capable of sensing the complete VIS/NIR/SWIR/MIR/TIR spectrum.
A disadvantage to using the Hyperion sensor, due to it being a relatively new
sensor, is that as of February 25th, 2011, only two scenes have been collected in the study
area. Therefore, it was necessary to submit a Data Acquisition Request (DAR) to the
United States Geological Survey‟s Center for Earth Observation and Science Center
(EROS). This was further complicated by the fact that only 12 scenes may be scheduled
per day (Beck 2003). Also, scene collection may be further restricted if only nadir scenes
are requested. This research, in order to increase the likelihood of collection, submitted
requests for nadir or pointed mode scenes. It is estimated that pointed mode scenes may
have look angles 5 to 17 degrees from true nadir.
By requesting pointed mode,
considering that the EO-1 follows the World Reference System-2 (WRS-2) path/row
16
system, and therefore has a 16-day repeat nadir collection opportunity, the sensor will
have three opportunities per 16-day cycle to collect the scenes over the study area (Beck
2003).
Regarding scenes collected by Hyperion for the study area, as previously
mentioned, only two scenes had been collected since the inception of the Fayetteville
Shale Gas Play. However, at the time of the collections, the study area had 70 percent and
90 percent cloud cover, therefore blocking the ground from view. See Figure 3 below. On
March 1, 2012, a scene was collected based on this author‟s DAR. The scene is ideal
considering it has zero percent cloud cover, and therefore, will be used for this project.
Figure 3. Fayetteville Shale Play in North Central Arkansas
17
5.3 Software
This study will utilize ERDAS Imagine 2010® software to perform all spectral
analyses. Environmental Systems Research Institute (ESRI) ARCINFO 10® will be
utilized for all spatial correlation analyses. The two software suites offer full
interoperability between one another.
5.4 Analysis
Specific areas within the Hyperion scenes within the study area will be targeted to
maximize probability of methane presence. This includes locations of gas processing
facilities, locations of gas custody transfer points, and gathering gas pipeline right-ofways.
The analysis will consist of a three-step method intended to systematically and
incrementally increase the intensity of the analysis of the data. If a preceding step in the
methodology produces positive CH4 detection, the next steps will still be conducted for
the purposes of accuracy and validation.
5.4.1
Imagery Pre-Processing
Prior to the three-step analysis, Hyperion scenes require pre-processing in order to
convert the digital numbers (DN) of the pixels to radiance and then true reflectance.
Radiance is defined as the amount of light that reaches the sensor that includes the
amount of light reflected by the atmosphere and does not account for the amount of light
absorbed by the atmosphere. Reflectance can be defined as the ratio of the amount of
light that is reflected from an object to the amount of light that reaches the target
(Borengasser 2008).
18
5.4.1.1 Conversion to Radiance
The digital values of Hyperion Level 1 scenes are scaled to maximize precision.
In order to convert the digital values to radiance, it is necessary to divide the visible nearinfrared bands by 40 and the short wave infrared bands by 80 (Beck 2003). Hyperion
bands 151 and 216, center wavelengths of 1.659 and 2.314µm, will be used since this
study will attempt to identify CH4 absorption. A complete list of the center wavelengths
of bands within Hyperion‟s range is listed in appendix D. Therefore, the digital values of
the pixels in this band will be divided by 80 using ESRI®‟s raster calculator (See Figure
4).
Radiance= DN Band 151
80
Figure 4. ESRI®‟s raster calculator
5.4.1.2 Conversion to Reflectance (Atmospheric Correction)
USGS‟s Earth Resources Observation and Science Center recommends using the
formula below to calculate reflectance for Hyperion scenes that are relatively clear
(EROS 2011). ESRI®‟s raster calculator will also be used to perform this operation.
Pp= π* L ‫ *ג‬d2___
ESUN ‫* ג‬cosθs
Where: Pp= Unitless planetary reflectance
L‫ =ג‬Spectral radiance at the sensor‟s aperture
d= Earth-Sun distance in astronomical units from nautical handbook or
interpolated from values listed in appendix B
ESUN ‫ = ג‬Hyperion solar irradiances listed in appendix C
θs = solar zenith angle in degrees
19
5.4.2 Anomaly Detection
As previously stated, this will be a three-step process which increases in intensity
with each step. Anomaly detection will be the first methodology used. This process is
designed to identify spectral signatures that significantly deviate from the background
spectra. The base image, which is not atmospherically corrected, will be used for this
process since ERDAS® designed this engine to use RAW digital number (DN) values.
This step will use the Orthogonal Subspace Projection (OSP) methodology. This
method was chosen because it is ideal for identification of target signatures within natural
environments and requires the target to be present in low concentrations. This matches
this scenario well considering that the study area is uniform, consisting mostly of graze
land and mixed forests. If any CH4 plumes are present it can be assumed that the CH4
will be in relatively low concentrations.
5.4.3 Target Detection
Where the presence of a certain material is known to occur in small
concentrations, it is possible to use target detection methods. The target detection process
attempts to nullify background spectra and identify or “target” pixels within an image
that exhibits a spectrum similar to a sample that has been provided as a parameter. The
sample or base signature of the target material is required in order to instruct the software
what spectrum to attempt to identify. To supply sample spectra, ERDAS Imagine® will
be used to pinpoint the pixels with the highest probability of containing CH4.
5.4.3.1 Signature Derivation
Even after atmospherical correction, it is possible that other atmospheric gases
will mask a laboratory collected spectra such as the ones contained in HITRAN. The
20
second step of analysis will be to derive a signature or sample from pixels believed to
contain CH4 in the same imagery being used for target detection. It is impossible to
simultaneously create a methane plume in the study area as Hyperion is collecting the
scene to scheduling complications with NASA. Therefore, it is necessary to identify a
source that continually produces CH4 such as farming operations that store large amounts
of manure, landfills, or waste treatment facilities to create a reference spectrum or
“training data”. Carman‟s (1996) (as sited in Vincent, 1997, p. 265) study estimated that
the atmospheric gas concentration above three landfills in Ohio ranged between 50008000 ppm dependent upon the atmospheric pressure. This is 2.5 to 4 times the
concentration that Vincent and Singleton (1995) (as sited in Vincent, 1997, p. 281)
estimated was the minimum concentration required for an airborne platform to detect
methane.
Several landfills exist within the study area and should provide good opportunities
to create spectra samples. One in particular, Waste Management Corporation‟s Two Pine
Landfill, has numerous shallow natural gas wells with a gathering system that supplies
on-site electricity generators fuel (AEDC 2012). The landfill is located near Jacksonville,
Arkansas, approximately 40 kilometers south of the southern edge of the known natural
gas production in the Fayetteville Shale Play. The scene that was collected for this
project, 185 kilometers in length, contains both the landfill and the eastern flank of the
Fayetteville Shale Play. This will allow for this site to be used as sample spectra that can
be applied, in-scene, to facilities located well within the limits of the Fayetteville Shale
Play (See Figure 5).
21
Figure 5. Location of sample spectra
5.4.4
Subpixel Analysis
The third, and most scrupulous step of this methodology, will be to perform
subpixel analysis to the scene in an attempt to identify the presence of methane in mixed
pixels. Essentially, subpixel analysis is similar to target detection except that it is a
process that discriminates between materials that may be mixed within one pixel. This is
in contrast to “Target Detection” where the spectra analyzed are a result of the total
spectral contributions of all the materials within the pixel combined.
ERDAS Imagine 2010 Sub-pixel Classifier® utilizes a mixed pixel fraction
methodology to identify “materials of interest” within individual pixels. To simplify, the
analysis initiates with Preprocessing where the program attempts to identify a set of
general background spectra. Next, automated environmental correction can be applied to
the scenes to account for atmospheric effects and sensor irregularities. Residuals from the
general background spectra and fractions are calculated that will be used later in the
process. Signature Derivation follows where the program operator is responsible for
22
manually choosing which pixels should be considered the material of interest. In this
case, pixels covering the Two Pines Landfill will be included to attempt to capture mixed
pixels that contain some mixture of emitted methane. The signature derived in section
6.4.3.1 will be utilized. The last step in this process is MOI Classification where the
program will calculate the mixed pixel fraction based on the material of interest training
data spectra and the potential background spectra (ERDAS 2009). The mixed pixel
fraction will be the value that will produce the intensity that the Hyperion satellite
observed. After all processing is completed, any positive samples will move to the next
step for geographic association.
5.4.5
Geographic Association
The resultant rasters will be overlaid with natural gas gathering system data using
ARCINFO 10®. The natural gas gathering system data were previously digitized for this
project utilizing 3-meter resolution natural color aerial imagery collected by the United
States Department of Agriculture (USDA) in 2009. According to the USGS Earth
Observing and Science Center (EROS 2012), Hyperion Level 1Gst imagery is spatially
registered and corrected for terrain and parallax error. However, it should be noted that
the preliminary research for this project has found that Hyperion Level 1Gst level data
appear to be shifted approximately 290 meters (950 feet) North 45 degrees West, using
quarter quadrant bearings, from its actual position in North Central Arkansas. Any
positives shown in the results will be corrected by this distance and bearing using
ARCINFO 10®. After spatial refinement, all data will be loaded into a Trimble Geo XH®
handheld mapping grade GPS so that it may be used for navigation to the area of the
positive results.
23
5.5 Validation
After navigating to the location of the spatially refined positive samples, each
location will be tested utilizing a Photovac MicroFID Flame Ionization Monitor®,
generically known as a “Flame Pack” (See Figure 6). Flame Packs are commonly used by
the oil and gas industry for leak detection and confined space entry. The Photovac
MicroFID® is capable of detecting methane in concentrations between 0.5 and 50,000
ppm (Photovac 2002). If any of the positive samples from the analysis show measurable
concentrations of CH4, then the sight will be considered a validated positive sample. In
addition, the concentration of the methane will be recorded and reported in the final
project for future research.
Figure 6. Photovac Micro FID Detector
Source: Pine Environmental Available Online: http://www.pineenvironmental.com/flameionization-detector/photovac-microfid.htm
24
Figure 7. Research Flow Diagram
6
Expected Results
True positives are not expected to be identified until the third and most scrupulous
process, sub-pixel analysis. In addition, it is anticipated that if positives are identified
they will not be limited to fugitive emissions generated by gas production and gathering.
Positives may also include emissions from swamps and agricultural operations.
Lastly, it is expected that any true positives identified will show absorption lines
near 1.66µm and 2.3µm, but may vary slightly either side of the peak absorption due to
the background contributions. Please see Figure 7 above, which depicts the entire
research flow for this thesis.
25
7
Timeline
26
8
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9.0 Appendices
Appendix A Cross-Comparison of NASA‟s Current Sensors (EROS 2012).
Appendix B Interpolated values of Earth-Sun Distance in Astronomical Units (EROS
2012).
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Appendix C (Source: EROS 2012)
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Appendix D (Source: EROS 2012)
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