METHANE PLUME DETECTION USING PASSIVE HYPER-SPECTRAL REMOTE SENSING Willard D. Barnhouse Jr. A Thesis Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE December 2005 Committee: Robert K. Vincent, Advisor Sheila Roberts Enrique GomezdelCampo ii ABSTRACT Robert K. Vincent, Advisor The ability to detect and measure atmospheric methane gas plumes is important for a number of reasons. Methane is a significant greenhouse gas, plume detection could help methane source/sink studies. Detecting gas plumes due to leakage is also important for energy resource production and transportation facilities. There have been various techniques developed to accomplish this task. The work in this thesis used passive hyperspectral remote sensing analysis with data collected form the high altitude MODIS Airborne Simulator (MAS). The MAS platform is the only publicly known sensor to operate at an altitude close to orbital parameters and which is equipped with high spectral resolution bands in the same wavelength region (3.314µm) as the fundamental C-H spectral absorption feature. This study examined multiple remote sensing band ratios designed to capitalize on the 3.314µm absorption feature. Other ratios were also developed to detect atmospheric gas changes associated with possible methane plumes. Much of the analysis utilized datasets covering two California regions known to contain active oil/gas seeps and production. One study area covered an off-shore environment, while the other area was over land. It was determined that no single MAS ratio algorithm could be used to confidently detect a methane gas plume. The expected or possible presence of other atmospheric gases has the potential to affect the algorithms and produce complications for interpretation. The relative uniform spectral and thermal properties of ocean waters provide a good background for passive remote sensing atmospheric studies. However, methane plume detection iii over marine enviornments is problematic due to the production of water vapor from methane atmospheric chemical reactions. By using a concurrence of ratio algorithm results, one suspected plume was thought to be detected in one of the off-shore datasets. The analysis for the land datasets found the high degree of surface material and temperature variations dramatically interfered with the ability to interpret the algorithm results with any significant confidence. The results produced many features of which it was not possible to distinguish potential positive results from the false positives. No methane plumes were identified in any of the land datasets, even though oil/gas facilities were targeted. Additional work with multi-temporal datasets could provide a means to address these issues. iv ACKNOWLEDGMENTS I would like to thank my advisor, Dr Robert Vincent, for all of his support and assistance in conducting this research. His enthusiasm for applying remote sensing techniques to pertinent and difficult studies is contagious. Without his extensive knowledge and willingness to share that knowledge, this work would not have been feasible. Many thanks to Bill Butcher, whose computer expertise and late nights insured I could keep working. I would also like to thank my committee members, Dr Sheila Roberts and Dr Enrique Gomezdelcampo, for their manuscript reviews and suggestions. v TABLE OF CONTENTS Page INTRODUCTION ................................................................................................................. 1 METHANE BACKGROUND............................................................................................... 2 Methane Sources........................................................................................................ 2 Methane in the Atmosphere ...................................................................................... 4 Spectral Properties of Methane ................................................................................. 6 Methane Detection and Previous Studies .................................................................. 8 THESIS OVERVIEW ............................................................................................................ 11 Using RS data Ratios ................................................................................................. 11 Viewing Conditions and Geometries......................................................................... 14 Selecting Areas of Interest......................................................................................... 18 Spatial Correlation..................................................................................................... 19 Use of HITRAN, MAS, and Analysis Software........................................................ 20 METHODOLOGY: PRE-ANALYTICAL........................................................................... 21 Details of HITRAN.................................................................................................... 21 Details of MAS .......................................................................................................... 22 Combining HITRAN Data and MAS Bands ............................................................. 24 RS Band Ratio Generation and Evaluation................................................................ 26 Expected Sensor Signal Strengths ............................................................................. 30 METHODOLOGY: DATA ANALYSIS ............................................................................. 34 ERMapper …............................................................................................................. 34 ArcMap ……. ........................................................................................................... 34 vi RESULTS ……................................................................................................................ 38 MAS Band Ratios...................................................................................................... 38 RS Ratio Analysis and Images................................................................................... 39 GIS Spatial Correlation.............................................................................................. 43 DISCUSSION …….............................................................................................................. 46 RS Ratio Algorithms.................................................................................................. 46 California Off-shore MAS 98031 ............................................................................. 48 California Inland MAS 97127 .................................................................................. 55 Toledo-Oregon, Ohio MAS 96144 ........................................................................... 60 GIS Analysis ............................................................................................................ 62 Quantitative Estimation ............................................................................................. 65 CONCLUSIONS ….............................................................................................................. 67 REFERENCES ...................................................................................................................... 70 APPENDIX A. ……............................................................................................................. 74 APPENDIX B. …….. ........................................................................................................... 92 APPENDIX C. …….. ........................................................................................................... 98 vii LIST OF FIGURES Figure Page 1 Atmospheric gas concentrations for CH4, CO, and OH ........................................... 6 2 Plot of infrared line list data for various hydrocarbons ............................................ 7 3 Reflectance and emittance curves of the Earth’s surface based upon Planck’s blackbody formula ................................................................... 12 4 Illustration for Linear Approximation Concept and Continuum Method.................. 12 5 Illustration of band selection for spectral analysis ratio algorithm............................ 13 6a Standard viewing conditions and geometries for land............................................... 15 6b Standard viewing conditions and geometries over water .......................................... 16 6c Daytime viewing conditions and geometries over land and water with atmospheric gas................................................................................. 17 7 Plot of HITRAN spectral lines for CH4 gas............................................................... 22 8 California mission locations...................................................................................... 24 9 CH4 spectra and MAS 98-031 Bands........................................................................ 25 10 BB Curve plots for solar reflected and 300K surface emission................................. 30 11 Ideal spectral profiles................................................................................................. 31 12 Ideal vs Actual sensor response ................................................................................. 32 13 General location of remote sensing images in figures 14 and 15 .............................