title: Hyperspectral detection of a subsurface CO2 leak in the presence of water stressed vegetation.
abstract: Remote sensing of vegetation stress has been posed as a possible large area monitoring tool for surface CO2 leakage from geologic carbon sequestration (GCS) sites since vegetation is adversely affected by elevated CO2 levels in soil. However, the extent to which remote sensing could be used for CO2 leak detection depends on the spectral separability of the plant stress signal caused by various factors, including elevated soil CO2 and water stress. This distinction is crucial to determining the seasonality and appropriateness of remote GCS site monitoring. A greenhouse experiment tested the degree to which plants stressed by elevated soil CO2 could be distinguished from plants that were water stressed. A randomized block design assigned Alfalfa plants (Medicago sativa) to one of four possible treatment groups: 1) a CO2 injection group; 2) a water stress group; 3) an interaction group that was subjected to both water stress and CO2 injection; or 4) a group that received adequate water and no CO2 injection. Single date classification trees were developed to identify individual spectral bands that were significant in distinguishing between CO2 and water stress agents, in addition to a random forest classifier that was used to further understand and validate predictive accuracies. Overall peak classification accuracy was 90% (Kappa of 0.87) for the classification tree analysis and 83% (Kappa of 0.77) for the random forest classifier, demonstrating that vegetation stressed from an underground CO2 leak could be accurately discerned from healthy vegetation and areas of co-occurring water stressed vegetation at certain times. Plants appear to hit a stress threshold, however, that would render detection of a CO2 leak unlikely during severe drought conditions. Our findings suggest that early detection of a CO2 leak with an aerial or ground-based hyperspectral imaging system is possible and could be an important GCS monitoring tool.
authors.affiliation: Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, Montana, United States of America.
authors.forename: Gabriel J
authors.initials: GJ
authors.lastname: Bellante
authors.affiliation: Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, Montana, United States of America.
authors.forename: Scott L
authors.initials: SL
authors.lastname: Powell
authors.affiliation: Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, Montana, United States of America.
authors.forename: Rick L
authors.initials: RL
authors.lastname: Lawrence
authors.affiliation: Department of Electrical and Computer Engineering, Montana State University, Bozeman, Montana, United States of America.
authors.forename: Kevin S
authors.initials: KS
authors.lastname: Repasky
authors.affiliation: Department of Plant Sciences and Plant Pathology, Montana State University, Bozeman, Montana, United States of America.
authors.forename: Tracy
authors.initials: T
authors.lastname: Dougher
mesh_terms: D002245:Carbon Dioxide / Q000032:analysis* / Q000378:metabolism; D057965:Carbon Sequestration; D055864:Droughts; D000455:Medicago sativa / Q000187:drug effects / Q000378:metabolism / Q000502:physiology*; D058998:Remote Sensing Technology; D012987:Soil / Q000737:chemistry; D013057:Spectrum Analysis; D013312:Stress, Physiological / Q000187:drug effects*; D014867:Water / Q000494:pharmacology*
chemical_list: D012987:Soil; D014867:Water; D002245:Carbon Dioxide