Aerial detection of a simulated CO2 leak from a geologic sequestration site using hyperspectral imagery

Abstract

Airborne remote sensing has been proposed as a potential large-area monitoring tool for geologic carbon sequestration (GCS) sites. Elevated soil CO2 levels from an underground CO2 leak could induce a plant stress response that is spectrally discernible from the air. A controlled subsurface CO2 release experiment was conducted during the growing season at the outdoor Zero Emissions Research and Technology (ZERT) center to simulate a CO2 leak scenario. Simultaneously, aerial imagery was collected to obtain a time series used to identify and characterize the simulated CO2 leak prior to, during, and after the three week CO2 release. A theoretical framework was developed for analytical strategies that could be implemented to detect a CO2 leak using aerial hyperspectral imagery with minimal a priori knowledge of when and where a surface leak is going to occur. Areas of inferred CO2 stressed vegetation were identified using an unsupervised clustering algorithm. The spectral signatures of this vegetation informed the development of a red edge index (REI) used to quantify the CO2 stress signal and chart vegetation health trajectories over the course of the CO2 release experiment. REI was found to be significantly lower (Welch's p-value<0.001) in CO2 stressed vegetation as compared to healthy vegetation. Furthermore, maximum differences in REI (REIhealthy vegetation−REICO2 stressed vegetation) were observed at the height of the CO2 release followed by a subsequent decrease in differences once CO2 injection ceased. These results suggest that there was a cumulative vegetation stress response followed by a possible vegetation recovery and that aerial hyperspectral imaging may be a plausible method for detecting CO2 leaks from GCS sites.