Leveraging Time Series Imaging Spectrometer Data and Deep Learning for
Methane Plume Detection and Delineation
Abstract
Methane is an important greenhouse gas, and anthropogenic methane
emissions from point sources are a frequent target for emissions
reductions. Airborne imaging spectrometers measuring shortwave infrared
radiance are becoming regular sources of data for methane point source
plume detection and flux estimation. Accurate and efficient detection
and delineation of methane plumes will play an essential role in
quantifying point source fluxes. Methane plumes are highly variable in
space and time, whereas surfaces that are typically “false positive”
detections in methane enhancement retrievals are more regularly shaped
and change on longer time scales. This work aims to take advantage of
plume variability by applying a fully convolutional network (FCN) to
detection and delineation of methane plumes within imaging spectrometer
time series data. Using a time series of matched filter methane
retrieval products derived from Airborne Visible and InfraRed Imaging
Spectrometer Next Generation (AVIRIS-NG) data, we demonstrate the
ability of a FCN to classify methane plumes at each time step.
Comparison with plume detection and delineation using conventional
statistical methods demonstrates the value of this approach. Automated
approaches incorporating deep learning will become increasingly
important as future global satellite missions greatly increase the
frequency at which methane point sources are imaged.