Automatic methane plume masking based on wavelet transform image processing: application to MethaneAIR and MethaneSAT data

Efficient and accurate detection and masking of emission plumes are essential for localizing and quantifying point-source emissions via remote sensing. This study presents an automated plume-masking method based on a 2D discrete wavelet transform and advanced image conditioning processes, designed to replace workflows that rely on manual human identification. The method applies a 2D discrete wavelet transform to a methane concentration enhancement image, without assuming prior knowledge of source locations, enhancing plume features while suppressing background noise. The resulting binary plume masks are refined using information from distributions of concentration enhancements, plume morphology, and wind directions. Tunable parameters enable the algorithm to maintain high detection accuracy under varying background and meteorological conditions. The algorithm detected 75 % more plumes than previous methods when applied to MethaneAIR and MethaneSAT images while reducing false positives, primarily by improving sensitivity to low emission sources. Enhanced sensitivity provides more comprehensive emission rate distributions. The wavelet method is computationally efficient compared to machine learning models. It is designed to be readily adaptable to multiple aircraft and satellite platforms and to be applicable to other trace gases that exhibit plume-like structures associated with discrete sources.