A method for detecting underground natural gas pipeline micro-leakage in vegetated areas using high spatial resolution hyperspectral imagery

Abstract

Natural gas is an important clean energy source that is mainly transported through buried pipelines. However, slight leakage during transportation is inevitable and needs to be identified in time to reduce the risk of related accidents. For this purpose, remote sensing can be used as an indirect and non-destructive technique to detect natural gas leakage through vegetation. Abundant spectral and detailed spatial information enables the high spatial resolution hyperspectral imagery to capture subtle stress responses from vegetation. In this study, we designed a simulation experiment of natural gas leakage in vegetated areas, and collected the high spatial resolution hyperspectral images of three plant species. Considering the shadow problem contained in high spatial resolution images, a new vegetation pixel extraction method to eliminate the influence of background shadows was proposed. Then, two spectral transformation methods, continuum removal (CR) and variational mode decomposition (VMD), were integrated to analyze the vegetation spectra, and a spectral index which could reflect the degree of vegetation stress was designed based on the spectral characteristics, named CVI (Continuum removal - Variational mode decomposition Index). Finally, the threshold segmentation methods and the cumulative minimum circumscribed circle method were used to extract the range and location of natural gas leakage. The study demonstrated the potential of using high spatial hyperspectral imagery to map and monitor natural gas leakage through its stress on vegetation. Results of this research can be a reference for the fine identification of stressed plants and are expected to be applied to provide support for related departments and stakeholders.