Identifying plants under natural gas micro-leakage stress using hyperspectral remote sensing

Natural gas is an important clean energy source. The demand for, and consumption of, natural gas have been increasing in recent years. Slight natural gas leakage can occur during transportation, which can have a negative impact on the environment, economy, and safety. However, it is relatively difficult to directly detect natural gas microleakage. Hyperspectral remote sensing technology is useful for analyzing the spectral characteristics of vegetation near leakage areas, thereby indirectly obtaining leakage information. In this study, a field experiment was designed to simulate natural gas leakage from an underground pipeline and gas stress on three plant species. The canopy spectral reflectance of the vegetation throughout the growth period of the plants was collected and analyzed. Variational mode decomposition was then used to decompose the spectra. Based on the stress distance (SD) and intrinsic mode functions, it was found that the second intrinsic mode function, with a decomposition scale of 32, was sensitive to gas stress. According to the results of SD, the bands (616 and 829 nm) sensitive to natural gas stress for the three plant species were extracted, and the variational mode decomposition index (VMDI) was constructed. The Jeffries–Matusita distance (JMD) was used to quantitatively evaluate the VMDI index and three indices were used to evaluate the ability to recognize stress. It was found that the index proposed in this study could identify stressed wheat and grass one week earlier than other indices and could better identify stressed vegetation throughout the phenological cycle (JMD > 1.8). The results show that the proposed index can be used as a reliable method to identify natural gas-stressed plants, and that hyperspectral technology is promising for detecting the location of natural gas leaks from underground pipelines.