Department of Civil, Architectural and Environmental Engineering, Missouri University of Science and Technology, Rolla, Missouri 65401, United States.
School of Infrastructure, Indian Institute of Technology, Bhubaneswar, Odisha 752050, India.
Environ Sci Technol. 2024 Jul 9;58(27):12018-12027. doi: 10.1021/acs.est.4c03345. Epub 2024 Jun 14.
The timely detection of underground natural gas (NG) leaks in pipeline transmission systems presents a promising opportunity for reducing the potential greenhouse gas (GHG) emission. However, existing techniques face notable limitations for prompt detection. This study explores the utility of Vegetation Indicators (VIs) to reflect vegetation health deterioration, thereby representing leak-induced stress. Despite the acknowledged potential of VIs, their sensitivity and separability remain understudied. In this study, we employed ground vegetation as biosensors for detecting methane emissions from underground pipelines. Hyperspectral imaging from vegetation was collected weekly at both plant and leaf scales over two months to facilitate stress detection using VIs and Deep Neural Networks (DNNs). Our findings revealed that plant pigment-related VIs, modified chlorophyll absorption reflectance index (MCARI), exhibit commendable sensitivity but limited separability in discerning stressed grasses. A NG-specialized VI, the optimized soil-adjusted vegetation index (OSAVI), demonstrates higher sensitivity and separability in early detection of methane leaks. Notably, the OSAVI proved capable of discriminating vegetation stress 21 days after methane exposure initiation. DNNs identified the methane leaks following a 3-week methane treatment with an accuracy of 98.2%. DNN results indicated an increase in visible (VIS) and a decrease in near-infrared (NIR) in spectra due to methane exposure.
及时检测管道输送系统中地下天然气 (NG) 泄漏为减少潜在温室气体 (GHG) 排放提供了一个有前途的机会。然而，现有的技术在快速检测方面存在明显的局限性。本研究探讨了植被指标 (VIs) 的实用性，以反映植被健康恶化，从而代表泄漏引起的胁迫。尽管 VIs 具有公认的潜力，但它们的敏感性和可分离性仍未得到充分研究。在这项研究中，我们利用地面植被作为生物传感器来检测地下管道中的甲烷排放。在两个月的时间里，每周在植物和叶片尺度上收集植被的高光谱图像，以便使用 VIs 和深度神经网络 (DNN) 进行胁迫检测。我们的研究结果表明，与植物色素相关的 VIs，即改良的叶绿素吸收反射指数 (MCARI)，在识别受胁迫的草时具有令人称道的敏感性，但可分离性有限。一种专门用于 NG 的 VI，即优化的土壤调整植被指数 (OSAVI)，在早期检测甲烷泄漏方面具有更高的敏感性和可分离性。值得注意的是，OSAVI 能够在甲烷暴露 21 天后区分植被胁迫。DNN 以 98.2%的准确率识别了甲烷泄漏。DNN 结果表明，由于甲烷暴露，光谱中的可见光 (VIS) 增加，近红外 (NIR) 减少。
