Abstract
Remote Sensing Detection of Natural Gas Leaks Remains Challenging When using Ground Vegetation Stress to Detect Underground Pipeline Leaks. Other Natural Stressors May Co-Present and Complicate Gas Leak Detection. This Study Explores the Feasibility of Identifying and Distinguishing Gas-Induced Stress from Other Natural Stresses by Analyzing the Hyperspectral Reflectance of Vegetation. the Effectiveness of This Discrimination is Assessed Across Three Distinct Spectral Ranges (VNIR, SWIR, and Full Spectra). Greenhouse Experiments Subjected Three Plant Species to Controlled Environmental Stressors, Including Gas Leakage, Salinity Impact, Heavy-Metal Contamination, and Drought Exposure. Spectral Curves Obtained from the Experiments Underwent Preprocessing Techniques Such as Standard Normal Variate, First-Order Derivative, and Second-Order Derivative. Principal Component Analysis Was Then Employed to Reduce Dimensionality in the Spectral Feature Space, Facilitating Input for Linear/quadratic Discriminant Analysis (LDA/QDA) to Identify and Discriminate Gas Leaks. Results Demonstrate an Average Accuracy of 80% in Identifying Gas-Stressed Plants from Unstressed Ones using LDA. Gas Leakage Can Be Discriminated from Scenarios Involving a Single Distracting Stressor with an Accuracy Ranging from 76.4% to 84.6%, with Drought Treatment Proving the Most Successful. Notably, First-Order Derivative Processing of VNIR Spectra Yields the Highest Accuracy in Gas Leakage Detection.
Recommended Citation
P. Ma et al., "Natural Gas Induced Vegetation Stress Identification and Discrimination from Hyperspectral Imaging for Pipeline Leakage Detection," Remote Sensing, vol. 16, no. 6, article no. 1029, MDPI, Mar 2024.
The definitive version is available at https://doi.org/10.3390/rs16061029
Department(s)
Civil, Architectural and Environmental Engineering
Publication Status
Open Access
Keywords and Phrases
climate change; hyperspectral imaging; methane/natural gas; multivariate analysis; pipeline leakage detection; remote sensing; vegetation stress
International Standard Serial Number (ISSN)
2072-4292
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Mar 2024
Comments
U.S. Department of Transportation, Grant 693JK31950005CAAP