Early Detection of Pipeline Natural Gas Leakage from Hyperspectral Imaging by Vegetation Indicators and Deep Neural Networks-Reference-Cited by-同舟云学术

Early Detection of Pipeline Natural Gas Leakage from Hyperspectral Imaging by Vegetation Indicators and Deep Neural Networks

Published:2024-06-14 Issue:27 Volume:58 Page:12018-12027
ISSN:0013-936X
Container-title:Environmental Science & Technology
language:en
Short-container-title:Environ. Sci. Technol.

Author:

Ma Pengfei¹,Mondal Tarutal Ghosh¹²,Shi Zhenhua¹,Afsharmovahed Mohammad Hossein¹^ORCID,Romans Kevin¹,Li Liujun¹³,Zhuo Ying¹,Chen Genda¹^ORCID

Affiliation:

1. Department of Civil, Architectural and Environmental Engineering, Missouri University of Science and Technology, Rolla, Missouri 65401, United States

2. School of Infrastructure, Indian Institute of Technology, Bhubaneswar, Odisha 752050, India

3. Department of Soil and Water Systems, University of Idaho, Moscow, Idaho 83844, United States

Funder

Pipeline and Hazardous Materials Safety Administration

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.est.4c03345

Reference57 articles.

1. Methane Emissions from the Natural Gas Transmission and Storage System in the United States

2. Satellites Detect Abatable Super-Emissions in One of the World’s Largest Methane Hotspot Regions

3. PHMSA report, https://www.phmsa.dot.gov/data-and-statistics/pipeline/pipeline-incident-20-year-trends (accessed 2024-06-12).

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

5. A multi-temporal method for detection of underground natural gas leakage using hyperspectral imaging

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