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A baseline slope index to detect natural gas microleakage-stressed vegetation considering shadow removal in hyperspectral imagery

Author

Listed:
  • Zhang, Wenxuan
  • Jiang, Jinbao
  • Li, Kangning
  • Wang, Xinda
  • Zhang, Feng
  • Zhang, Ruixia
  • Jiang, Dong
  • Yue, Guangtao

Abstract

Natural gas storage leakages cause significant energy waste, pollution, and even challenge human safety. Despite difficulties in direct detection by sensors, underground natural gas microleakages can be identified through stressed vegetation based on hyperspectral remote sensing. However, previous models often require substantial computing power due to complex network, while existing index-based models cause inaccurate identifications. Moreover, shadow effects, which can obscure spectral changes and cause misdetection, were not fully considered in previous models. Therefore, this study proposed a baseline slope index model considering shadow removal (BLSI-SR) to detect natural gas microleakage-stressed vegetation, which consists of two steps: (1) shadow removal. The endmembers were extracted from the sunlit regions in an unsupervised way, and input into the triple shadow multilinear mixing (triple-SMLM) model for vegetation canopy shadow removal. (2) Stressed range extraction. A spectral index called baseline slope index (BLSI), derived from the baseline slope between near-infrared and green bands, was used to extract the stressed range and locate the gas leakage. The results indicated that compared to other methods, the BLSI-SR achieves 100 % Precision and Recall with the best mean absolute location error of 6.52 cm. This study has the potential for enhancing leakage monitoring in underground natural gas storage.

Suggested Citation

  • Zhang, Wenxuan & Jiang, Jinbao & Li, Kangning & Wang, Xinda & Zhang, Feng & Zhang, Ruixia & Jiang, Dong & Yue, Guangtao, 2025. "A baseline slope index to detect natural gas microleakage-stressed vegetation considering shadow removal in hyperspectral imagery," Energy, Elsevier, vol. 331(C).
  • Handle: RePEc:eee:energy:v:331:y:2025:i:c:s0360544225026799
    DOI: 10.1016/j.energy.2025.137037
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    References listed on IDEAS

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    1. Peng, Jinghong & Zhou, Jun & Liang, Guangchuan & Li, Chengyu & Qin, Can, 2024. "Multi-period integrated scheduling optimization of complex natural gas pipeline network system with underground gas storage to ensure economic and environmental benefits," Energy, Elsevier, vol. 302(C).
    2. Liang, Xiaopeng & Ma, Hongling & Cai, Rui & Zhao, Kai & Zeng, Zhen & Li, Hang & Yang, Chunhe, 2023. "Feasibility analysis of natural gas storage in the voids of sediment within salt cavern——A case study in China," Energy, Elsevier, vol. 285(C).
    3. Ali, Aliyuda & Aliyuda, Kachalla & Elmitwally, Nouh & Muhammad Bello, Abdulwahab, 2022. "Towards more accurate and explainable supervised learning-based prediction of deliverability for underground natural gas storage," Applied Energy, Elsevier, vol. 327(C).
    4. Xu, Jiuping & Tang, Min & Liu, Tingting & Fan, Lurong, 2024. "Technological paradigm-based development strategy towards natural gas hydrate technology," Energy, Elsevier, vol. 289(C).
    5. Yao, Lizhong & Zhang, Yu & He, Tiantian & Luo, Haijun, 2023. "Natural gas pipeline leak detection based on acoustic signal analysis and feature reconstruction," Applied Energy, Elsevier, vol. 352(C).
    6. Chu, Hongyang & Zhang, Liang & Lu, Huimin & Chen, Danyang & Wang, Jianping & Zhu, Weiyao & Lee, W. John, 2024. "Transient pressure prediction in large-scale underground natural gas storage: A deep learning approach and case study," Energy, Elsevier, vol. 311(C).
    7. Jiang, Jinbao & Steven, Michael D. & He, Ruyan & Chen, Yunhao & Du, Peijun, 2016. "Identification of plants responding to CO2 leakage stress using band depth and the full width at half maxima of canopy spectra," Energy, Elsevier, vol. 100(C), pages 73-81.
    8. Li, Pengcheng & Wang, Dantong & Hou, Yaoqi & Hu, Zhan & Chen, Danqing & Wang, Yi & Song, Chunfeng, 2024. "Phytohormones enhanced carbon fixation and biomass production in CO2 absorption-microalgae conversion system under light stress," Energy, Elsevier, vol. 308(C).
    9. Ren, Simiao & Hu, Wayne & Bradbury, Kyle & Harrison-Atlas, Dylan & Malaguzzi Valeri, Laura & Murray, Brian & Malof, Jordan M., 2022. "Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis," Applied Energy, Elsevier, vol. 326(C).
    10. Meixuan Li & Xicun Zhu & Wei Li & Xiaoying Tang & Xinyang Yu & Yuanmao Jiang, 2022. "Retrieval of Nitrogen Content in Apple Canopy Based on Unmanned Aerial Vehicle Hyperspectral Images Using a Modified Correlation Coefficient Method," Sustainability, MDPI, vol. 14(4), pages 1-16, February.
    11. Ali, Aliyuda, 2021. "Data-driven based machine learning models for predicting the deliverability of underground natural gas storage in salt caverns," Energy, Elsevier, vol. 229(C).
    12. Chen, Yun & Guerschman, Juan P & Cheng, Zhibo & Guo, Longzhu, 2019. "Remote sensing for vegetation monitoring in carbon capture storage regions: A review," Applied Energy, Elsevier, vol. 240(C), pages 312-326.
    13. Omidkar, Ali & Alagumalai, Avinash & Li, Zhaofei & Song, Hua, 2024. "Machine learning assisted techno-economic and life cycle assessment of organic solid waste upgrading under natural gas," Applied Energy, Elsevier, vol. 355(C).
    14. Du, Ying & Jiang, Jinbao & Yu, Zijian & Liu, Ziwei & Pan, Yingyang & Xiong, Kangni, 2024. "A knowledge guided deep learning framework for underground natural gas micro-leaks detection from hyperspectral imagery," Energy, Elsevier, vol. 294(C).
    15. Wang, Jingfan & Ji, Jingwei & Ravikumar, Arvind P. & Savarese, Silvio & Brandt, Adam R., 2022. "VideoGasNet: Deep learning for natural gas methane leak classification using an infrared camera," Energy, Elsevier, vol. 238(PB).
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