An approach to detect gas flaring sites using sentinel-2 MSI and NOAA-20 VIIRS images
Detecting gas flaring activities during oil production on a regional scale is necessary, since it emits harmful gases and bring serious global environmental impacts. This study developed a new algorithm to detect gas flare sites (GFs) on daytime Sentinel-2 MSI images and Nighttime NOAA-20 VIIRS imag...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843223003588 |
| _version_ | 1797633255596359680 |
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| author | Chenglin Hu Xiuying Zhang Xuewen Xing Qian Gao |
| author_facet | Chenglin Hu Xiuying Zhang Xuewen Xing Qian Gao |
| author_sort | Chenglin Hu |
| collection | DOAJ |
| description | Detecting gas flaring activities during oil production on a regional scale is necessary, since it emits harmful gases and bring serious global environmental impacts. This study developed a new algorithm to detect gas flare sites (GFs) on daytime Sentinel-2 MSI images and Nighttime NOAA-20 VIIRS images. The algorithm includes three steps: Thermal Anomaly Index (TAI) on single-temporal MSI images was first used to detect potential GFs, then the daily night temperature of potential GFs was calculated based on Planck's law on VIIRS images, and finally the confirmed GFs were filtered based on the detection frequency and temperature. The algorithm was implemented in five regions with different surface covers in the top four countries (Russia, Iran, Iraq, and the USA) in terms of flared gas volumes in 2021. The algorithm achieves an average producer accuracy of 81.7 %, an average user accuracy of 80.3 %, and the root mean square error of the spatial position is 15.6 m for these five test areas. Compared with the existing global datasets of GFs, this algorithm detects the most GFs and the spatial location of the GFs is more accurate, indicating that the proposed method for GF detection has high spatial resolution and the result is reliable. This algorithm could be applied to detect the GFs globally and provide the scientific data to enact efficient measures to alleviate the environmental impacts. |
| first_indexed | 2024-03-11T11:51:30Z |
| format | Article |
| id | doaj.art-052b377a1f504321a4a824f11c386b94 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-03-11T11:51:30Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-052b377a1f504321a4a824f11c386b942023-11-09T04:11:49ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322023-11-01124103534An approach to detect gas flaring sites using sentinel-2 MSI and NOAA-20 VIIRS imagesChenglin Hu0Xiuying Zhang1Xuewen Xing2Qian Gao3International Institute for Earth System Science, Nanjing University, Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, ChinaInternational Institute for Earth System Science, Nanjing University, Nanjing 210023, China; Corresponding author.Research Institute of Petroleum Exploration & Development, PetroChina, Beijing 100083, ChinaInternational Institute for Earth System Science, Nanjing University, Nanjing 210023, ChinaDetecting gas flaring activities during oil production on a regional scale is necessary, since it emits harmful gases and bring serious global environmental impacts. This study developed a new algorithm to detect gas flare sites (GFs) on daytime Sentinel-2 MSI images and Nighttime NOAA-20 VIIRS images. The algorithm includes three steps: Thermal Anomaly Index (TAI) on single-temporal MSI images was first used to detect potential GFs, then the daily night temperature of potential GFs was calculated based on Planck's law on VIIRS images, and finally the confirmed GFs were filtered based on the detection frequency and temperature. The algorithm was implemented in five regions with different surface covers in the top four countries (Russia, Iran, Iraq, and the USA) in terms of flared gas volumes in 2021. The algorithm achieves an average producer accuracy of 81.7 %, an average user accuracy of 80.3 %, and the root mean square error of the spatial position is 15.6 m for these five test areas. Compared with the existing global datasets of GFs, this algorithm detects the most GFs and the spatial location of the GFs is more accurate, indicating that the proposed method for GF detection has high spatial resolution and the result is reliable. This algorithm could be applied to detect the GFs globally and provide the scientific data to enact efficient measures to alleviate the environmental impacts.http://www.sciencedirect.com/science/article/pii/S1569843223003588Gas flaringThermal Anomaly IndexSentinel-2 MSIDaytime observationNOAA-20 VIIRS |
| spellingShingle | Chenglin Hu Xiuying Zhang Xuewen Xing Qian Gao An approach to detect gas flaring sites using sentinel-2 MSI and NOAA-20 VIIRS images International Journal of Applied Earth Observations and Geoinformation Gas flaring Thermal Anomaly Index Sentinel-2 MSI Daytime observation NOAA-20 VIIRS |
| title | An approach to detect gas flaring sites using sentinel-2 MSI and NOAA-20 VIIRS images |
| title_full | An approach to detect gas flaring sites using sentinel-2 MSI and NOAA-20 VIIRS images |
| title_fullStr | An approach to detect gas flaring sites using sentinel-2 MSI and NOAA-20 VIIRS images |
| title_full_unstemmed | An approach to detect gas flaring sites using sentinel-2 MSI and NOAA-20 VIIRS images |
| title_short | An approach to detect gas flaring sites using sentinel-2 MSI and NOAA-20 VIIRS images |
| title_sort | approach to detect gas flaring sites using sentinel 2 msi and noaa 20 viirs images |
| topic | Gas flaring Thermal Anomaly Index Sentinel-2 MSI Daytime observation NOAA-20 VIIRS |
| url | http://www.sciencedirect.com/science/article/pii/S1569843223003588 |
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