Detecting Geothermal Anomalies Using Multi-Temporal Thermal Infrared Remote Sensing Data in the Damxung–Yangbajain Basin, Qinghai–Tibet Plateau
Geothermal energy is an eco-friendly, renewable source of underground thermal energy that exists in the interior of the earth. By tapping into these formations, fluids can be channeled to heat the rock formations above, resulting in a significantly higher land surface temperature (LST). However, LST...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/18/4473 |
| _version_ | 1797577251137519616 |
|---|---|
| author | Xiao Li Guangzheng Jiang Xiaoyin Tang Yinhui Zuo Shengbiao Hu Chao Zhang Yaqi Wang Yibo Wang Libo Zheng |
| author_facet | Xiao Li Guangzheng Jiang Xiaoyin Tang Yinhui Zuo Shengbiao Hu Chao Zhang Yaqi Wang Yibo Wang Libo Zheng |
| author_sort | Xiao Li |
| collection | DOAJ |
| description | Geothermal energy is an eco-friendly, renewable source of underground thermal energy that exists in the interior of the earth. By tapping into these formations, fluids can be channeled to heat the rock formations above, resulting in a significantly higher land surface temperature (LST). However, LST readings are influenced by various factors such as sun radiation, cyclical variations, and precipitation, which can mask the temperature anomalies caused by geothermal heat. To address these issues and highlight the LST anomalies caused by geothermal heat, this paper proposes a methodology to efficiently and quickly calculate the multi-temporal LST leveraging of the Google Earth Engine (GEE) in the Damxung–Yangbajain basin, Qinghai–Tibet Plateau. This method incorporates terrain correction, altitude correction, and multi-temporal series comparison to extract thermal anomaly signals. The existing geothermal manifestations are used as a benchmark to further refine the methodology. The results indicate that the annual mean winter LST is a sensitive indicator of geothermal anomaly signals. The annual mean winter LST between 2015 and 2020 varied from −14.7 °C to 26.7 °C, with an average of 8.6 °C in the study area. After altitude correction and water body removal, the annual mean winter LST varied from −22.1 °C to 23.3 °C, with an average of 6.2 °C. When combining the distribution of faults with the results of the annual mean winter LST, this study delineated the geothermal potential areas that are located predominantly around the fault zone at the southern foot of the Nyainqentanglha Mountains. Geothermal potential areas exhibited a higher LST, ranging from 12.6 °C to 23.3 °C. These potential areas extend to the northeast, and the thermal anomaly range reaches as high as 19.6%. The geothermal potential area makes up 8.2% of the entire study area. The results demonstrate that the approach successfully identified parts of known geothermal fields and indicates sweet spots for future research. This study highlights that utilizing the multi-temporal winter LST is an efficient and cost-effective method for prospecting geothermal resources in plateau environments. |
| first_indexed | 2024-03-10T22:05:34Z |
| format | Article |
| id | doaj.art-95e2d4e79e864fff93ac8d10eacb1609 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T22:05:34Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-95e2d4e79e864fff93ac8d10eacb16092023-11-19T12:48:12ZengMDPI AGRemote Sensing2072-42922023-09-011518447310.3390/rs15184473Detecting Geothermal Anomalies Using Multi-Temporal Thermal Infrared Remote Sensing Data in the Damxung–Yangbajain Basin, Qinghai–Tibet PlateauXiao Li0Guangzheng Jiang1Xiaoyin Tang2Yinhui Zuo3Shengbiao Hu4Chao Zhang5Yaqi Wang6Yibo Wang7Libo Zheng8College of Energy, Chengdu University of Technology, Chengdu 610059, ChinaCollege of Energy, Chengdu University of Technology, Chengdu 610059, ChinaInstitute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, ChinaCollege of Energy, Chengdu University of Technology, Chengdu 610059, ChinaState Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, ChinaCollege of Energy, Chengdu University of Technology, Chengdu 610059, ChinaState Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, ChinaState Key Laboratory of Lithospheric Evolution, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, ChinaThe First Institute of Hydrology and Engineering Geological Prospecting Anhui Geological Prospecting Bureau, Bengbu 233000, ChinaGeothermal energy is an eco-friendly, renewable source of underground thermal energy that exists in the interior of the earth. By tapping into these formations, fluids can be channeled to heat the rock formations above, resulting in a significantly higher land surface temperature (LST). However, LST readings are influenced by various factors such as sun radiation, cyclical variations, and precipitation, which can mask the temperature anomalies caused by geothermal heat. To address these issues and highlight the LST anomalies caused by geothermal heat, this paper proposes a methodology to efficiently and quickly calculate the multi-temporal LST leveraging of the Google Earth Engine (GEE) in the Damxung–Yangbajain basin, Qinghai–Tibet Plateau. This method incorporates terrain correction, altitude correction, and multi-temporal series comparison to extract thermal anomaly signals. The existing geothermal manifestations are used as a benchmark to further refine the methodology. The results indicate that the annual mean winter LST is a sensitive indicator of geothermal anomaly signals. The annual mean winter LST between 2015 and 2020 varied from −14.7 °C to 26.7 °C, with an average of 8.6 °C in the study area. After altitude correction and water body removal, the annual mean winter LST varied from −22.1 °C to 23.3 °C, with an average of 6.2 °C. When combining the distribution of faults with the results of the annual mean winter LST, this study delineated the geothermal potential areas that are located predominantly around the fault zone at the southern foot of the Nyainqentanglha Mountains. Geothermal potential areas exhibited a higher LST, ranging from 12.6 °C to 23.3 °C. These potential areas extend to the northeast, and the thermal anomaly range reaches as high as 19.6%. The geothermal potential area makes up 8.2% of the entire study area. The results demonstrate that the approach successfully identified parts of known geothermal fields and indicates sweet spots for future research. This study highlights that utilizing the multi-temporal winter LST is an efficient and cost-effective method for prospecting geothermal resources in plateau environments.https://www.mdpi.com/2072-4292/15/18/4473land surface temperature (LST)geothermal resourcemulti-temporal thermal infrared remote sensingGoogle Earth Engine (GEE)Qinghai–Tibet Plateau |
| spellingShingle | Xiao Li Guangzheng Jiang Xiaoyin Tang Yinhui Zuo Shengbiao Hu Chao Zhang Yaqi Wang Yibo Wang Libo Zheng Detecting Geothermal Anomalies Using Multi-Temporal Thermal Infrared Remote Sensing Data in the Damxung–Yangbajain Basin, Qinghai–Tibet Plateau Remote Sensing land surface temperature (LST) geothermal resource multi-temporal thermal infrared remote sensing Google Earth Engine (GEE) Qinghai–Tibet Plateau |
| title | Detecting Geothermal Anomalies Using Multi-Temporal Thermal Infrared Remote Sensing Data in the Damxung–Yangbajain Basin, Qinghai–Tibet Plateau |
| title_full | Detecting Geothermal Anomalies Using Multi-Temporal Thermal Infrared Remote Sensing Data in the Damxung–Yangbajain Basin, Qinghai–Tibet Plateau |
| title_fullStr | Detecting Geothermal Anomalies Using Multi-Temporal Thermal Infrared Remote Sensing Data in the Damxung–Yangbajain Basin, Qinghai–Tibet Plateau |
| title_full_unstemmed | Detecting Geothermal Anomalies Using Multi-Temporal Thermal Infrared Remote Sensing Data in the Damxung–Yangbajain Basin, Qinghai–Tibet Plateau |
| title_short | Detecting Geothermal Anomalies Using Multi-Temporal Thermal Infrared Remote Sensing Data in the Damxung–Yangbajain Basin, Qinghai–Tibet Plateau |
| title_sort | detecting geothermal anomalies using multi temporal thermal infrared remote sensing data in the damxung yangbajain basin qinghai tibet plateau |
| topic | land surface temperature (LST) geothermal resource multi-temporal thermal infrared remote sensing Google Earth Engine (GEE) Qinghai–Tibet Plateau |
| url | https://www.mdpi.com/2072-4292/15/18/4473 |
| work_keys_str_mv | AT xiaoli detectinggeothermalanomaliesusingmultitemporalthermalinfraredremotesensingdatainthedamxungyangbajainbasinqinghaitibetplateau AT guangzhengjiang detectinggeothermalanomaliesusingmultitemporalthermalinfraredremotesensingdatainthedamxungyangbajainbasinqinghaitibetplateau AT xiaoyintang detectinggeothermalanomaliesusingmultitemporalthermalinfraredremotesensingdatainthedamxungyangbajainbasinqinghaitibetplateau AT yinhuizuo detectinggeothermalanomaliesusingmultitemporalthermalinfraredremotesensingdatainthedamxungyangbajainbasinqinghaitibetplateau AT shengbiaohu detectinggeothermalanomaliesusingmultitemporalthermalinfraredremotesensingdatainthedamxungyangbajainbasinqinghaitibetplateau AT chaozhang detectinggeothermalanomaliesusingmultitemporalthermalinfraredremotesensingdatainthedamxungyangbajainbasinqinghaitibetplateau AT yaqiwang detectinggeothermalanomaliesusingmultitemporalthermalinfraredremotesensingdatainthedamxungyangbajainbasinqinghaitibetplateau AT yibowang detectinggeothermalanomaliesusingmultitemporalthermalinfraredremotesensingdatainthedamxungyangbajainbasinqinghaitibetplateau AT libozheng detectinggeothermalanomaliesusingmultitemporalthermalinfraredremotesensingdatainthedamxungyangbajainbasinqinghaitibetplateau |