Detecting Geothermal Anomalies Using Multi-Temporal Thermal Infrared Remote Sensing Data in the Damxung-Yangbajain Basin, Qinghai-Tibet Plateau

被引:3
|
作者
Li, Xiao [1 ,2 ]
Jiang, Guangzheng [1 ,2 ]
Tang, Xiaoyin [3 ]
Zuo, Yinhui [1 ,2 ]
Hu, Shengbiao [4 ,5 ]
Zhang, Chao [1 ,2 ]
Wang, Yaqi [4 ,5 ]
Wang, Yibo [4 ,5 ]
Zheng, Libo [6 ]
机构
[1] Chengdu Univ Technol, Coll Energy, Chengdu 610059, Peoples R China
[2] Chengdu Univ Technol, State Key Lab Oil & Gas Reservoir Geol & Exploitat, Chengdu 610059, Peoples R China
[3] Chinese Acad Geol Sci, Inst Geomech, Beijing 100081, Peoples R China
[4] Chinese Acad Sci, Inst Geol & Geophys, State Key Lab Lithospher Evolut, Beijing 100029, Peoples R China
[5] Univ Chinese Acad Sci, Beijing 100101, Peoples R China
[6] Anhui Geol Prospecting Bur, Inst Hydrol & Engn Geol Prospecting 1, Bengbu 233000, Peoples R China
关键词
land surface temperature (LST); geothermal resource; multi-temporal thermal infrared remote sensing; Google Earth Engine (GEE); Qinghai-Tibet Plateau; LAND-SURFACE TEMPERATURE; SATELLITE IMAGERY; HEAT-FLOW; AREA; TENGCHONG; VOLCANO; ENERGY; FIELD; PARK;
D O I
10.3390/rs15184473
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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 degrees C to 26.7 degrees C, with an average of 8.6 degrees C in the study area. After altitude correction and water body removal, the annual mean winter LST varied from -22.1 degrees C to 23.3 degrees C, with an average of 6.2 degrees 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 degrees C to 23.3 degrees 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.
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页数:20
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