Enhancing Geothermal Anomaly Detection with Multi-Source Thermal Infrared Data: A Case of the Yangbajing–Yangyi Basin, Tibet
Abstract
:1. Introduction
2. Geological Setting
3. Materials and Methods
3.1. Landsat 8 LST Data Products and Time-Series Processing
3.2. ASTER LST Data Product and D–S Evidence Theory Fusion Method
3.3. Combined Processing of Daytime and Nighttime LST Data
3.4. MODIS NLST Analysis and Magma Chamber Identification
4. Result
4.1. Daytime LST Anomalies Extracted from Landsat 8 Data
4.2. Nighttime LST Anomalies Extracted from ASTER LST Data
4.3. Geothermal Anomalies from Combined Daytime and Nighttime LST
4.4. Distribution Characteristics of Geothermal Anomalies Detected by MODIS NLST
5. Discussion
5.1. Integration and Optimization of Multi-Source LST Data for Geothermal Anomaly Detection
5.1.1. Anomaly Extraction and Method Optimization of Landsat-8 LST Data
5.1.2. Multi-Perspective Integration and Extraction Analysis of ASTER NLST Data
5.1.3. Geothermal Anomaly Stability from Joint Daytime and Nighttime LST Analysis
5.2. Magma Chamber Dynamics from MODIS NLST Data
5.3. Relationship Between Day–Night Anomalies and Magmatic Heat Sources
5.4. Limitations and Future Improvements
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Dataset | Resolution | Temporal Scope | Data Source | Total Scenes | Processing Steps | Research Role |
---|---|---|---|---|---|---|
Landsat-8 TIRS | 100 m | 2013–2023 (Winter: November–March) | USGS Landsat Collection 2 Surface Temperature Product | 134 | 1. Calculate annual winter mean LST and multi-year mean LST (2013–2023) using GEE. 2. Remove water bodies based on NDVI threshold. 3. Perform altitude correction on LST data. 4. Extract thermal anomalies using local threshold segmentation. | Detection of daytime geothermal anomalies through multi-year LST analysis. |
ASTER LST | 90 m | 3 November 2023, 9 December 2023 | NASA LP DAAC, ASTER Surface Temperature Product (AST_08 V003) | 2 | 1. Extract thermal anomalies using a multi-view approach (global, local, elevation, and structural thresholds). 2. Integrate multi-view data using D–S evidence theory. | Identification of nighttime geothermal anomalies from multiple perspectives. |
MODIS NLST | 1000 m | 2003–2023 (21 years, 252 months) | NASA LP DAAC, MODIS Land Surface Temperature Daily Product (MOD11A1 V061) | 7640 | 1. Construct a 252-month long-term LST time series. 2. Calculate the 21-year monthly mean LST. 3. Analyze interannual and monthly LST variation trends (2003–2023). | Analysis of magmatic chamber activity and long-term geothermal dynamic evolution. |
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Li, C.; Guo, N.; Li, Y.; Luo, H.; Zhuo, Y.; Deng, S.; Li, X. Enhancing Geothermal Anomaly Detection with Multi-Source Thermal Infrared Data: A Case of the Yangbajing–Yangyi Basin, Tibet. Appl. Sci. 2025, 15, 3740. https://doi.org/10.3390/app15073740
Li C, Guo N, Li Y, Luo H, Zhuo Y, Deng S, Li X. Enhancing Geothermal Anomaly Detection with Multi-Source Thermal Infrared Data: A Case of the Yangbajing–Yangyi Basin, Tibet. Applied Sciences. 2025; 15(7):3740. https://doi.org/10.3390/app15073740
Chicago/Turabian StyleLi, Chunhao, Na Guo, Yubin Li, Haiyang Luo, Yexin Zhuo, Siyuan Deng, and Xuerui Li. 2025. "Enhancing Geothermal Anomaly Detection with Multi-Source Thermal Infrared Data: A Case of the Yangbajing–Yangyi Basin, Tibet" Applied Sciences 15, no. 7: 3740. https://doi.org/10.3390/app15073740
APA StyleLi, C., Guo, N., Li, Y., Luo, H., Zhuo, Y., Deng, S., & Li, X. (2025). Enhancing Geothermal Anomaly Detection with Multi-Source Thermal Infrared Data: A Case of the Yangbajing–Yangyi Basin, Tibet. Applied Sciences, 15(7), 3740. https://doi.org/10.3390/app15073740