Abstract
Earthquakes are the sudden tremors of the ground leaving behind damages to life and property, ranging from smaller to massive scale. Earthquake prediction has been in the limelight of the scientific community since very early times. Prediction of earthquakes has been found in texts of ancient civilizations based on planetary positions with respect to the earth. With the advent of real-time observations on various data sources many attempts are going in this direction. The present paper investigates and put forward some facts based on data obtained from satellites for an earthquake which occurred in Imphal, India, in 2016. It studies the thermal anomaly data that took place before the earthquake. MODIS Land Surface Temperature (LST) product was used wherein daily nighttime images of six years have been used for the study. Quality Assurance of the datasets was performed to identify the good quality pixels having maximum information. A change detection technique for satellite data analysis namely Robust Satellite Technique has been used and RETIRA index has been calculated. The study of this RETIRA index has been done for three years and it has been found that the RETIRA index is considerably high for the earthquake year. But it cannot be concluded that the high value of RETIRA index is a sure indicator for an earthquake and hence it leaves scope for future studies.
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Acknowledgement
The authors are thankful to Dr. Virendra Singh Rathore, Assistant Professor, of the Department of Remote Sensing, Birla Institute of Technology, Mesra, Ranchi 835215, India, for his valuable suggestions.
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Mukhopadhyay, U.K., Sharma, R.N.K., Anwar, S., Dutta, A.D. (2020). Correlating Thermal Anomaly with Earthquake Occurrences Using Remote Sensing. In: Hassanien, A., Azar, A., Gaber, T., Bhatnagar, R., F. Tolba, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019). AMLTA 2019. Advances in Intelligent Systems and Computing, vol 921. Springer, Cham. https://doi.org/10.1007/978-3-030-14118-9_85
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DOI: https://doi.org/10.1007/978-3-030-14118-9_85
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