This thesis describes the application of time series analysis to satellite -derived thermal infrared imagery, in order to determine if spatiotemporally limited anomalies can be linked to earthquake occurrence as has been suggested by literature . For this purpose, a methodology is applied that can suppress large - scale patterns and highlight subtle localized fluctuations in hypertemporal satellite data. The methodology is tested on at- sensor longwave and midwave Thermal Infra -red (TIR) Brightness Temperature data and on TIR -derived Land Surface Temperature (LST) data. Experiments are first carried out to retrieve synthetic anomalies which are imposed in real- life datasets. The methodology is then applied for detection of volcanic activity , and the results are validated using published ground -based reports. The application of this methodology is found to facilitate utilization of longwave IR input for long -term volcanic monitoring, to complement existing hotspot detection techniques and to aid monitoring of lower temperature targets even in areas of constant activity. Finally, the methodology is applied to examine the presence of detectable localized (spatial extent up to 18225 km 2 ) increases in LST prior to twenty large, shallow, land -based earthquakes worldwide. The findings show that there is no statistically significant difference between the anomaly density detected at different distances from the earthquake, at different periods (before, during and after the earthquake), and in years with and without earthquake occurrence. It is not clear if the earthquakes have no influence on the LST registered by the satellite; if the influence exists but is overshadowed by local environmental and atmospheric influences; or if influences are linked to stress accumulation rather than stress release and therefore do not coincide with the earthquake. To clarify this, more research is required on the physical background behind the links between earthquakes and TIR anomalies which are suggested by literature.
|Qualification||Doctor of Philosophy|
|Award date||27 Sep 2017|
|Place of Publication||Enschede|
|Publication status||Published - 27 Sep 2018|