No abstract

No abstract

Earthquakes are reported to be preceded by anomalous increases in satellite-recorded thermal emissions, but published results are often contradicting and/or limited to short periods and areas around the earthquake. We apply a methodology that allows to detect subtle, localized spatio-temporal fluctuations in hyper-temporal, geostationary-based land surface temperature (LST) data. We study 10 areas worldwide, covering 20 large (Mw > 5.5) and shallow (<35 km) land-based earthquakes. We compare years and locations with and without earthquake, and we statistically evaluate our findings with respect to distance from epicentra and temporal coincidence with earthquakes. We detect anomalies throughout the duration of all datasets, at various distances from the earthquake, and in years with and without earthquake alike. We find no distinct repeated patterns in the case of earthquakes that happen in the same region in different years. We conclude that earthquakes do not have a significant effect on detected LST anomalies.Remote Sens. 2019, 11, 61 2 of 25 in [25]). Furthermore, anomalies are often found to relate to atmospheric influences and artefacts due to data processing [26].The objective of this study is to make use of the advantages provided by geostationary-based LST data and of a recently published methodological approach, in order to examine the presence of thermal anomalies shortly before large, shallow, land-based earthquakes. We consider that an earthquake is large when it has magnitude larger than Mw 5.5 and that an earthquake is shallow when it has focal depth <35 km. We study 20 earthquake cases in 10 study areas around the world, with different local environmental and climatic conditions. We apply a methodology which suppresses large-scale patterns in the satellite signal time series and isolates only spatially localized fluctuations [27]. This methodology allows to constrain the spatial extent of detected anomalies and the time of their occurrence. Thermal anomalies may appear for a variety of reasons other than earthquakes, including spatiotemporal variations of surface spectral emissivity [28] and local atmospheric conditions, like atmospheric inversions [29]. We therefore test the hypothesis that more anomalies would be detected at closer distances to the earthquake, shortly prior or during the earthquake, and only in years with earthquake occurrence. This hypothesis is supported by published research [30], concluding that anomalies increase with increasing earthquake magnitude; anomalies are found predominantly near the epicenter, one day before and on the day of the earthquake; and anomalies are more easily observed during shallow earthquakes than the deep ones. We statistically evaluate our findings, taking into account the spatial and temporal occurrence of detected anomalies and earthquakes. Materials and Methods Input DataResearch suggests that the use of satellite-derived land surface temperature (LST) data can provide significant advantages in this field of study [6,31,32]. LST products are esti...

We apply a method for detecting subtle spatiotemporal signal fluctuations to monitor volcanic activity. Whereas midwave infrared data are commonly used for volcanic hot spot detection, our approach utilizes hypertemporal longwave infrared‐based land surface temperature (LST) data. Using LST data of the second‐generation European Meteorological Satellites, we study (a) a paroxysmal, 1 day long eruption of Mount Etna (Italy); (b) a prolonged, 6 month period of effusive and lateral lava flows of the Nyamuragira volcano (Democratic Republic of Congo); and (c) intermittent activity in the permanent lava lake of Nyiragongo (Democratic Republic of Congo) over a period of 2 years (2011–2012). We compare our analysis with published ground‐based observations and satellite‐based alert systems; results agree on the periods of increased volcanic activity and quiescence. We further apply our analysis on mid‐infrared and long‐infrared brightness temperatures and compare the results. We conclude that our study enables the use of LST data for monitoring volcanic dynamics at different time scales, can complement existing methodologies, and allows for use of long time series from older sensors that do not provide midwave infrared data.

No abstract