Time series analysis of Land Surface Temperatures in 20 earthquake cases worldwide

DOI

The objective of the study was to examine if there are detectable localized increases in geostationary satellite-derived Land Surface Temperatures (LST) prior to twenty large (Mw>5.5) and shallow (<35km) land-based earthquakes. Two one-year-long datasets are constructed for every study area: one in a year with earthquakes and one in a year without. LST data are normalized based on the methodology described in Pavlidou et al., 2016. Anomalies are detected when normalized values exceed a threshold. Numbers of anomalies are counted in four spatial zones laying at different distances from the earthquakes and in five temporal periods before, during and after the earthquake. Anomaly densities (number of anomalies per zone and per period) are statistically evaluated to see if there exist significant differences between years, periods and locations relative to the earthquakes. The assumption is that a link between earthquakes and anomalies can be established only if significantly more anomalies appear prior to, or during, an earthquake; closer to the earthquake; and only in the year of the earthquake. The calculations and the comparisons are repeated for two different anomaly detection thresholds and for two different definitions of the length of a co-seismic period.

Identifier
DOI http://dx.doi.org/doi:10.17026/dans-xdz-wrwq
Metadata Access https://easy.dans.knaw.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:easy.dans.knaw.nl:easy-dataset:112692
Provenance
Creator Pavlidou, E.
Publisher Data Archiving and Networked Services (DANS)
Contributor Hecker, C.A.;van der Werff, H.M.A.;van der Meijde, M.
Publication Year 2018
Rights info:eu-repo/semantics/openAccess;License: http://creativecommons.org/publicdomain/zero/1.0
Contact Hecker, C.A.;ITC, University of Twente;van der Werff, H.M.A.;van der Meijde, M.;Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente
Representation
Language English
Resource Type Dataset
Format .csv;.sav;.por;.pro;application/pdf;.dta
Coverage
Discipline Not stated
Temporal Coverage Begin 2017-10-26T11:59:59Z
Temporal Coverage End 2018-12-30T11:59:59Z