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dc.creatorMilitino, Ana F.es_ES
dc.creatorMoradi, Mohammad Mehdies_ES
dc.creatorUgarte Martínez, María Doloreses_ES
dc.date.accessioned2020-07-06T08:41:21Z
dc.date.available2020-07-06T08:41:21Z
dc.date.issued2020
dc.identifier.issn2072-4292
dc.identifier.urihttps://hdl.handle.net/2454/37336
dc.description.abstractDetecting change-points and trends are common tasks in the analysis of remote sensing data. Over the years, many different methods have been proposed for those purposes, including (modified) Mann-Kendall and Cox-Stuart tests for detecting trends; and Pettitt, Buishand range, Buishand U, standard normal homogeneity (Snh), Meanvar, structure change (Strucchange), breaks for additive season and trend (BFAST), and hierarchical divisive (E. divisive) for detecting change-points. In this paper, we describe a simulation study based on including different artificial, abrupt changes at different time-periods of image time series to assess the performances of such methods. The power of the test, type I error probability, and mean absolute error (MAE) were used as performance criteria, although MAE was only calculated for change-point detection methods. The study reveals that if the magnitude of change (or trend slope) is high, and/or the change does not occur in the first or last time-periods, the methods generally have a high power and a low MAE. However, in the presence of temporal autocorrelation, MAE raises, and the probability of introducing false positives increases noticeably. The modified versions of the Mann-Kendall method for autocorrelated data reduce/moderate its type I error probability, but this reduction comes with an important power diminution. In conclusion, taking a trade-off between the power of the test and type I error probability, we conclude that the original Mann-Kendall test is generally the preferable choice. Although Mann-Kendall is not able to identify the time-period of abrupt changes, it is more reliable than other methods when detecting the existence of such changes. Finally, we look for trend/change-points in land surface temperature (LST), day and night, via monthly MODIS images in Navarre, Spain, from January 2001 to December 2018.en
dc.description.sponsorshipThis work has been supported by Project MTM2017-82553-R (AEI/ FEDER, UE). It has also received funding from la Caixa Foundation (ID1000010434), Caja Navarra Foundation, and UNED Pamplona, under agreement LCF/PR/PR15/51100007.en
dc.format.extent25 p.
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherMDPIen
dc.relation.ispartofRemote Sensing, 2020, 12(6), 1008en
dc.rights© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectLand surface temperatureen
dc.subjectMann-Kendall testen
dc.subjectPower of the testen
dc.subjectSpatio-temporal dataen
dc.subjectTime seriesen
dc.subjectType I error probabilityen
dc.titleOn the performances of trend and change-point detection methods for remote sensing dataen
dc.typeinfo:eu-repo/semantics/articleen
dc.typeArtículo / Artikuluaes
dc.contributor.departmentUniversidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticases_ES
dc.contributor.departmentNafarroako Unibertsitate Publikoa. Estatistika, Informatika eta Matematikak Sailaeu
dc.contributor.departmentUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoa. InaMat - Institute for Advanced Materialses_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.identifier.doi10.3390/rs12061008
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/2PE/MTM2017-82553-Ren
dc.relation.publisherversionhttps://doi.org/10.3390/rs12061008
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.type.versionVersión publicada / Argitaratu den bertsioaes


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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Except where otherwise noted, this item's license is described as © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.