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A new method for the attribution of breakpoints in segmentation of IWV difference time series / Khanh Ninh Nguyen (2022)  

Public Titre : A new method for the attribution of breakpoints in segmentation of IWV difference time series Type de document : Article/Communication Auteurs : Khanh Ninh Nguyen, Auteur ; Olivier Bock , Auteur ; Emilie Lebarbier, Auteur Editeur : Munich [Allemagne] : European Geosciences Union EGU Année de publication : 2022 Conférence : EGU 2022, General Assembly 23/05/2022 27/05/2022 Vienne Autriche OA Abstracts only Importance : 1 p. Format : 21 x 30 cm Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] données GNSS [Termes IGN] points de rupture [Termes IGN] segmentation [Termes IGN] série temporelle [Termes IGN] teneur intégrée en vapeur d'eau Résumé : (auteur) In recent years, the detection and correction of the non-natural irregularities in the long climatic records, so-called homogenization, has been studied. This work is motivated by the problem of identification of origins of the breakpoints in the segmentation of difference series (difference between a candidate series and a reference series). Several segmentation methods have been developed for the difference series, but many of them assume that the reference series is homogenous. However, the homogeneity of the reference series, in reality, is uncertain and unproven. In our study, we applied the segmentation method GNSSseg (Quarello et al., 2020) on the difference between the Integrated water vapour estimates of the CODE REPRO2015 GNSS data set and the ERA5 reanalysis. About 36.5% of change points can be validated from the GPS metadata, and the origins of the remaining 64.5% are questionable (Nguyen et al., 2021). The ambiguity can be leveraged when there is at least one nearby GPS station with respect to which the candidate series can be compared. The proposed method uses weighted t-tests combining the candidate GPS and ERA series and their homologues (denoted GPS' and ERA') from each nearby station. If sufficient consistency emerges from the six tests for all the nearby stations, a decision can be made whether the breakpoint detected in the candidate GPS-ERA series is due to GPS or, alternatively, to ERA. For each quadruplet (GPS, ERA, GPS', ERA'), six t-tests are performed, and the outcomes are combined. In a set of 81 globally distributed GNSS time series spanning more than 25 years, 56 series have at least one nearby station, where 171 breakpoints are detected in segmentation, in which 136 breakpoints are attributed to the GPS. Among those, 94 breakpoints have consistent results between all the nearby stations. GPS-related breakpoints are used for the correction of the mean shift in the difference series. The impact of the breakpoint correction on the GNSS IWV trend estimates is then evaluated. Numéro de notice : C2022-009 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : 10.5194/egusphere-egu22-6390 En ligne : https://doi.org/10.5194/egusphere-egu22-6390 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100713

A breakpoint detection in the mean model with heterogeneous variance on fixed time-intervals / Olivier Bock in Statistics and Computing, vol 29 n° 1 (February 2020)  

Public [article]  inStatistics and Computing > vol 29 n° 1 (February 2020) . - pp 1 - 13 Titre : A breakpoint detection in the mean model with heterogeneous variance on fixed time-intervals Type de document : Article/Communication Auteurs : Olivier Bock , Auteur ; Xavier Collilieux , Auteur ; François Guillamon, Auteur ; Emilie Lebarbier, Auteur ; Claire Pascal, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : pp 1 - 13 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] analyse de variance [Termes IGN] données GNSS [Termes IGN] inférence statistique [Termes IGN] méthode robuste [Termes IGN] points de rupture [Termes IGN] processus gaussien [Termes IGN] segmentation [Termes IGN] série temporelle [Termes IGN] variabilité Résumé : (Auteur) This work is motivated by an application for the homogenization of global navigation satellite system (GNSS)-derived integrated water vapour series. Indeed, these series are affected by abrupt changes due to equipment changes or environmental effects. The detection and correction of the series from these changes are a crucial step before any use for climate studies. In addition to these abrupt changes, it has been observed in the series a non-stationary of the variability. We propose in this paper a new segmentation model that is a breakpoint detection in the mean model of a Gaussian process with heterogeneous variance on known time intervals. In this segmentation case, the dynamic programming algorithm used classically to infer the breakpoints cannot be applied anymore. We propose a procedure in two steps: we first estimate robustly the variances and then apply the classical inference by plugging these estimators. The performance of our proposed procedure is assessed through simulation experiments. An application to real GNSS data is presented. Numéro de notice : A2020-368 Affiliation des auteurs : Géodésie+Ext (mi2018-2019) Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11222-019-09853-5 Date de publication en ligne : 03/05/2019 En ligne : https://doi.org/10.1007/s11222-019-09853-5 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92571 [article]

Spatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series / Meng Lu in ISPRS Journal of photogrammetry and remote sensing, vol 117 (July 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 117 (July 2016) . - pp 227 – 236 Titre : Spatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series Type de document : Article/Communication Auteurs : Meng Lu, Auteur ; Edzer J. Pebesma, Auteur ; Alber Sanchez, Auteur ; Jan Verbesselt, Auteur Année de publication : 2016 Article en page(s) : pp 227 – 236 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Amazonie [Termes IGN] Brésil [Termes IGN] corrélation automatique de points homologues [Termes IGN] déboisement [Termes IGN] détection de changement [Termes IGN] image Aqua-MODIS [Termes IGN] image Terra-MODIS [Termes IGN] points de rupture [Termes IGN] série temporelle Résumé : (auteur) Growing availability of long-term satellite imagery enables change modeling with advanced spatio-temporal statistical methods. Multidimensional arrays naturally match the structure of spatio-temporal satellite data and can provide a clean modeling process for complex spatio-temporal analysis over large datasets. Our study case illustrates the detection of breakpoints in MODIS imagery time series for land cover change in the Brazilian Amazon using the BFAST (Breaks For Additive Season and Trend) change detection framework. BFAST includes an Empirical Fluctuation Process (EFP) to alarm the change and a change point time locating process. We extend the EFP to account for the spatial autocorrelation between spatial neighbors and assess the effects of spatial correlation when applying BFAST on satellite image time series. In addition, we evaluate how sensitive EFP is to the assumption that its time series residuals are temporally uncorrelated, by modeling it as an autoregressive process. We use arrays as a unified data structure for the modeling process, R to execute the analysis, and an array database management system to scale computation. Our results point to BFAST as a robust approach against mild temporal and spatial correlation, to the use of arrays to ease the modeling process of spatio-temporal change, and towards communicable and scalable analysis. Numéro de notice : A2016-586 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.03.007 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.03.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81727 [article]