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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)
[article]
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] variabilitéRésumé : (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
in Statistics and Computing > vol 29 n° 1 (February 2020) . - pp 1 - 13[article]Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods / Elisa Kamir in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)
[article]
Titre : Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods Type de document : Article/Communication Auteurs : Elisa Kamir, Auteur ; François Waldner, Auteur ; Zvi Hochman, Auteur Année de publication : 2020 Article en page(s) : pp 124 - 135 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] Australie
[Termes IGN] blé (céréale)
[Termes IGN] carte agricole
[Termes IGN] climat
[Termes IGN] estimation de précision
[Termes IGN] fonction de base radiale
[Termes IGN] image satellite
[Termes IGN] modèle de croissance végétale
[Termes IGN] modèle non linéaire
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] régression
[Termes IGN] rendement agricole
[Termes IGN] série temporelle
[Termes IGN] variation saisonnièreRésumé : (Auteur) Closing the yield gap between actual and potential wheat yields in Australia is important to meet the growing global demand for food. The identification of hotspots of the yield gap, where the potential for improvement is the greatest, is a necessary step towards this goal. While crop growth models are well suited to quantify potential yields, they lack the ability to provide accurate large-scale estimates of actual yields, owing to the sheer quantity of data they require for parameterisation. In this context, we sought to provide accurate estimates of actual wheat yields across the Australian wheat belt based on machine-learning regression methods, climate records and satellite image time series. Out of nine base learners and two ensembles, support vector regression with radial basis function emerged as the single best learner (root mean square error of 0.55 t ha−1 and R2 of 0.77 at the pixel level). At national scale, this model explained 73% of the yield variability observed across statistical units. Benchmark approaches based on peak Normalised Difference Vegetation Index (NDVI) and on a harvest index were largely outperformed by the machine-learning regression models (R2 Numéro de notice : A2020-046 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.008 Date de publication en ligne : 20/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.008 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94556
in ISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 124 - 135[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2020021 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020023 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020022 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Mapping precipitable water vapor time series from Sentinel-1 interferometric SAR / Pedro Mateus in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)
[article]
Titre : Mapping precipitable water vapor time series from Sentinel-1 interferometric SAR Type de document : Article/Communication Auteurs : Pedro Mateus, Auteur ; João Catalão, Auteur ; Giovanni Nico, Auteur ; Pedro Benevides, Auteur Année de publication : 2020 Article en page(s) : pp 1373 - 1379 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Appalaches
[Termes IGN] cartographie
[Termes IGN] données GNSS
[Termes IGN] image Sentinel-SAR
[Termes IGN] interferométrie différentielle
[Termes IGN] itération
[Termes IGN] méthode des moindres carrés
[Termes IGN] modèle atmosphérique
[Termes IGN] optimisation (mathématiques)
[Termes IGN] phase GNSS
[Termes IGN] prévision météorologique
[Termes IGN] série temporelle
[Termes IGN] vapeur d'eauRésumé : (auteur) In this article, a methodology to retrieve the precipitable water vapor (PWV) from a differential interferometric time series is presented. We used external data provided by atmospheric weather models (e.g., ERA-Interim reanalysis) to constrain the initial state and by Global Navigation Satellite System (GNSS) to phase ambiguities elimination introduced by phase unwrapping algorithm. An iterative least-square is then used to solve the optimization problem. We applied the presented methodology to two time series of differential PWV maps estimated from synthetic aperture radar (SAR) images acquired by the Sentinel-1A, over the southwest part of the Appalachian Mountains (USA). The results were validated using an independent GNSS data set and also compared with atmospheric weather prediction data. The GNSS PWV observations show a strong correlation with the estimated PWV maps with a root-mean-square error less than 1 mm. These results are very encouraging, particularly for the meteorology community, providing crucial information to assimilate into numerical weather models and potentially improve the forecasts. Numéro de notice : A2020-098 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2946077 Date de publication en ligne : 28/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2946077 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94672
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 2 (February 2020) . - pp 1373 - 1379[article]MODIS-based land surface temperature for climate variability and change research: the tale of a typical semi-arid to arid environment / Salahuddin M. Jaber in European journal of remote sensing, vol 53 n° 1 (2020)
[article]
Titre : MODIS-based land surface temperature for climate variability and change research: the tale of a typical semi-arid to arid environment Type de document : Article/Communication Auteurs : Salahuddin M. Jaber, Auteur ; Mahmoud M. Abu-Allaban, Auteur Année de publication : 2020 Article en page(s) : pp 81 - 90 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] anthropisation
[Termes IGN] changement climatique
[Termes IGN] climat aride
[Termes IGN] image Aqua-MODIS
[Termes IGN] image Terra-MODIS
[Termes IGN] image thermique
[Termes IGN] Jordanie
[Termes IGN] MNS ASTER
[Termes IGN] nuit
[Termes IGN] régression linéaire
[Termes IGN] série temporelle
[Termes IGN] température au sol
[Termes IGN] variation diurne
[Termes IGN] zone semi-arideRésumé : (auteur) This study aims to (1) determine the seasonalities and spatial and temporal rates of change of MODIS-based daytime and nighttime land surface temperature (LST) for the last 19 years from 2000 to 2018 and (2) investigate whether these rates are induced by natural (represented by elevation) or anthropogenic (represented by population counts) forcing. The study area is Jordan – a typical Middle Eastern semi-arid to arid country. Time-series additive seasonal decomposition and simple linear regression produced the following results. (1) For both daytime and nighttime the highest LST values were observed in June while the lowest LST values were observed in December. (2) No significant linear rates of change of LST were noticed in daytime, while significant linear rates of increase of LST, which varied from 0.041°C/year to 0.119°C/year, were observed in nighttime in about one-third of the area of the country mainly in the western parts. (3) The significant linear rates of increase of nighttime LST increased significantly by 0.005°C/year for every 1,000 m increase in elevation and by 0.003°C/year for every 1,000 people increase in population counts. (4) Both natural and anthropogenic factors affected LST in nighttime; however, anthropogenic factors seemed to be more important than natural factors. Numéro de notice : A2020-166 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/22797254.2020.1735264 Date de publication en ligne : 06/03/2020 En ligne : https://doi.org/10.1080/22797254.2020.1735264 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94834
in European journal of remote sensing > vol 53 n° 1 (2020) . - pp 81 - 90[article]Prediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series / Mathieu Fauvel in Remote sensing of environment, Vol 237 (February 2020)
[article]
Titre : Prediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series Type de document : Article/Communication Auteurs : Mathieu Fauvel, Auteur ; Maylis Lopes, Auteur ; Titouan Dubo, Auteur ; Justine Rivers-Moore, Auteur ; Pierre-Louis Frison , Auteur ; Nicolas Gross, Auteur ; Annie Ouin, Auteur Année de publication : 2020 Projets : SEBIOREF / Ouin, Annie Article en page(s) : 13 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] biodiversité végétale
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] Haute-Garonne (31)
[Termes IGN] image radar moirée
[Termes IGN] image RapidEye
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] indice de diversité
[Termes IGN] indice de végétation
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] prairie
[Termes IGN] série temporelle
[Termes IGN] taxinomieRésumé : (auteur) The prediction of grasslands plant diversity using satellite image time series is considered in this article. Fifteen months of freely available Sentinel optical and radar data were used to predict taxonomic and functional diversity at the pixel scale (10 m × 10 m) over a large geographical extent (40,000 km2). 415 field measurements were collected in 83 grasslands to train and validate several statistical learning methods. The objective was to link the satellite spectro-temporal data to the plant diversity indices. Among the several diversity indices tested, Simpson and Shannon indices were best predicted with a coefficient of determination around 0.4 using a Random Forest predictor and Sentinel-2 data. The use of Sentinel-1 data was not found to improve significantly the prediction accuracy. Using the Random Forest algorithm and the Sentinel-2 time series, the prediction of the Simpson index was performed. The resulting map highlights the intra-parcel variability and demonstrates the capacity of satellite image time series to monitor grasslands plant taxonomic diversity from an ecological viewpoint. Numéro de notice : A2020-004 Affiliation des auteurs : UPEM-LASTIG+Ext (2016-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2019.111536 Date de publication en ligne : 26/11/2019 En ligne : https://doi.org/10.1016/j.rse.2019.111536 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94296
in Remote sensing of environment > Vol 237 (February 2020) . - 13 p.[article]Advanced GNSS tropospheric products for monitoring severe weather events and climate, ch. 5. Use of GNSS Tropospheric Products for Climate Monitoring (Working Group 3) / Olivier Bock (2020)PermalinkAnalyse des surcharges hydrologiques observées par géodésie spatiale avec l’outil Multi Singular Spectrum Analysis / Louis Bonhomme (2020)PermalinkApplication of geographic Information system and remote sensing in multiple criteria analysis to identify priority areas for biodiversity conservation in Vietnam / Xuan Dinh Vu (2020)PermalinkCaractérisation de la contribution des charges hydrologiques, atmosphériques et océaniques aux séries temporelles de position GNSS : analyse comparée des modèles de charge et de mouvement du géocentre / Elie-Alban Lescout (2020)PermalinkCartographie des essences forestières à partir de séries temporelles d’images satellitaires à hautes résolutions : stabilité des prédictions, autocorrélation spatiale et cohérence avec la phénologie observée in situ / Nicolas Karasiak (2020)PermalinkClassification of time series of Sentinel-2 images for large scale mapping in Cameroon / Hermann Tagne (2020)PermalinkDétermination conjointe des inondations et du type d’eau au moyen de l’imagerie multi-spectrale / Sabrine Amzil (2020)PermalinkDevelopment of new homogenisation methods for GNSS atmospheric data. Application to the analysis of climate trends and variability / Annarosa Quarello (2020)PermalinkDéveloppement d’outils ad-hoc open source pour des applications Web cartographiques / Bruno Verchère (2020)PermalinkÉtude de la vapeur d’eau atmosphérique à partir de données GNSS dans le bassin sud-ouest de l’océan Indien et applications à l’étude du climat et des cyclones tropicaux / Edouard Lees (2020)Permalink