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Real-time clock prediction of multi-GNSS satellites and its application in precise point positioning / Yaquan Peng in Advances in space research, vol 64 n°7 (1 October 2019)  

Public [article]  inAdvances in space research > vol 64 n°7 (1 October 2019) . - pp 1445 - 1454 Titre : Real-time clock prediction of multi-GNSS satellites and its application in precise point positioning Type de document : Article/Communication Auteurs : Yaquan Peng, Auteur ; Yidong Lou, Auteur ; Xiaopeng Gong, Auteur ; YinTong Wang, Auteur ; Xiaolei Dai, Auteur Année de publication : 2019 Article en page(s) : pp 1445 - 1454 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes descripteurs IGN] horloge atomique [Termes descripteurs IGN] horloge du satellite [Termes descripteurs IGN] positionnement par BeiDou [Termes descripteurs IGN] positionnement par Galileo [Termes descripteurs IGN] positionnement par GNSS [Termes descripteurs IGN] positionnement ponctuel précis [Termes descripteurs IGN] prédiction [Termes descripteurs IGN] temps réel Résumé : (auteur) With the development of Global Navigation Satellite System (GNSS), multi-GNSS is expected to greatly benefit precise point positioning (PPP), especially during the outage of real time service (RTS). In this paper, we focus on the performance of multi-GNSS satellite clock prediction and its application in real-time PPP. Based on the statistical analysis of multi-system satellite clock products, a model consisting of polynomial and periodic terms is employed for multi-system satellite clock prediction. To evaluate the method proposed, both post-processed and real-time satellite clock products are employed in simulated real-time processing mode. The results show that the accuracy of satellite clock prediction is related to atomic clock type and satellite type. For GPS satellites, the average standard deviations (STDs) of Cs atomic clocks will reach as high as 0.65 ns while the STD of Rb atomic clocks is only about 0.15 ns. As for BDS and Galileo, the average STD of 2-hour satellite clock prediction are 0.30 ns and 0.06 ns, respectively. In addition, it is validated that real-time PPP can still achieve positioning accuracy of one to three decimeters by using products of 2-hour satellite clock prediction. Moreover, compared to the results of GPS-only PPP, multi-system can greatly enhance the accuracy of real-time PPP from 12.5% to 18.5% in different situations. Numéro de notice : A2019-410 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.asr.2019.06.040 date de publication en ligne : 08/07/2019 En ligne : https://doi.org/10.1016/j.asr.2019.06.040 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93525 [article]

A methodology with a distributed algorithm for large-scale trajectory distribution prediction / QiuLei Guo in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > Vol 33 n° 3-4 (March - April 2019) . - pp 833 - 854 Titre : A methodology with a distributed algorithm for large-scale trajectory distribution prediction Type de document : Article/Communication Auteurs : QiuLei Guo, Auteur ; Hassan A. Karimi, Auteur Année de publication : 2019 Article en page(s) : pp 833 - 854 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] circulation urbaine [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] données massives [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] gestion de trafic [Termes descripteurs IGN] migration pendulaire [Termes descripteurs IGN] mobilité urbaine [Termes descripteurs IGN] New York (Etats-Unis ; ville) [Termes descripteurs IGN] Pékin (Chine) [Termes descripteurs IGN] population urbaine [Termes descripteurs IGN] prédiction [Termes descripteurs IGN] trajectoire Résumé : (Auteur) In this paper, we propose a method for predicting the distributions of people’s trajectories on the road network throughout a city. Specifically, we predict the number of people who will move from one area to another, their probable trajectories, and the corresponding likelihoods of those trajectories in the near future, such as within an hour. With this prediction, we will identify the hot road segments where potential traffic jams might occur and reveal the formation of those traffic jams. Accurate predictions of human trajectories at a city level in real time is challenging due to the uncertainty of people’s spatial and temporal mobility patterns, the complexity of a city level’s road network, and the scale of the data. To address these challenges, this paper proposes a method which includes several major components: (1) a model for predicting movements between neighboring areas, which combines both latent and explicit features that may influence the movements; (2) different methods to estimate corresponding flow trajectory distributions in the road network; (3) a MapReduce-based distributed algorithm to simulate large-scale trajectory distributions under real-time constraints. We conducted two case studies with taxi data collected from Beijing and New York City and systematically evaluated our method. Numéro de notice : A2019-218 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1536981 date de publication en ligne : 31/10/2018 En ligne : https://doi.org/10.1080/13658816.2018.1536981 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92690 [article]

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Analysis of GPS satellite clock prediction performance with different update intervals and application to real-time PPP / H. Yang in Survey review, vol 51 n° 364 (January 2019)  

Public [article]  inSurvey review > vol 51 n° 364 (January 2019) . - pp 43 - 52 Titre : Analysis of GPS satellite clock prediction performance with different update intervals and application to real-time PPP Type de document : Article/Communication Auteurs : H. Yang, Auteur ; C. Xu, Auteur ; Y. Gao, Auteur Année de publication : 2019 Article en page(s) : pp 43 - 52 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes descripteurs IGN] horloge du satellite [Termes descripteurs IGN] positionnement cinématique en temps réel [Termes descripteurs IGN] positionnement ponctuel précis [Termes descripteurs IGN] prédiction [Termes descripteurs IGN] temps réel Résumé : (Auteur) The GPS satellite clock offset prediction is investigated and applied to a real-time PPP system. First, the current situation of GPS satellite clock is introduced and analysed with respect to their stability. Then the satellite clock prediction with different update intervals is presented, in which the satellite clock day boundary jump is addressed. Afterwards, the investigation of the satellite clock prediction model for GPS satellite IIF clocks is carried out and the effects of periodic terms are discussed. After that, the verification of the satellite clock offset prediction will be carried out both in the time and positioning domain. Positioning accuracy at 0.021, 0.049, and 0.017 m in the east, north, and vertical directions can be obtained for 6-h static positioning using the predicted clock offset updating every hour, while the 3D RMS for kinematic real-time PPP is 0.360 m, with 28% improvement over that utilising the IGU predicted products. Numéro de notice : A2019-188 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2017.1359473 date de publication en ligne : 03/08/2017 En ligne : https://doi.org/10.1080/00396265.2017.1359473 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92620 [article]

Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar / Melissa Fedrigo in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 136 (February 2018) . - pp 106 - 119 Titre : Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar Type de document : Article/Communication Auteurs : Melissa Fedrigo, Auteur ; Glenn J. Newnham, Auteur ; Nicholas C. Coops, Auteur ; Darius S. Culvenor, Auteur ; Douglas K. Bolton, Auteur ; Craig R. Nitschke, Auteur Année de publication : 2018 Article en page(s) : pp 106 - 119 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] analyse linéaire des mélanges spectraux [Termes descripteurs IGN] Australie [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] Eucalyptus (genre) [Termes descripteurs IGN] forêt tempérée [Termes descripteurs IGN] peuplement forestier [Termes descripteurs IGN] prédiction [Termes descripteurs IGN] strate forestière Résumé : (Auteur) Light detection and ranging (lidar) data have been increasingly used for forest classification due to its ability to penetrate the forest canopy and provide detail about the structure of the lower strata. In this study we demonstrate forest classification approaches using airborne lidar data as inputs to random forest and linear unmixing classification algorithms. Our results demonstrated that both random forest and linear unmixing models identified a distribution of rainforest and eucalypt stands that was comparable to existing ecological vegetation class (EVC) maps based primarily on manual interpretation of high resolution aerial imagery. Rainforest stands were also identified in the region that have not previously been identified in the EVC maps. The transition between stand types was better characterised by the random forest modelling approach. In contrast, the linear unmixing model placed greater emphasis on field plots selected as endmembers which may not have captured the variability in stand structure within a single stand type. The random forest model had the highest overall accuracy (84%) and Cohen’s kappa coefficient (0.62). However, the classification accuracy was only marginally better than linear unmixing. The random forest model was applied to a region in the Central Highlands of south-eastern Australia to produce maps of stand type probability, including areas of transition (the ‘ecotone’) between rainforest and eucalypt forest. The resulting map provided a detailed delineation of forest classes, which specifically recognised the coalescing of stand types at the landscape scale. This represents a key step towards mapping the structural and spatial complexity of these ecosystems, which is important for both their management and conservation. Numéro de notice : A2018-074 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.11.018 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.11.018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89438 [article]

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Improving the prediction of African savanna vegetation variables using time series of MODIS products / Miriam Tsalyuk in ISPRS Journal of photogrammetry and remote sensing, vol 131 (September 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 131 (September 2017) . - pp 77 - 91 Titre : Improving the prediction of African savanna vegetation variables using time series of MODIS products Type de document : Article/Communication Auteurs : Miriam Tsalyuk, Auteur ; Maggi Kelly, Auteur ; Wayne M. Getz, Auteur Année de publication : 2017 Article en page(s) : pp 77 - 91 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Végétation [Termes descripteurs IGN] Afrique (géographie physique) [Termes descripteurs IGN] biomasse forestière [Termes descripteurs IGN] dégradation de la flore [Termes descripteurs IGN] Enhanced vegetation index [Termes descripteurs IGN] image Terra-MODIS [Termes descripteurs IGN] Leaf Area Index [Termes descripteurs IGN] Namibie [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] prédiction [Termes descripteurs IGN] savane [Termes descripteurs IGN] variation Résumé : (Auteur) African savanna vegetation is subject to extensive degradation as a result of rapid climate and land use change. To better understand these changes detailed assessment of vegetation structure is needed across an extensive spatial scale and at a fine temporal resolution. Applying remote sensing techniques to savanna vegetation is challenging due to sparse cover, high background soil signal, and difficulty to differentiate between spectral signals of bare soil and dry vegetation. In this paper, we attempt to resolve these challenges by analyzing time series of four MODIS Vegetation Products (VPs): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) for Etosha National Park, a semiarid savanna in north-central Namibia. We create models to predict the density, cover, and biomass of the main savanna vegetation forms: grass, shrubs, and trees. To calibrate remote sensing data we developed an extensive and relatively rapid field methodology and measured herbaceous and woody vegetation during both the dry and wet seasons. We compared the efficacy of the four MODIS-derived VPs in predicting vegetation field measured variables. We then compared the optimal time span of VP time series to predict ground-measured vegetation. We found that Multiyear Partial Least Square Regression (PLSR) models were superior to single year or single date models. Our results show that NDVI-based PLSR models yield robust prediction of tree density (R2 = 0.79, relative Root Mean Square Error, rRMSE = 1.9%) and tree cover (R2 = 0.78, rRMSE = 0.3%). EVI provided the best model for shrub density (R2 = 0.82) and shrub cover (R2 = 0.83), but was only marginally superior over models based on other VPs. FPAR was the best predictor of vegetation biomass of trees (R2 = 0.76), shrubs (R2 = 0.83), and grass (R2 = 0.91). Finally, we addressed an enduring challenge in the remote sensing of semiarid vegetation by examining the transferability of predictive models through space and time. Our results show that models created in the wetter part of Etosha could accurately predict trees’ and shrubs’ variables in the drier part of the reserve and vice versa. Moreover, our results demonstrate that models created for vegetation variables in the dry season of 2011 could be successfully applied to predict vegetation in the wet season of 2012. We conclude that extensive field data combined with multiyear time series of MODIS vegetation products can produce robust predictive models for multiple vegetation forms in the African savanna. These methods advance the monitoring of savanna vegetation dynamics and contribute to improved management and conservation of these valuable ecosystems. Numéro de notice : A2017-537 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.07.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.07.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86575 [article]

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Quantifying the sources of epistemic uncertainty in model predictions of insect disturbances in an uncertain climate / David R. Gray in Annals of Forest Science [en ligne], vol 74 n° 3 (September 2017)  

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TM-Based SOC models augmented by auxiliary data for carbon crediting programs in semi-arid environments / Salahuddin M. Jaber in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 6 (June 2017)  

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Modeling dynamic urban land-use change with geographical cellular automata and generalized pattern search-optimized rules / Yongjiu Feng in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)  

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Panda∗: A generic and scalable framework for predictive spatio-temporal queries / Abdeltawab M. Hendawi in Geoinformatica [en ligne], vol 21 n° 2 (April - June 2017)  

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Quantifying early-seral forest composition with remote sensing / Rayma A Cooley in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 11 (November 2016)  

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Propagating uncertainty through individual tree volume model predictions to large-area volume estimates / Ronald E. McRoberts in Annals of Forest Science [en ligne], vol 73 n° 3 (September 2016)  

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Predicting palustrine wetland probability using random forest machine learning and digital elevation data-derived terrain variables / Aaron E. Maxwell in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 6 (June 2016)  

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Approximating prediction uncertainty for random forest regression models / John W. Coulston in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 3 (March 2016)

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Projections démographiques / Françoise de Blomac in DécryptaGéo le mag, n° 172 (décembre 2015)

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The influence of application a simplified transformation model between reference frames ECEF and ECI onto prediction accuracy of position and velocity of GLONASS satellites / Robert Krzyzek in Reports on geodesy and geoinformatics, vol 99 (December 2015)  

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