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Ionospheric irregularity layer height and thickness estimation with a GNSS receiver array / Seebany Datta-Barua in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)
[article]
Titre : Ionospheric irregularity layer height and thickness estimation with a GNSS receiver array Type de document : Article/Communication Auteurs : Seebany Datta-Barua, Auteur ; Yang Su, Auteur ; Aurora López Rubio, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 6198 - 6207 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] Alaska (Etats-Unis)
[Termes IGN] hauteur de la couche ionosphérique
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] modèle ionosphérique
[Termes IGN] phase GNSS
[Termes IGN] rapport signal sur bruit
[Termes IGN] scintillation
[Termes IGN] série temporelle
[Termes IGN] signal GNSSRésumé : (auteur) This work develops a method by which a kilometer-spaced array of Global Navigation Satellite System (GNSS) scintillation receivers can be used to estimate the ionospheric irregularity layer height and thickness and associated uncertainties on those estimates. Spectra of filtered signal power and phase data are used to estimate these quantities by comparing the observed ratio of the log of the power spectrum to the phase spectrum with the Rytov weak scatter theoretical ratio. A Monte Carlo simulation of noise on the input signal and the irregularity drift velocity is used to quantify the error in estimates of height and thickness. The method is tested using data from the Scintillation Auroral Global Positioning System (GPS) Array (SAGA) sited in the auroral zone at Poker Flat Research Range, Alaska. For the 30-min scintillation period studied, the technique identifies ionospheric scattering from a thick F layer, which correlates well with on-site incoherent scatter radar measurements of peak electron density, for an event previously identified in the literature as likely due to F layer. Numéro de notice : A2021-539 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1109/TGRS.2020.3024173 Date de publication en ligne : 12/10/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3024173 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98013
in IEEE Transactions on geoscience and remote sensing > Vol 59 n° 7 (July 2021) . - pp 6198 - 6207[article]JUST: MATLAB and python software for change detection and time series analysis / Ebrahim Ghaderpour in GPS solutions, vol 25 n° 3 (July 2021)
[article]
Titre : JUST: MATLAB and python software for change detection and time series analysis Type de document : Article/Communication Auteurs : Ebrahim Ghaderpour, Auteur Année de publication : 2021 Article en page(s) : Article 85 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] analyse de données
[Termes IGN] détection de changement
[Termes IGN] Matlab
[Termes IGN] méthode des moindres carrés
[Termes IGN] Python (langage de programmation)
[Termes IGN] série temporelleRésumé : (Auteur) Change detection within unequally spaced and non-stationary time series is crucial in various applications, such as environmental monitoring and satellite navigation. The jumps upon spectrum and trend (JUST) is developed to detect potential jumps within the trend component of time series segments. JUST can simultaneously estimate the trend and seasonal components of any equally or unequally spaced time series by considering the observational uncertainties or measurement errors. JUST and its modules can also be applied to monitor vegetation time series in near-real-time. Herein, the details of the open-source software package for JUST, developed in both MATLAB and Python, are presented. Numéro de notice : A2021-330 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-021-01118-x Date de publication en ligne : 09/04/2021 En ligne : https://doi.org/10.1007/s10291-021-01118-x Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97500
in GPS solutions > vol 25 n° 3 (July 2021) . - Article 85[article]Marrying deep learning and data fusion for accurate semantic labeling of Sentinel-2 images / Guillemette Fonteix in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)
[article]
Titre : Marrying deep learning and data fusion for accurate semantic labeling of Sentinel-2 images Type de document : Article/Communication Auteurs : Guillemette Fonteix, Auteur ; M. Swaine, Auteur ; M. Leras, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 101 - 107 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] carte de confiance
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] fusion d'images
[Termes IGN] image optique
[Termes IGN] image Sentinel-MSI
[Termes IGN] segmentation sémantique
[Termes IGN] série temporelleRésumé : (auteur) The understanding of the Earth through global land monitoring from satellite images paves the way towards many applications including flight simulations, urban management and telecommunications. The twin satellites from the Sentinel-2 mission developed by the European Space Agency (ESA) provide 13 spectral bands with a high observation frequency worldwide. In this paper, we present a novel multi-temporal approach for land-cover classification of Sentinel-2 images whereby a time-series of images is classified using fully convolutional network U-Net models and then coupled by a developed probabilistic algorithm. The proposed pipeline further includes an automatic quality control and correction step whereby an external source can be introduced in order to validate and correct the deep learning classification. The final step consists of adjusting the combined predictions to the cloud-free mosaic built from Sentinel-2 L2A images in order for the classification to more closely match the reference mosaic image. Numéro de notice : A2021-492 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5194/isprs-annals-V-3-2021-101-2021 Date de publication en ligne : 17/06/2021 En ligne : http://dx.doi.org/10.5194/isprs-annals-V-3-2021-101-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97957
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2021 (July 2021) . - pp 101 - 107[article]Comparison and evaluation of high-resolution marine gravity recovery via sea surface heights or sea surface slopes / Shengjun Zhang in Journal of geodesy, vol 95 n° 6 (June 2021)
[article]
Titre : Comparison and evaluation of high-resolution marine gravity recovery via sea surface heights or sea surface slopes Type de document : Article/Communication Auteurs : Shengjun Zhang, Auteur ; Adili Abulaitijiang, Auteur ; Ole Baltazar Andersen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 66 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] données altimétriques
[Termes IGN] données Jason
[Termes IGN] géodésie marine
[Termes IGN] gravimétrie en mer
[Termes IGN] hauteurs de mer
[Termes IGN] image Cryosat
[Termes IGN] relief sous-marin
[Termes IGN] SARAL
[Termes IGN] série temporelle
[Termes IGN] surface de la merRésumé : (auteur) There are two dominating approaches of modeling the marine gravity field based on satellite altimetry observations. In this study, the marine gravity field is determined in four selected areas (Northwestern Atlantic, Hawaii ocean area, Mariana Trench area, and Aegean Sea) by using exact same input datasets but different methods which are based on sea surface height (SSH) and sea surface slope (SSS), respectively. The impact of the methodology is evaluated by conducting validations with shipborne gravity observation. The CryoSat-2, Jason-1/2, and SARAL/Altika geodetic mission data (similarly 3-year-long time series) are firstly retracked by the two-pass retracker. After that, the obtained SSHs are used for the derivation of geoid undulations and vertical deflections, and then for the resulting marine gravity field separately. The validation results indicate that the SSH-based method has advantages in robustly estimating marine gravity anomalies near the coastal zone. The SSS-based method has advantages over regions with intermedium ocean depths (2000–4000 m) where seamounts and ridges are found, but obvious disadvantages when the ocean currents flow along the north–south direction (e.g., western boundary currents) or the topography features north–south directional trenches. In the deep ocean where the seafloor topography is plain and smooth, the two methods have similar accuracy. Numéro de notice : A2021-433 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01506-8 Date de publication en ligne : 27/05/2021 En ligne : https://doi.org/10.1007/s00190-021-01506-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97799
in Journal of geodesy > vol 95 n° 6 (June 2021) . - n° 66[article]Identifying the effects of chronic saltwater intrusion in coastal floodplain swamps using remote sensing / Elliott White Jr in Remote sensing of environment, vol 258 (June 2021)
[article]
Titre : Identifying the effects of chronic saltwater intrusion in coastal floodplain swamps using remote sensing Type de document : Article/Communication Auteurs : Elliott White Jr, Auteur ; David Kaplan, Auteur Année de publication : 2021 Article en page(s) : n° 112385 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse aérienne
[Termes IGN] eau de mer
[Termes IGN] Enhanced vegetation index
[Termes IGN] Floride (Etats-Unis)
[Termes IGN] littoral
[Termes IGN] Louisiane (Etats-Unis)
[Termes IGN] marais
[Termes IGN] Mexique (golfe du)
[Termes IGN] montée du niveau de la mer
[Termes IGN] salinité
[Termes IGN] série temporelleRésumé : (auteur) Coastal floodplain swamps (CFS) are an important part of the coastal wetland mosaic, however they are threatened due to accelerated rates of sea level rise and saltwater intrusion (SWI). While remote sensing-based detection of wholesale coastal ecosystem shifts (i.e., from forest to marsh) are relatively straightforward, assessments of chronic, low-level SWI into CFS using remote sensing have yet to be developed and can provide a critical early-warning signal of ecosystem deterioration. In this study, we developed nine ecologically-based hypotheses to test whether remote sensing data could be used to reliably detect the presence of CFS experiencing SWI. Hypotheses were motivated by field- and literature-based understanding of the phenological and vegetative dynamics of CFS experiencing SWI relative to unimpacted, control systems. Hypotheses were organized into two primary groups: those that analyzed differences in summary measures (e.g., median and distribution) between SWI-impacted and unimpacted control sites and those that examined timeseries trends (e.g., sign and magnitude of slope). The enhanced vegetation index (EVI) was used as a proxy for production/biomass and was generated using MODIS surface reflectance data spanning 2000 to 2018. Experimental sites (n = 8) were selected from an existing network of long-term monitoring sites and included 4 pairs of impacted/non-impacted CFS across the northern Gulf of Mexico from Texas to Florida. The four best-supported hypotheses (81% across all sties) all used summary statistics, indicating that there were significant differences in the EVI of CFS experiencing chronic, low-level SWI compared to controls. These hypotheses were tested using data across a large and diverse region, supporting their implementation by researchers and managers seeking to identify CFS undergoing the first phases of SWI. In contrast, hypotheses that assessed CFS change over time were poorly supported, likely due to the slow and variable pace of ecological change, relatively short remote sensing data record, and/or specific site histories. Overall, these results show that remote sensing data can be used to identify differences in CFS vegetation associated with long-term, low-level SWI, but further methodological advancements are needed to reliably detect the temporal transition process. Numéro de notice : A2021-444 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112385 Date de publication en ligne : 12/03/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112385 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97851
in Remote sensing of environment > vol 258 (June 2021) . - n° 112385[article]Mapping fine-scale human disturbances in a working landscape with Landsat time series on Google Earth Engine / Tongxi Hu in ISPRS Journal of photogrammetry and remote sensing, vol 176 (June 2021)PermalinkResolution enhancement for large-scale land cover mapping via weakly supervised deep learning / Qiutong Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)PermalinkSpatio-temporal linking of multiple SAR satellite data from medium and high resolution Radarsat-2 images / Bin Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 176 (June 2021)PermalinkA compilation of snow cover datasets for Svalbard: A multi-sensor, multi-model study / Hannah Vickers in Remote sensing, vol 13 n°10 (May-2 2021)PermalinkDetection of rainstorm pattern in arid regions using MODIS NDVI time series analysis / Mohamed E. Hereher in Geocarto international, vol 36 n° 8 ([01/05/2021])PermalinkEvaluation of light pollution in global protected areas from 1992 to 2018 / Haowei Mu in Remote sensing, vol 13 n° 9 (May-1 2021)PermalinkNumerical modelling for analysis of the effect of different urban green spaces on urban heat load patterns in the present and in the future / Tamás Gál in Computers, Environment and Urban Systems, vol 87 (May 2021)PermalinkSNR-based water height retrieval in rivers: Application to high amplitude asymmetric tides in the Garonne river / Pierre Zeiger in Remote sensing, vol 13 n° 9 (May-1 2021)PermalinkA stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms / Dimitrios Bellos in Machine Vision and Applications, vol 32 n° 3 (May 2021)PermalinkAssessing forest phenology: A multi-scale comparison of near-surface (UAV, spectral reflectance sensor, PhenoCam) and satellite (MODIS, Sentinel-2) remote sensing / Shangharsha Thapa in Remote sensing, vol 13 n° 8 (April-2 2021)PermalinkLeaf area index estimation of wheat crop using modified water cloud model from the time-series SAR and optical satellite data / Vijay Pratap Yadav in Geocarto international, vol 36 n° 7 ([15/04/2021])Permalink1996–2017 GPS position time series, velocities and quality measures for the CORS Network / Jarir Saleh in Journal of applied geodesy, vol 15 n° 2 (April 2021)PermalinkAssessment of degree-2 order-1 gravitational changes from GRACE and GRACE Follow-on, Earth rotation, satellite laser ranging, and models / Jianli Chen in Journal of geodesy, vol 95 n° 4 (April 2021)PermalinkTemporal mosaicking approaches of Sentinel-2 images for extending topsoil organic carbon content mapping in croplands / Emmanuelle Vaudour in International journal of applied Earth observation and geoinformation, vol 96 (April 2021)PermalinkTime-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine / Dong Liang in Remote sensing of environment, Vol 256 (April 2020)PermalinkDétection des zones de dégradation et de régénération de la couverture végétale dans le sud du Sénégal à travers l'analyse des tendances de séries temporelles MODIS NDVI et des changements d'occupation des sols à partir d'images LANDSAT / Boubacar Solly in Revue Française de Photogrammétrie et de Télédétection, n° 223 (mars - décembre 2021)PermalinkAttribution of the Australian bushfire risk to anthropogenic climate change / Geert Jan Van Oldenborgh in Natural Hazards and Earth System Sciences, vol 21 n° 3 (March 2021)PermalinkCharacterizing urban land changes of 30 global megacities using nighttime light time series stacks / Qiming Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)PermalinkCluster-based empirical tropospheric corrections applied to InSAR time series analysis / Kyle Dennis Murray in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)PermalinkExtraction of impervious surface using Sentinel-1A time-series coherence images with the aid of a Sentinel-2A image / Wenfu Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 3 (March 2021)PermalinkRecent increase in European forest harvests as based on area estimates (Ceccherini et al. 2020a) not confirmed in the French case / Nicolas Picard in Annals of Forest Science, vol 78 n° 1 (March 2021)PermalinkSaline-soil deformation extraction based on an improved time-series InSAR approach / Wei Xiang in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)PermalinkComprehensive time-series analysis of bridge deformation using differential satellite radar interferometry based on Sentinel-1 / Matthias Schlögl in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)PermalinkDeveloping a site index model for P. Pinaster stands in NW Spain by combining bi-temporal ALS data and environmental data / Juan Guerra-Hernández in Forest ecology and management, vol 481 (February 2021)PermalinkGeo-spatially modelling dengue epidemics in urban cities: a case study of Lahore, Pakistan / Muhammad Imran in Geocarto international, vol 36 n° 2 ([01/02/2021])PermalinkStudy of systematic bias in measuring surface deformation with SAR interferometry / Homa Ansari in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)PermalinkMapping seasonal agricultural land use types using deep learning on Sentinel-2 image time series / Misganu Debella-Gilo in Remote sensing, Vol 13 n° 2 (January-2 2021)PermalinkAmélioration des systèmes de suivi des cultures à l’aide de la télédétection multi-source et des techniques d’apprentissage profond / Yawogan Gbodjo (2021)PermalinkPermalinkAre there detectable common aperiodic displacements at ITRF co-location sites? / Maylis Teyssendier de la Serve (2021)PermalinkAssessing the interest of a multi-modal gap-filling strategy for monitoring changes in grassland parcels / Anatol Garioud (2021)PermalinkBenchmarking of convolutional neural network approaches for vegetation land cover mapping / Benjamin Carpentier (2021)PermalinkCharacteristics of seasonal variations and noises of the daily double-difference and PPP solutions / Kamil Maciuk in Journal of applied geodesy, vol 15 n° 1 (January 2021)PermalinkPermalinkClustering et apprentissage profond sous contraintes pour l’analyse de séries temporelles : Application à l’analyse temporelle incrémentale en télédétection / Baptiste Lafabregue (2021)PermalinkCopula-based modeling of dependence structure in geodesy and GNSS applications: case study for zenith tropospheric delay in complex terrain / Roya Mousavian in GPS solutions, vol 25 n° 1 (January 2021)PermalinkPermalinkDeep learning for wildfire progression monitoring using SAR and optical satellite image time series / Puzhao Zhang (2021)PermalinkEnsemble learning methods on the space of covariance matrices : application to remote sensing scene and multivariate time series classification / Sara Akodad (2021)PermalinkEstimation et cartographie d’attributs forestiers haute résolution : Le potentiel des approches multisource / Cédric Vega (2021)PermalinkEvaluation of Sentinel-1 & 2 time series for the identification and characterization of ecological continuities, from wooded to crop-dominated landscapes / Audrey Mercier (2021)PermalinkEvaluation du stock de carbone aérien dans la végétation à partir de multiples observations satellites micro-ondes / Martin Cubaud (2021)PermalinkFrom local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2 / Yousra Hamrouni in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)PermalinkPermalinkA hybrid approach for recovering high-resolution temporal gravity fields from satellite laser ranging / Anno Löcher in Journal of geodesy, vol 95 n° 1 (January 2021)PermalinkImpact of forest disturbance on InSAR surface displacement time series / Paula M. Bürgi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)PermalinkInvestigation of Sentinel-1 time series for sensitivity to fern vegetation in an European temperate forest / Marlin Mueller (2021)PermalinkLearning disentangled representations of satellite image time series in a weakly supervised manner / Eduardo Hugo Sanchez (2021)PermalinkMonitoring tree-crown scale autumn leaf phenology in a temperate forest with an integration of PlanetScope and drone remote sensing observations / Shengbiao Wu in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)PermalinkPermalinkPanoptic segmentation of satellite image time series with convolutional temporal attention networks / Vivien Sainte Fare Garnot (2021)PermalinkPermalinkPermalinkRemotely-sensed rip current dynamics and morphological control in high-energy beach environments / Isaac Rodriguez Padilla (2021)PermalinkSeasonal flow variability of Greenlandic glaciers : satellite observations and numerical modeling to study driving processes / Anna Derkacheva (2021)PermalinkSensitivity of segmentation of GNSS IWV time series and trend estimates to data properties / Khanh Ninh Nguyen (2021)PermalinkStatistical analysis of vertical land motions and sea level measurements at the coast / Kevin Gobron (2021)PermalinkSuivi de la rotation des cultures à partir de séries temporelles d’images satellite / Félix Quinton (2021)PermalinkSupplementary material for: Panoptic segmentation of satellite image time series with convolutional temporal attention networks / Vivien Sainte Fare Garnot (2021)PermalinkTélédétection et intégration de connaissances via la modélisation spatiale pour une cartographie plus cohérente des systèmes agricoles complexes / Arthur Crespin-Boucaud (2021)PermalinkThe ambiguous sea level rise at Brest’s 212 yearlong record elucidated / Huseyin Baki Iz in Journal of geodetic science, vol 11 n° 1 (January 2021)PermalinkTime-series analysis of massive satellite images : Application to earth observation / Alexandre Constantin (2021)PermalinkTopographic, edaphic and climate influences on aspen (Populus tremuloides) drought stress on an intermountain bunchgrass prairie / Andrew Neary in Forest ecology and management, vol 479 ([01/01/2021])PermalinkUnderstanding the geodetic signature of large aquifer systems: Example of the Ozark Plateaus in Central United States / Stacy Larochelle (2021)PermalinkVariations of precipitable water vapor using GNSS CORS in Thailand / Chokchai Trakolkul in Survey review, vol 53 n°376 (January 2021)PermalinkClimate sensitive single tree growth modeling using a hierarchical Bayes approach and integrated nested Laplace approximations (INLA) for a distributed lag model / Arne Nothdurft in Forest ecology and management, vol 478 ([15/12/2020])PermalinkCharacterizing the spatial and temporal variation of the land surface temperature hotspots in Wuhan from a local scale / Chen Yang in Geo-spatial Information Science, vol 23 n° 4 (December 2020)PermalinkQuality assessment of photogrammetric methods - A workflow for reproducible UAS orthomosaics / Marvin Ludwig in Remote sensing, vol 12 n° 22 (December-1 2020)PermalinkUnprecedented pluri-decennial increase in the growing stock of French forests is persistent and dominated by private broadleaved forests / Jean-Daniel Bontemps in Annals of Forest Science, vol 77 n° 4 (December 2020)PermalinkCombination of Landsat 8 OLI and Sentinel-1 SAR time-series data for mapping paddy fields in parts of West and Central Java provinces, Indonesia / Sanjiwana Arjasakusuma in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)PermalinkDisplacement monitoring of upper Atbara dam based on time series InSAR / Q.Q. Wang in Survey review, vol 52 n° 375 (November 2020)PermalinkGeostatistical analysis and mitigation of the atmospheric phase screens in Ku-band terrestrial radar interferometric observations of an alpine glacier / Simone Baffelli in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)PermalinkNon-stationary extreme value analysis of ground snow loads in the French Alps: a comparison with building standards / Erwann Le Roux in Natural Hazards and Earth System Sciences, vol 20 n° 11 (November 2020)PermalinkSea surface temperature and high water temperature occurrence prediction using a long short-term memory model / Minkyu Kim in Remote sensing, vol 12 n° 21 (November 2020)PermalinkMonitoring population dynamics in the Pearl River Delta from 2000 to 2010 / Sisi Yu in Geocarto international, vol 35 n° 14 ([15/10/2020])PermalinkTime series potential assessment for biophysical characterization of orchards and crops in a mixed scenario with Sentinel-1A SAR data / Hemant Sahu in Geocarto international, vol 35 n° 14 ([15/10/2020])PermalinkAn advanced residual error model for tropospheric delay estimation / Szabolcs Rózsa in GPS solutions, Vol 24 n° 4 (October 2020)PermalinkAnalysis of shoreline changes in Vishakhapatnam coastal tract of Andhra Pradesh, India: an application of digital shoreline analysis system (DSAS) / Mirza Razi Imam Baig in Annals of GIS, vol 26 n° 4 (October 2020)PermalinkMapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data / Yaotong Cai in International journal of applied Earth observation and geoinformation, vol 92 (October 2020)PermalinkWide-area near-real-time monitoring of tropical forest degradation and deforestation using Sentinel-1 / Dirk Hoekman in Remote sensing, vol 12 n° 19 (October-1 2020)PermalinkAn overview of clustering methods for geo-referenced time series: from one-way clustering to co- and tri-clustering / Xiaojing Wu in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)PermalinkApplying multi-temporal Landsat satellite data and Markov-cellular automata to predict forest cover change and forest degradation of sundarban reserve forest, Bangladesh / Mohammad Emran Hasan in Forests, vol 11 n° 9 (September 2020)PermalinkAssessing local trends in indicators of ecosystem services with a time series of forest resource maps / Matti Katila in Silva fennica, vol 54 n° 4 (September 2020)PermalinkBenefits of non-tidal loading applied at distinct levels in VLBI analysis / Matthias Glomsda in Journal of geodesy, vol 94 n° 9 (September 2020)PermalinkCombining optical and radar satellite image time series to map natural vegetation: savannas as an example / Maylis Lopes in Remote sensing in ecology and conservation, vol 6 n° 3 (September 2020)PermalinkDeriving a frozen area fraction from Metop ASCAT backscatter based on Sentinel-1 / Helena Bergstedt in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)PermalinkIlluminating the spatio-temporal evolution of the 2008–2009 Qaidam earthquake sequence with the joint use of Insar time series and teleseismic data / Simon Daout in Remote sensing, vol 12 n° 17 (September-1 2020)PermalinkA novel algorithm to estimate phytoplankton carbon concentration in inland lakes using Sentinel-3 OLCI images / Heng Lyu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)PermalinkGipsyX/RTGx, a new tool set for space geodetic operations and research / Willy I. Bertiger in Advances in space research, vol 66 n° 3 (1 August 2020)PermalinkPredicting biomass dynamics at the national extent from digital aerial photogrammetry / Bronwyn Price in International journal of applied Earth observation and geoinformation, vol 90 (August 2020)PermalinkAutomated estimation and tools to extract positions, velocities, breaks, and seasonal terms from daily GNSS measurements: illuminating nonlinear Salton Trough deformation / Michael B. Heflin in Earth and space science, vol 7 n° 7 (July 2020)PermalinkEvaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification, visual interpretation and machine learning approaches / S.M. Hamylton in International journal of applied Earth observation and geoinformation, vol 89 (July 2020)PermalinkImproved crop classification with rotation knowledge using Sentinel-1 and -2 time series / Sébastien Giordano in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 7 (July 2020)PermalinkLong time-series remote sensing analysis of the periodic cycle evolution of the inlets and ebb-tidal delta of Xincun Lagoon, Hainan Island, China / Huaguo Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)PermalinkPredicting displacement of bridge based on CEEMDAN-KELM model using GNSS monitoring data / Qian Fan in Journal of applied geodesy, vol 14 n° 3 (July 2020)Permalink