Descripteur
Documents disponibles dans cette catégorie (3460)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Comprehensive study on the tropospheric wet delay and horizontal gradients during a severe weather event / Victoria Graffigna in Remote sensing, vol 14 n° 4 (February-2 2022)
[article]
Titre : Comprehensive study on the tropospheric wet delay and horizontal gradients during a severe weather event Type de document : Article/Communication Auteurs : Victoria Graffigna, Auteur ; Manuel Hernández-Pajares, Auteur ; Francisco Azpilicueta, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 888 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] données météorologiques
[Termes IGN] gradient de troposphère
[Termes IGN] phénomène climatique extrême
[Termes IGN] positionnement ponctuel précis
[Termes IGN] retard troposphérique zénithal
[Termes IGN] station GNSS
[Termes IGN] surveillance météorologique
[Termes IGN] tempête
[Termes IGN] Texas (Etats-Unis)
[Termes IGN] vapeur d'eauRésumé : (auteur) GNSS meteorology is today one of the most growing technologies to monitor severe weather events. In this paper, we present the usage of 160 GPS reference stations over the period of 14 days to monitor and track Hurricane Harvey, which struck Texas in August 2017. We estimate the Zenith Wet Delay (ZWD) and the tropospheric gradients with 30 s interval using TOMION v2 software and carry out the processing in Precise Point Positioning (PPP) mode. We study the relationship of these parameters with atmospheric variables extracted from Tropical Rainfall Measuring Mission (TRMM) satellite mission and climate reanalysis model ERA5. This research finds that the ZWD shows patterns related to the rainfall rate and to the location of the hurricane. We also find that the tropospheric gradients are correlated with water vapor gradients before and after the hurricane, and with the wind and the pressure gradients only after the hurricane. This study also shows a new finding regarding the spectral distribution of the gradients, with a clear diurnal period present, which is also found on the ZWD itself. This kind of study approaches the GNSS meteorology to the increasing requirements of meteorologist in terms of monitoring severe weather events. Numéro de notice : A2022-166 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.3390/rs14040888 Date de publication en ligne : 12/02/2022 En ligne : https://doi.org/10.3390/rs14040888 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99791
in Remote sensing > vol 14 n° 4 (February-2 2022) . - n° 888[article]Building footprint extraction in Yangon city from monocular optical satellite image using deep learning / Hein Thura Aung in Geocarto international, vol 37 n° 3 ([01/02/2022])
[article]
Titre : Building footprint extraction in Yangon city from monocular optical satellite image using deep learning Type de document : Article/Communication Auteurs : Hein Thura Aung, Auteur ; Sao Hone Pha, Auteur ; Wataru Takeuchi, Auteur Année de publication : 2022 Article en page(s) : pp 792 - 812 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] Birmanie
[Termes IGN] détection du bâti
[Termes IGN] empreinte
[Termes IGN] image Geoeye
[Termes IGN] image isolée
[Termes IGN] réseau antagoniste génératif
[Termes IGN] vision monoculaireRésumé : (auteur) In this research, building footprints in Yangon City, Myanmar are extracted only from monocular optical satellite image by using conditional generative adversarial network (CGAN). Both training dataset and validating dataset are created from GeoEYE image of Dagon Township in Yangon City. Eight training models are created according to the change of values in three training parameters; learning rate, β1 term of Adam, and number of filters in the first convolution layer of the generator and the discriminator. The images of the validating dataset are divided into four image groups; trees, buildings, mixed trees and buildings, and pagodas. The output images of eight trained models are transformed to the vector images and then evaluated by comparing with manually digitized polygons using completeness, correctness and F1 measure. According to the results, by using CGAN, building footprints can be extracted up to 71% of completeness, 81% of correctness and 69% of F1 score from only monocular optical satellite image. Numéro de notice : A2022-345 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1740949 Date de publication en ligne : 20/03/2020 En ligne : https://doi.org/10.1080/10106049.2020.1740949 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100526
in Geocarto international > vol 37 n° 3 [01/02/2022] . - pp 792 - 812[article]Calibrating GNSS phase biases with onboard observations of low earth orbit satellites / Xingxing Li in Journal of geodesy, vol 96 n° 2 (February 2022)
[article]
Titre : Calibrating GNSS phase biases with onboard observations of low earth orbit satellites Type de document : Article/Communication Auteurs : Xingxing Li, Auteur ; Jiaqi Wu, Auteur ; Xin Li, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 8 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] bande K
[Termes IGN] erreur de phase
[Termes IGN] erreur systématique
[Termes IGN] étalonnage des données
[Termes IGN] orbite basse
[Termes IGN] phase GNSS
[Termes IGN] positionnement ponctuel précis
[Termes IGN] résolution d'ambiguïtéRésumé : (auteur) recent years, numerous low earth orbit (LEO) satellites have been launched for different scientific tasks such as the Earth’s magnetic field, gravity recovering and ocean altimetry. The LEO satellites can cover the ocean area and are less affected by atmospheric delays and multipath errors, which provides new opportunities for calibrating the phase biases of the Global Navigation Satellite System (GNSS). In this contribution, we propose an alternative approach for uncalibrated phase delay (UPD) estimation by making full use of onboard observations of LEO satellites. Stable wide-lane (WL) and narrow-lane (NL) UPDs can be obtained from spaceborne GNSS observations and agree well with the UPD products derived from 106 IGS stations. To further verify the feasibility of the proposed method for UPD estimation, zero-difference (ZD) ambiguity resolution (AR) for precise point positioning (PPP) and LEO precise orbit determination (POD) are implemented. After applying the LEO-based UPDs, the averaged convergence time for PPP AR can be reduced to 15.2 min, with an improvement of 24% compared to float solutions. As for LEO AR, the fixing rates of WL and NL ambiguities exceed 98 and 92%, respectively. The accuracies of ambiguity-fixed orbits are validated by comparing with external satellite laser ranging (SLR) and K-band ranging (KBR) observations. Compared to float solutions, the standard deviations (STDs) of SLR residuals can be reduced by 8 ~ 43%, and the KBR residuals of 3.75 mm can be achieved for fixed solutions using LEO-based UPDs, with an improvement of 60%. Although the current UPD results derived from LEO satellites are slightly worse than those of ground-based UPD, it is anticipated that the performance of LEO-based UPD can be further improved in the near future with the rapidly increasing number of LEO satellites and the continuous refinements of the POD method. Numéro de notice : A2022-129 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s00190-022-01600-5 Date de publication en ligne : 31/01/2022 En ligne : https://doi.org/10.1007/s00190-022-01600-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99712
in Journal of geodesy > vol 96 n° 2 (February 2022) . - n° 8[article]Diffuse sunlight and cosmic rays: Missing pieces of the forest growth change attribution puzzle? / Jean-Daniel Bontemps in Science of the total environment, vol 806 n°1 (February 2022)
[article]
Titre : Diffuse sunlight and cosmic rays: Missing pieces of the forest growth change attribution puzzle? Type de document : Article/Communication Auteurs : Jean-Daniel Bontemps , Auteur ; Henrik Svensmark, Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : n° 150469 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] cerne
[Termes IGN] croissance des arbres
[Termes IGN] dioxyde de carbone
[Termes IGN] modèle de croissance végétale
[Termes IGN] modélisation de la forêt
[Termes IGN] photosynthèse
[Termes IGN] rayonnement cosmique
[Termes IGN] rayonnement lumineux
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Forest growth changes have been a matter of intense research efforts since the 1980s. Owing to the variety of their environmental causes – mainly atmospheric CO2 increase, atmospheric N deposition, changes in temperature and water availability, and their interactions – their interpretation has remained challenging. Recent isolated researches suggest further effects of neglected environmental factors, namely changes in the diffuse fraction of light, more efficient to photosynthesis, and galactic cosmic rays (GCR), both emphasized in this Discussion paper. With growing awareness of GCR influence on global cloudiness (the cosmoclimatologic theory by H. Svensmark), GCR may thus cause trends in diffuse-light, and distinguishing between their direct/indirect influences on forest growth remains uncertain. This link between cosmic rays and diffuse sunlight also forms an alternative explanation to the geological evidence of a negative correlation between GCR and atmospheric CO2 concentration over the past 500 Myr. After a careful scrutiny of this literature and of key contributions in the field, we draw research options to progress further in this attribution. These include i) observational strategies intending to build on differences in the spatio-temporal dynamics of environmental growth factors, ranging from quasi-experiments to meta-analyses, ii) simulation strategies intending to quantify environmental factor's effects based on process-based ecosystem modelling, in a context where progresses for accounting for diffuse-light fraction are ongoing. Also, the hunt for tree-ring based proxies of GCR may offer the perspective of testing the GCR hypothesis on fully coupled forest growth samples. Numéro de notice : A2022-001 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.scitotenv.2021.150469 Date de publication en ligne : 21/09/2021 En ligne : https://doi.org/10.1016/j.scitotenv.2021.150469 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98515
in Science of the total environment > vol 806 n°1 (February 2022) . - n° 150469[article]Landsat-based monitoring of southern pine beetle infestation severity and severity change in a temperate mixed forest / Ran Meng in Remote sensing of environment, vol 269 (February 2022)
[article]
Titre : Landsat-based monitoring of southern pine beetle infestation severity and severity change in a temperate mixed forest Type de document : Article/Communication Auteurs : Ran Meng, Auteur ; Renjie Gao, Auteur ; Feng Zhao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112847 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spatiale
[Termes IGN] dépérissement
[Termes IGN] forêt tempérée
[Termes IGN] image Landsat-8
[Termes IGN] insecte nuisible
[Termes IGN] mortalité
[Termes IGN] peuplement mélangé
[Termes IGN] Scolytinae
[Termes IGN] signature spectrale
[Termes IGN] surveillance forestière
[Termes IGN] xylophageRésumé : (auteur) The recent northward expansion of Southern Pine Beetle (SPB) outbreaks associated with warming winters has caused extensive tree mortality in temperate pine forests, significantly affecting forest dynamics, structure, and functioning. Spatially-explicit early warning and detection of SPB-induced tree mortality is critical for timely and sustainable forest management practices. The unique contributions of remote sensing technologies to mapping the location, extent, and severity of beetle outbreaks, as well as assisting in analyzing the potential drivers for outbreak predictions, have been well recognized. However, little is known about the performance of moderate resolution satellite multispectral imagery for early warning and detection of SPB-induced tree mortality. Thus, we conducted this study, as the first attempt, to capture the spatial-temporal patterns of SPB infestation severity at the regional scale and to understand the underlying environmental drivers in a spatially-explicit manner. First, we explored the spectral signatures of SPB-killed trees based on 30-m plot measurements and Landsat-8 imagery. Then, to improve detection accuracy for areas with low-moderate SPB infestation severity, we added spectral-temporal anomaly information in the form of a linear trend of the spectral index trajectory to a previously developed approach. The best overall accuracy increased from 84.7% to 90.1% and the best Macro F1 value increased from 0.832 to 0.900. Next, we compared the performances of spectral indices in mapping SPB infestation severity (i.e., % red stage within the 30-m grid cell). The results showed that the combination of Normalized Difference Moisture Index and Tasseled Cap Greenness had the best performance for mapping SPB infestation severity (2016: R2 = 0.754; RSME = 15.7; 2017: R2 = 0.787; RSME = 12.4). Finally, we found that climatic and landscape variables can explain the detected patterns of SPB infestation from 2014 to 2017 in our study area (R2 = 0.751; RSME = 9.67), providing valuable insights on possible predictors for early warning of SPB infestation. Specifically, in our study area, winter dew point temperature was found to be one of the most important predictors, followed by SPB infestation locations in the previous year, canopy cover of host species, elevation, and slope. In the context of continued global warming, our study not only provides a novel framework for efficient, spatially-explicit, and quantitative measurements of forest damage induced by SPB infestation over large scales, but also uncovers opportunities to predict future SPB outbreaks and take precautions against it. Numéro de notice : A2022-096 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112847 Date de publication en ligne : 15/12/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112847 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99538
in Remote sensing of environment > vol 269 (February 2022) . - n° 112847[article]Mapping burn severity in the western Italian Alps through phenologically coherent reflectance composites derived from Sentinel-2 imagery / Donato Morresi in Remote sensing of environment, vol 269 (February 2022)PermalinkSpatiotemporal fusion modelling using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria / Maninder Singh Dhillon in Remote sensing, vol 14 n° 3 (February-1 2022)PermalinkSurvival time and mortality rate of regeneration in the deep shade of a primeval beech forest / R. Petrovska in European Journal of Forest Research, vol 141 n° 1 (February 2022)PermalinkCo-seismic ionospheric disturbances following the 2016 West Sumatra and 2018 Palu earthquakes from GPS and GLONASS measurements / Mokhamad Nur Cahyadi in Remote sensing, vol 14 n° 2 (January-2 2022)PermalinkIncreasing territorial planning activities through viewshed analysis / Gheorghe-Gavrilă Hognogi in Geocarto international, vol 37 n° 2 ([15/01/2022])PermalinkUse of remotely sensed data to estimate tree species diversity as an indicator of biodiversity in Blouberg Nature Reserve, South Africa / Mangana Rampheri in Geocarto international, vol 37 n° 2 ([15/01/2022])PermalinkAdaptation d'un algorithme SLAM pour la vision panoramique multi-expositions dans des scènes à haute gamme dynamique / Eva Goichon (2022)PermalinkApport de la télédétection et des variables auxiliaires dans l'étude de l'évolution des périodes de sécheresse / Nesrine Farhani (2022)PermalinkAttributs de texture extraits d'images multispectrales acquises en conditions d'éclairage non contrôlées : application à l'agriculture de précision / Anis Amziane (2022)PermalinkCharacteristics of taiga and tundra snowpack in development and validation of remote sensing of snow / Henna-Reetta Hannula (2022)PermalinkConventional and neural network-based water vapor density model for GNSS troposphere tomography / Chen Liu in GPS solutions, vol 26 n° 1 (January 2022)PermalinkDART: An efficient 3D Monte Carlo vector radiative transfer model for remote sensing applications / Yingjie Wang (2022)PermalinkDetecting and visualizing observation hot-spots in massive volunteer-contributed geographic data across spatial scales using GPU-accelerated kernel density estimation / Guiming Zhang in ISPRS International journal of geo-information, vol 11 n° 1 (January 2022)PermalinkDetection and biomass estimation of phaeocystis globosa blooms off Southern China from UAV-based hyperspectral measurements / Xue Li in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)PermalinkPermalinkDevelopment of object detectors for satellite images by deep learning / Alissa Kouraeva (2022)PermalinkExamining the integration of Landsat operational land imager with Sentinel-1 and vegetation indices in mapping southern yellow pines (Loblolly, Shortleaf, and Virginia pines) / Clement E. Akumu in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 1 (January 2022)PermalinkFusion de données hyperspectrales et panchromatiques dans le domaine réflectif / Yohann Constans (2022)PermalinkGenerating GPS decoupled clock products for precise point positioning with ambiguity resolution / Shuai Liu in Journal of geodesy, vol 96 n° 1 (January 2022)PermalinkGénération d’un jeu de données d’entraînement et mise en oeuvre d’une architecture de détection par deep learning des numéros de parcelles sur les plans du cadastre Napoléonien / Tiecoumba Ibrahim Tamela (2022)PermalinkGlobal canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles / Nico Lang in Remote sensing of environment, vol 268 (January 2022)PermalinkImproving LSMA for impervious surface estimation in an urban area / Jin Wang in European journal of remote sensing, vol 55 n° 1 (2022)PermalinkIn situ C-band data for wheat physiological functioning monitoring in the South Mediterranean region / Nadia Ouaadi (2022)PermalinkLarge-scale dimensional metrology for geodesy: First results from the European GeoMetre project / Florian Pollinger (2022)PermalinkMapping burned areas and land-uses in Kangaroo Island using an object-based image classification framework and Landsat 8 Imagery from Google Earth Engine / Jiyu Liu in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkModeling of precipitable water vapor from GPS observations using machine learning and tomography methods / Mir Reza Ghaffari Razin in Advances in space research, vol 69 n° 7 (April 2022)PermalinkPermalinkNon-linear GNSS signal processing applied to land observation with high-rate airborne reflectometry / Hamza Issa (2022)PermalinkPython software to transform GPS SNR wave phases to volumetric water content / Angel Martín in GPS solutions, vol 26 n° 1 (January 2022)PermalinkLe radar révèle des montagnes cachées / Laurent Polidori in Géomètre, n° 2198 (janvier 2022)PermalinkPermalinkSalt tectonic imaging at crustal and experimental scales by seismic migration and adjoint method / Javier Abreu-Torres (2022)PermalinkSpatiotemporal analysis of precipitable water vapor using ANFIS and comparison against voxel-based tomography and radiosonde / Mir Reza Ghaffari Razin in GPS solutions, vol 26 n° 1 (January 2022)PermalinkPermalinkPermalinkBaseline-dependent clock offsets in VLBI data analysis / Hana Krásná in Journal of geodesy, vol 95 n° 12 (December 2021)PermalinkEarly detection of spruce vitality loss with hyperspectral data: Results of an experimental study in Bavaria, Germany / Kathrin Einzmann in Remote sensing of environment, vol 266 (December 2021)PermalinkIonospheric corrections tailored to the Galileo High Accuracy Service / Adria Rovira-Garcia in Journal of geodesy, vol 95 n° 12 (December 2021)PermalinkMulti-model estimation of forest canopy closure by using red edge bands based on Sentinel-2 images / Yiying Hua in Forests, vol 12 n° 12 (December 2021)PermalinkParticle swarm optimization based water index (PSOWI) for mapping the water extents from satellite images / Mohammad Hossein Gamshadzaei in Geocarto international, vol 36 n° 20 ([01/12/2021])PermalinkRadiative transfer modeling in structurally complex stands: towards a better understanding of parametrization / Frédéric André in Annals of Forest Science, vol 78 n° 4 (December 2021)PermalinkSpatial variability of suspended sediments in San Francisco Bay, California / Niky C. Taylor in Remote sensing, vol 13 n° 22 (November-2 2021)PermalinkA CNN-based approach for the estimation of canopy heights and wood volume from GEDI waveforms / Ibrahim Fayad in Remote sensing of environment, vol 265 (November 2021)PermalinkDiffuse attenuation coefficient (Kd) from ICESat-2 ATLAS spaceborne Lidar using random-forest regression / Forrest Corcoran in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 11 (November 2021)PermalinkDownscaling MODIS spectral bands using deep learning / Rohit Mukherjee in GIScience and remote sensing, vol 58 n° 8 (2021)Permalink