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Establishing vertical separation models for vulnerable coastlines in developing territories / Cassandra Nanlal in Marine geodesy, vol 44 n° 5 (September 2021)  

Public [article]  inMarine geodesy > vol 44 n° 5 (September 2021) . - pp 387 - 407 Titre : Establishing vertical separation models for vulnerable coastlines in developing territories Type de document : Article/Communication Auteurs : Cassandra Nanlal, Auteur ; Keith Miller, Auteur ; Dexter Davis, Auteur ; Michael Sutherland, Auteur Année de publication : 2021 Article en page(s) : pp 387 - 407 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux [Termes IGN] aménagement du littoral [Termes IGN] Amérique du sud [Termes IGN] changement climatique [Termes IGN] données altimétriques [Termes IGN] géodésie physique [Termes IGN] golfe [Termes IGN] hauteurs de mer [Termes IGN] niveau de la mer [Termes IGN] simulation hydrodynamique [Termes IGN] système de référence altimétrique [Termes IGN] système de référence local [Termes IGN] trait de côte [Termes IGN] vulnérabilité Résumé : (Auteur) Vertical separation models are valuable for coastal zone management and protection against the effects of climate change. To date, the development of such models has been undertaken in areas where long-term sea level measurements exist and there are resources for extensive offshore bathymetric and Global Navigation Satellite Systems surveys. Many small island developing states and other resource constrained territories host vulnerable coastal zones and would benefit from such models, however, financial constraints and data sparsity make it difficult. This article describes the establishment of a vertical separation model using an amalgamation of long- and short-term sea level measurements with hydrodynamic modeling. With existing vertical separations at only two coastal points for comparison, the model was designed to include a tidal prediction element which allowed for validation against sparse independently observed sea levels. Considering that unmodeled influences on sea levels in the study area can exceed 0.2 m at times, the method was tested against independently observed sea levels and can be considered successful with variances in the range of 1.3–4.5% of the average tidal range for the study area. This research provides the means of addressing a significant need in developing territories where long-term sea level records are unavailable and resource deficiencies exist. Numéro de notice : A2021-576 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01490419.2020.1844825 date de publication en ligne : 02/12/2020 En ligne : https://doi.org/10.1080/01490419.2020.1844825 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98227 [article]

Automatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning / Malarvizhi Arulraj in Remote sensing of environment, vol 257 (May 2021)  

Public [article]  inRemote sensing of environment > vol 257 (May 2021) . - n° 112355 Titre : Automatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning Type de document : Article/Communication Auteurs : Malarvizhi Arulraj, Auteur ; Ana P. Baros, Auteur Année de publication : 2021 Article en page(s) : n° 112355 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] Appalaches [Termes IGN] apprentissage automatique [Termes IGN] bande S [Termes IGN] classification automatique [Termes IGN] classification barycentrique [Termes IGN] classification par forêts aléatoires [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] image GPM [Termes IGN] orographie [Termes IGN] précipitation Résumé : (auteur) Ground-clutter is a significant cause of missed-detection and underestimation of precipitation in complex terrain from space-based radars such as the Global Precipitation Measurement Mission (GPM) Dual-frequency Precipitation Radar (DPR). This research proposes an Artificial Intelligence (AI) framework consisting of a precipitation detection model (PDM) and a precipitation regime classification model (PCM) to improve orographic precipitation retrievals from GPM-DPR using machine learning. The PDM is a Random Forest Classifier using GPM Microwave Imager (GMI) calibrated brightness temperatures (Tbs) and low-level precipitation mixing ratios from the High-Resolution Rapid Refresh (HRRR) analysis as inputs. The PCM is a Convolutional Neural Network that predicts the precipitation regime class, defined independently based on quantitative features of ground-based radar reflectivity profiles, using GPM DPR Ku-band (Ku-PR) reflectivity profiles and GMI Tbs. The AI framework is demonstrated for warm-season precipitation in the Southern Appalachian Mountains over. Numéro de notice : A2021-279 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112355 date de publication en ligne : 19/02/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112355 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97372 [article]

Integrated water vapour observations in the Caribbean arc from a network of ground-based GNSS receivers during EUREC4A / Olivier Bock in Earth System Science Data, vol 13 n° 5 (May 2021)  

Public [article]  inEarth System Science Data > vol 13 n° 5 (May 2021) . - pp 2407 - 2436 Titre : Integrated water vapour observations in the Caribbean arc from a network of ground-based GNSS receivers during EUREC4A Type de document : Article/Communication Auteurs : Olivier Bock , Auteur ; Pierre Bosser , Auteur ; Cyrille Flamant, Auteur ; Erik Doerflinger, Auteur ; Friedhelm Jansen, Auteur ; Romain Fagès , Auteur ; Sandrine Bony, Auteur ; Sabrina Schnitt, Auteur Année de publication : 2021 Projets : VEGAN / Bock, Olivier, EUREC4A / Bock, Olivier Article en page(s) : pp 2407 - 2436 Note générale : bibliographie This work was supported by the CNRS program LEFE/INSU through the project VEGAN. The EUREC4A project was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 694768). Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] Caraïbes [Termes IGN] données auxiliaires [Termes IGN] données GNSS [Termes IGN] données météorologiques [Termes IGN] erreur systématique [Termes IGN] humidité de l'air [Termes IGN] retard troposphérique zénithal [Termes IGN] teneur intégrée en vapeur d'eau Résumé : (auteur) Ground-based Global Navigation Satellite System (GNSS) measurements from nearly fifty stations distributed over the Caribbean Arc have been analysed for the period 1 January–29 February 2020 in the framework of the EUREC4A (Elucidate the Couplings Between Clouds, Convection and Circulation) field campaign. The aim of this effort is to deliver high-quality Integrated Water Vapour (IWV) estimates to investigate the moisture environment of mesoscale cloud patterns in the Tradewinds and their feedback on the large-scale circulation and energy budget. This paper describes the GNSS data processing procedures and assesses the quality of the GNSS IWV retrievals from four operational streams and one reprocessed research stream which is the main data set used for offline scientific applications. The uncertainties associated with each of the data sets, including the zenith tropospheric delay (ZTD) to IWV conversion methods and auxiliary data, are quantified and discussed. The IWV estimates from the reprocessed data set are compared to the Vaisala RS41 radiosonde measurements operated from the Barbados Cloud Observatory (BCO) and to the measurements from the operational radiosonde station at Grantley Adams international airport (GAIA). A significant dry bias is found in the GAIA humidity observations with respect to the BCO sondes (−2.9 kg m−2) and the GNSS results (−1.2 kg m−2). A systematic bias between the BCO sondes and GNSS is also observed (1.7 kg m−2) where the Vaisala RS41 measurements are moister than the GNSS retrievals. The IWV estimates from a colocated microwave radiometer agree with the BCO soundings after an instrumental update on 27 January, while they exhibit a dry bias compared to the soundings and to GNSS before that date. IWV estimates from the ECMWF fifth generation reanalysis (ERA5) are overall close to the GAIA observations, probably due to the assimilation of these observations in the reanalysis. However, during several events where strong peaks in IWV occurred, ERA5 is shown to significantly underestimate the GNSS derived IWV peaks. Two successive peaks are observed on 22 January and 23/24 January which were associated with heavy rain and deep moist layers extending from the surface up to altitudes of 3.5 and 5 km, respectively. ERA5 significantly underestimates the moisture content in the upper part of these layers. The origins of the various moisture biases are currently being investigated. We classified the cloud organisation for five representative GNSS stations across the Caribbean Arc and found that the environment of Fish cloud patterns to be moister than that of Flowers cloud patterns which, in turn, is moister than the environment of Gravel cloud patterns. The differences in the IWV means between Fish and Gravel were assessed to be significant. Finally, the Gravel moisture environment was found to be similar to that of clear, cloud-free conditions. The moisture environment associated with the Sugar cloud pattern has not been assessed because it was hardly observed during the first two months of 2020. The reprocessed ZTD and IWV data set from 49 GNSS stations used in this study are available from the AERIS data center (https://doi.org/10.25326/79; Bock (2020b)). Numéro de notice : A2021-172 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/essd-13-2407-2021 date de publication en ligne : 24/02/2021 En ligne : http://dx.doi.org/10.5194/essd-13-2407-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97213 [article]

Refining MODIS NIR atmospheric water vapor retrieval algorithm using GPS-derived water vapor data / Jia He in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 5 (May 2021) . - pp 3682 - 3694 Titre : Refining MODIS NIR atmospheric water vapor retrieval algorithm using GPS-derived water vapor data Type de document : Article/Communication Auteurs : Jia He, Auteur ; Zhizhao Liu, Auteur Année de publication : 2021 Article en page(s) : pp 3682 - 3694 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Amérique du nord [Termes IGN] données météorologiques [Termes IGN] données spatiotemporelles [Termes IGN] image Terra-MODIS [Termes IGN] modèle de régression [Termes IGN] modèle de transfert radiatif [Termes IGN] précision des données [Termes IGN] station GPS [Termes IGN] vapeur d'eau Résumé : (Auteur) A new algorithm of retrieving atmospheric water vapor from MODIS near-infrared (IR) (NIR) data by using a regression fitting method based on Global Positioning System (GPS)-derived water vapor is developed in this work. The algorithm has been used to retrieve total column water vapor from Moderate Resolution Imaging Spectroradiometer (MODIS) satellites both Terra and Aqua under cloud-free conditions from solar radiation in the NIR channels. Water vapor data estimated from GPS observations recorded from 2003 to 2017 by the SuomiNet GPS network over the western North America are used as ground truth references. The GPS stations were classified into six subsets based on the surface types adopted from MCD12Q1 IGBP legend. The differences in surface types are considered in the regression fitting procedure, thus different regression functions are trained for different surface types. Thus, the wet bias in the operational MODIS water vapor products has been significantly reduced. Water vapor retrieved from each of the three absorption channels and the weighted water vapor of combined three absorption channels are analyzed. Validation shows that the weighted water vapor performs better than the single-channel results. Compared to the MODIS/Terra water vapor products, the RMSE has been reduced by 50.78% to 2.229 mm using the two-channel ratio transmittance method and has been reduced by 53.06% to 2.126 mm using the three-channel ratio transmittance method. Compared to the MODIS/Aqua water vapor products, the RMSE has been reduced by 45.54% to 2.423 mm using the two-channel ratio transmittance method and has been reduced by 45.34% to 2.432 mm using the three-channel ratio transmittance method. Numéro de notice : A2021-338 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3016655 date de publication en ligne : 24/08/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3016655 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97569 [article]

Precipitable water vapor fusion based on a generalized regression neural network / Bao Zhang in Journal of geodesy, vol 95 n° 4 (April 2021)  

Public [article]  inJournal of geodesy > vol 95 n° 4 (April 2021) . - n° 36 Titre : Precipitable water vapor fusion based on a generalized regression neural network Type de document : Article/Communication Auteurs : Bao Zhang, Auteur ; Yibing Yao, Auteur Année de publication : 2021 Article en page(s) : n° 36 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] Amérique du nord [Termes IGN] coefficient d'étalonnage [Termes IGN] coefficient de corrélation [Termes IGN] données GNSS [Termes IGN] données météorologiques [Termes IGN] erreur systématique [Termes IGN] fusion de données [Termes IGN] image Aqua-MODIS [Termes IGN] image Terra-MODIS [Termes IGN] précipitation [Termes IGN] prévision météorologique [Termes IGN] régression [Termes IGN] réseau neuronal artificiel [Termes IGN] vapeur d'eau [Termes IGN] variation temporelle Résumé : (auteur) Water vapor plays an important role in Earth’s weather and climate processes and energy transfer. Plenty of techniques have developed to monitor precipitable water vapor (PWV), but joint use of different techniques has some problems, including systematic biases, different spatiotemporal coverages and resolutions among different datasets. To address the above problems and improve the data utilization, we propose to use a generalized regression neural network (GRNN) to fuse PWVs from Global Navigation Satellite System (GNSS), Moderate-Resolution Imaging Spectroradiometer (MODIS), and European Centre for Medium‐Range Weather Forecasts Reanalysis 5 (ERA5). The core idea of this method is to use the high-quality GNSS PWV to calibrate and optimize the relatively low-quality MODIS and ERA5 PWV through the constructed GRNNs. Using the proposed method, we generated more than 400 PWV maps that combine GNSS, MODIS, and ERA5 PWVs in North America in 2018. Results show that the overall bias, standard deviation (STD), and root-mean-square (RMS) error are 0.0 mm, 2.1 mm, and 2.2 mm for the improved MODIS PWV, and 0.0 mm, 1.6 mm, and 1.6 mm for the improved ERA5 PWV. Compared to the original MODIS and ERA5 PWV, the total improvements are 37.1% and 15.8% in terms of RMS. The RMS improvements are mainly contributed from the calibration of bias for the MODIS PWV and optimization for the ERA5 PWV. It also demonstrates that the original MODIS PWV tends to be greater than the GNSS PWV while the ERA5 PWV has very small biases. After calibration and optimization, the correlation coefficients between the modified PWV and the GNSS PWV are 0.96 for the MODIS PWV and 0.98 for the ERA5 PWV. The proposed method also diminishes the temporal and spatial variations in accuracy, generating homogeneous PWV products. Since the biases among the three datasets are well removed and data accuracies are improved to the same level, they are thus easily fused and jointly used. Numéro de notice : A2021-259 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01482-z date de publication en ligne : 01/03/2021 En ligne : https://doi.org/10.1007/s00190-021-01482-z Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97299 [article]

Les impacts spatiaux du changement climatique / Denis Mercier (2021)

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Is Xylella fastidiosa a serious threat to European forests? / Marie-Laure Desprez-Loustau in Forestry, an international journal of forest research, vol 94 n° 1 (January 2021)  

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Wide-area near-real-time monitoring of tropical forest degradation and deforestation using Sentinel-1 / Dirk Hoekman in Remote sensing, vol 12 n° 19 (October 2020)  

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What Is threatening forests in protected areas? A global assessment of deforestation in protected areas, 2001–2018 / Christopher M. Wade in Forests, vol 11 n° 5 (May 2020)  

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Genetic variation of introduced red oak (Quercus rubra) stands in Germany compared to North American populations / Tim Pettenkofer in European Journal of Forest Research, vol 139 n° 2 (April 2020)  

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Clinal variation along precipitation gradients in Patagonian temperate forests: unravelling demographic and selection signatures in three Nothofagus spp. / Carolina Soliani in Annals of Forest Science [en ligne], Vol 77 n° 1 (March 2020)  

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A discriminative tensor representation model for feature extraction and classification of multispectral LiDAR data / Qingwang Wang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)  

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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)  

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Détermination conjointe des inondations et du type d’eau au moyen de l’imagerie multi-spectrale / Sabrine Amzil (2020)  

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Coastal sea level and related fields from existing observing systems / Marta Marcos in Surveys in Geophysics, vol 40 n° 6 (November 2019)  

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