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Climate 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])  

Public [article]  inForest ecology and management > vol 478 [15/12/2020] . - 14 p. Titre : Climate sensitive single tree growth modeling using a hierarchical Bayes approach and integrated nested Laplace approximations (INLA) for a distributed lag model Type de document : Article/Communication Auteurs : Arne Nothdurft, Auteur Année de publication : 2020 Article en page(s) : 14 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] approche hiérarchique [Termes descripteurs IGN] Autriche [Termes descripteurs IGN] bioclimatologie [Termes descripteurs IGN] croissance végétale [Termes descripteurs IGN] dendrochronologie [Termes descripteurs IGN] données météorologiques [Termes descripteurs IGN] estimation bayesienne [Termes descripteurs IGN] Fagus sylvatica [Termes descripteurs IGN] intégrale de Laplace [Termes descripteurs IGN] larix decidua [Termes descripteurs IGN] modèle de croissance [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] peuplement mélangé [Termes descripteurs IGN] Picea abies [Termes descripteurs IGN] Pinus sylvestris [Termes descripteurs IGN] quercus sessiliflora [Termes descripteurs IGN] série temporelle [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) A novel methodological framework is presented for climate-sensitive modeling of annual radial stem increments using tree-ring width time series. The approach is based on a hierarchical Bayes model together with a distributed time lag model that take into account the effects of a series of monthly temperature and precipitation values, as well as their interactions. By using a set of random walk priors, the hierarchical Bayes model allows both the detrending of the individual time series and the regression modeling to be performed simultaneously in a single model step. The approach was applied to comprehensive tree-ring width data from Austria collected on sample plots arranged in triplets representing different mixture types. Bayesian predictions revealed that European larch (Larix decidua Mill.), Norway spruce (Picea abies (L.) H. Karst.), and Scots pine (Pinus sylvestris L.) show positive climate-related growth trends throughout higher elevation sites in Tyrol, and these trends remain unchanged under a mixed-stand scenario. At the lower Austrian sites, Norway spruce was found to show a severely negative growth trend under both the pure- and mixed-stand scenario. The increment rates of European beech (Fagus sylvatica L.) were found to have a negative climate-related trend in pure stands, and the trend diminished through an admixture of spruce or larch. The trends of European larch and sessile oak (Quercus petraea (Matt.) Liebl.) showed stationary behavior, irrespective of the mixture scenario. Scots pine data showed a positive trend at the lower elevation sites under both the pure- and mixed-stand scenario. These findings indicate that species mixing does not lower the climate-related increment fluctuations of beech, oak, pine, and spruce at lower elevation sites. Numéro de notice : A2020-625 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2020.118497 date de publication en ligne : 07/09/2020 En ligne : https://doi.org/10.1016/j.foreco.2020.118497 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96025 [article]

Sea 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)  

Public [article]  inRemote sensing > vol 12 n° 21 (November 2020) . - n° 3654 Titre : Sea surface temperature and high water temperature occurrence prediction using a long short-term memory model Type de document : Article/Communication Auteurs : Minkyu Kim, Auteur ; Hung Yang, Auteur ; Jonghwa Kim, Auteur Année de publication : 2020 Article en page(s) : n° 3654 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] aquaculture [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] Corée du sud [Termes descripteurs IGN] données météorologiques [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] pêche [Termes descripteurs IGN] réseau neuronal récurrent [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] température de surface de la mer Résumé : (auteur) Recent global warming has been accompanied by high water temperatures (HWTs) in coastal areas of Korea, resulting in huge economic losses in the marine fishery industry due to disease outbreaks in aquaculture. To mitigate these losses, it is necessary to predict such outbreaks to prevent or respond to them as early as possible. In the present study, we propose an HWT prediction method that applies sea surface temperatures (SSTs) and deep-learning technology in a long short-term memory (LSTM) model based on a recurrent neural network (RNN). The LSTM model is used to predict time series data for the target areas, including the coastal area from Goheung to Yeosu, Jeollanam-do, Korea, which has experienced frequent HWT occurrences in recent years. To evaluate the performance of the SST prediction model, we compared and analyzed the results of an existing SST prediction model for the SST data, and additional external meteorological data. The proposed model outperformed the existing model in predicting SSTs and HWTs. Although the performance of the proposed model decreased as the prediction interval increased, it consistently showed better performance than the European Center for Medium-Range Weather Forecast (ECMWF) prediction model. Therefore, the method proposed in this study may be applied to prevent future damage to the aquaculture industry. Numéro de notice : A2020-721 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12213654 date de publication en ligne : 07/11/2020 En ligne : https://doi.org/10.3390/rs12213654 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96311 [article]

Background tropospheric delay in geosynchronous synthetic aperture radar / Dexin Li in Remote sensing, vol 12 n° 18 (September 2020)  

Public [article]  inRemote sensing > vol 12 n° 18 (September 2020) . - 21 p. Titre : Background tropospheric delay in geosynchronous synthetic aperture radar Type de document : Article/Communication Auteurs : Dexin Li, Auteur ; Xiaoxiang Zhu, Auteur ; Zhen Dong, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 21 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] compensation [Termes descripteurs IGN] décorrélation [Termes descripteurs IGN] données météorologiques [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] modèle géométrique de prise de vue [Termes descripteurs IGN] propagation troposphérique [Termes descripteurs IGN] radar bistatique [Termes descripteurs IGN] retard troposphérique [Termes descripteurs IGN] synchronisation Résumé : (auteur) Spaceborne synthetic aperture radar (SAR) has been treated as a weather independent system for a long time. However, with the development of advanced SAR configurations, e.g., high resolution, bistatic, geosynchronous (GEO), the influence of tropospheric propagation error, which strongly depends on the weather, has begun to receive attention. In this paper, we focus on the effect of deterministic background tropospheric delay (BTD) during the image formation of GEO SAR. First, the decorrelation problems caused by the spatial variation and BTD are presented. Second, by combining with the SAR imaging geometry, the BTD error is decomposed as constant error, spatially variant error, and time variant error, the influences of which are analyzed under different circumstances. Third, an imaging method starting from the meteorological parameters and the GEO SAR systematic parameters is proposed to deal with the decorrelation problems. Finally, simulations with the dot-matrix targets are performed to validate the imaging method. Numéro de notice : A2020-632 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs12183081 date de publication en ligne : 20/09/2020 En ligne : https://doi.org/10.3390/rs12183081 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96053 [article]

Use of Bayesian modeling to determine the effects of meteorological conditions, prescribed burn season, and tree characteristics on litterfall of pinus nigra and pinus pinaster stands / Juncal Espinosa in Forests, vol 11 n° 9 (September 2020)  

Public [article]  inForests > vol 11 n° 9 (September 2020) . - N° 1006 Titre : Use of Bayesian modeling to determine the effects of meteorological conditions, prescribed burn season, and tree characteristics on litterfall of pinus nigra and pinus pinaster stands Type de document : Article/Communication Auteurs : Juncal Espinosa, Auteur ; Óscar Rodríguez de Rivera, Auteur ; Javier Madrigal, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : N° 1006 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] biomasse [Termes descripteurs IGN] classification bayesienne [Termes descripteurs IGN] données météorologiques [Termes descripteurs IGN] Espagne [Termes descripteurs IGN] estimation bayesienne [Termes descripteurs IGN] incendie de forêt [Termes descripteurs IGN] intégrale de Laplace [Termes descripteurs IGN] modèle linéaire [Termes descripteurs IGN] Pinus nigra [Termes descripteurs IGN] Pinus pinaster [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Research Highlights: Litterfall biomass after prescribed burning (PB) is significantly influenced by meteorological variables, stand characteristics, and the fire prescription. Some of the fire-adaptive traits of the species under study (Pinus nigra and Pinus pinaster) mitigate the effects of PB on litterfall biomass. The Bayesian approach, tested here for the first time, was shown to be useful for analyzing the complex combination of variables influencing the effect of PB on litterfall. Background and Objectives: The aims of the study focused on explaining the influence of meteorological conditions after PB on litterfall biomass, to explore the potential influence of stand characteristic and tree traits that influence fire protection, and to assess the influence of fire prescription and fire behavior. Materials and Methods: An experimental factorial design including three treatments (control, spring, and autumn burning), each with three replicates, was established at two experimental sites (N = 18; 50 × 50 m2 plots). The methodology of the International Co-operative Program on Assessment and Monitoring of Air Pollution Effects on Forests (ICP forests) was applied and a Bayesian approach was used to construct a generalized linear mixed model. Results: Litterfall was mainly affected by the meteorological variables and also by the type of stand and the treatment. The effects of minimum bark thickness and the height of the first live branch were random. The maximum scorch height was not high enough to affect the litterfall. Time during which the temperature exceeded 60 °C (cambium and bark) did not have an important effect. Conclusions: Our findings demonstrated that meteorological conditions were the most significant variables affecting litterfall biomass, with snowy and stormy days having important effects. Significant effects of stand characteristics (mixed and pure stand) and fire prescription regime (spring and autumn PB) were shown. The trees were completely protected by a combination of low-intensity PB and fire-adaptive tree traits, which prevent direct and indirect effects on litterfall. Identification of important variables can help to improve PB and reduce the vulnerability of stands managed by this method. Numéro de notice : A2020-753 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f11091006 date de publication en ligne : 18/09/2020 En ligne : https://doi.org/10.3390/f11091006 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96433 [article]

Global Climate [in “State of the Climate in 2019"] / A. Ades in Bulletin of the American Meteorological Society, vol 101 n° 8 (August 2020)  

Public [article]  inBulletin of the American Meteorological Society > vol 101 n° 8 (August 2020) . - pp S9 - S128 Titre : Global Climate [in “State of the Climate in 2019"] Type de document : Article/Communication Auteurs : A. Ades, Auteur ; R. Adler, Auteur ; et al., Auteur ; Olivier Bock , Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : pp S9 - S128 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Atmosphère [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] circulation atmosphérique [Termes descripteurs IGN] climat terrestre [Termes descripteurs IGN] cryosphère [Termes descripteurs IGN] données météorologiques [Termes descripteurs IGN] gaz à effet de serre [Termes descripteurs IGN] humidité de l'air [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] précipitation [Termes descripteurs IGN] sécheresse [Termes descripteurs IGN] température de l'air Résumé : (auteur) [introduction] The assessments and analyses presented in this chapter focus predominantly on the measured differences of climate and weather observables from previous conditions, years, and decades to place 2019 in context. Many of these differences have direct impacts on people, for example, their health and environment, as well as the wider biosphere, but are beyond the scope of these analyses. For the last few State of the Climate reports, an update on the number of warmer-than-average years has held no surprises, and this year is again no different. The year 2019 was among the three warmest years since records began in the mid-to-late 1800s. Only 2016, and for some datasets 2015, were warmer than 2019; all years after 2013 have been warmer than all others back to the mid-1800s. Each decade since 1980 has been successively warmer than the preceding decade, with the most recent (2010–19) being around 0.2°C warmer than the previous (2000–09). This warming of the land and ocean surface is reflected across the globe. For example, lake and permafrost temperatures have increased; glaciers have continued to lose mass, becoming thinner for the 32nd consecutive year, with the majority also becoming shorter during 2019. The period during which Northern Hemisphere (NH) lakes were covered in ice was seven days shorter than the 1981–2010 long-term average, based on in situ phenological records. There were fewer cool extremes and more warm extremes on land; regions including Europe, Japan, Pakistan, and India all experienced heat waves. More strong than moderate marine heat waves were recorded for the sixth consecutive year. And in Australia (discussed in more detail in section 7h4), moisture deficits and prolonged high temperatures led to severe impacts during late austral spring and summer, including devastating wildfires. Smoke from these wildfires was detected across large parts of the Southern Hemisphere (SH). [...] Numéro de notice : A2020-798 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1175/BAMS-D-20-0104.1 date de publication en ligne : 12/08/2020 En ligne : https://doi.org/10.1175/BAMS-D-20-0104.1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96582 [article]

Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques in Hanoi, Vietnam / Khanh Giang Le in Geo-spatial Information Science, vol 23 n° 2 (June 2020)  

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Improved optical image matching time series inversion approach for monitoring dune migration in North Sinai Sand Sea: Algorithm procedure, application, and validation / Eslam Ali in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

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Spatiotemporal variation of NDVI in the vegetation growing season in the source region of the yellow river, China / Mingyue Wang in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)  

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Advanced GNSS tropospheric products for monitoring severe weather events and climate / Jonathan Jones (2020)  

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IWV retrieval from ground and shipborne GPS receivers during NAWDEX [diaporama] / Pierre Bosser (2020)  

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IWV retrieval from shipborne GPS receiver on hydrographic ship Borda [diaporama] / Olivier Bock (2020)  

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Projet infos usagers et projet transport à la demande / Oriane Sobreira (2020) 

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Restitution de profils verticaux de la distribution de gouttes de pluie à partir de mesures au sol et en altitude / Christophe Samboun (2020) 

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Spatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods / Wolfgang B. Hamer in ISPRS International journal of geo-information, Vol 9 n° 1 (January 2020)  

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Spatiotemporal variation in the relationship between boreal forest productivity proxies and climate data / Clémentine Ols in Dendrochronologia, vol 58 (December 2019)  

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