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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 descripteurs IGN] Appalaches [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] bande S [Termes descripteurs IGN] classification automatique [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] image GPM [Termes descripteurs IGN] orographie [Termes descripteurs 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]

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 descripteurs IGN] Amérique du nord [Termes descripteurs IGN] données météorologiques [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] image Terra-MODIS [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] modèle de transfert radiatif [Termes descripteurs IGN] précision des données [Termes descripteurs IGN] station GPS [Termes descripteurs 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 descripteurs IGN] Amérique du nord [Termes descripteurs IGN] coefficient d'étalonnage [Termes descripteurs IGN] coefficient de corrélation [Termes descripteurs IGN] données GNSS [Termes descripteurs IGN] données météorologiques [Termes descripteurs IGN] erreur systématique [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] image Aqua-MODIS [Termes descripteurs IGN] image Terra-MODIS [Termes descripteurs IGN] précipitation [Termes descripteurs IGN] prévision météorologique [Termes descripteurs IGN] régression [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] vapeur d'eau [Termes descripteurs 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]

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)  

Public [article]  inForestry, an international journal of forest research > vol 94 n° 1 (January 2021) . - pp 1 - 17 Titre : Is Xylella fastidiosa a serious threat to European forests? Type de document : Article/Communication Auteurs : Marie-Laure Desprez-Loustau, Auteur ; Yialmaz Balci, Auteur ; Daniele Cornara, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1 - 17 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] Acer pseudoplatanus [Termes descripteurs IGN] Amérique du nord [Termes descripteurs IGN] dépérissement [Termes descripteurs IGN] écosystème forestier [Termes descripteurs IGN] Europe (géographie politique) [Termes descripteurs IGN] Italie [Termes descripteurs IGN] maladie bactérienne [Termes descripteurs IGN] Olea europaea [Termes descripteurs IGN] Quercus (genre) [Termes descripteurs IGN] Ulmus (genre) [Termes descripteurs IGN] viticulture [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) The recent emergence of Olive Quick Decline Syndrome in Italy, caused by Xylella fastidiosa, has drawn attention to the risks posed by this vector-borne bacterium to important crops in Europe (especially fruit trees and grapevine). Comparatively very little is known on actual and potential impacts of this pathogen in forests, in the native (North American) and introduced (European) regions, respectively. The present review aims to address important questions related to the threat posed by X. fastidiosa to European forests, such as the following: What are the symptoms, hosts and impact of bacterial leaf scorch caused by X. fastidiosa on trees in North America? Which forest tree species have been found infected in the introduction area in Europe? How does X. fastidiosa cause disease in susceptible hosts? Are there any X. fastidiosa genotypes (subspecies and sequence types) specifically associated with forest trees? How is X. fastidiosa transmitted? What are the known and potential vectors for forest trees? How does vector ecology affect disease? Is the distribution of X. fastidiosa, especially the strains associated with trees, restricted by climatic factors? Is disease risk for trees different in forest ecosystems as compared with urban settings? We conclude by pointing to important knowledge gaps related to all these questions and strongly advocate for more research about the Xylella-forest pathosystems, in both North America and Europe. Numéro de notice : A2021-072 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/forestry/cpaa029 date de publication en ligne : 06/08/2020 En ligne : https://doi.org/10.1093/forestry/cpaa029 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96805 [article]

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)  

Public [article]  inForests > vol 11 n° 5 (May 2020) . - 14 p. Titre : What Is threatening forests in protected areas? A global assessment of deforestation in protected areas, 2001–2018 Type de document : Article/Communication Auteurs : Christopher M. Wade, Auteur ; Kemen G. Austin, Auteur ; James Cajka, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 14 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] aire protégée [Termes descripteurs IGN] Amérique du nord [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] arbuste [Termes descripteurs IGN] biodiversité [Termes descripteurs IGN] Brésil [Termes descripteurs IGN] Congo [Termes descripteurs IGN] couvert forestier [Termes descripteurs IGN] déboisement [Termes descripteurs IGN] gaz à effet de serre [Termes descripteurs IGN] habitat forestier [Termes descripteurs IGN] indicateur de service écosystémique [Termes descripteurs IGN] Indonésie [Termes descripteurs IGN] protection des forêts [Termes descripteurs IGN] risque environnemental [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) The protection of forests is crucial to providing important ecosystem services, such as supplying clean air and water, safeguarding critical habitats for biodiversity, and reducing global greenhouse gas emissions. Despite this importance, global forest loss has steadily increased in recent decades. Protected Areas (PAs) currently account for almost 15% of Earth’s terrestrial surface and protect 5% of global tree cover and were developed as a principal approach to limit the impact of anthropogenic activities on natural, intact ecosystems and habitats. We assess global trends in forest loss inside and outside of PAs, and land cover following this forest loss, using a global map of tree cover loss and global maps of land cover. While forests in PAs experience loss at lower rates than non-protected forests, we find that the temporal trend of forest loss in PAs is markedly similar to that of all forest loss globally. We find that forest loss in PAs is most commonly—and increasingly—followed by shrubland, a broad category that could represent re-growing forest, agricultural fallows, or pasture lands in some regional contexts. Anthropogenic forest loss for agriculture is common in some regions, particularly in the global tropics, while wildfires, pests, and storm blowdown are a significant and consistent cause of forest loss in more northern latitudes, such as the United States, Canada, and Russia. Our study describes a process for screening tree cover loss and agriculture expansion taking place within PAs, and identification of priority targets for further site-specific assessments of threats to PAs. We illustrate an approach for more detailed assessment of forest loss in four case study PAs in Brazil, Indonesia, Democratic Republic of Congo, and the United States. Numéro de notice : A2020-655 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f11050539 date de publication en ligne : 12/05/2020 En ligne : https://doi.org/10.3390/f11050539 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96119 [article]

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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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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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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Monitoring climate sensitivity shifts in tree-rings of Eastern Boreal North America using model-data comparison : Shifts in tree growth sensivity to climate / Clémentine Ols in Ecosystems, vol 21 n° 5 (August 2018)  

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Hydrologically-driven crustal stresses and seismicity in the New Madrid seismic zone / Timothy J. Craig in Nature communications, vol 8 (2017)  

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Strong gradients in forest sensitivity to climate change revealed by dynamics of forest fire cycles in the post Little Ice Age Era / Igor Drobyshev in Journal of geophysical research : Biogeosciences, vol 122 n° 10 (October 2017)  

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Wireframing for interactive & web-based geographic visualization: designing the NOAA Lake Level Viewer / Robert Emmett Roth in Cartography and Geographic Information Science, Vol 44 n° 4 (July 2017)  

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Evidence for postglacial signatures in gravity gradients: A clue in lower mantle viscosity / Laurent Métivier in Earth and planetary science letters, vol 452 (October 2016)  

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MIDAS robust trend estimator for accurate GPS station velocities without step detection / Geoffrey Blewitt in Journal of geophysical research : Solid Earth, vol 121 n° 3 (March 2016)    

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