Centre dedocumentation
scientifique

Adding tree rings to North America's national forest inventories: An essential tool to guide drawdown of atmospheric CO2 / Margaret E.K. Evans in BioScience, vol 72 n° 3 (March 2022)  

Public [article]  inBioScience > vol 72 n° 3 (March 2022) . - pp 233 - 246 Titre : Adding tree rings to North America's national forest inventories: An essential tool to guide drawdown of atmospheric CO2 Type de document : Article/Communication Auteurs : Margaret E.K. Evans, Auteur ; R. Justin DeRose, Auteur ; Stefan Klesse, Auteur ; Martin P. Girardin, Auteur ; Kelly A. Heilman, Auteur ; M. Ross Alexander, Auteur ; André Arsenault, Auteur ; Flurin Babst, Auteur ; Mathieu Bouchard, Auteur ; Sean M. P. Cahoon, Auteur ; Elisabeth M. Campbell, Auteur ; Michael Dietze, Auteur ; Louis Duchesne, Auteur ; David Frank, Auteur ; Courtney L. Giebink, Auteur ; Armando Gómez-Guerrero, Auteur ; Genaro Gutiérrez García, Auteur ; Edward H. Hogg, Auteur ; Juha Metsaranta, Auteur ; Clémentine Ols , Auteur ; et al., Auteur Année de publication : 2022 Projets : ARBRE / AgroParisTech, LUE / Université de Lorraine Article en page(s) : pp 233 - 246 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Amérique du nord [Termes IGN] cerne [Termes IGN] dendrochronologie [Termes IGN] dioxyde de carbone [Termes IGN] gaz à effet de serre [Termes IGN] inventaire forestier étranger (données) [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Tree-ring time series provide long-term, annually resolved information on the growth of trees. When sampled in a systematic context, tree-ring data can be scaled to estimate the forest carbon capture and storage of landscapes, biomes, and—ultimately—the globe. A systematic effort to sample tree rings in national forest inventories would yield unprecedented temporal and spatial resolution of forest carbon dynamics and help resolve key scientific uncertainties, which we highlight in terms of evidence for forest greening (enhanced growth) versus browning (reduced growth, increased mortality). We describe jump-starting a tree-ring collection across the continent of North America, given the commitments of Canada, the United States, and Mexico to visit forest inventory plots, along with existing legacy collections. Failing to do so would be a missed opportunity to help chart an evidence-based path toward meeting national commitments to reduce net greenhouse gas emissions, urgently needed for climate stabilization and repair. Numéro de notice : A2022-031 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/biosci/biab119 Date de publication en ligne : 08/12/2021 En ligne : https://doi.org/10.1093/biosci/biab119 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99282 [article]

How geographic and climatic factors affect the adaptation of Douglas-fir provenances to the temperate continental climate zone in Europe / Marzena Niemczyk in European Journal of Forest Research, vol 140 n° 6 (December 2021)  

Public [article]  inEuropean Journal of Forest Research > vol 140 n° 6 (December 2021) . - pp 1341 - 1361 Titre : How geographic and climatic factors affect the adaptation of Douglas-fir provenances to the temperate continental climate zone in Europe Type de document : Article/Communication Auteurs : Marzena Niemczyk, Auteur ; Daniel J. Chmura, Auteur ; Jarosław Socha, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1341 - 1361 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] adaptation (biologie) [Termes IGN] Amérique du nord [Termes IGN] analyse de variance [Termes IGN] analyse diachronique [Termes IGN] climat tempéré [Termes IGN] croissance des arbres [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] gelée [Termes IGN] graine [Termes IGN] modélisation de la forêt [Termes IGN] Pologne [Termes IGN] Pseudotsuga menziesii [Termes IGN] régénération (sylviculture) [Termes IGN] sécheresse [Termes IGN] variation saisonnière [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) The contribution of Douglas-fir (Df) to European forests is likely to increase as the species is a potential adaptation option to climate change. In this study, we investigated growth and survival of Df seed sources to fill a knowledge gap regarding recommendations for the future use of Df provenances in Poland. Our experimental test site represents the most continental climate among all Df trials installed in the IUFRO 1966–67 test series in Europe. At this unique single site, we evaluated the performance of 46 Df provenances from North America, and nine local landraces of unknown origin. Repeated measurements of tree diameter, height, and volume were analysed, to age 48, representing integrated responses to geographic and climatic conditions. Significant variation in survival and productivity-related traits were found, with the interior Df provenances performing best, in contrast to previous European reports. The higher survivability and volume of the interior provenances resulted from their superior frost resistance. The low precipitation seasonality at the location of seed origin provided an additional advantage to the trees at the test site. Geographic and climatic factors of seed origin explained most of the variation in productivity (77 and 64%, respectively). The tested landraces exhibited diverse performance, implying that naturalized local seed sources in Poland need improvement and perhaps enrichment with new genetic material from North America, while considering geography and climate. Assisted migration programs should consider the limitations imposed by both frost and drought events in guiding future Df selections for continental climates. Further field testing, early greenhouse screening and DNA testing are also recommended. Numéro de notice : A2021-837 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s10342-021-01398-5 Date de publication en ligne : 22/07/2021 En ligne : https://doi.org/10.1007/s10342-021-01398-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99017 [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 d'arbres décisionnels [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]

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]

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)  

Permalink

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)  

Permalink

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)  

Permalink

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)  

Permalink

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)  

Permalink

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

Permalink

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)  

Permalink

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

Permalink

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)  

Permalink

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)  

Permalink