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Provisioning forest and conservation science with high-resolution maps of potential distribution of major European tree species under climate change / Debojyoti Chakraborty in Annals of Forest Science [en ligne], vol 78 n° 2 (June 2021)
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Titre : Provisioning forest and conservation science with high-resolution maps of potential distribution of major European tree species under climate change Type de document : Article/Communication Auteurs : Debojyoti Chakraborty, Auteur ; Norbert Móricz, Auteur ; Ervin Rasztovits, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : Article 26 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse de groupement
[Termes descripteurs IGN] arbre (flore)
[Termes descripteurs IGN] carte forestière
[Termes descripteurs IGN] changement climatique
[Termes descripteurs IGN] conservation des ressources forestières
[Termes descripteurs IGN] espèce végétale
[Termes descripteurs IGN] Europe (géographie politique)
[Termes descripteurs IGN] outil d'aide à la décision
[Termes descripteurs IGN] peuplement forestier
[Termes descripteurs IGN] vulnérabilité
[Vedettes matières IGN] Ecologie forestièreRésumé : (Auteur) We developed a dataset of the potential distribution of seven ecologically and economically important tree species of Europe in terms of their climatic suitability with an ensemble approach while accounting for uncertainty due to model algorithms. The dataset was documented following the ODMAP protocol to ensure reproducibility. Our maps are input data in a decision support tool “SusSelect” which predicts the vulnerability of forest trees in climate change and recommends adapted planting material. Numéro de notice : A2021-329 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-021-01029-4 date de publication en ligne : 22/03/2021 En ligne : https://doi.org/10.1007/s13595-021-01029-4 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97490
in Annals of Forest Science [en ligne] > vol 78 n° 2 (June 2021) . - Article 26[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)
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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] précipitationRésumé : (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
in Remote sensing of environment > vol 257 (May 2021) . - n° 112355[article]Learning from multimodal and multitemporal earth observation data for building damage mapping / Bruno Adriano in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)
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Titre : Learning from multimodal and multitemporal earth observation data for building damage mapping Type de document : Article/Communication Auteurs : Bruno Adriano, Auteur ; Naoto Yokoya, Auteur ; Junshi Xia, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 132 - 143 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes descripteurs IGN] apprentissage profond
[Termes descripteurs IGN] cartographie des risques
[Termes descripteurs IGN] catastrophe naturelle
[Termes descripteurs IGN] classification par réseau neuronal convolutif
[Termes descripteurs IGN] cyclone
[Termes descripteurs IGN] dommage
[Termes descripteurs IGN] données multitemporelles
[Termes descripteurs IGN] image à haute résolution
[Termes descripteurs IGN] image optique
[Termes descripteurs IGN] image radar moirée
[Termes descripteurs IGN] observation de la Terre
[Termes descripteurs IGN] segmentation sémantique
[Termes descripteurs IGN] séisme
[Termes descripteurs IGN] surveillance d'ouvrage
[Termes descripteurs IGN] tsunamiRésumé : (auteur) Earth observation (EO) technologies, such as optical imaging and synthetic aperture radar (SAR), provide excellent means to continuously monitor ever-growing urban environments. Notably, in the case of large-scale disasters (e.g., tsunamis and earthquakes), in which a response is highly time-critical, images from both data modalities can complement each other to accurately convey the full damage condition in the disaster aftermath. However, due to several factors, such as weather and satellite coverage, which data modality will be the first available for rapid disaster response efforts is often uncertain. Hence, novel methodologies that can utilize all accessible EO datasets are essential for disaster management. In this study, we developed a global multimodal and multitemporal dataset for building damage mapping. We included building damage characteristics from three disaster types, namely, earthquakes, tsunamis, and typhoons, and considered three building damage categories. The global dataset contains high-resolution (HR) optical imagery and high-to-moderate-resolution SAR data acquired before and after each disaster. Using this comprehensive dataset, we analyzed five data modality scenarios for damage mapping: single-mode (optical and SAR datasets), cross-modal (pre-disaster optical and post-disaster SAR datasets), and mode fusion scenarios. We defined a damage mapping framework for semantic segmentation of damaged buildings based on a deep convolutional neural network (CNN) algorithm. We also compared our approach to another state-of-the-art model for damage mapping. The results indicated that our dataset, together with a deep learning network, enabled acceptable predictions for all the data modality scenarios. We also found that the results from cross-modal mapping were comparable to the results obtained from a fusion sensor and optical mode analysis. Numéro de notice : A2021-272 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.02.016 date de publication en ligne : 17/03/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.02.016 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97343
in ISPRS Journal of photogrammetry and remote sensing > vol 175 (May 2021) . - pp 132 - 143[article]Numerical modelling for analysis of the effect of different urban green spaces on urban heat load patterns in the present and in the future / Tamás Gál in Computers, Environment and Urban Systems, vol 87 (May 2021)
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Titre : Numerical modelling for analysis of the effect of different urban green spaces on urban heat load patterns in the present and in the future Type de document : Article/Communication Auteurs : Tamás Gál, Auteur ; Sándor István Mahó, Auteur ; Norà Skarbit, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 101600 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes descripteurs IGN] arbre urbain
[Termes descripteurs IGN] changement climatique
[Termes descripteurs IGN] climat urbain
[Termes descripteurs IGN] espace vert
[Termes descripteurs IGN] flore urbaine
[Termes descripteurs IGN] forêt périurbaine
[Termes descripteurs IGN] Hongrie
[Termes descripteurs IGN] ilot thermique urbain
[Termes descripteurs IGN] image à haute résolution
[Termes descripteurs IGN] modèle de simulation
[Termes descripteurs IGN] modèle dynamique
[Termes descripteurs IGN] parc urbain
[Termes descripteurs IGN] planification urbaine
[Termes descripteurs IGN] série temporelle
[Termes descripteurs IGN] utilisation du solRésumé : (auteur) This paper focuses on urban green spaces in terms of climate and human thermal comfort containing their effect on heat load mitigation. It incorporates a modelling study in which the role of green spaces was investigated in terms of heat stress modification by applying MUKLIMO_3 model. During the experiment, the thermal effects of dense trees, scattered trees, grasslands and mixed green infrastructure has been investigated in the case of Szeged (Hungary) and assessed using different climate indices. The investigations encompassed 3 climatological time periods (1981–2010, 2021–2050 and 2071–2100) and two emission scenarios for future climate (RCP4.5 and RCP8.5). It was found that urban green spaces (e.g. parks) generally cool the environment, although, the cooling potential of the different green types differs. The highest reduction of heat load was found in the case of large urban parks comprising of dense trees near the downtown. The spatial extension of detected cooling was found small. However, it would increase during the future, especially in the case of grasslands. For urban planners, it is highly recommended to introduce new green sites within a city and to increase the spatial extension of the existing ones to mitigate and adapt to the impacts of climate change in the urban environment. Numéro de notice : A2021-276 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/URBANISME Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101600 date de publication en ligne : 25/01/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101600 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97362
in Computers, Environment and Urban Systems > vol 87 (May 2021) . - n° 101600[article]Detection of rainstorm pattern in arid regions using MODIS NDVI time series analysis / Mohamed E. Hereher in Geocarto international, vol 36 n° 8 ([15/04/2021])
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Titre : Detection of rainstorm pattern in arid regions using MODIS NDVI time series analysis Type de document : Article/Communication Auteurs : Mohamed E. Hereher, Auteur Année de publication : 2021 Article en page(s) : pp 861 - 873 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes descripteurs IGN] Arabie
[Termes descripteurs IGN] bassin hydrographique
[Termes descripteurs IGN] gestion de l'eau
[Termes descripteurs IGN] image MODIS
[Termes descripteurs IGN] Normalized Difference Vegetation Index
[Termes descripteurs IGN] orage
[Termes descripteurs IGN] pluie
[Termes descripteurs IGN] précipitation
[Termes descripteurs IGN] ressources en eau
[Termes descripteurs IGN] série temporelle
[Termes descripteurs IGN] zone arideRésumé : (auteur) The normalized difference vegetation index (NDVI) was used to delineate potential water suppliers west of the Arabian Peninsula. Time series NDVI data extracted from the moderate resolution imaging spectroradiometer NDVI product were used to develop a robust estimate of rainstorm frequency and intensity. A total of 216 NDVI images were acquired between February 2000 and January 2018 to carry out this investigation. As NDVI values of negative records correspond to water, it was possible to address and delineate the occurrence and duration of temporal ponded water. Results showed that at least 7 locations are potential to harvest water from flashfloods. Some locations witnessed 10, 11 and 13 rainstorms and ponding of water ranged from 1 to 20 months. These locations, if properly managed, could sustain a fresh water resource for local uses. The study demonstrates that NDVI time series curves could help identify the time/duration of previous rainstorms. Numéro de notice : A2021-306 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1629643 date de publication en ligne : 19/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1629643 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97433
in Geocarto international > vol 36 n° 8 [15/04/2021] . - pp 861 - 873[article]Electrical resistivity, remote sensing and geographic information system approach for mapping groundwater potential zones in coastal aquifers of Gurpur watershed / H.S. Virupaksha in Geocarto international, vol 36 n° 8 ([15/04/2021])
PermalinkCloud detection from paired CrIS water vapor and CO₂ channels using machine learning techniques / Miao Tian in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)
PermalinkA geographic information-driven method and a new large scale dataset for remote sensing cloud/snow detection / Xi Wu in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)
PermalinkPrecipitable water vapor fusion based on a generalized regression neural network / Bao Zhang in Journal of geodesy, vol 95 n° 4 (April 2021)
PermalinkTime-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine / Dong Liang in Remote sensing of environment, Vol 256 (April 2020)
PermalinkThe influence of urban form on the spatiotemporal variations in land surface temperature in an arid coastal city / Irshad Mir Parvez in Geocarto international, vol 36 n° 6 ([30/03/2021])
PermalinkApplication of fuzzy analytical hierarchy process for assessment of desertification sensitive areas in North West of Morocco / Hicham Ait Kacem in Geocarto international, vol 36 n° 5 ([15/03/2021])
PermalinkApplication of thermal imaging and hyperspectral remote sensing for crop water deficit stress monitoring / Gopal Krishna in Geocarto international, vol 36 n° 5 ([15/03/2021])
PermalinkAre pine-oak mixed stands in Mediterranean mountains more resilient to drought than their monospecific counterparts? / Francisco J. Muñoz-Gálvez in Forest ecology and management, vol 484 ([15/03/2021])
PermalinkTerrestrial laser scanning intensity captures diurnal variation in leaf water potential / S. Junttila in Remote sensing of environment, Vol 255 (March 2021)
PermalinkDevelopment and assessment of rainwater harvesting suitability map using analytical hierarchy process, GIS and RS techniques / Khaled S. Balkhair in Geocarto international, vol 36 n° 4 ([01/03/2021])
PermalinkImpact of atmospheric correction on spatial heterogeneity relations between land surface temperature and biophysical compositions / Xin-Ming Zhu in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)
PermalinkIntegrity investigation of global ionospheric TEC maps for high-precision positioning / Jiaojiao Zhao in Journal of geodesy, vol 95 n° 3 (March 2021)
PermalinkModélisation des délais ionosphériques appliquée au traitement PPP-RTK centimétrique avec ambiguïtés entières de phase / Camille Parra in XYZ, n° 166 (mars 2021)
PermalinkA multi-criteria analysis of forest restoration strategies to improve the ecosystem services supply: an application in Central Italy / Alessandro Paletto in Annals of Forest Science [en ligne], vol 78 n° 1 (March 2021)
PermalinkON GLONASS pseudo-range inter-frequency bias solution with ionospheric delay modeling and the undifferenced uncombined PPP / Zheng Zhang in Journal of geodesy, vol 95 n° 3 (March 2021)
PermalinkOn the polarimetric variable improvement via alignment of subarray channels in PPAR using weather returns / Igor R. Ivić in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)
PermalinkRadar measurements of snow depth over sea ice on an unmanned aerial vehicle / Adrian Eng-Choon Tan in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)
PermalinkRecent increase in European forest harvests as based on area estimates (Ceccherini et al. 2020a) not confirmed in the French case / Nicolas Picard in Annals of Forest Science [en ligne], vol 78 n° 1 (March 2021)
PermalinkAssessing spatial-temporal evolution processes and driving forces of karst rocky desertification / Fei Chen in Geocarto international, vol 36 n° 3 ([15/02/2021])
PermalinkIntegrating runoff map of a spatially distributed model and thematic layers for identifying potential rainwater harvesting suitability sites using GIS techniques / Hamid Karimi in Geocarto international, vol 36 n° 3 ([15/02/2021])
PermalinkAssessment of mass-induced sea level variability in the Tropical Indian Ocean based on GRACE and altimeter observations / Shiva Shankar Manche in Journal of geodesy, vol 95 n° 2 (February 2021)
PermalinkG-band radar for humidity and cloud remote sensing / Ken B. Cooper in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)
PermalinkIWV retrieval from ground GNSS receivers during NAWDEX / Pierre Bosser in Advances in geosciences, vol 55 ([01/02/2021])
PermalinkLong-term tree species population dynamics in Swiss forest reserves influenced by forest structure and climate / A.S. Mathys in Forest ecology and management, vol 481 (February 2021)
PermalinkPure and even-aged forestry of fast growing conifers under climate change: on the need of a silvicultural paradigm shift / Clémentine Ols in Environmental Research Letters, vol 16 n° 2 (February 2021)
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PermalinkReceiver DCB analysis and calibration in geomagnetic storm-time using IGS products / Jianfeng Li in Survey review, Vol 53 n° 377 (February 2021)
PermalinkA regional spatiotemporal analysis of large magnitude snow avalanches using tree rings / Erich Peitzsch in Natural Hazards and Earth System Sciences, Vol 21 n° 2 (February 2021)
PermalinkWeb‐based real‐time visualization of large‐scale weather radar data using 3D tiles / Mingyue Lu in Transactions in GIS, Vol 25 n° 1 (February 2021)
PermalinkComparing the performance of turbulent kinetic energy and K-profile parameterization vertical parameterization schemes over the tropical indian ocean / Lokesh Kumar Pandey in Marine geodesy, vol 44 n° 1 (January 2021)
PermalinkCopula-based modeling of dependence structure in geodesy and GNSS applications: case study for zenith tropospheric delay in complex terrain / Roya Mousavian in GPS solutions, vol 25 n° 1 (January 2021)
PermalinkDrought propagation and its impact on groundwater hydrology of wetlands: a case study on the Doode Bemde nature reserve (Belgium) / Buruk Kitachew Wossenyeleh in Natural Hazards and Earth System Sciences, vol 21 n° 1 (January 2021)
PermalinkDynamic mechanism of blown sand hazard formation at the Jieqiong section of the Lhasa–Shigatse railway / Shengbo Xie in Geomatics, Natural Hazards and Risk, vol 12 n° 1 (2021)
PermalinkESA UGI (Unified-GNSS-Ionosphere): An open-source software to compute precise ionosphere estimates / Raül Orús-Pérez in Advances in space research, vol 67 n° 1 (January 2021)
PermalinkPermalinkGeospatial analysis of September, 2019 floods in the lower gangetic plains of Bihar using multi-temporal satellites and river gauge data / C.M. Bhatt in Geomatics, Natural Hazards and Risk, vol 12 n° 1 (2021)
PermalinkGLONASS FDMA data for RTK positioning: a five-system analysis / Andreas Brack in GPS solutions, vol 25 n° 1 (January 2021)
PermalinkIntegrated water vapour content retrievals from ship-borne GNSS receivers during EUREC4A / Pierre Bosser in Earth System Science Data, vol 13 n° inconnu ([01/01/2021])
PermalinkIWV 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° inconnu ([01/01/2021])
PermalinkModelling landslide hazards under global changes: the case of a Pyrenean valley / Séverine Bernardie in Natural Hazards and Earth System Sciences, vol 21 n° 1 (January 2021)
PermalinkA new segmentation method for the homogenisation of GNSS-derived IWV time-series / Annarosa Quarello in Journal of the Royal Statistical Society: Series C Applied Statistics, vol inconnu ([01/01/2021])
PermalinkNorway spruce seedlings from an Eastern Baltic provenance show tolerance to simulated drought / Roberts Matisons in Forests, vol 12 n° 1 (January 2021)
PermalinkSeeing the trees in the world’s forests: An extension of the forest transition concept / Jean-Daniel Bontemps (2021)
PermalinkThe Loop Effect: how climate change impacts the mitigation potential of the French forest sector / Philippe Delacote in Journal of Forest Economics, vol 36 n° inconnu ([01/01/2021])
PermalinkTopographic, edaphic and climate influences on aspen (Populus tremuloides) drought stress on an intermountain bunchgrass prairie / Andrew Neary in Forest ecology and management, vol 479 ([01/01/2021])
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