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La puissance spatiale chinoise s’affirme / Laurent Polidori in Géomètre, n° 2203 (juin 2022)

Public [article]  inGéomètre > n° 2203 (juin 2022) . - pp 25 - 25 Titre : La puissance spatiale chinoise s’affirme Type de document : Article/Communication Auteurs : Laurent Polidori, Auteur Année de publication : 2022 Article en page(s) : pp 25 - 25 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Technologies spatiales [Termes IGN] BeiDou [Termes IGN] débris spatial [Termes IGN] Gaofen [Termes IGN] Lune [Termes IGN] Mars (planète) [Termes IGN] observation de la Terre [Termes IGN] programme spatial [Termes IGN] radar à antenne synthétique Résumé : (Auteur) Malgré un démarrage tardif, le programme spatial chinois est désormais présent sur tous les fronts, jusqu’à l’exploration martienne. Numéro de notice : A2022-520 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101054 [article]

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The number of tree species on Earth / Roberto Cazzolla Gatti in Proceedings of the National Academy of Sciences of the United States of America PNAS, vol 119 n° 6 (2022)  

Public [article]  inProceedings of the National Academy of Sciences of the United States of America PNAS > vol 119 n° 6 (2022) . - n° e2115329119 Titre : The number of tree species on Earth Type de document : Article/Communication Auteurs : Roberto Cazzolla Gatti, Auteur ; Peter B. Reich, Auteur ; Javier G. P. Gamarra, Auteur ; et al., Auteur ; Olivier Bouriaud , Auteur Année de publication : 2022 Article en page(s) : n° e2115329119 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] distribution spatiale [Termes IGN] essence d'arbre [Termes IGN] forêt [Termes IGN] richesse floristique [Termes IGN] Terre (planète) [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) One of the most fundamental questions in ecology is how many species inhabit the Earth. However, due to massive logistical and financial challenges and taxonomic difficulties connected to the species concept definition, the global numbers of species, including those of important and well-studied life forms such as trees, still remain largely unknown. Here, based on global ground-sourced data, we estimate the total tree species richness at global, continental, and biome levels. Our results indicate that there are ∼73,000 tree species globally, among which ∼9,000 tree species are yet to be discovered. Roughly 40% of undiscovered tree species are in South America. Moreover, almost one-third of all tree species to be discovered may be rare, with very low populations and limited spatial distribution (likely in remote tropical lowlands and mountains). These findings highlight the vulnerability of global forest biodiversity to anthropogenic changes in land use and climate, which disproportionately threaten rare species and thus, global tree richness. Numéro de notice : A2022-155 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article DOI : 10.1073/pnas.2115329119 Date de publication en ligne : 31/01/2022 En ligne : https://doi.org/10.1073/pnas.2115329119 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100309 [article]

Voir aussi

• Correction for Cazzolla Gatti et al., The number of tree species on Earth / Roberto Cazzolla Gatti in Proceedings of the National Academy of Sciences of the United States of America PNAS, vol 119 n° 13 (2022)  

MLMT-CNN for object detection and segmentation in multi-layer and multi-spectral images / Majedaldein Almahasneh in Machine Vision and Applications, vol 33 n° 1 (January 2022)  

Public [article]  inMachine Vision and Applications > vol 33 n° 1 (January 2022) . - n° 9 Titre : MLMT-CNN for object detection and segmentation in multi-layer and multi-spectral images Type de document : Article/Communication Auteurs : Majedaldein Almahasneh, Auteur ; Adeline Paiement, Auteur ; Xianghua Xie, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage dirigé [Termes IGN] apprentissage profond [Termes IGN] atmosphère solaire [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] couche thématique [Termes IGN] détection d'objet [Termes IGN] image multibande [Termes IGN] segmentation d'image Résumé : (auteur) Precisely localising solar Active Regions (AR) from multi-spectral images is a challenging but important task in understanding solar activity and its influence on space weather. A main challenge comes from each modality capturing a different location of the 3D objects, as opposed to typical multi-spectral imaging scenarios where all image bands observe the same scene. Thus, we refer to this special multi-spectral scenario as multi-layer. We present a multi-task deep learning framework that exploits the dependencies between image bands to produce 3D AR localisation (segmentation and detection) where different image bands (and physical locations) have their own set of results. Furthermore, to address the difficulty of producing dense AR annotations for training supervised machine learning (ML) algorithms, we adapt a training strategy based on weak labels (i.e. bounding boxes) in a recursive manner. We compare our detection and segmentation stages against baseline approaches for solar image analysis (multi-channel coronal hole detection, SPOCA for ARs) and state-of-the-art deep learning methods (Faster RCNN, U-Net). Additionally, both detection and segmentation stages are quantitatively validated on artificially created data of similar spatial configurations made from annotated multi-modal magnetic resonance images. Our framework achieves an average of 0.72 IoU (segmentation) and 0.90 F1 score (detection) across all modalities, comparing to the best performing baseline methods with scores of 0.53 and 0.58, respectively, on the artificial dataset, and 0.84 F1 score in the AR detection task comparing to baseline of 0.82 F1 score. Our segmentation results are qualitatively validated by an expert on real ARs. Numéro de notice : A2022-089 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00138-021-01261-y Date de publication en ligne : 29/11/2021 En ligne : https://doi.org/10.1007/s00138-021-01261-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99500 [article]

Automatic atmospheric correction for shortwave hyperspectral remote sensing data using a time-dependent deep neural network / Jian Sun in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 174 (April 2021) . - pp 117 - 131 Titre : Automatic atmospheric correction for shortwave hyperspectral remote sensing data using a time-dependent deep neural network Type de document : Article/Communication Auteurs : Jian Sun, Auteur ; Fangcao Xu, Auteur ; Guido Cervone, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 117 - 131 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] correction atmosphérique [Termes IGN] détection de cible [Termes IGN] image hyperspectrale [Termes IGN] modèle de transfert radiatif [Termes IGN] rayonnement solaire [Termes IGN] réflectivité Résumé : (auteur) Atmospheric correction is an essential step in hyperspectral imaging and target detection from spectrometer remote sensing data. State-of-the-art atmospheric correction approaches either require extensive filed experiments or prior knowledge of atmospheric characteristics to improve the predicted accuracy, which are computational expensive and unsuitable for real time application. To take full advantages of remote sensing observation in quickly and reliably acquiring data for a large area, an automatic and efficient processing tool is required for atmospheric correction. In this paper, we propose a time-dependent neural network for automatic atmospheric correction and target detection using multi-scan hyperspectral data under different elevation angles. In addition to the total radiance, the collection day and time are also incorporated to improve the time-dependency of the network and represent the seasonal and diurnal characteristics of atmosphere and solar radiation. Results show that the proposed network has the capacity to accurately provide atmospheric characteristics and estimate precise reflectivity spectra with 95,72% averaged accuracy for different materials, including vegetation, sea ice, and ocean. Additional experiments are designed to investigate the network’s temporal dependency and performance on missing data. The error analysis confirms that our proposed network is capable of estimating atmospheric characteristics under both seasonally and diurnally varying environments and handling the influence of missing data. Both the predicted results and error analysis are promising and demonstrate that our network has the ability of providing accurate atmospheric correction and target detection in real time. Numéro de notice : A2021-208 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.02.007 Date de publication en ligne : 24/02/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.02.007 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97186 [article]

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081-2021042	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Assessment of renewable energy resources with remote sensing / Fernando Ramos Martins (2021)  

Public Titre : Assessment of renewable energy resources with remote sensing Type de document : Monographie Auteurs : Fernando Ramos Martins, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 244 p. Format : 16 x 23 cm ISBN/ISSN/EAN : 978-3-0365-0481-0 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] climat [Termes IGN] détection des nuages [Termes IGN] données lidar [Termes IGN] énergie éolienne [Termes IGN] énergie géothermique [Termes IGN] énergie renouvelable [Termes IGN] énergie solaire [Termes IGN] Extreme Gradient Machine [Termes IGN] hydroélectricité [Termes IGN] image GOES [Termes IGN] Matlab [Termes IGN] prévision météorologique [Termes IGN] rayonnement solaire [Termes IGN] réseau neuronal artificiel Résumé : (éditeur) The book “Assessment of Renewable Energy Resources with Remote Sensing" focuses on disseminating scientific knowledge and technological developments for the assessment and forecasting of renewable energy resources using remote sensing techniques. The eleven papers inside the book provide an overview of remote sensing applications on hydro, solar, wind and geothermal energy resources and their major goal is to provide state of art knowledge to contribute with the renewable energy resource deployment, especially in regions where energy demand is rapidly expanding. Renewable energy resources have an intrinsic relationship with local environmental features and the regional climate. Even small and fast environment and/or climate changes can cause significant variability in power generation at different time and space scales. Methodologies based on remote sensing are the primary source of information for the development of numerical models that aim to support the planning and operation of an electric system with a substantial contribution of intermittent energy sources. In addition, reliable data and knowledge on renewable energy resource assessment are fundamental to ensure sustainable expansion considering environmental, financial and energetic security. Note de contenu : 1- Enhancement of cloudless skies frequency over a large tropical reservoir in Brazil 2- On the land-sea contrast in the surface solar radiation (SSR) in the Baltic region 3- Real-time automatic cloud detection using a low-cost sky camera 4- Attenuation factor estimation of direct normal irradiance combining sky camera images and mathematical models in an inter-tropical area 5- Multistep-ahead solar radiation forecasting scheme based on the light gradient boosting machine: A case study of Jeju Island 6- Modified search strategies assisted crossover whale optimization algorithm with selection operator for parameter extraction of solar photovoltaic models 7- Industry experience of developing day-ahead photovoltaic plant forecasting system based on machine learning 8- The global wind resource observed by scatterometer 9- Coastal wind measurements using a single scanning LiDAR 10- Characterizing geological heterogeneities for geothermal purposes through combined geophysical prospecting methods 11- A computational workflow for generating a voxel-based design approach based on subtractive shading envelopes and attribute information of point cloud data Numéro de notice : 28653 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-0365-0481-0 En ligne : https://doi.org/10.3390/books978-3-0365-0481-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99795

Determination of the lunar body tide from global laser altimetry data / Robin N. Thor in Journal of geodesy, vol 95 n° 1 (January 2021)  

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Crater detection and registration of planetary images through marked point processes, multiscale decomposition, and region-based analysis / David Solarna in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

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Novel communication channel model for signal propagation and loss through layered earth / David O. LeVan in IEEE Transactions on geoscience and remote sensing, vol 58 n° 8 (August 2020)  

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Galileo and QZSS precise orbit and clock determination using new satellite metadata / Xingxing Li in Journal of geodesy, vol 93 n° 8 (August 2019)  

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Seasonal pattern in time series of variances of GPS residual errors Anova estimates / Darko Anđić in Geodetski vestnik, vol 63 n° 2 (June - August 2019)  

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Temporal and spatial high-resolution climate data from 1961 to 2100 for the German National Forest Inventory (NFI) / Helge Dietrich in Annals of Forest Science [en ligne], vol 76 n° 1 (March 2019)  

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Simultaneous characterization of objects temperature and radiative properties through multispectral infrared thermography / Thibaud Toullier (2019)  

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A method of downscaling temperature maps based on analytical hillshading for use in species distribution modelling / Ángel M. Felicísimo in Cartography and Geographic Information Science, Vol 45 n° 4 (July 2018)  

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Use of LiDAR for calculating solar irradiance on roofs and façades of buildings at city scale: Methodology, validation, and analysis / Liang Cheng in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)  

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Self-shadowing of a spacecraft in the computation of surface forces : An example in planetary geodesy / Georges Balmino in Artificial satellites, vol 53 n° 1 (March 2018)  

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