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scientifique

Combination of Terrestrial Laser Scanning and Unmanned Aerial Vehicle Photogrammetry for Heritage Building Information Modeling: A Case Study of Tarsus St. Paul Church / Şafak Fidan in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 12 (December 2023)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 89 n° 12 (December 2023) . - pp 753 - 760 Titre : Combination of Terrestrial Laser Scanning and Unmanned Aerial Vehicle Photogrammetry for Heritage Building Information Modeling: A Case Study of Tarsus St. Paul Church Type de document : Article/Communication Auteurs : Şafak Fidan, Auteur ; Ulvi Ali, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 753 - 760 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] église [Termes IGN] image captée par drone [Termes IGN] modèle numérique [Termes IGN] patrimoine archéologique [Termes IGN] patrimoine immobilier Résumé : (auteur) Cultural heritage building information modeling (HBIM) is an emerging process allowing us to reconstruct built heritage virtually. The data of a digitally documented cultural heritage building offers significant advantages as it is accessible and modifiable by all professionals involved in the same or different projects. The most important factor affecting the accuracy and precision of the HBIM model is the ability to collect complete and accurate information about the physical structure. Combining terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) photogrammetry point clouds is one of the most efficient ways to capture accurate digital data on the building. This study provides the foundation for creating an HBIM model for cultural heritage the coupling of spatial data with TLS and UAV. This paper aims to generate synergy between TLS and UAV point cloud data and ensure that the spatial database contains sufficient data to model historical objects with HBIM tendencies. Numéro de notice : A2023-238 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.23-00031R2 En ligne : https://doi.org/10.14358/PERS.23-00031R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103599 [article]

Habitats, agricultural practices, and population dynamics of a threatened species: The European turtle dove in France / Christophe Sauser in Biological Conservation, vol 274 (octobre 2022)  

Public [article]  inBiological Conservation > vol 274 (octobre 2022) . - n° 109730 Titre : Habitats, agricultural practices, and population dynamics of a threatened species: The European turtle dove in France Type de document : Article/Communication Auteurs : Christophe Sauser, Auteur ; Loïc Commagnac , Auteur ; Cyril Eraud, Auteur ; et al., Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : n° 109730 Note générale : bibliographie Addendum : "The authors add: This study was partly funded and forms part of OFB's contribution to the European Commission contract ENV.D.3/SER /2019/0021 “Development of a population model and adaptive harvest mechanism for Turtle Dove (Streptopelia turtur)”. The authors would like to apologise for any inconvenience caused." Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] agronomie [Termes IGN] analyse diachronique [Termes IGN] Aves [Termes IGN] habitat animal [Termes IGN] haie [Termes IGN] impact sur l'environnement [Termes IGN] jachère [Termes IGN] lisière [Termes IGN] modèle numérique [Termes IGN] politique de conservation (biodiversité) [Termes IGN] R (langage) Mots-clés libres : tourterelle des bois  Streptopelia turtur Résumé : (auteur) Agricultural changes in recent decades have led to a widespread loss of biodiversity, with habitat loss considered as the main factor in the decline. The European turtle dove is one of the farmland birds that has declined markedly in Europe, leading the IUCN to downgrade its status in 2015 from “Near Threatened” to “Vulnerable”. Knowledge of how habitat factors and agricultural practices influence the turtle dove population is crucial for the conservation of this species through the implementation of targeted measures. Here we investigate how foraging and nesting habitats influence the abundance of turtle doves at national and regional scales, using a 23-year dataset from point counts carried out throughout France, a stronghold country for this species during the breeding season. We found that turtle dove abondance was positively affected by fallow lands, both at national and regional scales. Turtle dove abundance was also negatively affected by fodder crop area at national scale, but the effect was detected in only four of the 13 French regions. We also showed that an increase in hedgerows length had a positive effect on turtle dove abundance. On the other hand, forest edges length showed a bell-shaped trend, suggesting that an increase in forest edges length may have a favourable effect on turtle dove abundance only up to a given threshold. We suggest that targeted conservation measures combining an increase in fallow lands and hedgerows length could allow the stabilisation or even an increase in turtle dove abundance in France, but also in European countries with similar landscapes. Numéro de notice : A2022-687 Affiliation des auteurs : IGN+Ext (2020- ) Autre URL associée : Addendum Thématique : BIODIVERSITE/GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.biocon.2022.109730 Date de publication en ligne : 09/09/2022 En ligne : https://doi.org/10.1016/j.biocon.2022.109730 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101612 [article]

Mapping forest site quality at national level / Ana Aguirre in Forest ecology and management, vol 508 (March-15 2022)  

Public [article]  inForest ecology and management > vol 508 (March-15 2022) . - n° 120043 Titre : Mapping forest site quality at national level Type de document : Article/Communication Auteurs : Ana Aguirre, Auteur ; Daniel Moreno-Fernández, Auteur ; Iciar A. Alberdi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120043 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] autocorrélation spatiale [Termes IGN] carte forestière [Termes IGN] climat local [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] Espagne [Termes IGN] gestion forestière durable [Termes IGN] inventaire forestier étranger (données) [Termes IGN] krigeage [Termes IGN] modèle numérique [Termes IGN] sécheresse [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Determining site quality is essential in order to develop sustainable forest management, allowing more appropriate silvicultural decisions to be made. However, most studies carried out in Spain have focused on a few species and at local scale, which makes it difficult to apply the findings or conduct studies at larger scales. The aim of this study is to obtain a site quality map at national scale for the main forest species (Pinus sylvestris, Pinus uncinata, Pinus pinea, Pinus halepensis, Pinus nigra, Pinus pinaster, Pinus canariensis, Pinus radiata, Abies alba, Juniperus thurifera, Quercus robur, Querus petraea, Quercus pyrenaica, Quercus faginea, Quercus ilex, Quercus suber, Populus nigra, Eucalyptus globulus, Eucalyptus camaldulensis, Fagus sylvatica, Castanea sativa, Quercus pubescens, Populus × canadensis, Betula alba). National Forest Inventory (NFI) data has been used to develop site quality models using the site form (SF) concept (dominant height- dominant diameter relationship). Universal Kriging techniques have been used to identify both the geographical trend linked to site factors (climatic, soil and physiographic variables) and their spatial autocorrelation to estimate the SF for every species. Finally, the information was interpolated for each tile of the Spanish National Forest Map in which the species considered was present, thus obtaining a SF national map for each species. The results reveal biologically consistent SF models, indicating that both NFI data and SF are suitable for studying site quality at national level. The variables used differ among the species analyzed, altitude being the most important variable for estimating SF models, while aridity and soil variables are less important. The results obtained could provide an important tool for forest managers working at national level with the main forest species in Spain. This methodology could be used for larger areas, such as at European level, and would allow some species to be analyzed at larger scales. Numéro de notice : A2022-161 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.1016/j.foreco.2022.120043 Date de publication en ligne : 25/01/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120043 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99780 [article]

GNSS reflectometry global ocean wind speed using deep learning: Development and assessment of CyGNSSnet / Milad Asgarimehr in Remote sensing of environment, vol 269 (February 2022)  

Public [article]  inRemote sensing of environment > vol 269 (February 2022) . - n° 112801 Titre : GNSS reflectometry global ocean wind speed using deep learning: Development and assessment of CyGNSSnet Type de document : Article/Communication Auteurs : Milad Asgarimehr, Auteur ; Caroline Arnold, Auteur ; Tobias Weigel, Auteur ; Chris Ruf, Auteur ; Jens Wickert, Auteur Année de publication : 2022 Article en page(s) : n° 112801 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] apprentissage profond [Termes IGN] modèle numérique [Termes IGN] réflectométrie par GNSS [Termes IGN] réseau neuronal convolutif [Termes IGN] vent [Termes IGN] vitesse Résumé : (auteur) GNSS Reflectometry (GNSS-R) is a novel remote sensing technique for the monitoring of geophysical parameters using reflected GNSS signals from the Earth's surface. Ocean wind speed monitoring is the main objective of the recently launched Cyclone GNSS (CyGNSS), a GNSS-R constellation of eight microsatellites, launched in late 2016. In this study, the capability of deep learning, especially, for an operational wind speed data derivation from the measured Delay-Doppler Maps (DDMs) is characterized. CyGNSSnet is based on convolutional layers for the feature extraction from bistatic radar cross section (BRCS) DDMs, along with fully connected layers for processing ancillary technical and higher-level input parameters. The best architecture is determined on a validation set and is evaluated over a completely blind dataset from a different time span than that of the training data to validate the generality of the model for operational usage. After a data quality control, CyGNSSnet results in an RMSE of 1.36 m/s leading to a significant improvement by 28% in comparison to the officially operational retrieval algorithm. The RMSE is the lowest among those seen in the literature for any conventional or machine learning-based algorithm. The benefits of the convolutional layers, the advantages and weaknesses of the model are discussed. CyGNSSnet offers efficient processing of GNSS-R measurements for high-quality global ocean winds. Numéro de notice : A2022-079 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1016/j.rse.2021.112801 Date de publication en ligne : 23/11/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112801 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99764 [article]

Machine Learning: The Basics / Alexander Jung (2022)  

Public Titre : Machine Learning: The Basics Type de document : Guide/Manuel Auteurs : Alexander Jung, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2022 Importance : 280 p. Note générale : glossaire arXiv:1805.05052 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage automatique [Termes IGN] apprentissage dirigé [Termes IGN] apprentissage non-dirigé [Termes IGN] apprentissage par renforcement [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] intelligence artificielle [Termes IGN] modèle numérique Résumé : (auteur) Machine learning (ML) has become a commodity in our every-day lives. We routinely ask ML empowered smartphones to suggest lovely food places or to guide us through a strange place. ML methods have also become standard tools in many fields of science and engineering. A plethora of ML applications transform human lives at unprecedented pace and scale. This book portrays ML as the combination of three basic components: data, model and loss. ML methods combine these three components within computationally efficient implementations of the basic scientific principle "trial and error". This principle consists of the continuous adaptation of a hypothesis about a phenomenon that generates data. ML methods use a hypothesis to compute predictions for future events. We believe that thinking about ML as combinations of three components given by data, model, and loss helps to navigate the steadily growing offer for ready-to-use ML methods. Our three-component picture of ML allows a unified treatment of a wide range of concepts and techniques which seem quite unrelated at first sight. The regularization effect of early stopping in iterative methods is due to the shrinking of the effective hypothesis space. Privacy-preserving ML is obtained by particular choices for the features of data points. Explainable ML methods are characterized by particular choices for the hypothesis space. To make good use of ML tools it is instrumental to understand its underlying principles at different levels of detail. On a lower level, this tutorial helps ML engineers to choose suitable methods for the application at hand. The book also offers a higher-level view on the implementation of ML methods which is typically required to manage a team of ML engineers and data scientists. Numéro de notice : 17721 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/INFORMATIQUE Nature : Manuel de cours DOI : sans En ligne : https://arxiv.org/abs/1805.05052 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100081

Metamorphic transformation rate over large spatial and temporal scales constrained by geophysical data and coupled modelling / Gyorgy Hetényl in Journal of metamorphic geology, vol 39 n° 9 (December 2021)  

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The Loop Effect: how climate change impacts the mitigation potential of the French forest sector / Philippe Delacote in Journal of Forest Economics, vol 36 n° 3 ([01/06/2021])  

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Estimation et cartographie d’attributs forestiers haute résolution : Le potentiel des approches multisource / Cédric Vega (2021) 

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Modélisation numérique des paysages sonores urbains / Jonathan Siliézar (2021)

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Seasonal flow variability of Greenlandic glaciers : satellite observations and numerical modeling to study driving processes / Anna Derkacheva (2021)  

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Generating urban morphologies at large scales / Juste Raimbault (2019)    

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Retour d'expérience de l'école OpenMOLE "ExModelo", organisée en partenariat avec le méso-centre du CRIANN / Mathieu Leclaire (2019)  

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Les systèmes d'information géographique / Christina Aschan-Leygonie (2019)

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A comparison of two methods of data collection for modelling productivity of harvesters: manual time study and follow-up study using on-board-computer stem records / Julia Brewer in Annals of forest research, vol 61 n° 1 (January - June 2018)  

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Airborne laser scanning for tree diameter distribution modelling: a comparison of different modelling alternatives in a tropical single-species plantation / Matti Maltamo in Forestry, an international journal of forest research, vol 91 n° 1 (January 2018)  

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