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In situ C-band data for wheat physiological functioning monitoring in the South Mediterranean region / Nadia Ouaadi (2022)
Titre : In situ C-band data for wheat physiological functioning monitoring in the South Mediterranean region Type de document : Article/Communication Auteurs : Nadia Ouaadi, Auteur ; Ludovic Villard, Auteur ; Saïd Khabba, Auteur ; Pierre-Louis Frison , Auteur ; Jamal Ezzahar, Auteur ; Mohamed Kasbani, Auteur ; Adnane Chakir , Auteur ; Pascal Fanise, Auteur ; Valérie Le Dantec, Auteur ; Mehrez Zribi, Auteur ; Salah Er-Raki, Auteur ; Lionel Jarlan, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2022 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : IGARSS 2022, IEEE International Geoscience And Remote Sensing Symposium 17/07/2022 22/07/2022 Kuala Lumpur Malaysie Proceedings IEEE Importance : pp 4951 - 4954 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande C
[Termes IGN] blé (céréale)
[Termes IGN] coefficient de rétrodiffusion
[Termes IGN] cohérence photométrique
[Termes IGN] variation diurneRésumé : (auteur) Irrigated agriculture is the largest consumer of freshwater in the world, particularly in the South Mediterranean region that is already suffering from water shortages. Monitoring the water stress status of plants can contribute to an optimal use of irrigation. C-band radar data have shown great potential for monitoring soil and vegetation hydric conditions. While a diurnal cycle up to 1 dB has been observed over tropical forests, the behavior of annual crops is yet to be investigated. In this context, an experiment composed of a radar setup with 6 C-band antennas was installed in Morocco over a wheat field. 15 minutes full polarization acquisitions of the backscattering coefficient and the interferometric coherence are analyzed in relation with the physiological functioning of wheat. In this paper, the first results from the analysis of data collected during the 2020 growing season are presented. The results reveal the existence of a diurnal cycle of the interferometric coherence and the backscattering coefficient (up to 0.45 and 1.5 dB, respectively) with amplitudes increase in relation with vegetation development. Numéro de notice : C2022-041 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS46834.2022.9884289 Date de publication en ligne : 28/09/2022 En ligne : https://doi.org/10.1109/IGARSS46834.2022.9884289 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101769 Increasing and widespread vulnerability of intact tropical rainforests to repeated droughts / Shengli Tao in Proceedings of the National Academy of Sciences of the United States of America PNAS, vol 119 n° 37 (2022)
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Titre : Increasing and widespread vulnerability of intact tropical rainforests to repeated droughts Type de document : Article/Communication Auteurs : Shengli Tao, Auteur ; Jérôme Chave, Auteur ; Pierre-Louis Frison , Auteur ; et al., Auteur Année de publication : 2022 Projets : 3-projet - voir note / Article en page(s) : n° e2116626119 Note générale : bibliographie
This study was supported by an Investissement d’Avenir grant managed by the Agence Nationale de la Recherche (CEBA, ref. ANR-10-LABX-25-01; TULIP, ref. ANR-10-LABX-0041; ANAEE-France: ANR-11-INBS-0001), and by the National Natural Science Foundation of China (grant no. 31988102). This research was also supported by a Centre National d' Etudes Spatiales (CNES) postdoctoral fellowship to S.T., the CNES-BIOMASS pluriannual project, and the European Space Agency (ESA) Climate Change Initiative (CCI) Biomass project (contract no. 4000123662/18/I-NB).Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] forêt tropicale
[Termes IGN] image radar
[Termes IGN] sécheresse
[Termes IGN] série temporelle
[Termes IGN] vulnérabilitéRésumé : (auteur) Intact tropical rainforests have been exposed to severe droughts in recent decades, which may threaten their integrity, their ability to sequester carbon, and their capacity to provide shelter for biodiversity. However, their response to droughts remains uncertain due to limited high-quality, long-term observations covering extensive areas. Here, we examined how the upper canopy of intact tropical rainforests has responded to drought events globally and during the past 3 decades. By developing a long pantropical time series (1992 to 2018) of monthly radar satellite observations, we show that repeated droughts caused a sustained decline in radar signal in 93%, 84%, and 88% of intact tropical rainforests in the Americas, Africa, and Asia, respectively. Sudden decreases in radar signal were detected around the 1997–1998, 2005, 2010, and 2015 droughts in tropical Americas; 1999–2000, 2004–2005, 2010–2011, and 2015 droughts in tropical Africa; and 1997–1998, 2006, and 2015 droughts in tropical Asia. Rainforests showed similar low resistance (the ability to maintain predrought condition when drought occurs) to severe droughts across continents, but American rainforests consistently showed the lowest resilience (the ability to return to predrought condition after the drought event). Moreover, while the resistance of intact tropical rainforests to drought is decreasing, albeit weakly in tropical Africa and Asia, forest resilience has not increased significantly. Our results therefore suggest the capacity of intact rainforests to withstand future droughts is limited. This has negative implications for climate change mitigation through forest-based climate solutions and the associated pledges made by countries under the Paris Agreement. Numéro de notice : A2022-681 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1073/pnas.2116626119 En ligne : https://doi.org/10.1073/pnas.2116626119 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101538
in Proceedings of the National Academy of Sciences of the United States of America PNAS > vol 119 n° 37 (2022) . - n° e2116626119[article]Integration of radar and optical Sentinel images for land use mapping in a complex landscape (case study: Arasbaran Protected Area) / Vahid Nasiri in Arabian Journal of Geosciences, vol 15 n° 24 (December 2022)
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Titre : Integration of radar and optical Sentinel images for land use mapping in a complex landscape (case study: Arasbaran Protected Area) Type de document : Article/Communication Auteurs : Vahid Nasiri, Auteur ; Arnaud Le Bris , Auteur ; Ali Asghar Darvishsefat, Auteur ; Fardin Moradi, Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : n° 1759 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] aire protégée
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SARRésumé : (auteur) Considering the importance of accurate and up-to-date land use/cover (LULC) maps and in a situation of fast LULC changes, an accurate mapping of complex landscapes requires real-time high-resolution remote sensed data and powerful classification algorithms. The new ESA Copernicus satellites Sentinel-1 (S-1) and Sentinel-2 (S-2) have contributed to the effective monitoring of the Earth’s surface. This paper aims at assessing the potential of mono-temporal S-1 and S-2 satellite images and three common classification algorithms including maximum likelihood (ML), support vector machine (SVM), and random forest (RF) for LULC classification. The research methodology consists of a sequence of tasks including data collection and preprocessing, the extraction of texture and spectral features, the definition of several feature set configurations, classification, and accuracy assessment. Based on the results, using S-1 data alone leads to quite poor results, even though dual polarimetric C-band and texture features increased the classification accuracy. The S-2 data outperformed the S-1 data in terms of overall and class level accuracies. A combined use of S-1 and S-2 satellite images involving extracted features from both sources led to the best result for identifying all classes. This emphasizes the critical importance of using multi-modal datasets and different features in the LULC classification. Among classification algorithms, the SVM led to the highest accuracies irrespective of the dataset. To sum it up, according to the applied methodology and results, S-1 and S-2 data can provide optimal and up-to-date information for LULC mapping using non-parametric classifiers as SVM or RF. Numéro de notice : A2022-699 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12517-022-11035-z Date de publication en ligne : 07/12/2022 En ligne : https://doi.org/10.1007/s12517-022-11035-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102253
in Arabian Journal of Geosciences > vol 15 n° 24 (December 2022) . - n° 1759[article]
Titre : Is map generalisation a computational cartography problem? Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2022 Projets : LostInZoom / Touya, Guillaume Conférence : CompCarto 2022, 1st workshop on Computational Cartography 19/05/2022 20/05/2022 Bonn Allemagne programme Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] géomètrie algorithmique
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Map generalisation is one of the processes of map design, when the spatial data used to make the map is too detailed for the scale of the map. Map generalisation seeks to abstract and simplify this detailed spatial information to represent it on the map with a good legibility. Map generalisation involves different types of transformations: selection, simplification, displacement, aggregation, collapse, etc. Historically, computational geometry researchers greatly contributed to the automation of map generalisation by proposing algorithms to achieve some of these atomic transformations. But the research questions addressed by researchers on map generalisation have changed over the years, with the progress on the automation, and the changes in the way people use maps (apparition of multi-scale zoomable web maps). Is map generalisation still a computational cartography problem? To discuss this rhetorical question, this presentation illustrates the current issues of map generalisation, with past contribution of computational cartography and possible computational cartography problems. For instance, there are still needs for new specific algorithms, either because maps are much more diverse, or because the larger scale range calls for new transformations. As maps tend to mix elements of topographic maps and elements of schematized maps, computational cartography is also relevant to solve this task. Finally, as the current trend in map generalisation is the use of deep learning models, the presentation discusses the relevance of computational cartography techniques in this context. Numéro de notice : C2022-028 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Conférence invitée nature-HAL : Conf-Invitée DOI : sans En ligne : https://hal.science/hal-03677309v1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100959 Landslide evolution pattern revealed by multi-temporal DSMs obtained from historical aerial images / Michele Santangelo (2022)
Titre : Landslide evolution pattern revealed by multi-temporal DSMs obtained from historical aerial images Type de document : Article/Communication Auteurs : Michele Santangelo, Auteur ; Lulin Zhang , Auteur ; Ewelina Rupnik , Auteur ; Marc Pierrot-Deseilligny , Auteur ; Mauro Cardinali, Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2022 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B2 Projets : 3-projet - voir note / Touya, Guillaume Conférence : ISPRS 2022, Commission 2, 24th ISPRS international congress, Imaging today, foreseeing tomorrow 06/06/2022 11/06/2022 Nice France OA ISPRS Archives Importance : pp 1085 - 1092 Format : 21 x 30 cm Note générale : bibliographie
The research is supported by the Civil Protection of the Apulia region, in the framework of the project ‘Integrated assessment of geo-hydrological instability phenomena in the Apulia region, interpretative models and definition of rainfall thresholds for landslide triggering’ funded by the P.O.R. Puglia 2014-2020, Asse V - Azione 5.1. [Project identification number: B82F16003840006]Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] chaîne de traitement
[Termes IGN] effondrement de terrain
[Termes IGN] image ancienne
[Termes IGN] MicMac
[Termes IGN] modèle numérique de surface
[Termes IGN] photographie aérienne à axe vertical
[Termes IGN] Pouilles (Italie)Résumé : (auteur) Numéro de notice : C2022-017 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B2-2022-1085-2022 Date de publication en ligne : 30/05/2022 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-1085-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100843 Learning multi-view aggregation in the wild for large-scale 3D semantic segmentation / Damien Robert (2022)PermalinkLearning spatio-temporal representations of satellite time series for large-scale crop mapping / Vivien Sainte Fare Garnot (2022)PermalinkPermalinkLocation-enabled digital twins – understanding the role of NMCAs in a European context / Claire Ellul in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol X-4/W2 (October 2022)PermalinkPermalinkMachine learning models applied to a GNSS sensor network for automated bridge anomaly detection / Nicolas Manzini in Journal of structural engineering, Vol 148 n° 11 (November 2022)PermalinkA method to produce metadata describing and assessing the quality of spatial landmark datasets in mountain area / Marie-Dominique Van Damme (2022)PermalinkPermalinkPermalinkMonitoring grassland dynamics by exploiting multi-modal satellite image time series / Anatol Garioud (2022)Permalink