Descripteur
Termes IGN > imagerie > image spatiale > image satellite > image Sentinel > image Sentinel-MSI
image Sentinel-MSISynonyme(s)image sentinel-2Voir aussi |
Documents disponibles dans cette catégorie (99)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
A repeatable change detection approach to map extreme storm-related damages caused by intense surface runoff based on optical and SAR remote sensing: Evidence from three case studies in the South of France / Arnaud Cerbelaud in ISPRS Journal of photogrammetry and remote sensing, Vol 182 (December 2021)
[article]
Titre : A repeatable change detection approach to map extreme storm-related damages caused by intense surface runoff based on optical and SAR remote sensing: Evidence from three case studies in the South of France Type de document : Article/Communication Auteurs : Arnaud Cerbelaud, Auteur ; Laure Roupioz, Auteur ; Gwendoline Blanchet, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 153 - 175 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] Alpes-maritimes (06)
[Termes IGN] Aude (11)
[Termes IGN] bassin méditerranéen
[Termes IGN] catastrophe naturelle
[Termes IGN] classification dirigée
[Termes IGN] détection de changement
[Termes IGN] dommage matériel
[Termes IGN] image à très haute résolution
[Termes IGN] image optique
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] photo-interprétation
[Termes IGN] ruissellement
[Termes IGN] signature spectrale
[Termes IGN] tempêteRésumé : (auteur) Most flood hazards are induced either by river overflowing or intense overland flow following heavy rainfall, causing land surface damages under many forms. Until now, fine-scale detection of damages caused by intense rainwater runoff beyond the direct vicinity of major waterways has been scarcely explored using satellite remote sensing. In this work, three extreme storms in the Aude and Alpes-Maritimes departments in the South of France were investigated based on ground truths and very high resolution optical imagery (Pléiades satellite, IGN orthophotos). Plot delineation and land use information were combined to high revisit frequency and high resolution optical (Sentinel-2) and SAR (Sentinel-1) open-source data to test a simple automatic and replicable change detection method to locate damaged plots using supervised classification. Based on a unique training sample from the Aude floods of October 2018, combinations of plot-based spectral indicators allowed reaching overall detection accuracies greater than 85% on independent validation samples for all three events. A simple land use inter-class demeaning pre-processing used to account for land-specific seasonal variations improved event and site repeatability by lowering false detection rates down to a maximum of 13%. The benefits of introducing SWIR channel in addition to visible and near-infrared indices were limited to a few percentage points. SAR-derived proxies of soil moisture and roughness in weakly vegetated areas were consistent with the presence of degradations, with VV being the most sensitive polarization. However, classification accuracy was not significantly increased with Sentinel-1 data as compared to the exclusive use of Sentinel-2. Additional tests revealed that should the closest available optical images be rather distant in time because of persistent cloud cover, the method is reasonably robust as long as stable ground conditions were observed before the event. The need for images close in time was however emphasized through cross-site training. Indeed, efficient replicability from one site to another relied on using unaffected learning plots with slightly more inherent variability in time variations of spectral indices compared to the test site. Beyond the investigation of three case studies, this work demonstrates the performance and repeatability potential of a new probabilistic change detection method to expose various kinds of extreme rainfall-related disturbances, in particular those occurring far from the main hydrographic network. Should spatially accurate rainfall products be available, comprehensive mapping of intense stormwater runoff hazards using this original plot-based approach will then allow improving the understanding of overland flow generation mechanisms in hydrological models. Numéro de notice : A2021-852 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.10.013 Date de publication en ligne : 31/10/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.10.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99041
in ISPRS Journal of photogrammetry and remote sensing > Vol 182 (December 2021) . - pp 153 - 175[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2021121 SL Revue Centre de documentation Revues en salle Disponible 081-2021123 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021122 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Superpixel-based regional-scale grassland community classification using genetic programming with Sentinel-1 SAR and Sentinel-2 multispectral images / Zhenjiang Wu in Remote sensing, vol 13 n° 20 (October-2 2021)
[article]
Titre : Superpixel-based regional-scale grassland community classification using genetic programming with Sentinel-1 SAR and Sentinel-2 multispectral images Type de document : Article/Communication Auteurs : Zhenjiang Wu, Auteur ; Jiahua Zhang, Auteur ; Fan Deng, Auteur Année de publication : 2021 Article en page(s) : n° 4067 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] Chine
[Termes IGN] classification par algorithme génétique
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] indice de végétation
[Termes IGN] optimisation (mathématiques)
[Termes IGN] prairie
[Termes IGN] précision de la classification
[Termes IGN] superpixel
[Termes IGN] texture d'imageRésumé : (auteur) Grasslands are one of the most important terrestrial ecosystems on the planet and have significant economic and ecological value. Accurate and rapid discrimination of grassland communities is critical to the conservation and utilization of grassland resources. Previous studies that explored grassland communities were mainly based on field surveys or airborne hyperspectral and high-resolution imagery. Limited by workload and cost, these methods are typically suitable for small areas. Spaceborne mid-resolution RS images (e.g., Sentinel, Landsat) have been widely used for large-scale vegetation observations owing to their large swath width. However, there still keep challenges in accurately distinguishing between different grassland communities using these images because of the strong spectral similarity of different communities and the suboptimal performance of models used for classification. To address this issue, this paper proposed a superpixel-based grassland community classification method using Genetic Programming (GP)-optimized classification model with Sentinel-2 multispectral bands, their derived vegetation indices (VIs) and textural features, and Sentinel-1 Synthetic Aperture Radar (SAR) bands and the derived textural features. The proposed method was evaluated in the Siziwang grassland of China. Our results showed that the addition of VIs and textures, as well as the use of GP-optimized classification models, can significantly contribute to distinguishing grassland communities, and the proposed approach classified the seven communities in Siziwang grassland with an overall accuracy of 84.21% and a kappa coefficient of 0.81. We concluded that the classification method proposed in this paper is capable of distinguishing grassland communities with high accuracy at a regional scale. Numéro de notice : A2021-805 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13204067 Date de publication en ligne : 12/10/2021 En ligne : https://doi.org/10.3390/rs13204067 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98862
in Remote sensing > vol 13 n° 20 (October-2 2021) . - n° 4067[article]Détection des forêts dégradées en Guinée à partir des images satellites Sentinel-2 : évaluation de l'apport potentiel des nouveaux capteurs satellitaires optiques et radars / An Vo Quang in Blog de la RFPT, sans n° ([11/10/2021])
[article]
Titre : Détection des forêts dégradées en Guinée à partir des images satellites Sentinel-2 : évaluation de l'apport potentiel des nouveaux capteurs satellitaires optiques et radars Type de document : Article/Communication Auteurs : An Vo Quang, Auteur Année de publication : 2021 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Afrique occidentale
[Termes IGN] apprentissage profond
[Termes IGN] classification dirigée
[Termes IGN] dégradation de l'environnement
[Termes IGN] dégradation de la flore
[Termes IGN] détection automatique
[Termes IGN] forêt alpestre
[Termes IGN] Guinée
[Termes IGN] image Sentinel-MSI
[Termes IGN] photo-interprétation
[Termes IGN] série temporelleRésumé : (Auteur) [Contexte] Les travaux de la thèse CIFRE ont été réalisés dans le cadre d'un partenariat entre l'Institut interdisciplinaire de recherche en énergie de Paris (LIED) et IGN FI, une société d'ingénierie géographique (partenaire export de l'IGN - Institut national de l’information géographique et forestière) qui réalise des projets sur tous les continents et dans tous les domaines d'application de la géomatique, notamment l'aménagement du territoire, l'environnement, l'agriculture, l'administration foncière ou la gestion des risques. Plus spécifiquement, les travaux de thèse se sont intégrés au projet de Zonage Agro-Ecologique de Guinée (ZAEG) coordonné par IGN FI et financé par l'Agence Française de Développement (AFD) pour le ministère de l’Agriculture de Guinée. Contrairement à la déforestation, la dégradation forestière implique un changement de la structure forestière sans modification de l'utilisation du sol. Ce changement est subtil et moins visible que la déforestation. La dégradation des forêts est une préoccupation majeure car un potentiel de séquestration du carbone est perdu. Ce phénomène varie en fonction de l'emplacement géographique, des facteurs anthropiques, du climat, des types de forêts impactées, donc il n'existe pas de méthodologie de détection unique pour cartographier la dégradation des forêts à l'échelle mondiale. En Guinée, le principal processus de dégradation est l'exploitation forestière sélective dans la forêt de massif, en plus de la fragmentation de la forêt causée par le changement d'utilisation des terres. L’objectif est d’optimiser les méthodes de photo-interprétation utilisées par IGN FI pour détecter les zones de forêt dégradée. Le suivi du couvert forestier à l'aide des méthodes traditionnelles de télédétection nécessite un coût important en termes d'expertise en photo-interprétation. Nous proposons une approche de suivi par une procédure de classification semi-automatisée avec un coût de photo-interprétation minimum en incluant le contexte pixellaire, en intégrant les données du capteur Sentinel-2, acquises de manière répétitive. Numéro de notice : A2021-679 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : sans Date de publication en ligne : 11/10/2021 En ligne : https://rfpt-sfpt.github.io/blog/sentinel-2/s%C3%A9rie%20temporelle/deep%20learn [...] Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99040
in Blog de la RFPT > sans n° [11/10/2021][article]Deep-learning-based burned area mapping using the synergy of Sentinel-1&2 data / Qi Zhang in Remote sensing of environment, vol 264 (October 2021)
[article]
Titre : Deep-learning-based burned area mapping using the synergy of Sentinel-1&2 data Type de document : Article/Communication Auteurs : Qi Zhang, Auteur ; Linlin Ge, Auteur ; Ruiheng Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 112575 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] cartographie thématique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] coefficient de rétrodiffusion
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] incendie
[Termes IGN] réflectance du sol
[Termes IGN] réseau neuronal siamoisRésumé : (auteur) Around 350 million hectares of land are affected by wildfires every year influencing the health of ecosystems and leaving a trail of destruction. Accurate information over burned areas (BA) is essential for governments and communities to prioritize recovery actions. Prior research over the past decades has established the potentials and limitations of space-borne earth observation for mapping BA over large geographic areas at various scales. The operational deployment of Sentinel-1 and Sentinel-2 constellations significantly improved the quality and quantity of the imagery from the microwave (C-band) and optical regions on the spectrum. Based on that, this study set to investigate whether the existing coarse BA products can be further improved by the synergy of optical surface reflectance (SR), radar backscatter coefficient (BS), and/or radar interferometric coherence (COR) data with higher spatial resolutions. A Siamese Self-Attention (SSA) classification strategy is proposed for the multi-sensor BA mapping and a multi-source dataset is constructed at the object level for the training and testing. Results are analyzed by test sites, feature sources, and classification strategies to appraise the improvements achieved by the proposed method. Numéro de notice : A2021-807 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112575 Date de publication en ligne : 06/07/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112575 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98866
in Remote sensing of environment > vol 264 (October 2021) . - n° 112575[article]Field scale wheat LAI retrieval from multispectral Sentinel 2A-MSI and LandSat 8-OLI imagery: effect of atmospheric correction, image resolutions and inversion techniques / Rajkumar Dhakar in Geocarto international, vol 36 n° 18 ([01/10/2021])
[article]
Titre : Field scale wheat LAI retrieval from multispectral Sentinel 2A-MSI and LandSat 8-OLI imagery: effect of atmospheric correction, image resolutions and inversion techniques Type de document : Article/Communication Auteurs : Rajkumar Dhakar, Auteur ; Vinay Kumar Sehgal, Auteur ; Debasish Chakraborty, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2044 - 2064 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] blé (céréale)
[Termes IGN] correction atmosphérique
[Termes IGN] image Landsat-OLI
[Termes IGN] image multibande
[Termes IGN] image proche infrarouge
[Termes IGN] image Sentinel-MSI
[Termes IGN] Inde
[Termes IGN] indice foliaire
[Termes IGN] Leaf Area Index
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] réseau neuronal artificielRésumé : (auteur) This study assessed the effect of atmospheric correction algorithms, inversion techniques and image spatial and spectral resolution on wheat crop LAI retrieval using Sentinel-2 MSI and Landsat-8 OLI imagery. The LAI retrievals were validated with in-situ measurements collected in farmers’ fields. The MSI-based LAI retrievals improved significantly when images were atmospherically corrected using MODTRAN than using the libRadtran code. Among the two PROSAIL inversion approaches, look-up table outperforms artificial neural network for LAI retrievals. Using the best strategy of atmospheric correction and inversion, the effect of spatial resolution from 20 m (MSI) to 30 m (OLI) while using common six bands, showed non-significant improvement in LAI retrievals. The inclusion of additional two red-edge bands as available in MSI significantly reduced the uncertainly in LAI retrievals over that obtained by using six bands, while inclusion of only additional VNIR band did not show any significant effect on LAI retrievals. Numéro de notice : A2021-742 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1687591 Date de publication en ligne : 12/11/2019 En ligne : https://doi.org/10.1080/10106049.2019.1687591 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98666
in Geocarto international > vol 36 n° 18 [01/10/2021] . - pp 2044 - 2064[article]Integrating spatio-temporal-spectral information for downscaling Sentinel-3 OLCI images / Yijie Tang in ISPRS Journal of photogrammetry and remote sensing, vol 180 (October 2021)PermalinkInvestigating operational country-level crop monitoring with Sentinel~1 and~2 imagery / Nicolas David in Remote sensing letters, vol 12 n° 10 (October 2021)PermalinkPhenology-based delineation of irrigated and rain-fed paddy fields with Sentinel-2 imagery in Google Earth Engine / Daniel Marc G. dela Torre in Geo-spatial Information Science, vol 24 n° 4 (October 2021)PermalinkRecurrent-based regression of Sentinel time series for continuous vegetation monitoring / Anatol Garioud in Remote sensing of environment, vol 263 (15 September 2021)PermalinkThe real potential of current passive satellite data to map aboveground biomass in tropical forests / Nidhi Jha in Remote sensing in ecology and conservation, vol 7 n° 3 (September 2021)PermalinkImproving urban land cover classification with combined use of Sentinel-2 and Sentinel-1 imagery / Bin Hu in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)PermalinkMapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America / Bin Chen in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)PermalinkDetecting high-temperature anomalies from Sentinel-2 MSI images / Yongxue Liu in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)PermalinkEstimation of tree height and aboveground biomass of coniferous forests in North China using stereo ZY-3, multispectral Sentinel-2, and DEM data / Yueting Wang in Ecological indicators, vol 126 (July 2021)PermalinkFluvial gravel bar mapping with spectral signal mixture analysis / Liza Stančič in European journal of remote sensing, vol 54 sup 1 (2021)Permalink