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Crop identification by massive processing of multiannual satellite imagery for EU common agriculture policy subsidy control / Adolfo Lozano-Tello in European journal of remote sensing, vol 54 n° 1 (2021)
[article]
Titre : Crop identification by massive processing of multiannual satellite imagery for EU common agriculture policy subsidy control Type de document : Article/Communication Auteurs : Adolfo Lozano-Tello, Auteur ; Marcos Fernández-Sellers, Auteur ; Elia Quirós, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1 - 12 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] apprentissage automatique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification pixellaire
[Termes IGN] Estrémadure (Espagne)
[Termes IGN] image Sentinel-MSI
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] politique agricole commune
[Termes IGN] réseau neuronal artificiel
[Termes IGN] surface cultivée
[Termes IGN] surveillance agricoleRésumé : (auteur) The early and automatic identification of crops declared by farmers is essential for streamlining European Union Common Agricultural Policy (CAP) payment processes. Currently, field inspections are partial, expensive and entail a considerable delay in the process. Chronological satellite images of cultivated plots can be used so that neural networks can form the model of the declared crop. Once the patterns of a crop are obtained, the correspondence of the declaration with the model of the neural network can be systematically predicted, and can be used for monitoring the CAP. In this article, we propose a learning model with neural networks, using as examples of training the pixels of the cultivated plots from the satellite images over a period of time. We also propose using several years in the training model to generalise the patterns without linking them to the climatic characteristics of a specific year. The article also describes the use of the model in learning the multi-year pattern of tobacco cultivation with very good results. Numéro de notice : A2021-138 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/22797254.2020.1858723 Date de publication en ligne : 30/12/2020 En ligne : https://doi.org/10.1080/22797254.2020.1858723 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97012
in European journal of remote sensing > vol 54 n° 1 (2021) . - pp 1 - 12[article]Optimizing flood mapping using multi-synthetic aperture radar images for regions of the lower mekong basin in Vietnam / Vu Anh Tuan in European journal of remote sensing, vol 54 n° 1 (2021)
[article]
Titre : Optimizing flood mapping using multi-synthetic aperture radar images for regions of the lower mekong basin in Vietnam Type de document : Article/Communication Auteurs : Vu Anh Tuan, Auteur ; Nguyen Hong Quang, Auteur ; le Thi Thu Hang, Auteur Année de publication : 2021 Article en page(s) : pp 13 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bande L
[Termes IGN] cartographie des risques
[Termes IGN] crue
[Termes IGN] image ALOS
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] inondation
[Termes IGN] Mekong (fleuve)
[Termes IGN] optimisation spatiale
[Termes IGN] surveillance hydrologique
[Termes IGN] Viet NamRésumé : (auteur) One major characteristic of floods is flood extent. Information on this characteristic is indispensable for flood monitoring. Recently, synthetic aperture radar (SAR) data have been increasing in quality and quantity. This allows more flood studies conducted over large areas regardless of cloud and weather conditions and provides advantages including clear surface water classification based on SAR scattering mechanisms for low values (open water) and high values (inundated vegetation, etc.). However, challenges remain due to sources of uncertainties, such as atmospheric disturbances and vegetation masking parts of water surfaces. Therefore, in this study, we aim to optimize flood mapping processes on flooded vegetation that generated high-value pixels based on a SAR scattering mechanism called double bounce that classifies vegetative flooded water in L-band SAR images. This optimization is nearly impossible using Sentinel-1 scenes. Backscattering of time-series Sentinel-1 and ALOS-2 images acquired for the 2018 and 2019 flood season was analysed, thresholded and hybridized for flood mapping of a study site in the Tam Nong district of the Dong Thap Province of Vietnam. We found that the accuracy of SAR flood maps was improved compared to ground truth data when the SAR-extracted vegetative-flooded plains were considered flooded. Numéro de notice : A2021-139 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/22797254.2020.1859340 Date de publication en ligne : 30/12/2020 En ligne : https://doi.org/10.1080/22797254.2020.1859340 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97015
in European journal of remote sensing > vol 54 n° 1 (2021) . - pp 13 - 28[article]Reclaimed-airport surface-deformation monitoring by improved permanent-scatterer interferometric synthetic-aperture radar: a case study of Shenzhen Bao'an international airport, China / Lu Miao in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 2 (February 2021)
[article]
Titre : Reclaimed-airport surface-deformation monitoring by improved permanent-scatterer interferometric synthetic-aperture radar: a case study of Shenzhen Bao'an international airport, China Type de document : Article/Communication Auteurs : Lu Miao, Auteur ; Kailiang Deng, Auteur ; Guangcai Feng, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 105-116 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] aéroport
[Termes IGN] coin réflecteur
[Termes IGN] déformation d'édifice
[Termes IGN] déformation de surface
[Termes IGN] données spatiotemporelles
[Termes IGN] image Envisat-ASAR
[Termes IGN] image Sentinel-SAR
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] piste d'aéroport
[Termes IGN] Shenzhen
[Termes IGN] surveillance d'ouvrage
[Termes IGN] surveillance géologiqueRésumé : (Auteur) Reclaimed airports usually have fragile geological structures and are susceptible to the uneven ground settlements caused by filling-material consolidation, underground construction, and dynamic loading from takeoff and landing of aircrafts. Therefore, deformation monitoring is of great significance to the safe operation of reclaimed airports. This study adopts an improved permanent-scatterer interferometric synthetic-aperture radar strategy to map the spatiotemporal deformation of Shenzhen Bao'an International Airport in China using ascending and descending Envisat/ASAR data acquired from 2007 to 2010 and Sentinel-1 data from 2015 to 2019. The results show that uneven settlements of the airport concentrate in the new reclaimed land. Then we explore the settlement characteristics of each functional area. Furthermore, we separate out the dynamic-load settlement of runway No. 2 and confirm the settlements caused by dynamic load. This study provides new ideas for studying deformation in similar fields, and technical references for the future construction of Shenzhen Airport. Numéro de notice : A2021-098 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.2.105 Date de publication en ligne : 01/02/2021 En ligne : https://doi.org/10.14358/PERS.87.2.105 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97042
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 2 (February 2021) . - pp 105-116[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2021021 SL Revue Centre de documentation Revues en salle Disponible Spruce budworm tree host species distribution and abundance mapping using multi-temporal Sentinel-1 and Sentinel-2 satellite imagery / Rajeev Bhattarai in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)
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Titre : Spruce budworm tree host species distribution and abundance mapping using multi-temporal Sentinel-1 and Sentinel-2 satellite imagery Type de document : Article/Communication Auteurs : Rajeev Bhattarai, Auteur ; Parinaz Rahimzadeh-Bajgiran, Auteur ; Aaron R. Weiskittel, Auteur Année de publication : 2021 Article en page(s) : pp 28 - 40 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Abies balsamea
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] défoliation
[Termes IGN] dégradation de la flore
[Termes IGN] image multibande
[Termes IGN] image multitemporelle
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] indice de végétation
[Termes IGN] insecte phyllophage
[Termes IGN] Nouveau-Brunswick (Canada)
[Termes IGN] Picea abiesRésumé : (auteur) Spruce budworm (Choristoneura fumiferana; SBW) is the most destructive forest pest of northeastern Canada and United States. SBW occurrence as well as the extent and severity of its damage are highly dependent on the characteristics of the forests and the availability of host species namely, spruce (Picea sp.) and balsam fir (Abies balsamea (L.) Mill.). Remote sensing satellite imagery represents a valuable data source for seamless regional-scale mapping of forest composition. This study developed and evaluated new models to map the distribution and abundance of SBW host species at 20 m spatial resolution using Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery in combination with several site variables for a total of 191 variables in northern New Brunswick, Canada using the Random Forest (RF) algorithm. We found Sentinel-2 multi-temporal single spectral bands and numerous spectral vegetation indices (SVIs) yielded the classification of SBW host species with an overall accuracy (OA) of 72.6% and kappa coefficient (K) of 0.65. Incorporating Sentinel-1 SAR data with Sentinel-2 variables coupled with elevation, only marginally improved the performance of the model (OA: 73.0% and K: 0.66). The use of Sentinel-1 SAR data with elevation resulted in a reasonable OA of 57.5% and K of 0.47. These spatially explicit up-to-date SBW host species maps are essential for identifying susceptible forests, monitoring SBW defoliation, and minimizing forest losses from insect impacts at landscape scale in the current SBW outbreak in the region. Numéro de notice : A2021-085 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.11.023 Date de publication en ligne : 15/12/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.11.023 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96845
in ISPRS Journal of photogrammetry and remote sensing > vol 172 (February 2021) . - pp 28 - 40[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021021 SL Revue Centre de documentation Revues en salle Disponible 081-2021022 DEP-RECF Revue Nancy Bibliothèque Nancy IFN Exclu du prêt Study of systematic bias in measuring surface deformation with SAR interferometry / Homa Ansari in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)
[article]
Titre : Study of systematic bias in measuring surface deformation with SAR interferometry Type de document : Article/Communication Auteurs : Homa Ansari, Auteur ; Francesco De Zan, Auteur ; Alessandro Parizzi, Auteur Année de publication : 2021 Article en page(s) : pp 1285 - 1301 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] atténuation du signal
[Termes IGN] décorrélation
[Termes IGN] déformation de surface
[Termes IGN] erreur de phase
[Termes IGN] erreur systématique
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] processus stochastique
[Termes IGN] rapport signal sur bruit
[Termes IGN] série temporelleRésumé : (auteur) This article investigates the presence of a new interferometric signal in multilooked synthetic aperture radar (SAR) interferograms that cannot be attributed to the atmospheric or Earth-surface topography changes. The observed signal is short-lived and decays with the temporal baseline; however, it is distinct from the stochastic noise attributed to temporal decorrelation. The presence of such a fading signal introduces a systematic phase component, particularly in short temporal baseline interferograms. If unattended, it biases the estimation of Earth surface deformation from SAR time series. Here, the contribution of the mentioned phase component is quantitatively assessed. The biasing impact on the deformation-signal retrieval is further evaluated. A quality measure is introduced to allow the prediction of the associated error with the fading signals. Moreover, a practical solution for the mitigation of this physical signal is discussed; special attention is paid to the efficient processing of Big Data from modern SAR missions such as Sentinel-1 and NISAR. Adopting the proposed solution, the deformation bias is shown to decrease significantly. Based on these analyses, we put forward our recommendations for efficient and accurate deformation-signal retrieval from large stacks of multilooked interferograms. Numéro de notice : A2021-115 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3003421 Date de publication en ligne : 30/06/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3003421 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96929
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 1285 - 1301[article]Mapping seasonal agricultural land use types using deep learning on Sentinel-2 image time series / Misganu Debella-Gilo in Remote sensing, Vol 13 n° 2 (January-2 2021)PermalinkUsing Sentinel-2 images to estimate topography, tidal-stage lags and exposure periods over large intertidal areas / José P. Granadeiro in Remote sensing, Vol 13 n° 2 (January-2 2021)PermalinkAccurate sea surface heights from Sentinel-3A and Jason-3 retrackers by incorporating high-resolution marine geoid and hydrodynamic models / Mir Abolfazl Mostafavi in Journal of geodetic science, vol 11 n° 1 (January 2021)PermalinkAmélioration des systèmes de suivi des cultures à l’aide de la télédétection multi-source et des techniques d’apprentissage profond / Yawogan Gbodjo (2021)PermalinkApplications of remote sensing data in mapping of forest growing stock and biomass / Jose Aranha (2021)PermalinkApport des données satellitaires Sentinel-1 et Sentinel-2 pour la détection des surfaces irriguées et l'estimation des besoins et des consommations en eau des cultures d'été dans les zones tempérées / Yann Pageot (2021)PermalinkApport des données Sentinel-1 pour le suivi continu de la forêt tropicale : Cas de la Guyane / Marie Ballère (2021)PermalinkApport de la télédétection pour la simulation spatialisée des composantes du bilan carbone des cultures et des effets d'atténuation biogéochimiques et biogéophysiques des cultures intermédiaires / Gaétan Pique (2021)PermalinkApports des méthodes d'apprentissage profond pour la reconnaissance automatique des modes d'occupation des sols et d'objets par télédétection en milieu tropical / Guillaume Rousset (2021)PermalinkPermalink