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Evaluation of Sentinel-1 & 2 time series for the identification and characterization of ecological continuities, from wooded to crop-dominated landscapes / Audrey Mercier (2021)
Titre : Evaluation of Sentinel-1 & 2 time series for the identification and characterization of ecological continuities, from wooded to crop-dominated landscapes Type de document : Thèse/HDR Auteurs : Audrey Mercier, Auteur ; Laurence Hubert-Moy, Directeur de thèse ; Jacques Baudry, Directeur de thèse Editeur : Rennes : Université de Rennes 2 Année de publication : 2021 Importance : 305 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse de Doctorat présentée à l'Université de Rennes 2, Spécialité GéomatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] caractérisation
[Termes IGN] continuité écologique
[Termes IGN] cultures
[Termes IGN] données polarimétriques
[Termes IGN] forêt
[Termes IGN] habitat (nature)
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] occupation du sol
[Termes IGN] paysage agricole
[Termes IGN] précision de la classification
[Termes IGN] série temporelle
[Termes IGN] utilisation du solIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) Habitat loss is now considered one of the most serious threats to biodiversity. While many studies have focused on the circulation role of woodland features, very few have focused on the role of ecological continuities within agricultural mosaics. The objectives of this thesis were (1) to assess the combined use of Sentinel 1 and 2 time series to identify and characterize the elements of ecological continuities through land cover classifications and crop characterization in wooded and crop-dominated landscapes and (2) to estimate the impact of the spatio-temporal structuring of these landscape on biodiversity using metrics derived from Sentinel time series. The results showed that although S-2 data are more adapted than S-1 data to discriminate between land cover/land use types in wooded landscapes and phenological stages of wheat and rapeseed in crop-dominated landscapes, the combined use of S-2 and S-1 data improves their accuracy of the classifications, with S-1 data also showing a strong interest in cloudy areas. They also showed the interest of polarimetric indicators derived from S-1 data to characterize wheat and rapeseed crops. Finally, they highlighted the interest of the biophysical heterogeneity metrics derived from S-2 data to accurately estimate the distribution of carabid beetle species. The use of this metric, calculated with free images available everywhere on Earth, continuous and consistent from one site to another and from one type of crop to another,
should contribute to the study of the impact of ecological continuities on biodiversity.Note de contenu : General introduction
1. Ecological continuities from wooded to crop-dominated landscapes
2. The use of remote sensing imagery for the identification and characterization of ecological continuities
3. Study areas and data
4. Evaluation of Sentinel-1 and 2 time Series for land cover classification of forest–agriculture mosaics in temperate and tropical landscapes
5. Evaluation of Sentinel-1 and 2 time series for predicting wheat and rapeseed phenological stages
6. Evaluation of Sentinel-1 and 2 time series for estimating LAI and biomass of wheat and rapeseed crop types
7. Sentinel-2 images bring out functional biophysical heterogeneities in crop mosaics
General conclusion and perspectivesNuméro de notice : 26708 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Géomatique : Rennes 2 : 2021 Organisme de stage : Littoral, Environnement, Télédétection, Géomatique LETG nature-HAL : Thèse DOI : sans Date de publication en ligne : 14/10/2021 En ligne : https://tel.hal.science/tel-03377565 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99456 Examining the effectiveness of Sentinel-1 and 2 imagery for commercial forest species mapping / Mthembeni Mngadi in Geocarto international, vol 36 n° 1 ([01/01/2021])
[article]
Titre : Examining the effectiveness of Sentinel-1 and 2 imagery for commercial forest species mapping Type de document : Article/Communication Auteurs : Mthembeni Mngadi, Auteur ; John Odindi, Auteur ; Kabir Peerbhay, Auteur ; Onisimo Mutanga, Auteur Année de publication : 2021 Article en page(s) : pp 1 - 12 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse discriminante
[Termes IGN] carte forestière
[Termes IGN] Eucalyptus (genre)
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] KwaZulu-Natal (Afrique du Sud)
[Termes IGN] Pinus (genre)
[Termes IGN] télédétection spatialeRésumé : (Auteur) The successful launch and operation of the Sentinel satellite platform has provided access to freely available remotely sensed data useful for commercial forest species discrimination. Sentinel – 1 (S1) with a synthetic aperture radar (SAR) sensor and Sentinel – 2 (S2) multi-spectral sensor with additional and strategically positioned bands offer great potential for providing reliable information for discriminating and mapping commercial forest species. In this study, we sought to determine the value of S1 and S2 data characteristics in discriminating and mapping commercial forest species. Using linear discriminant analysis (LDA) algorithm, S2 multi-spectral imagery showed an overall classification accuracy of 84% (kappa = 0.81), with bands such as the red-edge (703.9–740.2 nm), narrow near infrared (835.1–864.8 nm), and short wave infrared (1613.7–2202.4 nm) particularly influential in discriminating individual forest species stands. When Sentinel 2’s spectral wavebands were fused with Sentinel 1’s (SAR) VV and VH polarimetric modes, overall classification accuracies improved to 87% (kappa = 0.83) and 88% (kappa = 0.85), respectively. These findings demonstrate the value of combining Sentinel’s multispectral and SAR structural information characteristics in improving commercial forest species discrimination. These, in addition to the sensors free availability, higher spatial resolution and larger swath width, offer unprecedented opportunities for improved local and large scale commercial forest species discrimination and mapping. Numéro de notice : A2021-050 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1585483 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1585483 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96719
in Geocarto international > vol 36 n° 1 [01/01/2021] . - pp 1 - 12[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2021011 RAB Revue Centre de documentation En réserve L003 Disponible Flood mapping from radar remote sensing using automated image classification techniques / Lisa Landuyt (2021)
Titre : Flood mapping from radar remote sensing using automated image classification techniques Type de document : Thèse/HDR Auteurs : Lisa Landuyt, Auteur ; Niko Verhoest, Directeur de thèse ; Frieke Vancoillie, Directeur de thèse Editeur : Gand [Belgique] : Universiteit Gent Année de publication : 2021 Importance : 227 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-94-6357-415-0 Note générale : bibliographie
Dissertation submitted in fulfillment of the requirements for the degree of Doctor (PhD) of Bioscience EngineeringLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] bande C
[Termes IGN] cartographie des risques
[Termes IGN] détection de changement
[Termes IGN] extraction de la végétation
[Termes IGN] Flandre (Belgique)
[Termes IGN] gestion de l'eau
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] inondation
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] modèle de simulation
[Termes IGN] surveillance hydrologiqueRésumé : (auteur) Floods are a hazard of major concern, causing substantial fatalities and eco-nomic losses. These losses are expected to further accumulate in the future, as both the frequency and magnitude of flood events are projected to increase dueto climate change. Insights into the occurrence and dynamics of these disastrous events are thus of paramount importance for the protection of livelihoods across the world, both in the near and far future.Synthetic Aperture Radar (SAR) satellite imagery is particularly suited to observe floods due to the synoptic view, low cost and timely availability ofsatellite imagery and the all-weather imaging capabilities of SAR sensors. The resulting observations are crucial for various purposes, including emergency relief, post-disaster damage assessment, the calibration and validation of floodprediction models, and risk assessment.Despite the clear advantages of SAR imagery, several factors complicate the flood extent retrieval from this imagery type. These include surfaces or land dynamics characterized by a SAR backscatter similar to that of water/flooding,as well as the presence of urban features and vegetation. Moreover, existing approaches often lack the robustness and automation necessary for operational purposes. This thesis aims to contribute to the accuracy and automation of SAR-based flood mapping approaches, by elaborating on several of theremaining challenges. More specifically, the objectives of this thesis are:
1.to investigate the state of the art in SAR-based flood mapping andidentify the strengths and limitations of existing methods, as well as possible trends;
2.to assess the potential of C-band SAR for the delineation of floodedvegetation, and suggested an approach for doing so in an automated way;
3.to identify the main obstacles with respect to automated flood monitoring,and develop an approach that allows putting science into practice.
In the process of pursuing these objectives, special attention is given to automation, as this is key for objective and timely observations, and to optimally employing available data, as additional data can substantially improve flood observations but not handling these critically may be have adverse effects. Additionally, the potential of object-based image analysis (OBIA) techniques is investigated, as they have proven their added value using optical imagery but SAR-based applications remain limited. Sentinel-1imagery is the main datasource considered in this thesis, as this medium-resolution C-band imagery is freely available and provides consistent global coverage.First, the state of the art in SAR-based flood mapping is investigated. Distin-guishing between approaches for the retrieval of open water, flooded vegetationand urban flooding, deployed input data and classification techniques are discussed. As it is difficult to draw conclusions regarding the strengths and limitations of these classification techniques based on their scientific publications, an in-depth assessment and comparison of a selection of these is carried out. This selection includes thresholding, active contour modeling and theHSBA-Flood method, and both single scene and change detection-based maps are generated. To tackle the second objective of this thesis, the detectability of both woody and herbaceous vegetation using Sentinel-1 is investigated. Moreover, an automated, object-based clustering approach, making use of globally and freely available data only, is presented and applied on four study areas with varying characteristics. The resulting flood maps discriminate between dryland, permanent water, open flooding and flooded vegetation. Forests are indicated too, in order to underline the uncertainty related to these areas where flooding cannot or only to a limited extent be detected.In the last part of this thesis, an approach for operational flood monitoringin Flanders is presented. This approach was developed for and with input of the local water manager,i.e.the Flanders Environment Agency, and makesuse of high-resolution ancillary data available for the region of interest. By combining a pixel-based and an object-based approach, a discrimination is made between dry land, permanent water, open flooding, probable flooding, flooded vegetation and probably flooded forests. The approach is extensively tested on flood events of different sizes that occurred between 2016 and 2020. Both the detectability of these flood events and the accuracy of the developed algorithm, in the presence and absence of flooding, are assessed and discussed.Note de contenu : 1- Introduction
2- Synthetic aperture radar: theoretical background
3- State of the art in SAR-based flood mapping
4- An assessment of establish
ed SAR-based flood mappingapproaches
5- Flood mapping in vegetated areas using an unsupervisedclustering approach on Sentinel-1 and -2 imagery
6- Flood monitoring in Flanders using Sentinel-1 imagery
7- Conclusion and outlookNuméro de notice : 28303 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD Thesis : Bioscience Engineering : Universiteit Gent : 2021 DOI : sans En ligne : https://biblio.ugent.be/publication/8709595/file/8709639.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98053
Titre : Forest biomass : from trees to energy Type de document : Monographie Auteurs : Ana Cristina Goncalves, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2021 ISBN/ISSN/EAN : 978-1-83962-971-6 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse (combustible)
[Termes IGN] biomasse forestière
[Termes IGN] bois énergie
[Termes IGN] image Sentinel-MSI
[Termes IGN] occupation du sol
[Termes IGN] Pinus pinaster
[Termes IGN] télédétection spatiale
[Termes IGN] utilisation du solIndex. décimale : 35.41 Applications de télédétection - végétation Résumé : (Editeur) Forests are responsible for the largest net biomass carbon production. They store the most standing biomass and carbon and thus they are an important source of bioenergy. Their importance is linked to their relative abundance and uniformity worldwide and the neutrality of CO2 emissions from biomass conversion to energy. Yet, the use of biomass for energy presents risks related to forest system sustainability and demands for new environmentally sustainable strategies for its use. This book provides a comprehensive overview of the current state of the art in a multitude of subjects related to forest bioenergy, ranging from trees, forest stand management, and biomass assessment to waste management, conversion technologies, and routes and energy applications. Note de contenu : 1. Energy Production from Forest Biomass: An Overview / By Ana Cristina Gonçalves, Isabel Malico and Adélia M.O. Sousa
2. The Potential of Sentinel-2 Satellite Images for Land-Cover/Land-Use and Forest Biomass Estimation: A Review / By Crismeire Isbaex and Ana Margarida Coelho
3. Biomass Estimation Using Satellite-Based Data / By Patrícia Lourenço
4. Management of Maritime Pine: Energetic Potential with Alternative Silvicultural Guidelines / By Teresa Fonseca and José Lousada
5. Evergreen Oak Biomass Residues for Firewood / By Isabel Malico, Ana Cristina Gonçalves and Adélia M.O. Sousa
6. Koroch (Pongamia pinnata): A Promising Unexploited Resources for the Tropics and Subtropics / By Abul Kalam Mohammad Aminul Islam, Swapan Chakrabarty, Zahira Yaakob, Mohammad Ahiduzzaman and Abul Kalam Mohammad Mominul Islam
7. Case Study: Pathways from Forest to Energy in a Circular Economy at Lafões / By Ana d’Espiney, Isabel Paula Marques and Helena Maria Pinheiro
8. Methodology for the Evaluation of the Electrical Energy Potential of Residual Biomass from the Wood Industry: A Case Study in Brazil / By Augusto César de Mendonça Brasil
9. Opportunities of Circular Economy in a Complex System of Woody Biomass and Municipal Sewage Plants / By Attila Bai and Zoltán GabnaiNuméro de notice : 26710 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.90324 Date de publication en ligne : 10/02/2021 En ligne : https://doi.org/10.5772/intechopen.90324 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99479 From local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2 / Yousra Hamrouni in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)
[article]
Titre : From local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2 Type de document : Article/Communication Auteurs : Yousra Hamrouni, Auteur ; Eric Paillassa, Auteur ; Véronique Chéret, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 76 - 100 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] base de données forestières
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] couvert forestier
[Termes IGN] échantillonnage
[Termes IGN] France (administrative)
[Termes IGN] image Sentinel-MSI
[Termes IGN] mise à jour de base de données
[Termes IGN] Populus (genre)
[Termes IGN] série temporelleRésumé : (auteur) Reliable estimates of poplar plantations area are not available at the French national scale due to the unsuitability and low update rate of existing forest databases for this short-rotation species. While supervised classification methods have been shown to be highly accurate in mapping forest cover from remotely sensed images, their performance depends to a great extent on the labelled samples used to build the models. In addition to their high acquisition cost, such samples are often scarce and not fully representative of the variability in class distributions. Consequently, when classification models are applied to large areas with high intra-class variance, they generally yield poor accuracies because of data shift issues. In this paper, we propose the use of active learning to efficiently adapt a classifier trained on a source image to spatially distinct target images with minimal labelling effort and without sacrificing the classification performance. The adaptation consists in actively adding to the initial local model new relevant training samples from other areas in a cascade that iteratively improves the generalisation capabilities of the classifier leading to a global model tailored to these different areas. This active selection relies on uncertainty sampling to directly focus on the most informative pixels for which the algorithm is the least certain of their class labels. Experiments conducted on Sentinel-2 time series revealed their high capacity to identify poplar plantations at a local scale with an average F-score ranging from 89.5% to 99.3%. For large area adaptation, the results showed that when the same number of training samples was used, active learning outperformed random sampling by up to 5% of the overall accuracy and up to 12% of the class F-score. Additionally, and depending on the class considered, the random sampling model required up to 50% more samples to achieve the same performance of an active learning-based model. Moreover, the results demonstrate the suitability of the derived global model to accurately map poplar plantations among other tree species with overall accuracy values up to 14% higher than those obtained with local models. The proposed approach paves the way for a national scale mapping in an operational context. Numéro de notice : A2021-013 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.018 Date de publication en ligne : 20/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.018 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96417
in ISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 76 - 100[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021011 SL Revue Centre de documentation Revues en salle Disponible 081-2021013 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021012 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Geospatial analysis of September, 2019 floods in the lower gangetic plains of Bihar using multi-temporal satellites and river gauge data / C.M. Bhatt in Geomatics, Natural Hazards and Risk, vol 12 n° 1 (2021)PermalinkPermalinkImpact of forest disturbance on InSAR surface displacement time series / Paula M. Bürgi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)PermalinkInvestigation of Sentinel-1 time series for sensitivity to fern vegetation in an European temperate forest / Marlin Mueller (2021)PermalinkLearning disentangled representations of satellite image time series in a weakly supervised manner / Eduardo Hugo Sanchez (2021)PermalinkPermalinkNear-real-time identification of the drivers of deforestation in French Guiana / Marie Ballère (2021)PermalinkProduction et mise à jour d’un produit BD Forêt V3 par apprentissage profond / Sébastien Giordano (2021)PermalinkQualification des données LiDAR GEDI pour le suivi de l’impact climatique sur la forêt de Südharz / Iris Jeuffrard (2021)PermalinkPermalinkPermalinkSAR data for tropical forest disturbance alerts in French Guiana: Benefit over optical imagery / Marie Ballère in Remote sensing of environment, Vol 252 (January 2021)PermalinkSeasonal flow variability of Greenlandic glaciers : satellite observations and numerical modeling to study driving processes / Anna Derkacheva (2021)PermalinkSemantic segmentation of sea ice type on Sentinel-1 SAR data using convolutional neural networks / Alissa Kouraeva (2021)PermalinkSuivi de la déforestation à partir de données Sentinel-1 en contexte tropical / Lucile Auzeméry (2021)PermalinkSuivi de la rotation des cultures à partir de séries temporelles d’images satellite / Félix Quinton (2021)PermalinkPermalinkPermalinkTélédétection et intégration de connaissances via la modélisation spatiale pour une cartographie plus cohérente des systèmes agricoles complexes / Arthur Crespin-Boucaud (2021)PermalinkThe use of deep machine learning for the automated selection of remote sensing data for the determination of areas of arable land degradation processes distribution / Dimitri I. 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Phalke in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)PermalinkA novel algorithm to estimate phytoplankton carbon concentration in inland lakes using Sentinel-3 OLCI images / Heng Lyu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)PermalinkA spaceborne SAR-based procedure to support the detection of landslides / Giuseppe Esposito in Natural Hazards and Earth System Sciences, vol 20 n° 9 (September 2020)PermalinkX-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data / Danfeng Hong in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)PermalinkAccuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets / Lamin R. Mansaray in Geocarto international, vol 35 n° 10 ([01/08/2020])PermalinkCan SPOT-6/7 CNN semantic segmentation improve Sentinel-2 based land cover products? sensor assessment and fusion / Olivier Stocker in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)PermalinkConjugate ruptures and seismotectonic implications of the 2019 Mindanao earthquake sequence inferred from Sentinel-1 InSAR data / Bingquan Li in International journal of applied Earth observation and geoinformation, vol 90 (August 2020)PermalinkDevelopment and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping / Alvin B. Baloloy in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)PermalinkRecent changes in two outlet glaciers in the Antarctic Peninsula using multi-temporal Landsat and Sentinel-1 data / Carolina L. Simões in Geocarto international, vol 35 n° 11 ([01/08/2020])PermalinkTowards a semi-automated mapping of Australia native invasive alien Acacia trees using Sentinel-2 and radiative transfer models in South Africa / Cecilia Masemola in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)PermalinkCartographie des surfaces pastorales à l’aide des données Sentinel 2 L3A et des données ouvertes : Promesses et réalités / Urcel Kalenga Tshingomba in Revue internationale de géomatique, vol 30 n° 3-4 (juillet - décembre 2020)PermalinkClassification of sea ice types in Sentinel-1 SAR data using convolutional neural networks / Hugo Boulze in Remote sensing, vol 12 n° 13 (July-1 2020)PermalinkCross-calibration of MODIS reflective solar bands with Sentinel 2A/2B MSI instruments / Amit Angal in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)PermalinkImproved crop classification with rotation knowledge using Sentinel-1 and -2 time series / Sébastien Giordano in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 7 (July 2020)PermalinkA novel framework based on polarimetric change vectors for unsupervised multiclass change detection in dual-pol intensity SAR images / David Pirrone in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)PermalinkCoastline change modelling induced by climate change using geospatial techniques in Togo (West Africa) / Yawo Konko in Advances in Remote Sensing, vol 9 n° 2 (June 2020)PermalinkDiscriminant analysis for lodging severity classification in wheat using RADARSAT-2 and Sentinel-1 data / Sugandh Chauhan in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)PermalinkImproved optical image matching time series inversion approach for monitoring dune migration in North Sinai Sand Sea: Algorithm procedure, application, and validation / Eslam Ali in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)PermalinkMapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data / Johannes Schumacher in Forest ecosystems, vol 7 (2020)PermalinkValidation of Sentinel-3A SRAL coastal sea level data at high posting rate: 80 Hz / Ana Aldarias in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)PermalinkAssessment of winter season land surface temperature in the Himalayan regions around the Kullu area in India using Landsat-8 data / Divyesh Varade in Geocarto international, vol 35 n° 6 ([01/05/2020])PermalinkIncorporating Sentinel-1 SAR imagery with the MODIS MCD64A1 burned area product to improve burn date estimates and reduce burn date uncertainty in wildland fire mapping / Kristofer Lasko in Geocarto international, vol 35 n° 6 ([01/05/2020])PermalinkIntertidal topography mapping using the waterline method from Sentinel-1 & -2 images: The examples of Arcachon and Veys Bays in France / Edward Salameh in ISPRS Journal of photogrammetry and remote sensing, vol 163 (May 2020)PermalinkMangrove forest classification and aboveground biomass estimation using an atom search algorithm and adaptive neuro-fuzzy inference system / Minh Hai Pham in Plos one, vol 15 n° 5 (May 2020)PermalinkSeasonal Deformation of Permafrost in Wudaoliang Basin in Qinghai-Tibet Plateau Revealed by StaMPS-InSAR / Ping Lu in Marine geodesy, Vol 43 n° 3 (May 2020)PermalinkShrub biomass estimates in former burnt areas using Sentinel 2 images processing and classification / Jose Aranha in Forests, vol 11 n° 5 (May 2020)PermalinkSoil moisture estimation with SVR and data augmentation based on alpha approximation method / Wei Xu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)PermalinkA Fusion Approach for Water Area Classification Using Visible, Near Infrared and Synthetic Aperture Radar for South Asian Conditions / Shahryar K. Ahmad in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)PermalinkImproving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series / Maylis Lopes in Methods in ecology and evolution, vol 11 n° 4 (April 2020)PermalinkMonitoring of landslide activity at the Sirobagarh landslide, Uttarakhand, India, using LiDAR, SAR interferometry and geodetic surveys / Ashutosh Tiwari in Geocarto international, vol 35 n° 5 ([01/04/2020])PermalinkDeep SAR-Net: learning objects from signals / Zhongling Huang in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)PermalinkLarge-scale two-phase estimation of wood production by poplar plantations exploiting Sentinel-2 data as auxiliary information / Agnese Marcelli in Silva fennica, vol 54 n° 2 (March 2020)PermalinkCloud detection by luminance and inter-band parallax analysis for pushbroom satellite imagers / Tristan Dagobert in IPOL Journal, Image Processing On Line, vol 10 (2020)PermalinkA LiDAR–optical data fusion approach for identifying and measuring small stream impoundments and dams / Benjamin Swan in Transactions in GIS, Vol 24 n° 1 (February 2020)PermalinkMapping precipitable water vapor time series from Sentinel-1 interferometric SAR / Pedro Mateus in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)PermalinkMulti-Spatial Resolution Satellite and sUAS Imagery for Precision Agriculture on Smallholder Farms in Malawi / Brad G. Peter in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 2 (February 2020)PermalinkPrediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series / Mathieu Fauvel in Remote sensing of environment, Vol 237 (February 2020)PermalinkRed-edge band vegetation indices for leaf area index estimation from Sentinel-2/MSI imagery / Yuanheng Sun in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)PermalinkThe potentiality of Sentinel-2 to assess the effect of fire events on Mediterranean mountain vegetation / Walter de Simone in Plant sociology, vol 57 n° 1 ([01/02/2020])PermalinkExtracting soil salinization information with a fractional-order filtering algorithm and grid-search support vector machine (GS-SVM) model / Xiaoping Wang in International Journal of Remote Sensing IJRS, vol 41 n° 3 (15 - 22 janvier 2020)PermalinkApplication of geographic Information system and remote sensing in multiple criteria analysis to identify priority areas for biodiversity conservation in Vietnam / Xuan Dinh Vu (2020)PermalinkCartographie des essences forestières à partir de séries temporelles d’images satellitaires à hautes résolutions : stabilité des prédictions, autocorrélation spatiale et cohérence avec la phénologie observée in situ / Nicolas Karasiak (2020)PermalinkClassification of poplar trees with object-based ensemble learning algorithms using Sentinel-2A imagery / H. Tombul in Journal of geodetic science, vol 10 n° 1 (January 2020)PermalinkClassification of time series of Sentinel-2 images for large scale mapping in Cameroon / Hermann Tagne (2020)PermalinkCombination of linear regression lines to understand the response of Sentinel-1 dual polarization SAR data with crop phenology - case study in Miyazaki, Japan / Emal Wali in Remote sensing, vol 12 n° 1 (January 2020)PermalinkComparison of multi-seasonal Landsat 8, Sentinel-2 and hyperspectral images for mapping forest alliances in Northern California / Matthew L. Clark in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)PermalinkDeep learning for remote sensing images with open source software / Rémi Cresson (2020)PermalinkDétermination conjointe des inondations et du type d’eau au moyen de l’imagerie multi-spectrale / Sabrine Amzil (2020)PermalinkEstimation et suivi de la ressource en bois en France métropolitaine par valorisation des séries multi-temporelles à haute résolution spatiale d'images optiques (Sentinel-2) et radar (Sentinel-1, ALOS-PALSAR) / David Morin (2020)PermalinkEtudes des dynamiques spatiales d’évolution de l’occupation et de l’utilisation des sols dans la fenêtre lacustre camerounaise du lac Tchad et son arrière-pays à partir des grandes sécheresses sahéliennes de 1970 / Paul Gérard Gbetkom (2020)PermalinkPermalinkGlobal investigation of marine atmospheric boundary layer rolls using Sentinel-1 SAR data / Chen Wang (2020)PermalinkLightweight temporal self-attention for classifying satellite images time series / Vivien Sainte Fare Garnot (2020)PermalinkMise en place d'une méthode de détermination de la hauteur d'eau des océans à partir d'un capteur LiDAR aéroporté dans le cadre de la calibration/validation de l'altimètre SWOT / Romain Serthelon (2020)PermalinkNational scale identification and characterization of braided rivers in New Zealand using Google Earth Engine / Alexis Jean (2020)PermalinkOn the joint exploitation of optical and SAR satellite imagery for grassland monitoring / Anatol Garioud (2020)PermalinkRadar interferometry of unstable slopes / Theeba Raveendran (2020)PermalinkPermalinkPermalinkSurface soil moiture retrieval over irrigated wheat crops in semi-arid areas using Sentinel-1 data / Nadia Ouaadi (2020)PermalinkPermalinkUnsupervised satellite image time series analysis using deep learning techniques / Ekaterina Kalinicheva (2020)PermalinkUso de QGIS en la teledetección, Vol. 2. QGIS y sus aplicaciones en la agricultura y la silvicultura / Nicolas Baghdadi (2020)PermalinkWater stress detection over irrigated wheat crops in semi-arid areas using the diurnal differences of Sentinel-1 backscatter / Nadia Ouaadi (2020)PermalinkShip identification and characterization in Sentinel-1 SAR images with multi-task deep learning / Clément Dechesne in Remote sensing, Vol 11 n° 24 (December-2 2019)PermalinkCombining Sentinel-1 and Sentinel-2 Satellite image time series for land cover mapping via a multi-source deep learning architecture / Dino Lenco in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)PermalinkMatching of TerraSAR-X derived ground control points to optical image patches using deep learning / Tatjana Bürgmann in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)PermalinkOn the value of corner reflectors and surface models in InSAR precise point positioning / Mengshi Yang in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)PermalinkAccurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits / Tawanda W. Gara in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)PermalinkDeep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery / Yuri Shendryk in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)PermalinkNew method for environmental monitoring in armed conflict zones: a case study of Syria / Samira Mobaied in Environmental Monitoring and Assessment, vol 191 n° 11 (November 2019)PermalinkPotential of Landsat-8 and Sentinel-2A composite for land use land cover analysis / Divyesh Varade in Geocarto international, vol 34 n° 14 ([30/10/2019])PermalinkSea ice extent detection in the Bohai Sea using Sentinel-3 OLCI data / Hua Su in Remote sensing, Vol 11 n° 20 (October-2 2019)PermalinkAutomatic canola mapping using time series of Sentinel 2 images / Davoud Ashourloo in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)PermalinkComparative analysis of the accuracy of surface soil moisture estimation from the C- and L-bands / Mohammad El Hajj in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)PermalinkUn été brûlant sous l’oeil des satellites / Laurent Polidori in Géomètre, n° 2173 (octobre 2019)PermalinkMapping of forest tree distribution and estimation of forest biodiversity using Sentinel-2 imagery in the University Research Forest Taxiarchis in Chalkidiki, Greece / Maria Kampouri in Geocarto international, vol 34 n° 12 ([15/09/2019])PermalinkSentinel-2 sharpening using a reduced-rank method / Magnus Orn Ulfarsson in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)PermalinkThe Parallel SBAS approach for Sentinel-1 interferometric wide swath deformation time-series generation: algorithm description and products quality assessment / Michele Manunta in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)PermalinkCalculating potential evapotranspiration and single crop coefficient based on energy balance equation using Landsat 8 and Sentinel-2 / Ali Mokhtari in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)PermalinkEstimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images / Jie Wang in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)PermalinkIntegration of corner reflectors for the monitoring of mountain glacier areas with Sentinel-1 time series / Matthias Jauvin in Remote sensing, vol 11 n° 8 (August 2019)PermalinkLocal climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network / Chunping Qiu in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)PermalinkOn the use of Sentinel-2 for coastal habitat mapping and satellite-derived bathymetry estimation using downscaled coastal aerosol band / Dimitris Poursanidis in International journal of applied Earth observation and geoinformation, vol 80 (August 2019)PermalinkClassification of glacial lakes using integrated approach of DFPS technique and gradient analysis using Sentinel 2A data / Prateek Verma in Geocarto international, vol 34 n° 10 ([15/07/2019])PermalinkEvaluating the potential of the red edge channel for C3 (Festuca spp.) grass discrimination using Sentinel-2 and Rapid Eye satellite image data / Charles Otunga in Geocarto international, vol 34 n° 10 ([15/07/2019])PermalinkMapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model / Roshanak Darvishzadeh in International journal of applied Earth observation and geoinformation, vol 79 (July 2019)PermalinkComprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data / P. Kumar in Geocarto international, vol 34 n° 9 ([15/06/2019])PermalinkMise en oeuvre d'outils open source pour le suivi opérationnel de l'occupation des sols et de la déforestation à partir des données Sentinel radar optique : études de cas en Guyane et au Togo / Cédric Lardeux in Revue Française de Photogrammétrie et de Télédétection, n° 219-220 (juin - octobre 2019)PermalinkPolarimétrie radar complète et partielle pour le suivi des surfaces terrestres / Pierre-Louis Frison in Revue Française de Photogrammétrie et de Télédétection, n° 219-220 (juin - octobre 2019)PermalinkUsing Sentinel-1A DInSAR interferometry and Landsat 8 data for monitoring water level changes in two lakes in Crete, Greece / D.D. Alexakis in Geocarto international, vol 34 n° 7 ([01/06/2019])PermalinkCoastline extraction from SAR images using robust ridge tracing / Dailiang Wang in Marine geodesy, vol 42 n° 3 (May 2019)PermalinkEstimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM imagery / Yanan Liu in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)PermalinkIncluding Sentinel-1 radar data to improve the disaggregation of MODIS land surface temperature data / Abdelhakim Amazirh in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)PermalinkCalibration of the normalized radar cross section for sentinel-1 wave mode / Huimin Li in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)PermalinkDeveloping a subswath-based wind speed retrieval model for sentinel-1 VH-Polarized SAR data over the ocean surface / Kangyu Zhang in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)PermalinkDuPLO: A DUal view Point deep Learning architecture for time series classificatiOn / Roberto Interdonato in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)PermalinkFeasibility study of vegetation indices derived from Sentinel-2 and PlanetScope satellite images for validating the LAI biophysical parameter to monitoring development stages of winter wheat / Radoslaw Gurdak in Geoinformation issues, Vol 10 n°1 (2018)PermalinkA novel sharpening approach for superresolving multiresolution optical images / Claudia Paris in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)PermalinkNear real-time deforestation detection in Malaysia and Indonesia using change vector analysis with three sensors / Pauline Perbet in International Journal of Remote Sensing IJRS, vol 40 n°19 (February 2019)PermalinkTree cover mapping using hybrid fuzzy C-means method and multispectral satellite images / Linda Gulbe in Baltic forestry, vol 25 n° 1 ([01/02/2019])PermalinkAnalyse de la déformation récente dans le Grand Tunis par interférométrie radar SAR / Anis Chaabani (2019)PermalinkApport des mesures du radar à synthèse d'ouverture de Sentinel-1 pour l'étude des propriétés du manteau neigeux / Gaëlle Veyssière (2019)PermalinkApports de l'imagerie satellitaire pour caractériser les évolutions morphologiques de l'embouchure du Tage / Anne Jaouen (2019)PermalinkAssessment of different vegetation parameters for parameterizing the coupled water cloud model and advanced integral equation model for soil moisture retrieval using time series Sentinel-1A data / Long Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)PermalinkCartographie des déformations sur le site de colocalisation de Grasse par méthode INSAR / Isabelle Delprat (2019)PermalinkChallenges in grassland mowing event detection with multimodal Sentinel images / Anatol Garioud (2019)PermalinkClassification du type et de la concentration de la banquise, à partir d’images Sentinel-1 SAR, grâce à des réseaux de neurones convolutifs / Hugo Boulze (2019)PermalinkPermalinkDiscriminating ship from radio frequency interference based on noncircularity and non-gaussianity in sentinel-1 SAR imagery / Xiangguang Leng in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)PermalinkEarth observation, remote sensing and geoscientific ground investigations for archaeological and heritage research / Deodato Tapete (2019)PermalinkEvaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest / Aline Bernarda Debastiani in Annals of forest research, vol 62 n° 1 (January - June 2019)PermalinkEvaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands / Fabio Castaldi in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkEvaluation of time-series SAR and optical images for the study of winter land-use / Julien Denize (2019)PermalinkExploitation de séries temporelles d'images multi-sources pour la cartographie des surfaces en eau / Filsa Bioresita (2019)PermalinkGéomatique webmapping en open source / David Collado (2019)PermalinkPermalinkGlobal observations of ocean surface winds and waves using spaceborne synthetic aperture radar measurements / Huimin Li (2019)PermalinkPermalinkJoint analysis of SAR and optical satellite images time series for grassland event detection / Anatol Garioud (2019)PermalinkMéthodes d'exploitation de données historiques pour la production de cartes d'occupation des sols à partir d'images de télédétection et en absence de données de référence de la période à cartographier / Benjamin Tardy (2019)PermalinkMonitoring crops water needs at high spatio-temporal resolution by synergy of optical / thermal and radar observations / Abdelhakim Amazirh (2019)PermalinkMultimodal scene understanding: algorithms, applications and deep learning, ch. 11. Decision fusion of remote-sensing data for land cover classification / Arnaud Le Bris (2019)PermalinkMultitemporal SAR images denoising and change detection : applications to Sentinel-1 data / Weiying Zhao (2019)PermalinkPermalinkPermalinkPermalinkPermalinkTime-space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series / Vivien Sainte Fare Garnot (2019)PermalinkToward global soil moisture monitoring with sentinel-1 : harnessing assets and overcoming obstacles / Bernhard Bauer-Marschallinger in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)PermalinkUrban deformation monitoring using persistent scatterer Interferometry and SAR tomography / Michele Crosetto (2019)PermalinkUrban morpho-types classification from SPOT-6/7 imagery and Sentinel-2 time series / Arnaud Le Bris (2019)PermalinkUtilisation de données Sentinel-2 et SPOT 6/7 pour la classification de l’occupation du sol / Olivier Stocker (2019)PermalinkAtmospheric artifacts correction with a covariance-weighted linear model over mountainous regions / Zhongbo Hu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkLong-term land deformation monitoring using quasi-persistent scatterer (Q-PS) technique observed by sentinel-1A : case study Kelok Sembilan / Pakhrur Razi in Advances in Remote Sensing, vol 7 n° 4 (December 2018)PermalinkPolarimetric radar vegetation index for biomass estimation in desert fringe ecosystems / Jisung Geba Chang in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkPotential of Sentinel-1 data for monitoring temperate mixed forest phenology / Pierre-Louis Frison in Remote sensing, vol 10 n° 12 (December 2018)PermalinkSuper-resolution of Sentinel-2 images : Learning a globally applicable deep neural network / Charis Lanaras in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)PermalinkMulti-scale object detection in remote sensing imagery with convolutional neural networks / Zhipeng Deng in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)PermalinkEstimation of forest above-ground biomass by geographically weighted regression and machine learning with Sentinel imagery / Lin Chen in Forests, vol 9 n° 10 (October 2018)PermalinkEstimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data / P. Kumar in Geocarto international, vol 33 n° 9 (September 2018)PermalinkSynergetic use of Sentinel-1 and Sentinel-2 for assessments of heathland conservation status / Johannes Schmidt in Remote sensing in ecology and conservation, vol 4 n° 3 (September 2018)PermalinkThe 2015 Mw 6.4 Pishan earthquake, China: geodetic modelling inferred from Sentinel-1A TOPS interferometry / Yongsheng Li in Survey review, vol 50 n° 363 (September 2018)PermalinkAssessment of Sentinel-1A data for rice crop classification using random forests and support vector machines / Nguyen-Thanh Son in Geocarto international, vol 33 n° 6 (June 2018)PermalinkFusion tardive d’images SPOT 6/7 et de données multitemporelles Sentinel-2 pour la détection de la tache urbaine / Cyril Wendl in Revue Française de Photogrammétrie et de Télédétection, n° 217-218 (juin - septembre 2018)PermalinkAn object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery / Luis Angel Ruiz in Geocarto international, vol 33 n° 5 (May 2018)PermalinkImproving the analysis of biogeochemical patterns associated with internal waves in the strait of Gibraltar using remote sensing images / Gabriel Navarro in Estuarine, Coastal and Shelf Science, vol 204 (May 2018)PermalinkCartographie des défoliations du massif forestier du Pays des étangs en Lorraine : Apports potentiels de la télédétection / Thierry Bélouard in Revue forestière française, vol 70 n° 5 (2018)PermalinkMapping spatial variability of foliar nitrogen in coffee (Coffea arabica L.) plantations with multispectral Sentinel-2 MSI data / Abel Chemura in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)PermalinkMapping tree cover with Sentinel-2 data using the Support Vector Machine (SVM) / Anna Mirończuk in Geoinformation issues, Vol 9 n° 1 (2017)PermalinkUnderstanding the temporal dimension of the red-edge spectral region for forest decline detection using high-resolution hyperspectral and Sentinel-2a imagery / Pablo J. Zarco-Tejada in ISPRS Journal of photogrammetry and remote sensing, vol 137 (March 2018)PermalinkAdapting an existing semi-automatized image processing chain to enable Sentinel-2 data classification. / Hiyam Elbadri (2018)PermalinkPermalinkAutomated delineation of wildfire areas using Sentinel-2 satellite imagery / Mira Weirather in GI Forum, vol 2018 n° 1 ([01/01/2018])PermalinkCartographie des déformations de surface sur l’île de Taiwan par interférométrie RADAR Sentinel-1 / Miloud Fekaouni (2018)Permalink