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Application of geographic Information system and remote sensing in multiple criteria analysis to identify priority areas for biodiversity conservation in Vietnam / Xuan Dinh Vu (2020)
Titre : Application of geographic Information system and remote sensing in multiple criteria analysis to identify priority areas for biodiversity conservation in Vietnam Type de document : Thèse/HDR Auteurs : Xuan Dinh Vu, Auteur Editeur : Dresde [Allemagne] : Technische Universität Dresden Année de publication : 2020 Importance : 294 p. Format : 21 x 30 cm Note générale : bibliographie
Dissertation for awarding the academic degree Doctor of Natural ScienceLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] aire protégée
[Termes IGN] classification dirigée
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] image Landsat-OLI
[Termes IGN] image Landsat-TM
[Termes IGN] image Sentinel-MSI
[Termes IGN] occupation du sol
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] protection de la biodiversité
[Termes IGN] réflectance de surface
[Termes IGN] série temporelle
[Termes IGN] système d'information géographique
[Termes IGN] télédétection
[Termes IGN] Viet Nam
[Termes IGN] zonage (urbanisme)Index. décimale : THESE Thèses et HDR Résumé : (auteur) There has been an increasing need for methods to define priority areas for biodiversity conservation since the effectiveness of biodiversity conservation in protected areas planning depends on available resources (human resources and funds) for the conservation. The identification of priority areas requires the integration of biodiversity data together with social data on human pressures and responses. However, the deficit of comprehensive data and reliable methods are key challenges in zoning where the demand for conservation is most urgent and where the outcomes of conservation strategies can be maximized. In order to fill this gap, the environmental model Pressure–State–Response (PSR) was applied to suggest a set of criteria to identify priority areas for biodiversity conservation. The empirical data have been compiled from 185 respondents, categorizing into three main groups: Governmental Administration, Research Institutions, and Protected Areas in Vietnam, by using a well-designed questionnaire. Then, the Analytic Hierarchy Process (AHP) theory was used to identify the weight of all criteria. These results show that three main factors could identify the priority level for biodiversity conservation: Pressure, State, and Response, with weights of 41%, 26%, and 33%, respectively. Based on the three factors, seven criteria and 17 sub-criteria were developed to determine priority areas for biodiversity conservation. In addition, this study also indicates that the groups of Governmental Administration and Protected Areas put a focus on the “Pressure” factor while the group of Research Institutions emphasized the importance of the “Response” factor in the evaluation process. Then these suggested criteria were applied by integrating with Geographic Information System (GIS) and Remote Sensing (RS) to define priority areas for biodiversity conservation in a particular conservation area (Pu Luong-Cuc Phuong area) in Vietnam. The results also reveal the proportion of very high and high priority areas, accounting for 84.9%, 96%, and 65.9% for Cuc Phuong National Park, Pu Luong Nature Reserve, and Ngoc Son Ngo Luong Nature Reserve, respectively. Based on these results, recommendations were provided to apply the developed criteria for identifying priority areas for biodiversity conservation in Vietnam. Note de contenu : 1- Introduction
2- Literature review
3- Research methodology
4- Establishment of criteria
5- Application of criteria
6- Conclusions and recommandationsNuméro de notice : 28465 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/GEOMATIQUE/IMAGERIE Nature : Thèse étrangère Note de thèse : PhD thesis : Natural Science : Dresde : 2020 DOI : sans En ligne : http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa2-737808 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99087 Cartographie 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)
Titre : Cartographie 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 Type de document : Thèse/HDR Auteurs : Nicolas Karasiak, Auteur ; Claude Monteil, Directeur de thèse ; Jean-Français Dejoux, Directeur de thèse ; David Sheeren , Directeur de thèse Editeur : Toulouse : Université de Toulouse Année de publication : 2020 Importance : 240 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse en vue de l'obtention du Doctorat de l'Université de Toulouse, délivré par l'Institut National Polytechnique de Toulouse, spécialité : Agrosystèmes, Écosystèmes et EnvironnementLangues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] autocorrélation spatiale
[Termes IGN] bibliothèque logicielle
[Termes IGN] carte forestière
[Termes IGN] essence d'arbre
[Termes IGN] image à haute résolution
[Termes IGN] image Formosat/COSMIC
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] phénologie
[Termes IGN] série temporelleIndex. décimale : THESE Thèses et HDR Résumé : (auteur) La forêt a un rôle essentiel sur terre, que ce soit pour stocker le carbone et ainsi lutter contre le réchauffement climatique ou encore fournir un habitat à de nombreuses espèces. Or, la composition de la forêt (la localisation des essences ou leur diversité) a une influence sur les services écologiques rendus. Dans ce contexte, il est important de cartographier les forêts et les essences qui la composent. La télédétection, en particulier à partir d’images satellitaires, apparait comme le moyen le plus adéquat pour caractériser un vaste territoire. Avec l’arrivée de constellations satellitaires comme Sentinel-2 ou Landsat-8 et leur gratuité d’acquisition pour l’utilisateur, il devient possible d’envisager l’usage de séries temporelles d’images satellites à haute résolution spatiale, spectrale et temporelle à l’aide d’algorithmes d’apprentissage automatique. Si de nombreux travaux ont étudié le potentiel des images satellitaires pour identifier les essences, rares sont ceux qui utilisent des séries temporelles (plusieurs images par an) avec une haute résolution spatiale et en tenant compte de l’autocorrélation spatiale des références, i.e. la ressemblance des échantillons spatialement proches les uns des autres. Or, en ne prenant pas en compte ce phénomène, des biais d’évaluation peuvent survenir et ainsi surestimer la qualité des modèles d’apprentissage. Il s’agit aussi de mieux cerner les verrous méthodologiques afin de comprendre pourquoi il peut être facile ou compliqué pour un algorithme d’identifier une essence d’une autre. L’objectif général de la thèse vise à étudier le potentiel et les verrous concernant la reconnaissance des essences forestières à partir des séries temporelles d’images satellite à haute résolution spatiale, spectrale et temporelle. Le premier objectif consiste à étudier la stabilité temporelle des prédictions à partir d’une archive de neuf ans du satellite Formosat-2. Plus particulièrement, les travaux portent sur la mise en place d’une méthode de validation qui soit le plus fidèle à la qualité observée des cartographies. Le second objectif s’intéresse au lien entre les évènements phénologiques in situ (pousse des feuilles en début de saison, ou perte et coloration des feuilles en fin de saison) et ce qui est observable par télédétection. Outre la capacité de détecter ces évènements, il sera étudié si ce qui permet aux algorithmes de différencier les essences les unes des autres est lié à des comportements spécifiques par espèce. Note de contenu : 1- Introduction
2- Etude de la stabilité spatiale et statistique des prédictions
3- Etude de l’impact de l’autocorrélation spatiale
4- Etude et apport de la phénologie
5- Partage des outils et des données : assurer une reproductibilité des travaux
6- Conclusion généraleNuméro de notice : 28326 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Agrosystèmes, Écosystèmes et Environnement : Toulouse : 2020 Organisme de stage : DYNAFOR DOI : sans En ligne : http://www.theses.fr/2020INPT0115 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98378 Classification 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)
[article]
Titre : Classification of poplar trees with object-based ensemble learning algorithms using Sentinel-2A imagery Type de document : Article/Communication Auteurs : H. Tombul, Auteur ; Ismail Colkesen, Auteur ; Taskin Kavzoglu, Auteur Année de publication : 2020 Article en page(s) : pp 14 - 22 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme d'apprentissage
[Termes IGN] analyse canonique
[Termes IGN] analyse comparative
[Termes IGN] bande spectrale
[Termes IGN] boosting adapté
[Termes IGN] carte de la végétation
[Termes IGN] carte thématique
[Termes IGN] classification orientée objet
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image Sentinel-MSI
[Termes IGN] jeu de données
[Termes IGN] Populus (genre)
[Termes IGN] précision de la classification
[Termes IGN] Rotation Forest classification
[Termes IGN] segmentation multi-échelle
[Termes IGN] TurquieRésumé : (auteur) The poplar species in the forest ecosystems are one of the most valuable and beneficial species for the society and environment. Conventional methods require high cost, time and labor need, and the results obtained vary and are insu˚cient in terms of achieved accuracy level. Determination of poplar cultivated fields and mapping of their spatial sites play a vital role for decision-makers and planners to enhance the economic and ecological value of poplar trees. The study aims to map Poplar (P. deltoides) cultivated areas in Akyazi district of Sakarya, Turkey province using various combinations of the Sentinel-2A image bands. For this purpose, object-based classification based on multi-resolution segmentation algorithm was utilized to produce image objects and ensemble learning algorithms, namely, Adaboost (AdaB), Random Forest (RF), Rotation Forest (RotFor) and Canonical correlation forest (CCF) were applied to produce thematic maps. In order to analyze the effects of the spectral bands of the Sentinel-2A image on the object-based classification performance, three datasets consisting of different spectral band combinations (i.e. four 10 m bands, six 20 m bands and ten 10m pan-sharpened bands) were used. The results showed that the RotFor and CCF classifiers produced superior classification performances compared to the AdaB and RF classifiers for the band combinations regarded in this study. Moreover, it was found that determination of poplar tree class level accuracy reached to ~94% in terms of F-score. It was also observed that the inclusion of the six spectral bands at 20 m resolution resulted in a noteworthy increase in classification accuracy (up to 6%) compared to single 10m band combination. Numéro de notice : A2020-420 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jogs-2020-0003 Date de publication en ligne : 04/05/2020 En ligne : https://doi.org/10.1515/jogs-2020-0003 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95477
in Journal of geodetic science > vol 10 n° 1 (January 2020) . - pp 14 - 22[article]Classification of time series of Sentinel-2 images for large scale mapping in Cameroon / Hermann Tagne (2020)
Titre : Classification of time series of Sentinel-2 images for large scale mapping in Cameroon Type de document : Article/Communication Auteurs : Hermann Tagne, Auteur ; Arnaud Le Bris , Auteur ; David Monkam, Auteur ; Clément Mallet , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2020 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B3 Projets : TOSCA Parcelle / Le Bris, Arnaud Conférence : ISPRS 2020, Commission 3, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Archives Commission 3 Importance : pp 633 - 640 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Cameroun
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification
[Termes IGN] image optique
[Termes IGN] image Sentinel-MSI
[Termes IGN] mise à jour de base de données
[Termes IGN] série temporelleRésumé : (auteur) Sentinel-2 satellites provide dense image time series exhibiting high spectral, spatial and temporal resolution. These images are in particular of utter interest to map Land-Cover (LC) at large scale. LC maps can now be computed on a yearly basis at the scale of a country with efficient supervised classifiers, assuming suitable training data are available. However, the efficient exploitation of large amount of Sentinel-2 imagery still remain challenging on unexplored areas where state-of-the-art classifiers are prone to fail. This paper focuses on Land-Cover mapping over Cameroon for the purpose of updating the national topographic geodatabase. The ι2 framework is adopted and tested for the specificity of the country. Here, experiments focus on generic classes (five) which enables providing robust focusing masks for higher resolution classifications. Two strategies are compared: (i) a LC map is calculated out of a year long time series and (ii) monthly LC maps are generated and merged into a single yearly map. Satisfactory accuracy scores are obtained, allowing to provide a first step towards finer-grained map retrieval. Numéro de notice : C2020-006 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLIII-B3-2020-633-2020 Date de publication en ligne : 21/08/2020 En ligne : https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-633-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95656 Combination 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)
[article]
Titre : Combination of linear regression lines to understand the response of Sentinel-1 dual polarization SAR data with crop phenology - case study in Miyazaki, Japan Type de document : Article/Communication Auteurs : Emal Wali, Auteur ; Masahiro Tasumi, Auteur ; Masao Moriyama, Auteur Année de publication : 2020 Article en page(s) : 17 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse
[Termes IGN] coefficient de rétrodiffusion
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] indice foliaire
[Termes IGN] Japon
[Termes IGN] polarisation
[Termes IGN] régression linéaire
[Termes IGN] rizière
[Termes IGN] surveillance agricole
[Termes IGN] variable biophysique (végétation)Résumé : (auteur) This study investigated the relationship between backscattering coefficients of a synthetic aperture radar (SAR) and the four biophysical parameters of rice crops—plant height, green vegetation cover, leaf area index, and total dry biomass. A paddy rice field in Miyazaki, Japan was studied from April to July of 2018, which is the rice cultivation season. The SAR backscattering coefficients were provided by Sentinel-1 satellite. Backscattering coefficients of two polarization settings—VH (vertical transmitting, horizontal receiving) and VV (vertical transmitting, vertical receiving)—were investigated. Plant height, green vegetation cover, leaf area index, and total dry biomass were measured at ground level, on the same dates as satellite image acquisition. Polynomial regression lines indicated relationships between backscattering coefficients and plant biophysical parameters of the rice crop. The biophysical parameters had stronger relationship to VH than to VV polarization. A disadvantage of adopting polynomial regression equations is that the equation can have two biophysical parameter solutions for a particular backscattering coefficient value, which prevents simple conversion from backscattering coefficients to plant biophysical parameters. To overcome this disadvantage, the relationships between backscattering coefficients and the plant biophysical parameters were expressed using a combination of two linear regression lines, one line for the first sub-period and the other for the second sub-period during the entire cultivation period. Following this approach, all four plant biophysical parameters were accurately estimated from the SAR backscattering coefficient, especially with VH polarization, from the date of transplanting to about two months, until the mid-reproductive stage. However, backscattering coefficients saturate after two months from the transplanting, and became insensitive to the further developments in plant biophysical parameters. This research indicates that SAR can effectively and accurately monitor rice crop biophysical parameters, but only up to the mid reproductive stage. Numéro de notice : A2020-223 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs12010189 Date de publication en ligne : 05/01/2020 En ligne : https://doi.org/10.3390/rs12010189 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94936
in Remote sensing > vol 12 n° 1 (January 2020) . - 17 p.[article]Comparison 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)PermalinkTroposphere delay modeling with horizontal gradients for satellite laser ranging / Mateusz Drożdżewski in Journal of geodesy, vol 93 n°10 (October 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)Permalink