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Mapping temperate forest tree species using dense Sentinel-2 time series / Jan Hemmerling in Remote sensing of environment, vol 267 (December-15 2021)
[article]
Titre : Mapping temperate forest tree species using dense Sentinel-2 time series Type de document : Article/Communication Auteurs : Jan Hemmerling, Auteur ; Dirk Pflugmacher, Auteur ; Patrick Hostert, Auteur Année de publication : 2021 Article en page(s) : n° 112743 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte de la végétation
[Termes IGN] espèce végétale
[Termes IGN] Europe centrale
[Termes IGN] filtrage numérique d'image
[Termes IGN] forêt tempérée
[Termes IGN] image Sentinel-MSI
[Termes IGN] série temporelleRésumé : (auteur) Precise information on tree species composition is critical for forest management and conservation, but mapping tree species with satellite data over large areas is still a challenge. Since 2017, Sentinel-2A/B provide multi-spectral time series with global coverage at an unprecedented spatial and temporal resolution. This is a new opportunity for mapping tree species over large areas that has not yet been fully explored. Because of the high spatial and temporal resolution, Sentinel-2 time series improve the characterization of vegetation phenology and canopy structure, parameters that are intrinsically linked to tree species. The objective of this study was to test the utility of a Sentinel-2 time-series based approach for mapping tree species in a temperate forest region in Central Europe. Using stand-wise forest inventory data for single species stands we assess how well main and minor tree species can be mapped, and if the addition of environmental variables and spatial texture metrics improves the classification accuracy. Our time series approach utilizes all available Sentinel-2 observations and an ensemble of radial basis convolution filters to build cloud-free 5-day time series for each spectral band. The time series are then used as input features to classify seventeen tree species. Our results show the potential of Sentinel-2 time-series based classification, but they also show the challenges associated with mapping a diverse portfolio of tree species. Accuracy of the nine main species, with an area proportion greater than 0.5%, ranged between 98.9% and 66.8%, which is promising for a large area. Adding detailed environmental data and texture metrics to the spectral model only marginally increased the accuracy of a few minor tree species. Overall, the eight minor tree species with area proportions less than 0.5% were most strongly affected by classification errors. Although the absolute mapped area of minor species correlated well with the estimated reference area, the small class areas of minor species lead to high classification errors in relative terms. Mapping minor tree species is challenging for statistical reasons (i.e., class imbalance, small sample size and class variance). Using all available Sentinel-2 data allows building dense time series at high spatial resolution that are mandatory for improved tree species mapping. We were able to show that the spectral time series is the prime explanatory information, even when complementing our analyses with texture information and various environmental data. The results suggest that with the applied data harmonization approach precise regional tree species mapping is feasible. Numéro de notice : A2021-939 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112743 Date de publication en ligne : 13/10/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112743 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99748
in Remote sensing of environment > vol 267 (December-15 2021) . - n° 112743[article]Multi-model estimation of forest canopy closure by using red edge bands based on Sentinel-2 images / Yiying Hua in Forests, vol 12 n° 12 (December 2021)
[article]
Titre : Multi-model estimation of forest canopy closure by using red edge bands based on Sentinel-2 images Type de document : Article/Communication Auteurs : Yiying Hua, Auteur ; Xuesheng Zhao, Auteur Année de publication : 2021 Article en page(s) : n° 1768 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] bande infrarouge
[Termes IGN] coefficient de corrélation
[Termes IGN] couvert forestier
[Termes IGN] détection de contours
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] modèle statistique
[Termes IGN] Mongolie intérieure (Chine)
[Termes IGN] régression
[Termes IGN] surveillance de la végétationRésumé : (auteur) In remote sensing, red edge bands are important indicators for monitoring vegetation growth. To examine the application potential of red edge bands in forest canopy closure estimation, three types of commonly used models—empirical statistical models (multiple stepwise regression (MSR)), machine learning models (back propagation neural network (BPNN)) and physical models (Li–Strahler geometric-optical (Li–Strahler GO) models)—were constructed and verified based on Sentinel-2 data, DEM data and measured data. In addition, we set up a comparative experiment without red edge bands. The relative error (ER) values of the BPNN model, MSR model, and Li–Strahler GO model with red edge bands were 16.97%, 20.76% and 24.83%, respectively. The validation accuracy measures of these models were higher than those of comparison models. For comparative experiments, the ER values of the MSR, Li–Strahler GO and BPNN models were increased by 13.07%, 4% and 1.22%, respectively. The experimental results demonstrate that red edge bands can effectively improve the accuracy of forest canopy closure estimation models to varying degrees. These findings provide a reference for modeling and estimating forest canopy closure using red edge bands based on Sentinel-2 images. Numéro de notice : A2021-125 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f12121768 Date de publication en ligne : 14/12/2021 En ligne : https://doi.org/10.3390/f12121768 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99318
in Forests > vol 12 n° 12 (December 2021) . - n° 1768[article]National scale mapping of larch plantations for Wales using the Sentinel-2 data archive / Suvarna M. Punalekar in Forest ecology and management, vol 501 (December-1 2021)
[article]
Titre : National scale mapping of larch plantations for Wales using the Sentinel-2 data archive Type de document : Article/Communication Auteurs : Suvarna M. Punalekar, Auteur ; Carole Planque, Auteur ; Richard M. Lucas, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 119679 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] arbre de décision
[Termes IGN] carte forestière
[Termes IGN] coupe rase (sylviculture)
[Termes IGN] gestion forestière
[Termes IGN] image infrarouge
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice de végétation
[Termes IGN] Larix decidua
[Termes IGN] maladie phytosanitaire
[Termes IGN] modélisation de la forêt
[Termes IGN] Pays de Galles
[Termes IGN] surveillance forestièreRésumé : (auteur) Accurate spatial information regarding forest types and tree species is immensely important for efficient forest management strategies. In the UK and particularly in Wales, creating a spatial inventory of larch (Larix sps.) plantations that encompasses both the public and private forests has become one of the highest priorities of woodland management policies, particularly given the need to respond to the rapid spread of Phytophthora ramorum fungal disease. For directing disease control measures, national scale, regularly updated mapping of larch distributions is essential. In this study, we applied a ExtraTree classifier machine learning algorithm to multi-year (June 2015 and December 2019) multi-path composites of vegetation indices derived from 10 m Sentinel-2 satellite data (spectral range used in this study: 490–2190 nm) to map the extent of larch plantations across Wales. For areas identified as woody vegetation, areas under larch plantations were associated with a needle-leaved leaf type and deciduous phenology, allowing differentiation from broad-leaved deciduous and needle-leaved evergreen types. The model accuracies for validation, which included overall accuracy, producer’s and user’s accuracies, exceeded 95% and the F1-score was greater than 0.97 for all forest types. Comparison against an independent reference dataset indicated all map accuracies above 90% (F1-score higher than 0.92) with the lowest value being 90.3% for the producer’s accuracy for larch. Short wave infrared and red-edge based indices were particularly useful for discriminating larch from other forest types. Capacity for updating information on clear-felling of larch stands through annual updates of a woody mask was also introduced. The resulting maps of larch plantations for Wales are the most current for Wales covering public as well as private woodlands and can be routinely updated. The classification approach has potential to be transferred to a wider geographical area given the availability of open-source multi-year Sentienl-2 datasets and robust calibration datasets. Numéro de notice : A2021-741 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.foreco.2021.119679 Date de publication en ligne : 20/09/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119679 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98657
in Forest ecology and management > vol 501 (December-1 2021) . - n° 119679[article]Crop rotation modeling for deep learning-based parcel classification from satellite time series / Félix Quinton in Remote sensing, vol 13 n° 22 (November-2 2021)
[article]
Titre : Crop rotation modeling for deep learning-based parcel classification from satellite time series Type de document : Article/Communication Auteurs : Félix Quinton , Auteur ; Loïc Landrieu , Auteur Année de publication : 2021 Projets : 3-projet - voir note / Article en page(s) : n° 4599 Note générale : bibliographie
This research was funded by the French Payment Agency ASP.Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] carte agricole
[Termes IGN] données étiquetées d'entrainement
[Termes IGN] image Sentinel-MSI
[Termes IGN] parcelle agricole
[Termes IGN] rotation de culture
[Termes IGN] série temporelleRésumé : (auteur) While annual crop rotations play a crucial role for agricultural optimization, they have been largely ignored for automated crop type mapping. In this paper, we take advantage of the increasing quantity of annotated satellite data to propose to model simultaneously the inter- and intra-annual agricultural dynamics of yearly parcel classification with a deep learning approach. Along with simple training adjustments, our model provides an improvement of over 6.3% mIoU over the current state-of-the-art of crop classification, and a reduction of over 21% of the error rate. Furthermore, we release the first large-scale multi-year agricultural dataset with over 300,000 annotated parcels. Numéro de notice : A2021-934 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13224599 Date de publication en ligne : 16/11/2021 En ligne : https://doi.org/10.3390/rs13224599 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99539
in Remote sensing > vol 13 n° 22 (November-2 2021) . - n° 4599[article]Spatial variability of suspended sediments in San Francisco Bay, California / Niky C. Taylor in Remote sensing, vol 13 n° 22 (November-2 2021)
[article]
Titre : Spatial variability of suspended sediments in San Francisco Bay, California Type de document : Article/Communication Auteurs : Niky C. Taylor, Auteur ; Raphael M. Kudela, Auteur Année de publication : 2021 Article en page(s) : n° 4625 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] baie
[Termes IGN] échantillonnage
[Termes IGN] estuaire
[Termes IGN] image Sentinel-MSI
[Termes IGN] pas d'échantillonnage au sol
[Termes IGN] qualité des eaux
[Termes IGN] réflectance
[Termes IGN] San Francisco
[Termes IGN] sédiment
[Termes IGN] spectroradiométrie
[Termes IGN] surface de l'eau
[Termes IGN] surveillance du littoral
[Termes IGN] turbidité des eaux
[Termes IGN] variabilitéRésumé : (auteur) Understanding spatial variability of water quality in estuary systems is important for making monitoring decisions and designing sampling strategies. In San Francisco Bay, the largest estuary system on the west coast of North America, tracking the concentration of suspended materials in water is largely limited to point measurements with the assumption that each point is representative of its surrounding area. Strategies using remote sensing can expand monitoring efforts and provide a more complete view of spatial patterns and variability. In this study, we (1) quantify spatial variability in suspended particulate matter (SPM) concentrations at different spatial scales to contextualize current in-water point sampling and (2) demonstrate the potential of satellite and shipboard remote sensing to supplement current monitoring methods in San Francisco Bay. We collected radiometric data from the bow of a research vessel on three dates in 2019 corresponding to satellite overpasses by Sentinel-2, and used established algorithms to retrieve SPM concentrations. These more spatially comprehensive data identified features that are not picked up by current point sampling. This prompted us to examine how much variability exists at spatial scales between 20 m and 10 km in San Francisco Bay using 10 m resolution Sentinel-2 imagery. We found 23–80% variability in SPM at the 5 km scale (the scale at which point sampling occurs), demonstrating the risk in assuming limited point sampling is representative of a 5 km area. In addition, current monitoring takes place along a transect within the Bay’s main shipping channel, which we show underestimates the spatial variance of the full bay. Our results suggest that spatial structure and spatial variability in the Bay change seasonally based on freshwater inflow to the Bay, tidal state, and wind speed. We recommend monitoring programs take this into account when designing sampling strategies, and that end-users account for the inherent spatial uncertainty associated with the resolution at which data are collected. This analysis also highlights the applicability of remotely sensed data to augment traditional sampling strategies. In sum, this study presents ways to supplement water quality monitoring using remote sensing, and uses satellite imagery to make recommendations for future sampling strategies. Numéro de notice : A2021-839 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13224625 Date de publication en ligne : 17/11/2021 En ligne : https://doi.org/10.3390/rs13224625 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99022
in Remote sensing > vol 13 n° 22 (November-2 2021) . - n° 4625[article]Above-ground biomass change estimation using national forest inventory data with Sentinel-2 and Landsat / Stefano Puliti in Remote sensing of environment, vol 265 (November 2021)PermalinkLand subsidence in Beijing’s sub-administrative center and its relationship with urban expansion inferred from Sentinel-1/2 observations / Jin Cao in Canadian journal of remote sensing, vol 47 n° 6 ([01/11/2021])PermalinkMulti-sensor aboveground biomass estimation in the broadleaved hyrcanian forest of Iran / Ghasem Ronoud in Canadian journal of remote sensing, vol 47 n° 6 ([01/11/2021])PermalinkA novel cotton mapping index combining Sentinel-1 SAR and Sentinel-2 multispectral imagery / Lan Xun in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)PermalinkPersistent scatterer interferometry for Pettimudi (India) landslide monitoring using Sentinel-1A images / Hari Shankar in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 11 (November 2021)PermalinkA repeatable change detection approach to map extreme storm-related damages caused by intense surface runoff based on optical and SAR remote sensing: Evidence from three case studies in the South of France / Arnaud Cerbelaud in ISPRS Journal of photogrammetry and remote sensing, Vol 182 (December 2021)PermalinkSuperpixel-based regional-scale grassland community classification using genetic programming with Sentinel-1 SAR and Sentinel-2 multispectral images / Zhenjiang Wu in Remote sensing, vol 13 n° 20 (October-2 2021)PermalinkDétection des forêts dégradées en Guinée à partir des images satellites Sentinel-2 : évaluation de l'apport potentiel des nouveaux capteurs satellitaires optiques et radars / An Vo Quang in Blog de la RFPT, sans n° ([11/10/2021])PermalinkBi- and three-dimensional urban change detection using sentinel-1 SAR temporal series / Meiqin Che in Geoinformatica, vol 25 n° 4 (October 2021)PermalinkDeep-learning-based burned area mapping using the synergy of Sentinel-1&2 data / Qi Zhang in Remote sensing of environment, vol 264 (October 2021)PermalinkEvaluation of methods for connecting InSAR to a terrestrial reference frame in the Latrobe Valley, Australia / P.J. Johnston in Journal of geodesy, vol 95 n° 10 (October 2021)PermalinkField scale wheat LAI retrieval from multispectral Sentinel 2A-MSI and LandSat 8-OLI imagery: effect of atmospheric correction, image resolutions and inversion techniques / Rajkumar Dhakar in Geocarto international, vol 36 n° 18 ([01/10/2021])PermalinkIntegrating spatio-temporal-spectral information for downscaling Sentinel-3 OLCI images / Yijie Tang in ISPRS Journal of photogrammetry and remote sensing, vol 180 (October 2021)PermalinkInvestigating operational country-level crop monitoring with Sentinel~1 and~2 imagery / Nicolas David in Remote sensing letters, vol 12 n° 10 (October 2021)PermalinkInvestigation of the landslides in Beylikdüzü-Esenyurt districts of Istanbul from InSAR and GNSS observations / Caglar Bayik in Natural Hazards, vol 109 n° 1 (October 2021)PermalinkOrbit error removal in InSAR/MTInSAR with a patch-based polynomial model / Yanan Du in International journal of applied Earth observation and geoinformation, vol 102 (October 2021)PermalinkPhenology-based delineation of irrigated and rain-fed paddy fields with Sentinel-2 imagery in Google Earth Engine / Daniel Marc G. dela Torre in Geo-spatial Information Science, vol 24 n° 4 (October 2021)PermalinkRecurrent-based regression of Sentinel time series for continuous vegetation monitoring / Anatol Garioud in Remote sensing of environment, vol 263 (15 September 2021)PermalinkA deep translation (GAN) based change detection network for optical and SAR remote sensing images / Xinghua Li in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)PermalinkSentinel-1 sensitivity to soil moisture at high incidence angle and the impact on retrieval over seasonal crops / Davide Palmisano in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)PermalinkThe real potential of current passive satellite data to map aboveground biomass in tropical forests / Nidhi Jha in Remote sensing in ecology and conservation, vol 7 n° 3 (September 2021)PermalinkEstimation of surface deformation due to Pasni earthquake using RADAR interferometry / Muhammad Ali in Geocarto international, vol 36 n° 14 ([01/08/2021])PermalinkImproving urban land cover classification with combined use of Sentinel-2 and Sentinel-1 imagery / Bin Hu in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)PermalinkMapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America / Bin Chen in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)PermalinkRapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning / Xin Jiang in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)PermalinkDetecting high-temperature anomalies from Sentinel-2 MSI images / Yongxue Liu in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)PermalinkEstimation of tree height and aboveground biomass of coniferous forests in North China using stereo ZY-3, multispectral Sentinel-2, and DEM data / Yueting Wang in Ecological indicators, vol 126 (July 2021)PermalinkFluvial gravel bar mapping with spectral signal mixture analysis / Liza Stančič in European journal of remote sensing, vol 54 sup 1 (2021)PermalinkMultisensor data fusion for cloud removal in global and all-season Sentinel-2 imagery / Patrick Ebel in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)PermalinkSemantic unsupervised change detection of natural land cover with multitemporal object-based analysis on SAR images / Donato Amitrano in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)PermalinkMarrying deep learning and data fusion for accurate semantic labeling of Sentinel-2 images / Guillemette Fonteix in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)PermalinkFast unsupervised multi-scale characterization of urban landscapes based on Earth observation data / Claire Teillet in Remote sensing, vol 13 n° 12 (June-2 2021)PermalinkCloud-native seascape mapping of Mozambique’s Quirimbas National Park with Sentinel-2 / Dimitris Poursanidis in Remote sensing in ecology and conservation, vol 7 n° 2 (June 2021)PermalinkDiscovery of new colonies by Sentinel2 reveals good and bad news for emperor penguins / Peter T. Fretwell in Remote sensing in ecology and conservation, vol 7 n° 2 (June 2021)PermalinkMultiscale cloud detection in remote sensing images using a dual convolutional neural network / Markku Luotamo in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)PermalinkResolution enhancement for large-scale land cover mapping via weakly supervised deep learning / Qiutong Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)PermalinkA compilation of snow cover datasets for Svalbard: A multi-sensor, multi-model study / Hannah Vickers in Remote sensing, vol 13 n°10 (May-2 2021)PermalinkAboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data : The superiority of deep learning over a semi-empirical model / S.M. Ghosh in Computers & geosciences, vol 150 (May 2021)PermalinkAssessing forest phenology: A multi-scale comparison of near-surface (UAV, spectral reflectance sensor, PhenoCam) and satellite (MODIS, Sentinel-2) remote sensing / Shangharsha Thapa in Remote sensing, vol 13 n° 8 (April-2 2021)PermalinkDecision-level and feature-level integration of remote sensing and geospatial big data for urban land use mapping / Jiadi Yin in Remote sensing, vol 13 n° 8 (April-2 2021)PermalinkLeaf area index estimation of wheat crop using modified water cloud model from the time-series SAR and optical satellite data / Vijay Pratap Yadav in Geocarto international, vol 36 n° 7 ([15/04/2021])PermalinkPotentialité des données satellitaires Sentinel-2 pour la cartographie de l’impact des feux de végétation en Afrique tropicale : application au Togo / Yawo Konko in Bois et forêts des tropiques, n° 347 ([02/04/2021])PermalinkAtmospheric correction of Sentinel-3/OLCI data for mapping of suspended particulate matter and chlorophyll-a concentration in Belgian turbid coastal waters / Quinten Vanhellemont in Remote sensing of environment, Vol 256 (April 2020)PermalinkExtraction of sea ice cover by Sentinel-1 SAR based on support vector machine with unsupervised generation of training data / Xiao-Ming Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkShoreline changes along Northern Ibaraki Coast after the great East Japan earthquake of 2011 / Quang Nguyen Hao in Remote sensing, vol 13 n° 7 (April-1 2021)PermalinkSpectral–spatial-aware unsupervised change detection with stochastic distances and support vector machines / Rogério Galante Negri in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkTemporal mosaicking approaches of Sentinel-2 images for extending topsoil organic carbon content mapping in croplands / Emmanuelle Vaudour in International journal of applied Earth observation and geoinformation, vol 96 (April 2021)PermalinkTime-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine / Dong Liang in Remote sensing of environment, Vol 256 (April 2020)PermalinkComplémentarité des images optiques Sentinel-2 avec les images radar Sentinel-1 et ALOS-PALSAR-2 pour la cartographie de la couverture végétale : application à une aire protégée et ses environs au Nord-Ouest du Maroc via trois algorithmes d’apprentissage automatique / Siham Acharki in Revue Française de Photogrammétrie et de Télédétection, n° 223 (mars - décembre 2021)PermalinkEvaluation du potentiel des series d’images multi-temporelles optique et radar des satellites Sentinel 1 & 2 pour le suivi d’une zone côtière en contexte tropical: cas de l’estuaire du Cameroun pour la période 2015-2020 / Nourdi Njutapvoui in Revue Française de Photogrammétrie et de Télédétection, n° 223 (mars - décembre 2021)PermalinkSRP, une base de calage 3D de très haute précision sur le continent africain / Laure Chandelier in Revue Française de Photogrammétrie et de Télédétection, n° 223 (mars - décembre 2021)PermalinkBasin-scale high-resolution extraction of drainage networks using 10-m Sentinel-2 imagery / Zifeng Wang in Remote sensing of environment, Vol 255 (March 2021)PermalinkEarly detection of forest stress from European spruce bark beetle attack, and a new vegetation index: Normalized distance red & SWIR (NDRS) / Langning Huo in Remote sensing of environment, Vol 255 (March 2021)PermalinkA soil texture categorization mapping from empirical and semi-empirical modelling of target parameters of synthetic aperture radar / Shoba Periasamy in Geocarto international, vol 36 n° 5 ([15/03/2021])PermalinkApports de la télédétection au calcul d’indicateurs agri-environnementaux au service de la PAC, des agriculteurs et porteurs d’enjeu / Christian Bockstaller in Innovations Agronomiques, vol 83 (Mars 2021)PermalinkCluster-based empirical tropospheric corrections applied to InSAR time series analysis / Kyle Dennis Murray in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)PermalinkDenoising Sentinel-1 extra-wide mode cross-polarization images over sea ice / Yan Sun in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)PermalinkExtraction of impervious surface using Sentinel-1A time-series coherence images with the aid of a Sentinel-2A image / Wenfu Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 3 (March 2021)PermalinkGridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates / Franz Schug in Plos one, vol 16 n° 3 (March 2021)PermalinkIntegration of an InSAR and ANN for sinkhole susceptibility mapping: A case study from Kirikkale-Delice (Turkey) / Hakan Nefeslioglu in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)PermalinkSaline-soil deformation extraction based on an improved time-series InSAR approach / Wei Xiang in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)PermalinkSimple method for identification of forest windthrows from Sentinel-1 SAR data incorporating PCA / Milan Lazecky in Procedia Computer Science, vol 181 (2021)PermalinkCoastal water remote sensing from sentinel-2 satellite data using physical, statistical, and neural network retrieval approach / Frank S. Marzano in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)PermalinkComprehensive time-series analysis of bridge deformation using differential satellite radar interferometry based on Sentinel-1 / Matthias Schlögl in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)PermalinkCrop 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)PermalinkOptimizing 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)PermalinkReclaimed-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)PermalinkSpruce 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)PermalinkStudy 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)PermalinkMapping 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)PermalinkAssessing the interest of a multi-modal gap-filling strategy for monitoring changes in grassland parcels / Anatol Garioud (2021)PermalinkAssessment of chlorophyll-a concentration from Sentinel-3 satellite images at the Mediterranean Sea using CMEMS open source in situ data / Ioannis Moutzouris-Sidiris in Open geosciences, vol 13 n° 1 (January 2021)PermalinkAssessment of combining convolutional neural networks and object based image analysis to land cover classification using Sentinel 2 satellite imagery (Tenes region, Algeria) / N. Zaabar (2021)PermalinkBeach morphology and its dynamism from remote sensing for coastal management support / Carlos Cabezas Rabadán (2021)PermalinkBenchmarking of convolutional neural network approaches for vegetation land cover mapping / Benjamin Carpentier (2021)PermalinkCentrality and city size effects on NO2 ground and tropospheric concentrations within European cities / Yufei Wei (2021)PermalinkChange detection of land use and land cover, using landsat-8 and sentinel-2A images / Mohammed Abdulmohsen Alhedyan (2021)PermalinkPermalinkPermalinkDeep learning for wildfire progression monitoring using SAR and optical satellite image time series / Puzhao Zhang (2021)PermalinkDétection de changement d’occupation du sol à l’aide de données Sentinel en contexte tropical / Lucas Martelet (2021)PermalinkDiurnal cycles of C-band temporal coherence and backscattering coefficient over an olive orchard in a semi-arid area: Comparison of in situ and Sentinel-1 radar observations / Adnane Chakir (2021)PermalinkDiurnal cycles of C-band temporal coherence and backscattering coefficient over a wheat field in a semi-arid area / Nadia Ouaadi (2021)PermalinkDynamics of inundation events in the rivers-estuaries-ocean continuum in Bengal delta : synergy between hydrodynamic modelling and spaceborne remote sensing / Md Jamal Uddin Kahn (2021)PermalinkEnsemble learning methods on the space of covariance matrices : application to remote sensing scene and multivariate time series classification / Sara Akodad (2021)PermalinkPermalinkÉvaluation de l'évapotranspiration des zones irriguées en piémont du Haut Atlas, Maroc / Jamal Elfarkh (2021)Permalink