Biblio-Sentinel
Ajouter le résultat dans votre panier Affiner la recherche
Estimation 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)
[article]
Titre : Estimation of forest above-ground biomass by geographically weighted regression and machine learning with Sentinel imagery Type de document : Article/Communication Auteurs : Lin Chen, Auteur ; Chunying Ren, Auteur ; Bai Zhang, Auteur ; Zongming Wang, Auteur ; Yanbiao Xi, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] arbre caducifolié
[Termes IGN] biomasse aérienne
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] modèle de simulation
[Termes IGN] montagne
[Termes IGN] régression géographiquement pondérée
[Termes IGN] surveillance forestière
[Termes IGN] texture d'image
[Termes IGN] variable biophysique (végétation)Résumé : (Auteur) Accurate forest above-ground biomass (AGB) is crucial for sustaining forest management and mitigating climate change to support REDD+ (reducing emissions from deforestation and forest degradation, plus the sustainable management of forests, and the conservation and enhancement of forest carbon stocks) processes. Recently launched Sentinel imagery offers a new opportunity for forest AGB mapping and monitoring. In this study, texture characteristics and backscatter coefficients of Sentinel-1, in addition to multispectral bands, vegetation indices, and biophysical variables of Sentinal-2, based on 56 measured AGB samples in the center of the Changbai Mountains, China, were used to develop biomass prediction models through geographically weighted regression (GWR) and machine learning (ML) algorithms, such as the artificial neural network (ANN), support vector machine for regression (SVR), and random forest (RF). The results showed that texture characteristics and vegetation biophysical variables were the most important predictors. SVR was the best method for predicting and mapping the patterns of AGB in the study site with limited samples, whose mean error, mean absolute error, root mean square error, and correlation coefficient were 4 × 10−3, 0.07, 0.08 Mg·ha−1, and 1, respectively. Predicted values of AGB from four models ranged from 11.80 to 324.12 Mg·ha−1, and those for broadleaved deciduous forests were the most accurate, while those for AGB above 160 Mg·ha−1 were the least accurate. The study demonstrated encouraging results in forest AGB mapping of the normal vegetated area using the freely accessible and high-resolution Sentinel imagery, based on ML techniques. Numéro de notice : A2018-478 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f9100582 Date de publication en ligne : 20/09/2018 En ligne : https://doi.org/10.3390/f9100582 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91180
in Forests > vol 9 n° 10 (October 2018)[article]Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data / P. Kumar in Geocarto international, vol 33 n° 9 (September 2018)
[article]
Titre : Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data Type de document : Article/Communication Auteurs : P. Kumar, Auteur ; R. Prasad, Auteur ; D. K. Gupta, Auteur ; V. N. Mishra, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 942 - 956 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bande C
[Termes IGN] blé (céréale)
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] croissance végétale
[Termes IGN] cultures
[Termes IGN] données polarimétriques
[Termes IGN] estimation statistique
[Termes IGN] hiver
[Termes IGN] image Sentinel-SAR
[Termes IGN] Leaf Area Index
[Termes IGN] régression
[Termes IGN] régression linéaire
[Termes IGN] réseau neuronal artificiel
[Termes IGN] séparateur à vaste marge
[Termes IGN] teneur en eau de la végétationRésumé : (Auteur) In the present study, Sentinel-1A Synthetic Aperture Radar analysis of time series data at C-band was carried out to estimate the winter wheat crop growth parameters. Five different date images were acquired during January 2015–April 2015 at different growth stages from tillering to ripening in Varanasi district, India. The winter wheat crop parameters, i.e. leaf area index, vegetation water content (VWC), fresh biomass (FB), dry biomass (DB) and plant height (PH) were estimated using random forest regression (RFR), support vector regression (SVR), artificial neural network regression (ANNR) and linear regression (LR) algorithms. The Ground Range Detected products of Interferometric Wide (IW) Swath were used at VV polarization. The three different subplots of 1 m2 area were taken for the measurement of crop parameters at every growth stage. In total, 73 samples were taken as the training data-sets and 39 samples were taken as testing data-sets. The highest sensitivity (adj. R2 = 0.95579) of backscattering with VWC was found using RFR algorithm, whereas the lowest sensitivity (adj. R2 = 0.66201) was found for the PH using LR algorithm. Overall results indicate more accurate estimation of winter wheat parameters by the RFR algorithm followed by SVR, ANNR and LR algorithms. Numéro de notice : A2018-337 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1316781 Date de publication en ligne : 18/04/2017 En ligne : https://doi.org/10.1080/10106049.2017.1316781 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90551
in Geocarto international > vol 33 n° 9 (September 2018) . - pp 942 - 956[article]Synergetic 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)
[article]
Titre : Synergetic use of Sentinel-1 and Sentinel-2 for assessments of heathland conservation status Type de document : Article/Communication Auteurs : Johannes Schmidt, Auteur ; Fabian E. Fassnacht, Auteur ; Michael Förster, Auteur ; Sebastian Schmidtlein, Auteur Année de publication : 2018 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Calluna vulgaris
[Termes IGN] directive européenne
[Termes IGN] état de conservation
[Termes IGN] habitat (nature)
[Termes IGN] image Sentinel-SAR
[Termes IGN] site Natura 2000Résumé : (auteur) Habitat quality assessments often demand wall-to-wall information about the state of vegetation. Remote sensing can provide this information by capturing optical and structural attributes of plant communities. Although active and passive remote sensing approaches are considered as complementary techniques, they have been rarely combined for conservation mapping. Here, we combined spaceborne multispectral Sentinel-2 and Sentinel-1 SAR data for a remote sensing-based habitat quality assessment of dwarf shrub heathland, which was inspired by nature conservation field guidelines. Therefore, three earlier proposed quality layers representing (1) the coverage of the key dwarf shrub species, (2) stand structural diversity and (3) an index reflecting co-occurring vegetation were mapped via linking in situ data and remote sensing imagery. These layers were combined in an RGB-representation depicting varying stand attributes, which afterwards allowed for a rule-based derivation of pixel-wise habitat quality classes. The links between field observations and remote sensing data reached correlations between 0.70 and 0.94 for modeling the single quality layers. The spatial patterns shown in the quality layers and the map of discrete quality classes were in line with the field observations. The remote sensing-based mapping of heathland conservation status showed an overall agreement of 76% with field data. Transferring the approach in time (applying a second set of Sentinel-1 and -2 data) caused a decrease in accuracy to 73%. Our findings suggest that Sentinel-1 SAR contains information about vegetation structure that is complimentary to optical data and therefore relevant for nature conservation. While we think that rule-based approaches for quality assessments offer the possibility for gaining acceptance in both communities applied conservation and remote sensing, there is still need for developing more robust and transferable methods. Numéro de notice : A2018-005 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET Nature : Article DOI : 10.1002/rse2.68 En ligne : https://doi.org/10.1002/rse2.68 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88738
in Remote sensing in ecology and conservation > vol 4 n° 3 (September 2018)[article]Documents numériques
en open access
Synergetic use of Sentinel-1 and Sentinel-2 - pdf éditeurAdobe Acrobat PDF The 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)
[article]
Titre : The 2015 Mw 6.4 Pishan earthquake, China: geodetic modelling inferred from Sentinel-1A TOPS interferometry Type de document : Article/Communication Auteurs : Yongsheng Li, Auteur ; Yi Luo, Auteur ; Jingfa Zhang, Auteur ; Wenliang Jiang, Auteur Année de publication : 2018 Article en page(s) : pp 522 - 530 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] déformation de la croute terrestre
[Termes IGN] faille géologique
[Termes IGN] image Sentinel-SAR
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] modèle géologique
[Termes IGN] séisme
[Termes IGN] Sinkiang (Chine)Résumé : (Auteur) This study investigates the 3 July 2015 (Mw=6.4) earthquake that affected Pishan County in Xinjiang, China. Using Synthetic Aperture Radar interferometry (InSAR), we mapped the near-field surface displacement of this event to better characterize the seismic source parameters. The inverted results suggest that a conjugate fault system could produce very similar deformation patterns as those observed in both ascending and descending InSAR observations, and the south-dipping fault is selected as the optimal fault responsible for the event. The selected fault was concentrated on a thrust fault with a strike of 112°, a dip of 25° and an average rake angle of 89°. The InSAR analysis reveals a fragmentation of the Western Kunlun north margin fault, and the overall deformation of the seismogenic fault is characterized by thrust motion, which is consistent with the transpressional tectonic characteristics of the major faults in this region. Numéro de notice : A2018-451 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2017.1335381 Date de publication en ligne : 07/06/2017 En ligne : https://doi.org/10.1080/00396265.2017.1335381 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91037
in Survey review > vol 50 n° 363 (September 2018) . - pp 522 - 530[article]Precise orbit determination of the Sentinel-3A altimetry satellite using ambiguity-fixed GPS carrier phase observations / Oliver Montenbruck in Journal of geodesy, vol 92 n° 7 (July 2018)
[article]
Titre : Precise orbit determination of the Sentinel-3A altimetry satellite using ambiguity-fixed GPS carrier phase observations Type de document : Article/Communication Auteurs : Oliver Montenbruck, Auteur ; Stefan Hackel, Auteur ; Adrian Jäggi, Auteur Année de publication : 2018 Article en page(s) : pp 711 - 726 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Techniques orbitales
[Termes IGN] données altimétriques
[Termes IGN] double différence
[Termes IGN] orbitographie
[Termes IGN] phase
[Termes IGN] Sentinel-3Résumé : (Auteur) The Sentinel-3 mission takes routine measurements of sea surface heights and depends crucially on accurate and precise knowledge of the spacecraft. Orbit determination with a targeted uncertainty of less than 2 cm in radial direction is supported through an onboard Global Positioning System (GPS) receiver, a Doppler Orbitography and Radiopositioning Integrated by Satellite instrument, and a complementary laser retroreflector for satellite laser ranging. Within this study, the potential of ambiguity fixing for GPS-only precise orbit determination (POD) of the Sentinel-3 spacecraft is assessed. A refined strategy for carrier phase generation out of low-level measurements is employed to cope with half-cycle ambiguities in the tracking of the Sentinel-3 GPS receiver that have so far inhibited ambiguity-fixed POD solutions. Rather than explicitly fixing double-difference phase ambiguities with respect to a network of terrestrial reference stations, a single-receiver ambiguity resolution concept is employed that builds on dedicated GPS orbit, clock, and wide-lane bias products provided by the CNES/CLS (Centre National d’Études Spatiales/Collecte Localisation Satellites) analysis center of the International GNSS Service. Compared to float ambiguity solutions, a notably improved precision can be inferred from laser ranging residuals. These decrease from roughly 9 mm down to 5 mm standard deviation for high-grade stations on average over low and high elevations. Furthermore, the ambiguity-fixed orbits offer a substantially improved cross-track accuracy and help to identify lateral offsets in the GPS antenna or center-of-mass (CoM) location. With respect to altimetry, the improved orbit precision also benefits the global consistency of sea surface measurements. However, modeling of the absolute height continues to rely on proper dynamical models for the spacecraft motion as well as ground calibrations for the relative position of the altimeter reference point and the CoM. Numéro de notice : A2018-453 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-017-1090-2 Date de publication en ligne : 27/11/2017 En ligne : https://doi.org/10.1007/s00190-017-1090-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91044
in Journal of geodesy > vol 92 n° 7 (July 2018) . - pp 711 - 726[article]Assessment 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)PermalinkPermalinkClassification à très haute résolution (THR) spatiale et fusion d'occupation des sols (OCS) / Tristan Postadjian (2018)PermalinkConception d’une méthode radar de suivi bimensuel des déforestations et d’une méthode optique de classification d’occupation des sols / Luc Baudoux (2018)PermalinkCrop-rotation structured classification using multi-source sentinel images and LPIS for crop type mapping / Simon Bailly (2018)PermalinkDecision fusion of SPOT6 and multitemporal Sentinel2 images for urban area detection / Cyril Wendl (2018)PermalinkDétection de changement par imagerie radar sur les zones naturelles et agricoles en milieu tropical / Jérôme Lebreton (2018)PermalinkEstimation of surface roughness over bare agricultural soil from Sentinel-1 data / Mohammad Choker (2018)PermalinkEtude préalable à l'installation d'un coin radar sur le site de co-localisation de Calern / Guillaume Schmidt (2018)Permalink