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Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data / Yaotong Cai in International journal of applied Earth observation and geoinformation, vol 92 (October 2020)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 92 (October 2020) . - n° 102164 Titre : Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data Type de document : Article/Communication Auteurs : Yaotong Cai, Auteur ; Xinyu Li, Auteur ; Meng Zhang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 102164 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] algorithme de généralisation [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] cartographie thématique [Termes descripteurs IGN] Chine [Termes descripteurs IGN] filtre de déchatoiement [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] modélisation spatio-temporelle [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] prairie [Termes descripteurs IGN] rétrodiffusion [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] zone humide Résumé : (auteur) Wetland ecosystems have experienced dramatic challenges in the past few decades due to natural and human factors. Wetland maps are essential for the conservation and management of terrestrial ecosystems. This study is to obtain an accurate wetland map using an object-based stacked generalization (Stacking) method on the basis of multi-temporal Sentinel-1 and Sentinel-2 data. Firstly, the Robust Adaptive Spatial Temporal Fusion Model (RASTFM) is used to get time series Sentinel-2 NDVI, from which the vegetation phenology variables are derived by the threshold method. Subsequently, both vertical transmit-vertical receive (VV) and vertical transmit-horizontal receive (VH) polarization backscatters (σ0 VV, σ0 VH) are obtained using the time series Sentinel-1 images. Speckle noise inherent in SAR data, resulting in over-segmentation or under-segmentation, can affect image segmentation and degrade the accuracies of wetland classification. Therefore, we segment Sentinel-2 multispectral images to delineate meaningful objects in this study. Then, in order to reduce data redundancy and computation time, we analyze the optimal feature combination using the Sentinel-2 multispectral images, Sentinel-2 NDVI time series, phenological variables and other vegetation index derived from Sentinel-2 multispectral images, as well as time series Sentinel-1 backscatters at the object level. Finally, the stacked generalization algorithm is utilized to extract the wetland information based on the optimal feature combination in the Dongting Lake wetland. The overall accuracy and Kappa coefficient of the object-based stacked generalization method are 92.46% and 0.92, which are 3.88% and 0.04 higher than that using the pixel-based method. Moreover, the object-based stacked generalization algorithm is superior to single classifiers in classifying vegetation of high heterogeneity areas. Numéro de notice : A2020-748 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2020.102164 date de publication en ligne : 07/06/2020 En ligne : https://doi.org/10.1016/j.jag.2020.102164 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96398 [article]

Deriving a frozen area fraction from Metop ASCAT backscatter based on Sentinel-1 / Helena Bergstedt in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6008 - 6019 Titre : Deriving a frozen area fraction from Metop ASCAT backscatter based on Sentinel-1 Type de document : Article/Communication Auteurs : Helena Bergstedt, Auteur ; Annett Bartsch, Auteur ; Anton Neureiter, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 6008 - 6019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Autriche [Termes descripteurs IGN] bande C [Termes descripteurs IGN] courbe de Pearson [Termes descripteurs IGN] dégel [Termes descripteurs IGN] Finlande [Termes descripteurs IGN] fonte des glaces [Termes descripteurs IGN] hétérogénéité spatiale [Termes descripteurs IGN] image MetOp-ASCAT [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] pergélisol [Termes descripteurs IGN] rétrodiffusion [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] télédétection en hyperfréquence [Termes descripteurs IGN] température au sol Résumé : (auteur) Surface state data derived from spaceborne microwave sensors with suitable temporal sampling are to date only available in low spatial resolution (25–50 km). Current approaches do not adequately resolve spatial heterogeneity in landscape-scale freeze–thaw processes. We propose to derive a frozen fraction instead of binary freeze–thaw information. This introduces the possibility to monitor the gradual freezing and thawing of complex landscapes. Frozen fractions were retrieved from Advanced Scatterometer (ASCAT, C-band) backscatter on a 12.5-km grid for three sites in noncontinuous permafrost areas in northern Finland and the Austrian Alps. To calibrate the retrieval approach, frozen fractions based on Sentinel-1 synthetic aperture radar (SAR, C-band) were derived for all sites and compared to ASCAT backscatter. We found strong relationships for ASCAT backscatter with Sentinel-1 derived frozen fractions (Pearson correlations of −0.85 to −0.96) for the sites in northern Finland and less strong relationships for the Alpine site (Pearson correlations −0.579 and −0.611, including and excluding forested areas). Applying the derived linear relationships, predicted frozen fractions using ASCAT backscatter values showed root mean square error (RMSE) values between 7.26% and 16.87% when compared with Sentinel-1 frozen fractions. The validation of the Sentinel-1 derived freeze–thaw classifications showed high accuracy when compared to in situ near-surface soil temperature (84.7%–94%). Results are discussed with regard to landscape type, differences between spring and autumn, and gridding. This article serves as a proof of concept, showcasing the possibility to derive frozen fraction from coarse spatial resolution scatterometer time series to improve the representation of spatial heterogeneity in landscape-scale surface state. Numéro de notice : A2020-525 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2967364 date de publication en ligne : 13/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2967364 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95702 [article]

Digital terrain, surface, and canopy height models from InSAR backscatter-height histograms / Gustavo H.X. Shiroma in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 6 (June 2020) . - pp 754 - 3777 Titre : Digital terrain, surface, and canopy height models from InSAR backscatter-height histograms Type de document : Article/Communication Auteurs : Gustavo H.X. Shiroma, Auteur ; Marco Lavalle, Auteur Année de publication : 2020 Article en page(s) : pp 754 - 3777 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] bande L [Termes descripteurs IGN] décomposition de Gauss [Termes descripteurs IGN] Gabon [Termes descripteurs IGN] histogramme [Termes descripteurs IGN] image captée par drone [Termes descripteurs IGN] interféromètrie par radar à antenne synthétique [Termes descripteurs IGN] modèle numérique de surface de la canopée [Termes descripteurs IGN] modèle numérique de terrain [Termes descripteurs IGN] modélisation 3D [Termes descripteurs IGN] polarimétrie radar [Termes descripteurs IGN] rétrodiffusion [Termes descripteurs IGN] structure de la végétation Résumé : (auteur) This article demonstrates how 3-D vegetation structure can be approximated by interferometric synthetic aperture radar (InSAR) backscatter-height histograms. Single-look backscatter measurements are plotted against the InSAR phase height and are aggregated spatially over a forest patch to form a 3-D histogram, referred to as InSAR backscatter-height histogram or simply InSAR histogram. InSAR histograms resemble LiDAR waveforms, suggesting that existing algorithms used to retrieve canopy height and ground topography from radar tomograms or LiDAR waveforms can be applied to InSAR histograms. Three algorithms are evaluated to generate maps of digital terrain, surface, and canopy height models: Gaussian decomposition, quantile, and backscatter threshold. Full-polarimetric L-band uninhabited aerial vehicle synthetic aperture radar (UAVSAR) data collected over the Gabonese Lopé National Park during the 2016 AfriSAR campaign are used to illustrate and compare the performance of the algorithms for the HH, HV, VV, HH+VV, and HH−VV polarimetric channels. Results show that radar-derived maps using the InSAR histograms differ by 4 m (top-canopy), 5 m (terrain), and 6 m (forest height) in terms of average root-mean-square errors (RMSEs) from standard maps derived from full-waveform laser, vegetation, and ice sensor (LVIS) LiDAR measurements. Numéro de notice : A2020-279 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2956989 date de publication en ligne : 16/01/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2956989 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95099 [article]

Potential of texture from SAR tomographic images for forest aboveground biomass estimation / Zhanmang Liao in International journal of applied Earth observation and geoinformation, vol 88 (June 2020)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 88 (June 2020) . - 15 p. Titre : Potential of texture from SAR tomographic images for forest aboveground biomass estimation Type de document : Article/Communication Auteurs : Zhanmang Liao, Auteur ; Binbin He, Auteur ; Xingwen Quan, Auteur Année de publication : 2020 Article en page(s) : 15 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse texturale [Termes descripteurs IGN] bande P [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] canopée [Termes descripteurs IGN] données TomoSAR [Termes descripteurs IGN] rétrodiffusion [Termes descripteurs IGN] tomographie radar Résumé : (auteur) Synthetic Aperture Radar (SAR) texture has been demonstrated to have the potential to improve forest biomass estimation using backscatter. However, forests are 3D objects with a vertical structure. The strong penetration of SAR signals means that each pixel contains the contributions of all the scatterers inside the forest canopy, especially for the P-band. Consequently, the traditional texture derived from SAR images is affected by forest vertical heterogeneity, although the influence on texture-based biomass estimation has not yet been explicitly explored. To separate and explore the influence of forest vertical heterogeneity, we introduced the SAR tomography technique into the traditional texture analysis, aiming to explore whether TomoSAR could improve the performance of texture-based aboveground biomass (AGB) estimation and whether texture plus tomographic backscatter could further improve the TomoSAR-based AGB estimation. Based on the P-band TomoSAR dataset from TropiSAR 2009 at two different sites, the results show that ground backscatter variance dominated the texture features of the original SAR image and reduced the biomass estimation accuracy. The texture from upper vegetation layers presented a stronger correlation with forest biomass. Texture successfully improved tomographic backscatter-based biomass estimation, and the texture from upper vegetation layers made AGB models much more transferable between different sites. In addition, the correlation between texture indices varied greatly among different tomographic heights. The texture from the 10 to 30 m layers was able to provide more independent information than the other layers and the original images, which helped to improve the backscatter-based AGB estimation. Numéro de notice : A2020-447 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2020.102049 date de publication en ligne : 12/02/2020 En ligne : https://doi.org/10.1016/j.jag.2020.102049 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95523 [article]

Wheat leaf area index retrieval using RISAT-1 hybrid polarized SAR data / Thota Sivasankar in Geocarto international, Vol 35 n° 8 ([01/06/2020])  

Public [article]  inGeocarto international > Vol 35 n° 8 [01/06/2020] . - pp 905 - 915 Titre : Wheat leaf area index retrieval using RISAT-1 hybrid polarized SAR data Type de document : Article/Communication Auteurs : Thota Sivasankar, Auteur ; Dheeraj Kumar, Auteur ; Hari Shanker Srivastava, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 905 - 915 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] bande C [Termes descripteurs IGN] blé (céréale) [Termes descripteurs IGN] coefficient de corrélation [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Risat-1 [Termes descripteurs IGN] indice foliaire [Termes descripteurs IGN] polarisation [Termes descripteurs IGN] régression non linéaire [Termes descripteurs IGN] rétrodiffusion [Termes descripteurs IGN] séparateur à vaste marge [Termes descripteurs IGN] surveillance de la végétation Résumé : (auteur) Leaf Area Index (LAI) is a key parameter to characterize the canopy–atmosphere interface, where most of the energy fluxes exchange. Space-borne satellite images have shown their relevance for various applications including LAI retrieval over large areas. Although optical data have been used for this purpose in previous studies, the constraints to acquire optical data during extreme weather conditions due to the presence of clouds, haze, smoke etc. hinders its use for uninterrupted monitoring. This study aims to analyze the relationships of C-band RISAT-1 hybrid polarized SAR data (σ˚RH and σ˚RV) with wheat LAI. The results have shown the correlation coefficient (|r|) of 0.57 and 0.73 for RH and RV backscatter, respectively, using non-linear regression approach. It is also observed that the accuracy of LAI retrieval has been significantly improved with |r| and RMSE of 0.81 and 0.54 (m2/m2), respectively, by considering both RH and RV backscatter as inputs for support vector machine-based model. Numéro de notice : A2020-341 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10106049.2019.1566404 date de publication en ligne : 07/02/2019 En ligne : https://doi.org/10.1080/10106049.2019.1566404 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95219 [article]

Integration 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)  

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Imagerie radar en ondes millimétriques appliquée à la viticulture / Dominique Henry (2018)  

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Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery / Jose Alan A. Castillo in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)  

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A time-series approach to estimating soil moisture from vegetated surfaces using L-band radar backscatter / Jeffrey D. Ouellette in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)  

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Mise en place d'une méthode semi-automatique de cartographie de l'occupation des sols à partir d'images SAR polarimétriques / Monique Moine in Revue Française de Photogrammétrie et de Télédétection, n° 215 (mai - août 2017)

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Passive microwave remote sensing of soil moisture based on dynamic vegetation scattering properties for AMSR-E / Jinyang Du in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)  

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Understanding the effects of ALS pulse density for metric retrieval across diverse forest types / Phil Wilkes in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 8 (August 2015)

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Non-invasive forest litter characterization using full-wave inversion of microwave radar data / Frédéric André in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)  

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Modelling electrical conductivity of soil from backscattering coefficient of microwave remotely sensed data using artificial neural network / Walaiporn Phonphan in Geocarto international, vol 29 n° 7 - 8 (November - December 2014)  

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Measurements of forest biomass change using P-Band synthetic aperture radar backscatter / Gustaf Sandberg in IEEE Transactions on geoscience and remote sensing, vol 52 n° 10 tome 1 (October 2014)

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