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Auteur Bin Chen |
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Estimating 10-m land surface albedo from Sentinel-2 satellite observations using a direct estimation approach with Google Earth Engine / Xingwen Lin in ISPRS Journal of photogrammetry and remote sensing, vol 194 (December 2022)
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Titre : Estimating 10-m land surface albedo from Sentinel-2 satellite observations using a direct estimation approach with Google Earth Engine Type de document : Article/Communication Auteurs : Xingwen Lin, Auteur ; Shengbiao Wu, Auteur ; Bin Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1 - 20 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] albedo
[Termes IGN] bande spectrale
[Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF
[Termes IGN] Google Earth Engine
[Termes IGN] hétérogénéité spatiale
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Terra-MODIS
[Termes IGN] Leaf Area Index
[Termes IGN] modèle de transfert radiatif
[Termes IGN] phénologie
[Termes IGN] réflectance de surfaceRésumé : (auteur) Land surface albedo plays an important role in controlling the surface energy budget and regulating the biophysical processes of natural dynamics and anthropogenic activities. Satellite remote sensing is the only practical approach to estimate surface albedo at regional and global scales. It nevertheless remains challenging for current satellites to capture fine-scale albedo variations due to their coarse spatial resolutions from tens to hundreds of meters. The emerging Sentinel-2 satellites, with a high spatial resolution of 10 m and an approximate 5-day revisiting cycle, provide a promising solution to address these observational limitations, yet their potentials remain underexplored. In this study, we integrated the Sentinel-2 observations with an updated direct estimation approach to improve the estimation and monitoring of fine-scale surface albedo. To enable the capability of the direct estimation approach at a 10-m scale, we combined the 10-m resolution European Space Agency (ESA) WorldCover land cover data and the 500-m resolution Moderate-Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF)/albedo product to build a high-quality and representative BRDF training database. To evaluate our approach, we proposed an integrated evaluation framework leveraging 3-D physical model simulations, ground measurements, and satellite observations. Specifically, we first simulated a comprehensive dataset of Sentinel-2-like surface reflectance and broadband albedo across a variety of geometric configurations using the MODIS BRDF training samples. With this dataset, we built the Look-Up-Tables (LUTs) that connect surface broadband albedo and Sentinel-2 reflectance through a direct angular bin-based linear regression approach, and further coupled these LUTs with the Google Earth Engine (GEE) cloud-computing platform. We next evaluated the proposed algorithm at two spatial levels: (1) 10-m scale for absolute accuracy assessment using the references from the Discrete Anisotropic Radiative Transfer (DART) simulations and flux-site observations, and (2) 500-m scale for large-scale mapping assessment by comparing the estimated albedo with the MODIS albedo product. Lastly, we presented four examples to show the capability of Sentinel-2 albedo in detecting fine-scale characteristics of vegetation and urban covers. Results show that: (1) the proposed algorithm accurately estimates surface albedo from Sentinel-2-like reflectance across different landscape configurations (overall root-mean-square-error (RMSE) = 0.018, bias = 0.005, and coefficient of determination (R2) = 0.88); (2) the Sentinel-2-derived surface albedo agrees well with ground measurements (overall RMSE = 0.030, bias = -0.004, and R2 = 0.94) and MODIS products (overall RMSE = 0.030, bias = 0.021, and R2 = 0.97); and (3) Sentinel-2-derived albedo accurately captures seasonal leaf phenology and rapid snow events, and detects the interspecific (or interclass) variations of tree species and colored urban rooftops. These results demonstrate the capability of the proposed approach to map high-resolution surface albedo from Sentinel-2 satellites over large spatial and temporal contexts, suggesting the potential of using such fine-scale datasets to improve our understanding of albedo-related biophysical processes in the coupled human-environment system. Numéro de notice : A2022-823 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.09.016 Date de publication en ligne : 14/10/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.09.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101999
in ISPRS Journal of photogrammetry and remote sensing > vol 194 (December 2022) . - pp 1 - 20[article]Mapping 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)
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Titre : Mapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America Type de document : Article/Communication Auteurs : Bin Chen, Auteur ; Ying Tu, Auteur ; Yimeng Song, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 203 - 218 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algorithme d'apprentissage
[Termes IGN] carte d'utilisation du sol
[Termes IGN] données massives
[Termes IGN] données multisources
[Termes IGN] Etats-Unis
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] métropole
[Termes IGN] OpenStreetMap
[Termes IGN] planification urbaine
[Termes IGN] zone urbaineRésumé : (auteur) Urban land-use maps outlining the distribution, pattern, and composition of various land use types are critically important for urban planning, environmental management, disaster control, health protection, and biodiversity conservation. Recent advances in remote sensing and social sensing data and methods have shown great potentials in mapping urban land use categories, but they are still constrained by mixed land uses, limited predictors, non-localized models, and often relatively low accuracies. To inform these issues, we proposed a robust and cost-effective framework for mapping urban land use categories using openly available multi-source geospatial “big data”. With street blocks generated from OpenStreetMap (OSM) data as the minimum classification unit, we integrated an expansive set of multi-scale spatially explicit information on land surface, vertical height, socio-economic attributes, social media, demography, and topography. We further proposed to apply the automatic ensemble learning that leverages a bunch of machine learning algorithms in deriving optimal urban land use classification maps. Results of block-level urban land use classification in five metropolitan areas of the United States found the overall accuracies of major-class (Level-I) and minor-class (Level-II) classification could be high as 91% and 86%, respectively. A multi-model comparison revealed that for urban land use classification with high-dimensional features, the multi-layer stacking ensemble models achieved better performance than base models such as random forest, extremely randomized trees, LightGBM, CatBoost, and neural networks. We found without very-high-resolution National Agriculture Imagery Program imagery, the classification results derived from Sentinel-1, Sentinel-2, and other open big data based features could achieve plausible overall accuracies of Level-I and Level-II classification at 88% and 81%, respectively. We also found that model transferability depended highly on the heterogeneity in characteristics of different regions. The methods and findings in this study systematically elucidate the role of data sources, classification methods, and feature transferability in block-level land use classifications, which have important implications for mapping multi-scale essential urban land use categories. Numéro de notice : A2021-564 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.06.010 Date de publication en ligne : 25/06/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.06.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98129
in ISPRS Journal of photogrammetry and remote sensing > vol 178 (August 2021) . - pp 203 - 218[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021081 SL Revue Centre de documentation Revues en salle Disponible 081-2021083 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021082 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt The national geographic characteristics of online public opinion propagation in China based on WeChat network / Chuan Ai in Geoinformatica, vol 22 n° 2 (April 2018)
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Titre : The national geographic characteristics of online public opinion propagation in China based on WeChat network Type de document : Article/Communication Auteurs : Chuan Ai, Auteur ; Bin Chen, Auteur ; Lingnan He, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 311 - 334 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] caractérisation
[Termes IGN] Chine
[Termes IGN] interaction spatiale
[Termes IGN] réseau social
[Termes IGN] villeRésumé : (Auteur) Offline networks have been the subject of intense academic scrutiny for many decades, but we still know little about the nationwide spatial interaction patterns and its application for public opinion management of online social networks. With the aim of uncovering the geographic interaction characteristics of online public opinion propagation, we analyze a large dataset obtained from WeChat, the most popular social media application in China, and construct the spatial interaction network G, which contains 359 city-nodes. It is found that the communities in the network and the administrative division corresponded well with each other, and cities with high betweenness and degree also develop well in the economy. Public opinion propagation depends on the state of online interaction. The findings indicate that public opinion should be managed separately by regions divided according to the community division, and different regions should adopt different management methods according to their economic, historical and political characteristics. In our work, the possibility and opportunity is presented to study the spatial interaction patterns of online public opinion propagation with the massive behavioral data and the methods of complex network. Numéro de notice : A2018-366 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-017-0311-4 En ligne : https://doi.org/10.1007/s10707-017-0311-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90731
in Geoinformatica > vol 22 n° 2 (April 2018) . - pp 311 - 334[article]