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Automatic detection of suspected sewage discharge from coastal outfalls based on Sentinel-2 imagery / Yuxin Wang in Science of the total environment, vol 853 (December 2022)
[article]
Titre : Automatic detection of suspected sewage discharge from coastal outfalls based on Sentinel-2 imagery Type de document : Article/Communication Auteurs : Yuxin Wang, Auteur ; Xianqiang He, Auteur ; Yan Bai, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 158374 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par nuées dynamiques
[Termes IGN] couleur de l'océan
[Termes IGN] détection automatique
[Termes IGN] eau usée
[Termes IGN] image Sentinel-MSI
[Termes IGN] littoral
[Termes IGN] perturbation écologique
[Termes IGN] qualité des eauxRésumé : (auteur) Terrestrial pollution has a great impact on the coastal ecological environment, and widely distributed coastal outfalls act as the final gate through which pollutants flow into rivers and oceans. Thus, effectively monitoring the water quality of coastal outfalls is the key to protecting the ecological environment. Satellite remote sensing provides an attractive way to monitor sewage discharge. Selecting the coastal areas of Zhejiang Province, China, as an example, this study proposes an innovative method for automatically detecting suspected sewage discharge from coastal outfalls based on high spatial resolution satellite imageries from Sentinel-2. According to the accumulated in situ observations, we established a training dataset of water spectra covering various optical water types from satellite-retrieved remote sensing reflectance (Rrs). Based on the clustering results from unsupervised classification and different spectral indices, a random forest (RF) classification model was established for the optical water type classification and detection of suspected sewage. The final classification covers 14 optical water types, with type 12 and type 14 corresponding to the high eutrophication water type and suspected sewage water type, respectively. The classification result of model training datasets exhibited high accuracy with only one misclassified sample. This model was evaluated by historical sewage discharge events that were verified by on-site observations and demonstrated that it could successfully recognize sewage discharge from coastal outfalls. In addition, this model has been operationally applied to automatically detect suspected sewage discharge in the coastal area of Zhejiang Province, China, and shows broad application value for coastal pollution supervision, management, and source analysis. Numéro de notice : A2022-859 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.scitotenv.2022.158374 Date de publication en ligne : 28/08/2022 En ligne : https://doi.org/10.1016/j.scitotenv.2022.158374 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102135
in Science of the total environment > vol 853 (December 2022) . - n° 158374[article]Eco-environment and coupling coordination and quantification of urbanization in Yangtze River delta considering spatial non-stationarity / Yaqiu Zhang in Geocarto international, vol 37 n° 27 ([20/12/2022])
[article]
Titre : Eco-environment and coupling coordination and quantification of urbanization in Yangtze River delta considering spatial non-stationarity Type de document : Article/Communication Auteurs : Yaqiu Zhang, Auteur ; Quanhua Zhao, Auteur ; Peizhen Peng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 14843 - 14862 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse spatio-temporelle
[Termes IGN] delta
[Termes IGN] distribution spatiale
[Termes IGN] Fleuve bleu (Chine)
[Termes IGN] mégalopole
[Termes IGN] protection de l'environnement
[Termes IGN] surveillance de l'urbanisation
[Termes IGN] urbanisationRésumé : (auteur) Since the 21st century, the rapid development of China’s mega-city clusters has posed a major threat to the healthy and coordinated development of cities. Therefore, it is necessary to be develop the comprehend the state of coupling coordination among mega-city cluster and EEQ under mesoscale. In this study, the largest Yangtze River Delta urban agglomeration is taken as the research object, NS-RSEI is constructed to evaluate the EEQ of the Yangtze River Delta, and the coupling coordination mechanism on the long-time series of the Yangtze River Delta in recent 20 years is explored by means of spatio-temporal analysis. The outcome verify that CCD of the Yangtze River Delta growth with a strong spatial dependence from 2001 to 2020, showing a spatial distribution pattern of " East West high-low". Above all, this study shows that urbanization is the main factor determining the development of CCD. In addition, compared with the traditional remote sensing eco-environment monitoring model, NS-RSEI proposed in this study shows better ability in mesoscale environmental monitoring, and provides great convenience for mesoscale EEQ evaluation. This study fills the research gap of the interactive coupling mechanism between urbanization and eco-environment quality of mesoscale mega-city group, and provides a new perspective on the sustainable development of megacity clusters. Numéro de notice : A2022-935 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2091161 Date de publication en ligne : 08/10/2022 En ligne : https://doi.org/10.1080/10106049.2022.2091161 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102673
in Geocarto international > vol 37 n° 27 [20/12/2022] . - pp 14843 - 14862[article]Bathymetry and benthic habitat mapping in shallow waters from Sentinel-2A imagery: A case study in Xisha islands, China / Wei Huang in IEEE Transactions on geoscience and remote sensing, vol 60 n° 12 (December 2022)
[article]
Titre : Bathymetry and benthic habitat mapping in shallow waters from Sentinel-2A imagery: A case study in Xisha islands, China Type de document : Article/Communication Auteurs : Wei Huang, Auteur ; Jun Zhao, Auteur ; Bin Ai, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 4212412 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bathymétrie
[Termes IGN] carte thématique
[Termes IGN] Chine
[Termes IGN] correction atmosphérique
[Termes IGN] fond marin
[Termes IGN] habitat d'espèce
[Termes IGN] image hyperspectrale
[Termes IGN] image Sentinel-MSI
[Termes IGN] profondeur
[Termes IGN] réflectance spectraleRésumé : (auteur) Mapping of benthic habitats and bathymetry is crucial for sustainable development and assessment of climate change and human activities. In this study, Hyperspectral Optimization Process Exemplar (HOPE) was modified, renamed as M-HOPE, to simultaneously obtain bathymetry and benthic habitat in shallow waters in Xisha Island, China. A local lookup table (LUT) for benthic reflectance spectra was established. Validation using in situ measurements demonstrated good performance of M-HOPE with a R2 of 0.76 for bathymetry using the local LUT. Application of M-HOPE to Sentinel-2A imagery further proved good accuracy of M-HOPE derived bathymetry with a R2 of 0.86 against in situ observations and a R2 of 0.92 against ICESat-2 measurements. M-HOPE-derived benthic classification also agreed well with field observations with probability of detection (POD) >0.6 and false alarm ratio (FAR) Numéro de notice : A2022-907 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3229029 Date de publication en ligne : 14/12/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3229029 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102338
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 12 (December 2022) . - n° 4212412[article]Establishing a GIS-based evaluation method considering spatial heterogeneity for debris flow susceptibility mapping at the regional scale / Shengwu Qin in Natural Hazards, vol 114 n° 3 (December 2022)
[article]
Titre : Establishing a GIS-based evaluation method considering spatial heterogeneity for debris flow susceptibility mapping at the regional scale Type de document : Article/Communication Auteurs : Shengwu Qin, Auteur ; Shuangshuang Qiao, Auteur ; Jingyu Yao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2709 - 2738 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] aléa
[Termes IGN] analyse de sensibilité
[Termes IGN] cartographie des risques
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] éboulement
[Termes IGN] hétérogénéité spatiale
[Termes IGN] prévention des risquesRésumé : (auteur) Susceptibility mapping is an effective means of preventing debris flow disasters. However, previous studies have failed to solve spatial heterogeneity well, especially at the regional scale. The main objective of this study is to solve the spatial heterogeneity of regional-scale debris flow susceptibility (DFS) mapping by establishing a geographic information system (GIS)-based processing framework. The framework was realized by integrating the determination factor (DFactor) model with machine learning models. The DFactor model established different combinations of evaluation factors in each local region and clarified the differing contributions of influencing factors to DFS. To test the feasibility of the framework, the support vector machine (SVM) and two-dimensional convolutional neural network (CNN) were integrated with the DFactor model (DFactor-SVM and DFactor-CNN) to evaluate DFS in Jilin Province, China. The individual models (SVM and CNN) were also used to map the DFS for comparison with the integrated models. For debris flow modeling, 868 debris flow samples were collected and randomly divided into two datasets: 70% of the samples were used for training and the result was used for verification. The results of the receiver operating characteristic curve showed that the integrated models performed better. The DFactor-CNN model had the highest predictive accuracy, followed by the DFactor-SVM, CNN and SVM models. In general, the GIS-based processing framework maximizes the contribution of the influencing factors to debris flows and enhances the prediction ability of models. Furthermore, it provides a reliable means to predict debris flows at the regional scale. Numéro de notice : A2022-854 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-022-05487-5 Date de publication en ligne : 06/08/2022 En ligne : https://doi.org/10.1007/s11069-022-05487-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102101
in Natural Hazards > vol 114 n° 3 (December 2022) . - pp 2709 - 2738[article]Extracting built-up land area of airports in China using Sentinel-2 imagery through deep learning / Fanxuan Zeng in Geocarto international, vol 37 n° 25 ([01/12/2022])
[article]
Titre : Extracting built-up land area of airports in China using Sentinel-2 imagery through deep learning Type de document : Article/Communication Auteurs : Fanxuan Zeng, Auteur ; Xin Wang, Auteur ; Mengqi Zha, Auteur Année de publication : 2022 Article en page(s) : pp 7753 - 7773 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] aéroport
[Termes IGN] apprentissage profond
[Termes IGN] architecture de réseau
[Termes IGN] Chine
[Termes IGN] détection du bâti
[Termes IGN] image Sentinel-MSIRésumé : (auteur) In China, airports have a profound impact on people’s lives, and understanding their dimensions has great significance for research and development. However, few existing airport databases contain such details, which can be reflected indirectly by the built-up land in the airport. In this study, a deep learning-based method was used for extraction of built-up land of airports in China using Sentinel-2 imagery and for further estimating their area. Here, a benchmark generation method is introduced by fusing two reference maps and cropping images into patches. Following this, a series of experiments were conducted to evaluate the network architectures and select the positive impact bands in Sentinel-2 imagery. A well-trained model was used to extract the built-up land for China airports, and the relationship between China airports’ built-up land and the carrying capacity of air transportation was further analysed. Results show that ResUNet-a outperformed U-Net, ResUNet, and SegNet, and the B2, B4, B6, B11, and B12 bands of Sentinel-2 had a positive impact on built-up land extraction. A well-trained model with an overall accuracy of 0.9423 and an F1 score of 0.9041 and 434 China airports’ built-up land was extracted. The four most developed airports are located in Beijing, Shanghai, and Guangzhou, which matches China’s political and economic development. The area of built-up land influenced the passenger throughput and aircraft movements. The total area influenced the cargo throughput, and we found a certain correlation among the built-up land, carrying capacity, and nighttime light. Numéro de notice : A2022-929 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10106049.2021.1983034 Date de publication en ligne : 01/10/2021 En ligne : https://doi.org/10.1080/10106049.2021.1983034 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102662
in Geocarto international > vol 37 n° 25 [01/12/2022] . - pp 7753 - 7773[article]Geographic named entity recognition by employing natural language processing and an improved BERT model / Liufeng Tao in ISPRS International journal of geo-information, vol 11 n° 12 (December 2022)PermalinkHigh-precision positioning using plane-constrained RTK method in urban environments / Chen Zhuang in Navigation : journal of the Institute of navigation, vol 69 n° 4 (Fall 2022)PermalinkMapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution / Zhenfeng Shao in Geo-spatial Information Science, vol 25 n° 4 (December 2022)PermalinkA novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds / Xiaoqiang Liu in Remote sensing of environment, vol 282 (December 2022)PermalinkA whale optimization algorithm–based cellular automata model for urban expansion simulation / Yuan Ding in International journal of applied Earth observation and geoinformation, vol 115 (December 2022)PermalinkAn advanced bidirectional reflectance factor (BRF) spectral approach for estimating flavonoid content in leaves of Ginkgo plantations / Kai Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 193 (November 2022)PermalinkExploring the influencing factors in identifying soil texture classes using multitemporal Landsat-8 and Sentinel-2 data / Yanan Zhou in Remote sensing, vol 14 n° 21 (November-1 2022)PermalinkA GIS and hybrid simulation aided environmental impact assessment of city-scale demolition waste management / Zhikun Ding in Sustainable Cities and Society, vol 86 (November 2022)PermalinkComparison of change and static state as the dependent variable for modeling urban growth / Yongjiu Feng in Geocarto international, vol 37 n° 23 ([15/10/2022])PermalinkAnalysis of the spatial range of service and accessibility of hospitals designated for coronavirus disease 2019 in Yunnan Province, China / Liangting Zheng in Geocarto international, vol 37 n° 22 ([10/10/2022])PermalinkApplication of a graph convolutional network with visual and semantic features to classify urban scenes / Yongyang Xu in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)PermalinkDeep learning-based local climate zone classification using Sentinel-1 SAR and Sentinel-2 multispectral imagery / Lin Zhou in Geo-spatial Information Science, vol 25 n° 3 (October 2022)PermalinkDSNUNet: An improved forest change detection network by combining Sentinel-1 and Sentinel-2 images / Jiawei Jiang in Remote sensing, vol 14 n° 19 (October-1 2022)PermalinkEstimating urban functional distributions with semantics preserved POI embedding / Weiming Huang in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)PermalinkIdentify urban building functions with multisource data: a case study in Guangzhou, China / Yingbin Deng in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)PermalinkNovel algorithm based on geometric characteristics for tree branch skeleton extraction from LiDAR point cloud / Jie Yang in Forests, vol 13 n° 10 (October 2022)PermalinkThe fractional vegetation cover (FVC) and associated driving factors of modeling in mining areas / Jun Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 10 (October 2022)PermalinkThe use of gravity data to determine orthometric heights at the Hong Kong territories / Albertini Nsiah Ababio in Journal of applied geodesy, vol 16 n° 4 (October 2022)PermalinkAdaptive block modeling of time dependent variations of datum reference points in a tectonically active area / Chun-Yun Chou in Survey review, vol 54 n° 386 (September 2022)PermalinkDiscontinuity interpretation and identification of potential rockfalls for high-steep slopes based on UAV nap-of-the-object photogrammetry / Wei Wang in Computers & geosciences, vol 166 (September 2022)Permalink