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scientifique

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)  

Public [article]  inScience of the total environment > vol 853 (December 2022) . - n° 158374 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] 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 eaux Résumé : (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 [article]

Integrating Bayesian networks to forecast sea-level rise impacts on barrier island characteristics and habitat availability / Benjamin T. Gutierrez in Earth and space science, vol 9 n° 11 (November 2022)  

Public [article]  inEarth and space science > vol 9 n° 11 (November 2022) . - 24 p. Titre : Integrating Bayesian networks to forecast sea-level rise impacts on barrier island characteristics and habitat availability Type de document : Article/Communication Auteurs : Benjamin T. Gutierrez, Auteur ; Sarah Zeigler, Auteur ; Erika Lentz, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : 24 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de sensibilité [Termes IGN] changement climatique [Termes IGN] géomorphologie [Termes IGN] habitat animal [Termes IGN] île [Termes IGN] modèle de simulation [Termes IGN] montée du niveau de la mer [Termes IGN] New York (Etats-Unis ; ville) [Termes IGN] planification côtière [Termes IGN] réseau bayesien [Termes IGN] submersion marine [Termes IGN] surveillance du littoral [Termes IGN] trait de côte Résumé : (auteur) Evaluation of sea-level rise (SLR) impacts on coastal landforms and habitats is a persistent need for informing coastal planning and management, including policy decisions, particularly those that balance human interests and habitat protection throughout the coastal zone. Bayesian networks (BNs) are used to model barrier island change under different SLR scenarios that are relevant to management and policy decisions. BNs utilized here include a shoreline change model and two models of barrier island biogeomorphological evolution at different scales (50 and 5 m). These BNs were then linked to another BN to predict habitat availability for piping plovers (Charadrius melodus), a threatened shorebird reliant on beach habitats. We evaluated the performance of the two linked geomorphology BNs and further examined error rates by generating hindcasts of barrier island geomorphology and habitat availability for 2014 conditions. Geomorphology hindcasts revealed that model error declined with a greater number of known inputs, with error rates reaching 55% when multiple outputs were hindcast simultaneously. We also found that, although error in predictions of piping plover nest presence/absence increased when outputs from the geomorphology BNs were used as inputs in the piping plover habitat BN, the maximum error rate for piping plover habitat suitability in the fully-linked BNs was only 30%. Our findings suggest this approach may be useful for guiding scenario-based evaluations where known inputs can be used to constrain variables that produce higher uncertainty for morphological predictions. Overall, the approach demonstrates a way to assimilate data and model structures with uncertainty to produce forecasts to inform coastal planning and management. Numéro de notice : A2022-883 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1029/2022EA002286 Date de publication en ligne : 14/10/2022 En ligne : https://doi.org/10.1029/2022EA002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102024 [article]

Tidal level prediction using combined methods of harmonic analysis and deep neural networks in Southern coastline of Iran / Kourosh Shahryari Nia in Marine geodesy, vol 45 n° 6 (November 2022)  

Public [article]  inMarine geodesy > vol 45 n° 6 (November 2022) . - pp 645 - 669 Titre : Tidal level prediction using combined methods of harmonic analysis and deep neural networks in Southern coastline of Iran Type de document : Article/Communication Auteurs : Kourosh Shahryari Nia, Auteur ; Mohammad Ali Sharifi, Auteur ; Saeed Farzaneh, Auteur Année de publication : 2022 Article en page(s) : pp 645 - 669 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse harmonique [Termes IGN] classification par Perceptron multicouche [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] données marégraphiques [Termes IGN] Iran [Termes IGN] marée océanique [Termes IGN] modèle de simulation [Termes IGN] niveau de la mer [Vedettes matières IGN] Altimétrie Résumé : (auteur) Predicting tides and water levels had always been such an important topic for researchers and professionals since the study of tidal level has pivotal role in supporting marine economy, port construction projects and maritime transportation. Tidal water levels are a combination of astronomical (deterministic part) and non-astronomical (stochastic part) water levels. In this study, we combined Harmonic Analysis (HA) with three Deep Neural Networks (DNNs), namely the Long-Short Term Memory (LSTM), Convolution Neural Network (CNN), and Multilayer Perceptron (MLP). The HA method is used for predicting the astronomical components, while DNNs are used to predict the non-astronomical water level. We have used tide gauge data from three stations along the southern coastline of Iran to demonstrate the effectiveness and accuracy of our model. We utilized RMSE, MAE, R2 (r-squared), and MAPE to evaluate the performance of the model. Finally, The LSTM network shown superior performance in most of the cases, although other networks also show good results. All three DNNs have R2 of 0.99, and the RMSE, MAE, and MAPE indicate that errors are low. Numéro de notice : A2022-783 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1080/01490419.2022.2116615 Date de publication en ligne : 28/08/2022 En ligne : https://doi.org/10.1080/01490419.2022.2116615 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101880 [article]

Use of GIS and dasymetric mapping for estimating tsunami-affected population to facilitate humanitarian relief logistics: a case study from Phuket, Thailand / Kiatkulchai Jitt-Aer in Natural Hazards, vol 113 n° 1 (August 2022)  

Public [article]  inNatural Hazards > vol 113 n° 1 (August 2022) . - pp 185 - 211 Titre : Use of GIS and dasymetric mapping for estimating tsunami-affected population to facilitate humanitarian relief logistics: a case study from Phuket, Thailand Type de document : Article/Communication Auteurs : Kiatkulchai Jitt-Aer, Auteur ; Graham Wall, Auteur ; Dylan Jones, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 185 - 211 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] analyse spatiale [Termes IGN] ArcGIS [Termes IGN] figuration de la densité [Termes IGN] gestion de crise [Termes IGN] interpolation spatiale [Termes IGN] planification côtière [Termes IGN] population [Termes IGN] prévention des risques [Termes IGN] Thaïlande [Termes IGN] tsunami Résumé : (auteur) The 2004 Indian Ocean tsunami led to improvements in Thailand’s early warning systems and evacuation procedures. However, there was no consideration of better aid delivery, which critically depends on estimates of the affected population. With the widespread use of geographical information systems (GIS), there has been renewed interest in spatial population estimation. This study has developed an application to determine the number of disaster-impacted people in a given district, by integrating GIS and population estimation algorithms, to facilitate humanitarian relief logistics. A multi-stage spatial interpolation is used for estimating the affected populations using ArcGIS software. We present a dasymetric mapping approach using a population-weighted technique coupled with remote sensing data. The results in each target area show the coordinates of each shelter location for evacuees, with the minimum and maximum numbers of people affected by the tsunami inundation. This innovative tool produces not only numerical solutions for decision makers, but also a variety of maps that improve visualisation of disaster severity across neighbourhoods. A case study in Patong, a town of Phuket, illustrates the application of this GIS-based approach. The outcomes can be used as key decision-making factors in planning and managing humanitarian relief logistics in the preparedness and response phases to improve performance with future tsunami occurrences, or with other types of flood disaster. Numéro de notice : A2022-703 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-022-05295-x Date de publication en ligne : 09/03/2022 En ligne : https://doi.org/10.1007/s11069-022-05295-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101566 [article]

Littoraux sous double surveillance / Laurent Polidori in Géomètre, n° 2204 (juillet-août 2022)

Public [article]  inGéomètre > n° 2204 (juillet-août 2022) . - pp 23 - 23 Titre : Littoraux sous double surveillance Type de document : Article/Communication Auteurs : Laurent Polidori, Auteur Année de publication : 2022 Article en page(s) : pp 23 - 23 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Litto3D [Termes IGN] marée océanique [Termes IGN] montée du niveau de la mer [Termes IGN] satellite d'observation de la mer [Termes IGN] satellite d'observation de la Terre [Termes IGN] surveillance du littoral [Termes IGN] trait de côte Résumé : (Auteur) Concentré d’enjeux écologiques et sociaux, le littoral est sous la surveillance permanente des satellites. Mais cet objet complexe et changeant se dérobe parfois à l’observation. Numéro de notice : A2022-526 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101295 [article]

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
063-2022071	SL	Revue	Centre de documentation	Revues en salle	En circulation Exclu du prêt

How can Sentinel-2 contribute to seagrass mapping in shallow, turbid Baltic Sea waters? / Katja Kuhwald in Remote sensing in ecology and conservation, vol 8 n° 3 (June 2022)  

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Modeling gravimetric signatures of third-degree ocean tides and their detection in superconducting gravimeter records / Roman Sulzbach in Journal of geodesy, vol 96 n° 5 (May 2022)  

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Coastal observation of sea surface tide and wave height using opportunity signal from Beidou GEO satellites: analysis and evaluation / Feng Wang in Journal of geodesy, vol 96 n° 4 (April 2022)  

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Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3 / Nima Pahlevan in Remote sensing of environment, vol 270 (March 2022)  

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Validating a new GNSS-based sea level instrument (CalNaGeo) at Senetosa Cape / Pascal Bonnefond in Marine geodesy, vol 45 n° 2 (March 2022)  

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Co-seismic ionospheric disturbances following the 2016 West Sumatra and 2018 Palu earthquakes from GPS and GLONASS measurements / Mokhamad Nur Cahyadi in Remote sensing, vol 14 n° 2 (January-2 2022)  

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Apport des nouveaux systèmes GNSS de cartographie du niveau marin à l’exploitation des données altimétriques en zone côtière / Clémence Chupin (2022)  

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Cartographie dynamique de la topographie de l'océan de surface par assimilation de données altimétriques / Florian Le Guillou (2022)  

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Detection and biomass estimation of phaeocystis globosa blooms off Southern China from UAV-based hyperspectral measurements / Xue Li in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)  

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Modélisation du lien entre éruptions et glissements de flancs au Piton de la Fournaise / Quentin Dumont (2022)  

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