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Plastic waste cleanup priorities to reduce marine pollution: A spatiotemporal analysis for Accra and Lagos with satellite data / Susmita Dasgupta in Science of the total environment, vol 839 (May 2022)  

Public [article]  inScience of the total environment > vol 839 (May 2022) . - n° 156319 Titre : Plastic waste cleanup priorities to reduce marine pollution: A spatiotemporal analysis for Accra and Lagos with satellite data Type de document : Article/Communication Auteurs : Susmita Dasgupta, Auteur ; Maria Sarraf, Auteur ; David M. Wheeler, Auteur Année de publication : 2022 Article en page(s) : n° 156319 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] déchet [Termes IGN] distribution spatiale [Termes IGN] enquête [Termes IGN] géoréférencement [Termes IGN] Ghana [Termes IGN] image à haute résolution [Termes IGN] Lagos [Termes IGN] littoral [Termes IGN] matière plastique [Termes IGN] méthode du maximum de vraisemblance (estimation) [Termes IGN] pollution des mers [Termes IGN] variation saisonnière Résumé : (auteur) Plastic waste, with an estimated lifetime of centuries, accounts for the major share of marine litter. Each year, thousands of fish, sea birds, sea turtles, and other marine species are killed by ingesting or becoming entangled with plastic debris. Reducing marine plastic pollution is particularly challenging for developing countries owing to the wide dispersal of plastic waste disposal and scarce public cleanup resources. To costeffectively reduce marine pollution, resources should target “hotspot” areas, where large volumes of plastic litter have a high likelihood of ending up in the ocean. Using new public information, this study develops a hotspot targeting strategy for Accra and Lagos, which are major sources of marine plastic pollution in West Africa. The same global information sources can support hotspot analyses for many other coastal cities that generate marine plastic waste. The methodology combines georeferenced household survey data on plastic use, measures of seasonal variation in marine plastic pollution from satellite imagery, and a model of plastic waste transport to the ocean that uses information on topography, seasonal rainfall, drainage to rivers, and river transport to the ocean. For cleanup, the results for West Africa assign the highest locational priority to areas with heavy plastic-waste disposal along river channels or in steeply sloped locations with high rainfall runoff potential near rivers. They assign the highest temporal priority to just before the onset of the first-semester rainy season, when runoff from the first rains transports large volumes of plastic waste that have accumulated during the dry season. Numéro de notice : A2022-471 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.scitotenv.2022.156319 Date de publication en ligne : 28/05/2022 En ligne : https://doi.org/10.1016/j.scitotenv.2022.156319 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100816 [article]

Decision fusion of deep learning and shallow learning for marine oil spill detection / Junfang Yang in Remote sensing, vol 14 n° 3 (February-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 3 (February-1 2022) . - n° 666 Titre : Decision fusion of deep learning and shallow learning for marine oil spill detection Type de document : Article/Communication Auteurs : Junfang Yang, Auteur ; Yi Ma, Auteur ; Yabin Hu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 666 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme de fusion [Termes IGN] analyse multiéchelle [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] hydrocarbure [Termes IGN] image hyperspectrale [Termes IGN] marée noire [Termes IGN] milieu marin [Termes IGN] pollution des mers [Termes IGN] précision de la classification [Termes IGN] sous ensemble flou [Termes IGN] surveillance écologique [Termes IGN] transformation en ondelettes Résumé : (auteur) Marine oil spills are an emergency of great harm and have become a hot topic in marine environmental monitoring research. Optical remote sensing is an important means to monitor marine oil spills. Clouds, weather, and light control the amount of available data, which often limit feature characterization using a single classifier and therefore difficult to accurate monitoring of marine oil spills. In this paper, we develop a decision fusion algorithm to integrate deep learning methods and shallow learning methods based on multi-scale features for improving oil spill detection accuracy in the case of limited samples. Based on the multi-scale features after wavelet transform, two deep learning methods and two classical shallow learning algorithms are used to extract oil slick information from hyperspectral oil spill images. The decision fusion algorithm based on fuzzy membership degree is introduced to fuse multi-source oil spill information. The research shows that oil spill detection accuracy using the decision fusion algorithm is higher than that of the single detection algorithms. It is worth noting that oil spill detection accuracy is affected by different scale features. The decision fusion algorithm under the first-level scale features can further improve the accuracy of oil spill detection. The overall classification accuracy of the proposed method is 91.93%, which is 2.03%, 2.15%, 1.32%, and 0.43% higher than that of SVM, DBN, 1D-CNN, and MRF-CNN algorithms, respectively. Numéro de notice : A2022-125 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14030666 Date de publication en ligne : 30/01/2022 En ligne : https://doi.org/10.3390/rs14030666 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99688 [article]

A novel deep learning instance segmentation model for automated marine oil spill detection / Shamsudeen Temitope Yekeen in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 167 (September 2020) . - pp 190 - 200 Titre : A novel deep learning instance segmentation model for automated marine oil spill detection Type de document : Article/Communication Auteurs : Shamsudeen Temitope Yekeen, Auteur ; Abdul‐Lateef Balogun, Auteur ; Khamaruzaman B. Wan Yusof, Auteur Année de publication : 2020 Article en page(s) : pp 190 - 200 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection automatique [Termes IGN] extraction de traits caractéristiques [Termes IGN] hydrocarbure [Termes IGN] image radar moirée [Termes IGN] marée noire [Termes IGN] segmentation sémantique [Termes IGN] vision par ordinateur [Termes IGN] zone d'intérêt Résumé : (auteur) The visual similarity of oil slick and other elements, known as look-alike, affects the reliability of synthetic aperture radar (SAR) images for marine oil spill detection. So far, detection and discrimination of oil spill and look-alike are still limited to the use of traditional machine learning algorithms and semantic segmentation deep learning models with limited accuracy. Thus, this study developed a novel deep learning oil spill detection model using computer vision instance segmentation Mask-Region-based Convolutional Neural Network (Mask R-CNN) model. The model training was conducted using transfer learning on the ResNet 101 on COCO as backbone in combination with Feature Pyramid Network (FPN) architecture for feature extraction at 30 epochs with 0.001 learning rate. Testing of the model was conducted using the least training and validation loss value on the withheld testing images. The model’s performance was evaluated using precision, recall, specificity, IoU, F1-measure and overall accuracy values. Ship detection and segmentation had the highest performance with overall accuracy of 98.3%. The model equally showed a higher accuracy for oil spill and look-alike detection and segmentation although oil spill detection outperformed look-alike with overall accuracy values of 96.6% and 91.0% respectively. The study concluded that the deep learning instance segmentation model performs better than conventional machine learning models and deep learning semantic segmentation models in detection and segmentation. Numéro de notice : A2020-548 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.07.011 Date de publication en ligne : 28/07/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.07.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95774 [article]

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Applying the environmental sensitivity index for the assessment of the prospective oil spills along the Nile Delta Coast, Egypt / Rasha M. Abou Samra in Geocarto international, vol 35 n° 6 ([01/05/2020])  

Public [article]  inGeocarto international > vol 35 n° 6 [01/05/2020] . - pp 589 - 601 Titre : Applying the environmental sensitivity index for the assessment of the prospective oil spills along the Nile Delta Coast, Egypt Type de document : Article/Communication Auteurs : Rasha M. Abou Samra, Auteur ; Rasha Eissa, Auteur ; Maie El-Gammal, Auteur Année de publication : 2020 Article en page(s) : pp 589 - 601 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] analyse de sensibilité [Termes IGN] environnement [Termes IGN] hydrocarbure [Termes IGN] impact sur l'environnement [Termes IGN] Nil (delta du) [Termes IGN] Nil (fleuve) [Termes IGN] pétrole [Termes IGN] pollution des mers [Termes IGN] rivage [Termes IGN] Suez, canal de Résumé : (auteur) The Nile Delta is located very close to the Suez Canal, the main route for oil transport in the world, makes it prone to pollution from any accidental oil spills in the Mediterranean Sea. The coast of the Nile Delta is generally arcuate and highly exposed to waves and currents. The present study attempted to perform the environmental sensitivity of the shoreline to oil slicks. Six variables were incorporated together in order to determine the environmental sensitivity index (ESI). Data were collected from different resources and from in situ observations. Results showed that the ESI is generally high for the western section of the Nile Delta, particularly along Alexandria region. Low ESI was observed along the shorelines facing the coastal sand dunes at the middle part of the delta coast. The ESI is an effective approach to delineate the vulnerable coastal areas to marine oil pollution. Numéro de notice : A2020-200 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1533592 Date de publication en ligne : 06/11/2018 En ligne : https://doi.org/10.1080/10106049.2018.1533592 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94870 [article]

A novel nonlinear hyperspectral unmixing approach for images of oil spills at sea / Ying Li in International Journal of Remote Sensing IJRS, vol 41 n° 12 (20 - 30 March 2020)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 41 n° 12 (20 - 30 March 2020) . - pp 4684 - 4701 Titre : A novel nonlinear hyperspectral unmixing approach for images of oil spills at sea Type de document : Article/Communication Auteurs : Ying Li, Auteur ; Huimin Lu, Auteur ; Zhenduo Zhang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 4684 - 4701 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] équation polynomiale [Termes IGN] hydrocarbure [Termes IGN] image hyperspectrale [Termes IGN] marée noire [Termes IGN] modèle non linéaire [Termes IGN] pollution des mers [Termes IGN] trigonométrie Résumé : (auteur) Hyperspectral remote sensing is currently being used to detect and monitor marine oil spills that cause damage to the environment. However, nonlinear interactions of oil and water make it difficult to extract their fractional abundances from the spectral response. Improving the modelling of nonlinear hyperspectral mixtures, which is required for a thorough and reliable characterization of the materials in an image, remains a challenging yet fundamental task. This study proposes a new model that combines polynomial and trigonometric systems to understand the nonlinear effects of oil and water spectral response. Although the model is nonlinear, unmixing is performed by solving a linear problem, thus allowing fast computation. Compared to classic polynomial models, the details of nonlinear interactions are better expressed and quantified, and the reconstruction accuracy and endmember abundance estimation are improved for both synthetic and real datasets. Both the polynomial and trigonometric parts of the model play important roles in characterizing nonlinearities, with statistically linear dependence areas covering more than 90% and 30%, respectively, in oil spill images sampled after the Deepwater Horizon explosion. Analysis of the experimental results suggests that the proposed model provides an efficient and accurate unmixing method that can be used to help design oil spill response plans. Numéro de notice : A2020-452 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2020.1723179 Date de publication en ligne : 27/02/2020 En ligne : https://doi.org/10.1080/01431161.2020.1723179 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95540 [article]

Monitoring of marine pollution / Houma Bachari Fouzia (2019)  

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Progress in marine oil spill optical remote sensing: Detected targets, spectral response characteristics, and theories / Lu yingcheng in Marine geodesy, vol 36 n° 3 (September - November 2013)  

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On the degree of polarization for SAR sea oil slick observation / Ferdinando Nunziata in ISPRS Journal of photogrammetry and remote sensing, vol 78 (April 2013)  

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Le développement durable à découvert / Agathe Euzen (2013)

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A multifrequency polarimetric SAR processing chain to observe oil fields in the Gulf of Mexico / M. Migliaccio in IEEE Transactions on geoscience and remote sensing, vol 49 n° 12 Tome 1 (December 2011)  

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Vigisat surveille l'océan depuis la Bretagne / J. Brami in CNES Magazine, n° 44 (01/01/2010)

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Du naufrage de l'Amoco Cadiz à Litto3D / G. Bessero in XYZ, n° 119 (juin - août 2009)    

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Potentiality of feed-forward neural networks for classifying dark formations to oil spills and look-alikes / Konstantinos Topouzelis in Geocarto international, vol 24 n° 3 (June - July 2009)  

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Multisensor satellite monitoring of seawater state and oil pollution in the northeastern coastal zone of the Black Sea / S. Shcherbak in International Journal of Remote Sensing IJRS, vol 29 n° 21 (October 2008)  

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Accès à l'information : expérimentation d'un nouvel outil / Benoit Chanavas in Info DFCI, n° 59 (décembre 2007)

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