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Spatial variability of suspended sediments in San Francisco Bay, California / Niky C. Taylor in Remote sensing, vol 13 n° 22 (November-2 2021)  

Public [article]  inRemote sensing > vol 13 n° 22 (November-2 2021) . - n° 4625 Titre : Spatial variability of suspended sediments in San Francisco Bay, California Type de document : Article/Communication Auteurs : Niky C. Taylor, Auteur ; Raphael M. Kudela, Auteur Année de publication : 2021 Article en page(s) : n° 4625 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] baie [Termes IGN] échantillonnage [Termes IGN] estuaire [Termes IGN] image Sentinel-MSI [Termes IGN] pas d'échantillonnage au sol [Termes IGN] qualité des eaux [Termes IGN] réflectance [Termes IGN] San Francisco [Termes IGN] sédiment [Termes IGN] spectroradiométrie [Termes IGN] surface de l'eau [Termes IGN] surveillance du littoral [Termes IGN] turbidité des eaux [Termes IGN] variabilité Résumé : (auteur) Understanding spatial variability of water quality in estuary systems is important for making monitoring decisions and designing sampling strategies. In San Francisco Bay, the largest estuary system on the west coast of North America, tracking the concentration of suspended materials in water is largely limited to point measurements with the assumption that each point is representative of its surrounding area. Strategies using remote sensing can expand monitoring efforts and provide a more complete view of spatial patterns and variability. In this study, we (1) quantify spatial variability in suspended particulate matter (SPM) concentrations at different spatial scales to contextualize current in-water point sampling and (2) demonstrate the potential of satellite and shipboard remote sensing to supplement current monitoring methods in San Francisco Bay. We collected radiometric data from the bow of a research vessel on three dates in 2019 corresponding to satellite overpasses by Sentinel-2, and used established algorithms to retrieve SPM concentrations. These more spatially comprehensive data identified features that are not picked up by current point sampling. This prompted us to examine how much variability exists at spatial scales between 20 m and 10 km in San Francisco Bay using 10 m resolution Sentinel-2 imagery. We found 23–80% variability in SPM at the 5 km scale (the scale at which point sampling occurs), demonstrating the risk in assuming limited point sampling is representative of a 5 km area. In addition, current monitoring takes place along a transect within the Bay’s main shipping channel, which we show underestimates the spatial variance of the full bay. Our results suggest that spatial structure and spatial variability in the Bay change seasonally based on freshwater inflow to the Bay, tidal state, and wind speed. We recommend monitoring programs take this into account when designing sampling strategies, and that end-users account for the inherent spatial uncertainty associated with the resolution at which data are collected. This analysis also highlights the applicability of remotely sensed data to augment traditional sampling strategies. In sum, this study presents ways to supplement water quality monitoring using remote sensing, and uses satellite imagery to make recommendations for future sampling strategies. Numéro de notice : A2021-839 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : https://doi.org/10.3390/rs13224625 Date de publication en ligne : 17/11/2021 En ligne : https://doi.org/10.3390/rs13224625 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99022 [article]

Remote sensing method for extracting topographic information on tidal flats using spatial distribution features / Yang Lijun in Marine geodesy, vol 44 n° 5 (September 2021)  

Public [article]  inMarine geodesy > vol 44 n° 5 (September 2021) . - pp 408 - 431 Titre : Remote sensing method for extracting topographic information on tidal flats using spatial distribution features Type de document : Article/Communication Auteurs : Yang Lijun, Auteur ; Xiao Yao, Auteur ; Jie Jiang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 408 - 431 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] alluvion [Termes IGN] arpentage [Termes IGN] Chine [Termes IGN] distribution spatiale [Termes IGN] données topographiques [Termes IGN] extraction de données [Termes IGN] Fleuve bleu (Chine) [Termes IGN] géomorphologie locale [Termes IGN] image Landsat [Termes IGN] marée océanique [Termes IGN] modèle numérique de surface [Termes IGN] positionnement cinématique en temps réel [Termes IGN] Shanghai (Chine) [Termes IGN] vase Résumé : (Auteur) A remote sensing method combining remote sensing and ground surveying is proposed to extract tidal flat topographic information via the spatial distribution characteristics of tidal flat surface features. Based on the eastern Chongming beach of the Yangtze Estuary and Landsat-5 satellite images, this study identifies the spatial distribution characteristics of tidal flat features using field-based RTK data and spectral data. The remote sensing method for extracting the geometric and physical characteristics of linear and surface geographical elements on tidal flats and the elevation assignment method are discussed. The effectiveness of this method is verified by the quality of the resultant tidal flat DEM. The results show that the use of spatial distribution features in remote sensing images can provide rich topographic information. The DEM results have an accuracy of 0.16 m, are in line with the basic topographic patterns of tidal flats, and can describe local microscale geomorphic features. This technique solves the problem of a single topographic information source in current remote sensing measurement methods and provides technical support for detecting dynamic changes in coastal zones by using remote sensing technology. Numéro de notice : A2021-577 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01490419.2021.1925791 Date de publication en ligne : 04/06/2021 En ligne : https://doi.org/10.1080/01490419.2021.1925791 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98230 [article]

Shore zone classification from ICESat-2 data over Saint Lawrence Island / Huan Xie in Marine geodesy, vol 44 n° 5 (September 2021)  

Public [article]  inMarine geodesy > vol 44 n° 5 (September 2021) . - pp 454 - 466 Titre : Shore zone classification from ICESat-2 data over Saint Lawrence Island Type de document : Article/Communication Auteurs : Huan Xie, Auteur ; Yuan Sun, Auteur ; Xiaoshuai Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 454 - 466 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Alaska (Etats-Unis) [Termes IGN] Bering, mer de [Termes IGN] données ICEsat [Termes IGN] Google Earth [Termes IGN] indicateur environnemental [Termes IGN] littoral [Termes IGN] modèle de régression [Termes IGN] photon [Termes IGN] sédiment Résumé : (Auteur) The shore zone is the most active zone in the atmosphere, hydrosphere, biosphere and lithosphere of nature, and has the environmental characteristics of both ocean and land. The ICESat-2 satellite provides height measurements of shore zone using a photon-counting LiDAR. The purpose of this study is to explore the application potential of ICESat-2 satellite data in shore zone classification. Saint Lawrence Island, Alaska, was chosen as the study area. Firstly, in this study, the upper and lower boundaries of the shore zone of the study area were extracted based on Google Earth images. The slope and width between the two boundaries were then calculated according to the formula. Secondly, six statistical indicators (standard deviation, relative standard deviation, average absolute deviation, relative average deviation, absolute median error and quartile deviation) related to the substrate and sediment classification that could reflect the characteristics of the shore zone profile were extracted, and the statistical indicators were used as input parameters of the softmax regression model for classification. Finally, the accuracy of the shore zone classification was validated using the ShoreZone classification system. The results show that, among the 246 shore zone sections in the study area, 86% (212) has been correctly classified. The results therefore indicate that ICESat-2 data can be used to support the characterization of shore zone morphology. Numéro de notice : A2021-578 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01490419.2021.1898498 Date de publication en ligne : 29/03/2021 En ligne : https://doi.org/10.1080/01490419.2021.1898498 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98234 [article]

Evolution of the beaches in the regional Park of Salinas and Arenales of San Pedro del Pinatar (Southeast of Spain) (1899–2019) / Daniel Ibarra-Marinas in ISPRS International journal of geo-information, vol 10 n° 4 (April 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 4 (April 2021) . - n° 200 Titre : Evolution of the beaches in the regional Park of Salinas and Arenales of San Pedro del Pinatar (Southeast of Spain) (1899–2019) Type de document : Article/Communication Auteurs : Daniel Ibarra-Marinas, Auteur ; Francisco Belmonte-Serrato, Auteur ; Gustavo A. Ballesteros-Pelegrín, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 200 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] analyse spatio-temporelle [Termes IGN] anthropisation [Termes IGN] cartographie ancienne [Termes IGN] construction [Termes IGN] érosion côtière [Termes IGN] impact sur l'environnement [Termes IGN] Murcie (Espagne) [Termes IGN] photographie aérienne [Termes IGN] plage [Termes IGN] QGIS [Termes IGN] sédiment [Termes IGN] trait de côte Résumé : (auteur) Coastal erosion is an issue which affects beaches all over the world and that signifies enormous economic and environmental losses. Classed as a slow phenomenon, the evolution of the coastline requires long-term analysis. In this study, old cartography and aerial photographs from various dates have been used to study the evolution of the coastline. The information has been processed with free software (QGIS) and for the calculation of sediment transport the Coastal Modeling System (SMC) software. The results show the accretion/erosion phenomena that occurred after the construction of the port in San Pedro del Pinatarin 1954 and which changed the coastal dynamics of a highly protected area. In some sectors, the beach has been reduced almost in its entirety, with retreat rates of up to −2.05 m per year and a total area loss of 66,419.81 m2 in Las Salinas beach and 76,891.13 m2 on Barraca Quemada beach. Numéro de notice : A2021-479 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10040200 Date de publication en ligne : 25/03/2021 En ligne : https://doi.org/10.3390/ijgi10040200 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97423 [article]

Study on offshore seabed sediment classification based on particle size parameters using XGBoost algorithm / Fengfan Wang in Computers & geosciences, vol 149 (April 2021)  

Public [article]  inComputers & geosciences > vol 149 (April 2021) . - n° 104713 Titre : Study on offshore seabed sediment classification based on particle size parameters using XGBoost algorithm Type de document : Article/Communication Auteurs : Fengfan Wang, Auteur ; Jia Yu, Auteur ; Zhijie Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 104713 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse spatiale [Termes IGN] calcul matriciel [Termes IGN] Chine [Termes IGN] classification par forêts aléatoires [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] diagramme [Termes IGN] échantillon [Termes IGN] Extreme Gradient Machine [Termes IGN] fond marin [Termes IGN] gravier [Termes IGN] image à haute résolution [Termes IGN] sédiment [Termes IGN] texture Résumé : (auteur) Folk's textual classification scheme which is widely used for sediment study operates with the proportions of gravel, sand, silt and clay fractions conventionally. However, dealing with data from different sources usually needs to face missing values that may make the classification difficult. To solve this problem and discover other methods of analyzing the scheme, with samples of offshore seabed sediment, a two-stage model was established to predict a sample's class using the XGBoost algorithm as well as the grain size parameters as input features. The final model was evaluated with quantitative performance measures of recall, precision and F1 score, and by comparing sediment texture maps using the predicted and the actual data. The results show that the model performs well on extraction of sediment samples without gravel fraction, and prediction of classes that have independent characteristics of grain size parameters or samples not near the boundaries of classes in the ternary diagram. The predicted sediment texture is close to the actual and could be reliable due to errors with little impact on further applications. It is demonstrated that the model could be an auxiliary or alternative approach to offshore sediment texture mapping, as well as supplementary to the analysis of sedimentary environment. Numéro de notice : A2021-289 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2021.104713 Date de publication en ligne : 12/02/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104713 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97400 [article]

Beach morphology and its dynamism from remote sensing for coastal management support / Carlos Cabezas Rabadán (2021)  

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Boreal peatland forests: ditch network maintenance effort and water protection in a forest rotation framework / Jenny Miettinen in Canadian Journal of Forest Research, vol 50 n° 10 (October 2020)  

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Comparative study of different models for soil erosion and sediment yield in Pairi watershed, Chhattisgarh, India / Tarun Kumar in Geocarto international, vol 35 n° 11 ([01/08/2020])  

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Modeling soil erosion after mechanized logging operations on steep terrain in the Northern Black Forest, Germany / Julian Haas in European Journal of Forest Research, vol 139 n°4 (August 2020)  

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Hydrogeology of the western Po plain (Piedmont, NW Italy) / Domenico Antonio De Luca in Journal of maps, vol 16 n° 2 ([01/06/2020])  

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Intertidal topography mapping using the waterline method from Sentinel-1 & -2 images: The examples of Arcachon and Veys Bays in France / Edward Salameh in ISPRS Journal of photogrammetry and remote sensing, vol 163 (May 2020)  

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Le sol s'affaisse, l'eau monte [Delta du Gange-Brahmapoutre-Meghna] / Marielle Mayo in Géomètre, n° 2179 (avril 2020)

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Estimation of soil surface water contents for intertidal mudflats using a near-infrared long-range terrestrial laser scanner / Kai Tan in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)  

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Application of photogrammetry to generate quantitative geobody data in ephemeral fluvial systems / Charlotte L. Priddy in Photogrammetric record, vol 34 n° 168 (December 2019)  

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Utilisation de la photogrammétrie numérique pour la modélisation de fonds mobiles de modèles physiques / Maxime Cartier Xatard in XYZ, n° 154 (mars - mai 2018)

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