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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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 163 (May 2020) . - pp 98 - 120 Titre : Intertidal topography mapping using the waterline method from Sentinel-1 & -2 images: The examples of Arcachon and Veys Bays in France Type de document : Article/Communication Auteurs : Edward Salameh, Auteur ; Frédéric Frappart, Auteur ; Imen Turki, Auteur ; Benoit Laignel, Auteur Année de publication : 2020 Article en page(s) : pp 98 - 120 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] aménagement du littoral [Termes descripteurs IGN] Arcachon (bassin d') [Termes descripteurs IGN] carte topographique [Termes descripteurs IGN] Cotentin [Termes descripteurs IGN] estran [Termes descripteurs IGN] France (administrative) [Termes descripteurs IGN] hydrodynamique [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] niveau de l'eau [Termes descripteurs IGN] sédiment [Termes descripteurs IGN] télédétection spatiale [Termes descripteurs IGN] trait de côte [Termes descripteurs IGN] zone tampon Résumé : (auteur) Intertidal flats lying as a buffer zone between land and sea provide critical services including protection against storm surges and coastal flooding. These environments are characterized by a continuous redistribution of sediment and changes in topography. Sea level rise, anthropogenic pressures, and their related stressors have a considerable impact on these areas and are expected to put them under more stress; hence the increased need for frequent and updated topography maps. Comparing to traditional surveying approaches, spaceborne remote sensing is able to provide topography maps more frequently with a lower cost and a higher coverage. The latter is currently considered as an established tool for measuring intertidal topography. In this study, an improved approach of the waterline method was developed to derive intertidal Digital Elevation Models (DEMs). The changes include a faster, more efficient and quasi-automatic detection and post-processing of waterlines. The edge detection technique consists in combining a k-means based segmentation and an active contouring procedure. This method was designed to generate closed contours in order to enable an automatization of the post-processing of the extracted waterlines. The waterlines were extracted from Sentinel-1 and Sentinel-2 images for two bays located on the French Coast: the Arcachon lagoon and the Bay of Veys. DEMs were generated for the Arcachon Bay between 2015 and 2018, and for the Bay of Veys between 2016 and 2018 using satellite acquisitions made during summer (low storm activity period). The comparison of the generated DEMs with lidar observations showed an error of about 19–25 cm. This study also demonstrated that the waterline method applied to Sentinel images is suitable for monitoring the morpho-sedimentary evolution in intertidal areas. By comparing the DEMs generated between 2016 and 2018, the Arcachon Bay and the Bay of Veys experienced net volume losses of 1.12 × 106 m3 and 0.70 × 106 m3 respectively. The generated DEMs provide useful and needed information for several scientific applications (e.g., sediment balance, hydrodynamic modelling), but also for authorities and stakeholders for coastal management and implementation of ecosystem protection policies. Numéro de notice : A2020-138 Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.03.003 date de publication en ligne : 13/03/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.03.003 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94756 [article]

Dimension reduction methods applied to coastline extraction on hyperspectral imagery / Ozan Arslan in Geocarto international, vol 35 n° 4 ([15/03/2020])  

Public [article]  inGeocarto international > vol 35 n° 4 [15/03/2020] . - pp 376 - 390 Titre : Dimension reduction methods applied to coastline extraction on hyperspectral imagery Type de document : Article/Communication Auteurs : Ozan Arslan, Auteur ; özer Akyürek, Auteur ; Sinasi Kaya, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 376 - 390 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] Bosphore [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] détection de contours [Termes descripteurs IGN] extraction [Termes descripteurs IGN] image EO1-Hyperion [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] Istanbul (Turquie) [Termes descripteurs IGN] littoral [Termes descripteurs IGN] rapport signal sur bruit [Termes descripteurs IGN] réduction [Termes descripteurs IGN] télédétection [Termes descripteurs IGN] trait de côte Résumé : (auteur) In this study, dimensionality reduction (DR) methods on a hyperspectral dataset to explore the influence on the process of extraction of coastlines were examined and performance of different DR algorithms on the detection of coastline in Bosphorus, Istanbul was investigated. Among these methods, principal component (PC) analysis, maximum noise fraction and independent component (IC) analysis were used in the experiments with the aim of comparing. The study was carried out using these well-known DR techniques on a real hyperspectral image, an Hyperion data set with 161 bands, in the course of the experiments. Three different classifiers (i.e. ML, SVM and neural network) were used for the classification of dimensionally reduced and original images to detect coastline in the region. The DR results were evaluated quantitatively and visually in order to determine the reduced dimensions of the image subsets. Findings show that there is no significant influence of using DR methods on the dataset on the detection of coastline. Numéro de notice : A2020-099 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1520920 date de publication en ligne : 22/10/2018 En ligne : https://doi.org/10.1080/10106049.2018.1520920 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94690 [article]

Assessment of salt marsh change on Assateague Island National Seashore between 1962 and 2016 / Anthony Campbell in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 3 (March 2020) . - pp 187 - 194 Titre : Assessment of salt marsh change on Assateague Island National Seashore between 1962 and 2016 Type de document : Article/Communication Auteurs : Anthony Campbell, Auteur ; Yeqiao Wang, Auteur Année de publication : 2020 Article en page(s) : pp 187 - 194 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] approche hiérarchique [Termes descripteurs IGN] Atlantique (océan) [Termes descripteurs IGN] biodiversité [Termes descripteurs IGN] cartographie thématique [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] Etats-Unis [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] lidar bathymétrique [Termes descripteurs IGN] littoral [Termes descripteurs IGN] marais salant [Termes descripteurs IGN] montée du niveau de la mer [Termes descripteurs IGN] service écosystémique [Termes descripteurs IGN] surveillance du littoral Résumé : (auteur) Salt marshes provide extensive ecosystem services, including high biodiversity, denitrification, and wave attenuation. In the mid-Atlantic, sea level rise is predicted to affect salt marsh ecosystems severely. This study mapped the entirety of Assateague Island with Very High Resolution satellite imagery and object-based methods to determine an accurate salt marsh baseline for change analysis. Topobathy-metric light detection and ranging was used to map the salt marsh and model expected tidal effects. The satellite imagery, collected in 2016 and classified at two hierarchical thematic schemes, were compared to determine appropriate thematic richness. Change analysis between this 2016 map and both a manually delineated 1962 salt marsh extent and image classification of the island from 1994 determined rates off change. The study found that from 1962 to 1994, salt marsh expanded by 4.01 ha/year, and from 1994 to 2016 salt marsh was lost at a rate of -3.4 ha/ year. The study found that salt marsh composition, (percent vegetated salt marsh) was significantly influenced by elevation, the length of mosquito ditches, and starting salt marsh composition. The study illustrates the importance of remote sensing monitoring for understanding site-specific changes to salt marsh environments and the barrier island system. Numéro de notice : A2020-148 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.3.187 date de publication en ligne : 01/03/2020 En ligne : https://doi.org/10.14358/PERS.86.3.187 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94777 [article]

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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 159 (January 2020) . - pp 129 - 139 Titre : Estimation of soil surface water contents for intertidal mudflats using a near-infrared long-range terrestrial laser scanner Type de document : Article/Communication Auteurs : Kai Tan, Auteur ; Jin Chen, Auteur ; Weiguo Zhang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 129 - 139 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] Chine [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données TLS (télémétrie) [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] littoral [Termes descripteurs IGN] positionnement cinématique en temps réel [Termes descripteurs IGN] rayonnement proche infrarouge [Termes descripteurs IGN] réflectance [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] télémétrie laser terrestre [Termes descripteurs IGN] teneur en vapeur d'eau [Termes descripteurs IGN] vase Résumé : (Auteur) Estimations of the soil surface water contents and distributions play a key role in the ecological, environmental, and topographical investigations for intertidal mudflats. However, existing techniques have limitations. Long-range terrestrial laser scanners (TLSs) can record the co-located intensity value which refers to a measure of the backscattered laser from each scanned point. Most long-range TLSs emit near-infrared lasers that can be strongly absorbed by water. Thus, the intensity values can be used as proxies for water contents. In this study, the intensity data of long-range TLSs are corrected for the incidence angle and distance effects to quantitatively estimate the soil surface water contents of intertidal mudflats. A case study for a mudflat in Chongming Island, Shanghai, China, is conducted. Results indicate that compared with traditional techniques, the corrected intensity data of long-range TLSs are extremely effective data sources for a quick, accurate, and detailed estimation of water contents for large-area mudflats. The estimation root mean square error is approximately 3%. Furthermore, the 3D distributions of the water contents can be accurately mapped by combining the point cloud of the mudflats to potentially analyze the intrinsic association among water contents and topography, vegetation coverage, and habitation of creatures in mudflats. Numéro de notice : A2020-013 Thématique : POSITIONNEMENT Nature : Article DOI : 10.1016/j.isprsjprs.2019.11.003 date de publication en ligne : 26/11/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.003 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94402 [article]

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Sea-Land Segmentation Using Deep Learning Techniques for Landsat-8 OLI Imagery / Ting Yang in Marine geodesy, Vol 43 n° 1 (janvier 2020)  

Public [article]  inMarine geodesy > Vol 43 n° 1 (janvier 2020) . - 29 p. Titre : Sea-Land Segmentation Using Deep Learning Techniques for Landsat-8 OLI Imagery Type de document : Article/Communication Auteurs : Ting Yang, Auteur Année de publication : 2020 Article en page(s) : 29 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] Chine [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] données localisées de référence [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] littoral [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] segmentation sémantique Résumé : (auteur) Automated coastline extraction from optical satellites is fundamental to coastal mapping, and sea-land segmentation is the core technology of coastline extraction. Deep convolutional neural networks (DCNNs) have performed well in semantic segmentation in recent years. However, sea-land segmentation using deep learning techniques remains a challenging task, due to the lack of a benchmark dataset and the difficulty of deciding which semantic segmentation model to use. We present a comparative framework of sea-land segmentation to Landsat-8 OLI imagery via semantic segmentation in deep learning techniques. Three issues are investigated: (1) constructing a sea-land benchmark dataset using Landsat-8 Operational Land Imager (OLI) imagery consisting of 18,000 km2 of coastline around China; (2) evaluating the feasibility and performance of sea-land segmentation by comparing the accuracy assessment, time complexity, spatial complexity and stability of state-of-the-art DCNNs methods; (3) choosing the most suitable semantic segmentation model for sea-land segmentation in accordance with Akaike information criterion (AIC) and Bayesian information criterion (BIC) model selection. Results show that the average test accuracy achieves over 99% accuracy, and the mean Intersection over Unions (mean IoU) is above 92%. These findings demonstrate that the Fully Convolutional DenseNet (FC-DenseNet) performs better than other state-of-the-art methods in sea-land segmentation, based on both AIC and BIC. Considering training time efficiency, DeeplabV3+ performs better for sea-land segmentation. The sea-land segmentation benchmark dataset is available at: https://pan.baidu.com/s/1BlnHiltOLbLKe4TG8lZ5xg. Numéro de notice : A2020-051 Thématique : POSITIONNEMENT Nature : Article DOI : 10.1080/01490419.2020.1713266 date de publication en ligne : 20/01/2020 En ligne : https://doi.org/10.1080/01490419.2020.1713266 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94518 [article]

Evolution of sand encroachment using supervised classification of Landsat data during the period 1987–2011 in a part of Laâyoune-Tarfaya basin of Morocco / Ali Aydda in Geocarto international, vol 34 n° 13 ([15/10/2019])  

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Unmanned aerial vehicles (UAVs) for monitoring macroalgal biodiversity: comparison of RGB and multispectral imaging sensors for biodiversity assessments / Leigh Tait in Remote sensing, vol 11 n° 19 (October 2019)  

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Coastline extraction from SAR images using robust ridge tracing / Dailiang Wang in Marine geodesy, vol 42 n° 3 (May 2019)  

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Analysis of ocean tide loading displacements by GPS kinematic precise point positioning: a case study at the China coastal site SHAO / H. Zhao in Survey review, vol 51 n° 365 (March 2019)  

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EU priority habitats: rethinking Mediterranean coastal pine forests / Gianmaria Bonari in Rendiconti Lincei. Scienze Fisiche e Naturali, vol 29 n° 2 (June 2018)  

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Le trait de côte recule, la carte suit ... / Hubert d' Erceville in SIGmag, n° 17 (juin 2018)

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Exploring the sensitivity of coastal inundation modelling to DEM vertical error / Harry West in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)  

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Littoral, "Ricochet" ausculte / Marielle Mayo in Géomètre, n° 2155 (février 2018)

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Détermination d’un modèle géopotentiel à haute résolution en zone littorale aidé par des mesures d’horloges atomiques / Hugo Lecomte (2018) 

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Différencier les représentations spatiales selon leurs statuts : Expérimentation en gestion intégrée des zones côtières / Lucille Ritschard in Revue internationale de géomatique, vol 28 n° 1 (janvier - mars 2018)  

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