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Evaluating the suitability of multi-scale terrain attribute calculation approaches for seabed mapping applications / Benjamin Misiuk in Marine geodesy, vol 44 n° 4 (July 2021)  

Public [article]  inMarine geodesy > vol 44 n° 4 (July 2021) . - pp 327 - 385 Titre : Evaluating the suitability of multi-scale terrain attribute calculation approaches for seabed mapping applications Type de document : Article/Communication Auteurs : Benjamin Misiuk, Auteur ; Vincent Lecours, Auteur ; M.F.J. Dolan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 327 - 385 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] analyse multiéchelle [Termes IGN] artefact [Termes IGN] attribut géomètrique [Termes IGN] carte bathymétrique [Termes IGN] cartographie hydrographique [Termes IGN] fond marin [Termes IGN] géomorphométrie [Termes IGN] habitat animal [Termes IGN] pente [Termes IGN] réalité de terrain [Termes IGN] rugosité [Termes IGN] sondeur multifaisceaux [Termes IGN] Terre-Neuve, île de (Terre-Neuve-et-Labrador) Résumé : (auteur) The scale dependence of benthic terrain attributes is well-accepted, and multi-scale methods are increasingly applied for benthic habitat mapping. There are, however, multiple ways to calculate terrain attributes at multiple scales, and the suitability of these approaches depends on the purpose of the analysis and data characteristics. There are currently few guidelines establishing the appropriateness of multi-scale raster calculation approaches for specific benthic habitat mapping applications. First, we identify three common purposes for calculating terrain attributes at multiple scales for benthic habitat mapping: (i) characterizing scale-specific terrain features, (ii) reducing data artefacts and errors, and (iii) reducing the mischaracterization of ground-truth data due to inaccurate sample positioning. We then define criteria that calculation approaches should fulfill to address these purposes. At two study sites, five raster terrain attributes, including measures of orientation, relative position, terrain variability, slope, and rugosity were calculated at multiple scales using four approaches to compare the suitability of the approaches for these three purposes. Results suggested that specific calculation approaches were better suited to certain tasks. A transferable parameter, termed the ‘analysis distance’, was necessary to compare attributes calculated using different approaches, and we emphasize the utility of such a parameter for facilitating the generalized comparison of terrain attributes across methods, sites, and scales. Numéro de notice : A2021-526 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/01490419.2021.1925789 Date de publication en ligne : 04/06/2021 En ligne : https://doi.org/10.1080/01490419.2021.1925789 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97967 [article]

Extracting Shallow-Water Bathymetry from Lidar point clouds using pulse attribute data: Merging density-based and machine learning approaches / Kim Lowell in Marine geodesy, vol 44 n° 4 (July 2021)  

Public [article]  inMarine geodesy > vol 44 n° 4 (July 2021) . - pp 259 - 286 Titre : Extracting Shallow-Water Bathymetry from Lidar point clouds using pulse attribute data: Merging density-based and machine learning approaches Type de document : Article/Communication Auteurs : Kim Lowell, Auteur ; Brian Calder, Auteur Année de publication : 2021 Article en page(s) : pp 259 - 286 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] angle d'incidence [Termes IGN] apprentissage automatique [Termes IGN] bathymétrie laser [Termes IGN] classification barycentrique [Termes IGN] données lidar [Termes IGN] Extreme Gradient Machine [Termes IGN] Floride (Etats-Unis) [Termes IGN] lever bathymétrique [Termes IGN] profondeur [Termes IGN] semis de points Résumé : (auteur) To automate extraction of bathymetric soundings from lidar point clouds, two machine learning (ML1) techniques were combined with a more conventional density-based algorithm. The study area was four data “tiles” near the Florida Keys. The density-based algorithm determined the most likely depth (MLD) for a grid of “estimation nodes” (ENs). Unsupervised k-means clustering determined which EN’s MLD depth and associated soundings represented ocean depth rather than ocean surface or noise to produce a preliminary classification. An extreme gradient boosting (XGB) model was fitted to pulse return metadata – e.g. return intensity, incidence angle – to produce a final Bathy/NotBathy classification. Compared to an operationally produced reference classification, the XGB model increased global accuracy and decreased the false negative rate (FNR) – i.e. undetected bathymetry – that are most important for nautical navigation for all but one tile. Agreement between the final XGB and operational reference classifications ranged from 0.84 to 0.999. Imbalance between Bathy and NotBathy was addressed using a probability decision threshold that equalizes the FNR and the true positive rate (TPR). Two methods are presented for visually evaluating differences between the two classifications spatially and in feature-space. Numéro de notice : A2021-525 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article DOI : 10.1080/01490419.2021.1925790 Date de publication en ligne : 25/05/2021 En ligne : https://doi.org/10.1080/01490419.2021.1925790 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97964 [article]

Coral habitat mapping: a comparison between maximum likelihood, Bayesian and Dempster–Shafer classifiers / Mohammad Shawkat Hossain in Geocarto international, vol 36 n° 11 ([15/06/2021])  

Public [article]  inGeocarto international > vol 36 n° 11 [15/06/2021] . - pp 1217 - 1235 Titre : Coral habitat mapping: a comparison between maximum likelihood, Bayesian and Dempster–Shafer classifiers Type de document : Article/Communication Auteurs : Mohammad Shawkat Hossain, Auteur ; Aidy M. Muslim, Auteur ; Muhammad Izuan Nadzri, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1217 - 1235 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte thématique [Termes IGN] classification bayesienne [Termes IGN] classification de Dempster-Shafer [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification pixellaire [Termes IGN] fond marin [Termes IGN] Google Earth [Termes IGN] habitat d'espèce [Termes IGN] image Quickbird [Termes IGN] Malaisie [Termes IGN] précision infrapixellaire [Termes IGN] récif corallien Résumé : (auteur) This study deals with the mixed-pixel problem of detecting benthic habitat class membership and evaluates two soft classifiers for coral habitat mapping on Lang Tengah island (Malaysia). A comparison was made between the Bayesian and Dempster–Shafer (D–S) with a traditional maximum likelihood (ML). The heterogeneous pattern of reef environment, established by field observation, four classes of coral habitats containing various combinations of live coral, dead coral with algae, rubble coral and sand. Posterior probability and belief maps, generated by Bayesian and D–S, respectively, were evaluated by visual inspection and final coral habitat distribution maps were validated via accuracy assessment estimates. The accuracy validation tests agreed with the visual inspection of the probability, uncertainty and coral distribution maps. The Bayesian algorithm performed better, with a 34.7–68.5% improvement in accuracy compared to D–S and ML, respectively. Probability maps demonstrate the advantages of the soft classifier over the hard classifier for coral mapping. Numéro de notice : A2021-435 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1637466 Date de publication en ligne : 10/07/2019 En ligne : https://doi.org/10.1080/10106049.2019.1637466 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97803 [article]

Cloud-native seascape mapping of Mozambique’s Quirimbas National Park with Sentinel-2 / Dimitris Poursanidis in Remote sensing in ecology and conservation, vol 7 n° 2 (June 2021)  

Public [article]  inRemote sensing in ecology and conservation > vol 7 n° 2 (June 2021) . - pp 275 - 291 Titre : Cloud-native seascape mapping of Mozambique’s Quirimbas National Park with Sentinel-2 Type de document : Article/Communication Auteurs : Dimitris Poursanidis, Auteur ; Dimosthenis Traganos, Auteur ; Luisa Teixeira, Auteur ; Aurélie Shapiro, Auteur ; Lara Muaves, Auteur Année de publication : 2021 Article en page(s) : pp 275 - 291 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse des mélanges spectraux [Termes IGN] classification et arbre de régression [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] écosystème [Termes IGN] Google Earth Engine [Termes IGN] habitat (nature) [Termes IGN] image Sentinel-MSI [Termes IGN] Mozambique [Termes IGN] récif corallien [Termes IGN] réserve naturelle [Termes IGN] surveillance écologique Résumé : (auteur) The lack of detailed spatial information on coastal resources, notably shallow water coral reefs and associated benthic habitats, impedes our ability to protect and manage them in the face of global climate change and anthropogenic impacts. Here, we develop a semi-automated workflow in the cloud that uses freely available Sentinel-2 data from the European Space Agency (ESA) Copernicus programme to derive information on near-shore coral reef habitats in the Quirimbas National Park (QNP), a recently declared biosphere reserve in northern Mozambique. We use an end-to-end cloud-based framework within the Google Earth Engine cloud geospatial platform to process imagery from raw pixels to cloud-free composites which are corrected for glint and surface artefacts, water column and derived estimated depth and then classified into four benthic habitats. Using independent training and validation data, we apply three supervised classification algorithms: random forests (RF), support vector machine (SVM) and classification and regression trees (CART). Our results show that random forests are the most accurate supervised algorithm with over 82% overall accuracy. We mapped over 105 000 ha of shallow water habitat inside the protected area, of which 18% are dominated by coral and hardbottom; 27.5% are seagrass and submerged aquatic vegetation and another 23.4% are soft and sandy substrates, and the remaining area is optically deep water. We employ satellite-derived bathymetry to assess slope, bathymetric position, rugosity and underwater topography of these habitats. Finally, a spectral unmixing model provides further sub-pixel–level information of habitats with the potential to monitor changes over time. This effort provides the first, consistent and repeatable and also scalable coastal information system for an east African tropical marine protected area, which hosts shallow-water ecosystems which are of great significance to local communities and building resilience towards climate change. Numéro de notice : A2021-733 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1002/rse2.187 Date de publication en ligne : 29/11/2020 En ligne : https://doi.org/10.1002/rse2.187 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98679 [article]

Comparison and evaluation of high-resolution marine gravity recovery via sea surface heights or sea surface slopes / Shengjun Zhang in Journal of geodesy, vol 95 n° 6 (June 2021)  

Public [article]  inJournal of geodesy > vol 95 n° 6 (June 2021) . - n° 66 Titre : Comparison and evaluation of high-resolution marine gravity recovery via sea surface heights or sea surface slopes Type de document : Article/Communication Auteurs : Shengjun Zhang, Auteur ; Adili Abulaitijiang, Auteur ; Ole Baltazar Andersen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 66 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] données altimétriques [Termes IGN] données Jason [Termes IGN] géodésie marine [Termes IGN] gravimétrie en mer [Termes IGN] hauteurs de mer [Termes IGN] image Cryosat [Termes IGN] relief sous-marin [Termes IGN] SARAL [Termes IGN] série temporelle [Termes IGN] surface de la mer Résumé : (auteur) There are two dominating approaches of modeling the marine gravity field based on satellite altimetry observations. In this study, the marine gravity field is determined in four selected areas (Northwestern Atlantic, Hawaii ocean area, Mariana Trench area, and Aegean Sea) by using exact same input datasets but different methods which are based on sea surface height (SSH) and sea surface slope (SSS), respectively. The impact of the methodology is evaluated by conducting validations with shipborne gravity observation. The CryoSat-2, Jason-1/2, and SARAL/Altika geodetic mission data (similarly 3-year-long time series) are firstly retracked by the two-pass retracker. After that, the obtained SSHs are used for the derivation of geoid undulations and vertical deflections, and then for the resulting marine gravity field separately. The validation results indicate that the SSH-based method has advantages in robustly estimating marine gravity anomalies near the coastal zone. The SSS-based method has advantages over regions with intermedium ocean depths (2000–4000 m) where seamounts and ridges are found, but obvious disadvantages when the ocean currents flow along the north–south direction (e.g., western boundary currents) or the topography features north–south directional trenches. In the deep ocean where the seafloor topography is plain and smooth, the two methods have similar accuracy. Numéro de notice : A2021-433 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01506-8 Date de publication en ligne : 27/05/2021 En ligne : https://doi.org/10.1007/s00190-021-01506-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97799 [article]

Flood risk mapping using uncertainty propagation analysis on a peak discharge: case study of the Mille Iles River in Quebec / Jean-Marie Zokagoa in Natural Hazards, vol 107 n° 1 (May 2021)  

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Mapping and quantification of the dwarf eelgrass Zostera noltii using a random forest algorithm on a SPOT 7 satellite image / Salma Benmokhtar in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)  

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

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Horizontal calibration of vessels with UASs / Casey O'Heran in Marine geodesy, vol 44 n° 2 (March 2021)  

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Coastal water remote sensing from sentinel-2 satellite data using physical, statistical, and neural network retrieval approach / Frank S. Marzano in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)  

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Using Sentinel-2 images to estimate topography, tidal-stage lags and exposure periods over large intertidal areas / José P. Granadeiro in Remote sensing, Vol 13 n° 2 (January-2 2021)  

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Correcting misclassification errors in crowdsourced ecological data: A Bayesian perspective / Edgar Santos-Fernandez in Journal of the Royal Statistical Society: Series C Applied Statistics, vol 70 n° 1 (January 2021)  

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Determination of the under water position of objects by reflectorless total stations / Štefan Rákay in Survey review, vol 53 n°376 (January 2021)  

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A method of hydrographic survey technology selection based on the decision tree supervised learning / Ivana Golub Medvešek (2021)  

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The Influence of camera calibration on nearshore bathymetry estimation from UAV Vvdeos / Gonzalo Simarro in Remote sensing, vol 13 n° 1 (January-1 2021)  

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