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scientifique

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)  

Public [article]  inJournal of geodesy > vol 96 n° 5 (May 2022) . - n° 35 Titre : Modeling gravimetric signatures of third-degree ocean tides and their detection in superconducting gravimeter records Type de document : Article/Communication Auteurs : Roman Sulzbach, Auteur ; Hartmut Wziontek, Auteur ; Michael Hart-Davis, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 35 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] données marégraphiques [Termes IGN] gravimètre supraconducteur [Termes IGN] levé gravimétrique [Termes IGN] marée océanique [Termes IGN] montée du niveau de la mer [Termes IGN] simulation hydrodynamique [Termes IGN] surcharge océanique Résumé : (auteur) We employ the barotropic, data-unconstrained ocean tide model TiME to derive an atlas for degree-3 tidal constituents including monthly to terdiurnal tidal species. The model is optimized with respect to the tide gauge data set TICON-td that is extended to include the respective tidal constituents of diurnal and higher frequencies. The tide gauge validation shows a root-mean-square (RMS) deviation of 0.9–1.3 mm for the individual species. We further model the load tide-induced gravimetric signals by two means (1) a global load Love number approach and (2) evaluating Greens-integrals at 16 selected locations of superconducting gravimeters. The RMS deviation between the amplitudes derived using both methods is below 0.5 nGal (1 nGal =0.01nms2) when excluding near-coastal gravimeters. Utilizing ETERNA-x, a recently upgraded and reworked tidal analysis software, we additionally derive degree-3 gravimetric tidal constituents for these stations, based on a hypothesis-free wave grouping approach. We demonstrate that this analysis is feasible, yielding amplitude predictions of only a few 10 nGal, and that it agrees with the modeled constituents on a level of 63–80% of the mean signal amplitude. Larger deviations are only found for lowest amplitude signals, near-coastal stations, or shorter and noisier data sets. Numéro de notice : A2022-299 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-022-01609-w Date de publication en ligne : 30/04/2022 En ligne : https://doi.org/10.1007/s00190-022-01609-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100511 [article]

Assessing surface drainage conditions at the street and neighborhood scale: A computer vision and flow direction method applied to lidar data / Cheng-Chun Lee in Computers, Environment and Urban Systems, vol 93 (April 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 93 (April 2022) . - n° 101755 Titre : Assessing surface drainage conditions at the street and neighborhood scale: A computer vision and flow direction method applied to lidar data Type de document : Article/Communication Auteurs : Cheng-Chun Lee, Auteur ; Nasir G. Gharaibeh, Auteur Année de publication : 2022 Article en page(s) : n° 101755 Note générale : bibliogrphie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] drainage [Termes IGN] écoulement des eaux [Termes IGN] Houston (Texas) [Termes IGN] inondation [Termes IGN] lidar mobile [Termes IGN] modèle numérique de surface [Termes IGN] ruissellement [Termes IGN] segmentation sémantique Résumé : (auteur) Surface drainage at the neighborhood and street scales plays an important role in conveying stormwater and mitigating urban flooding. Surface drainage at the local scale is often ignored due to the lack of up-to-date fine-scale topographical information. This paper addresses this issue by providing a novel method for evaluating surface drainage at the neighborhood and street scales based on mobile lidar (light detection and ranging) measurements. The developed method derives topographical properties and runoff accumulation by applying a semantic segmentation (SS) model (a computer vision technique) and a flow direction model (a hydrology technique) to lidar data. Fifty lidar images representing 50 street blocks were used to train, validate, and test the SS model. Based on the test dataset, the SS model has 80.3% IoU and 88.5% accuracy. The results suggest that the proposed method can effectively evaluate surface drainage conditions at both the neighborhood and street scales and identify problematic low points that could be susceptible to water ponding. Municipalities and property owners can use this information to take targeted corrective maintenance actions. Numéro de notice : A2022-120 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101755 Date de publication en ligne : 13/01/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101755 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99661 [article]

Characterizing stream morphological features important for fish habitat using airborne laser scanning data / Spencer Dakin Kuiper in Remote sensing of environment, vol 272 (April 2022)  

Public [article]  inRemote sensing of environment > vol 272 (April 2022) . - n° 112948 Titre : Characterizing stream morphological features important for fish habitat using airborne laser scanning data Type de document : Article/Communication Auteurs : Spencer Dakin Kuiper, Auteur ; Nicholas C. Coops, Auteur ; Piotr Tompalski, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112948 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] bassin hydrographique [Termes IGN] cours d'eau [Termes IGN] données de terrain [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] écosystème forestier [Termes IGN] forêt ripicole [Termes IGN] géomorphologie locale [Termes IGN] gestion forestière durable [Termes IGN] habitat animal [Termes IGN] modèle numérique de surface [Termes IGN] poisson (faune aquatique) [Termes IGN] semis de points [Termes IGN] structure d'un peuplement forestier [Termes IGN] Vancouver (Colombie britannique) Résumé : (auteur) Understanding changes in salmonid populations and their habitat is a critical issue given changing climate, their importance as a keystone species, and their cultural significance. Terrain features such as slope, gradient, and morphology, as well as forest structure attributes including canopy cover, height, and presence of on ground coarse wood, all influence the quality and quantity of salmonid habitat in forested ecosystems. The increasing availability of Airborne Laser Scanning (ALS) data for forest applications offers an opportunity to utilize these data for assessing the quality and quantity of habitat, which is often costly and difficult to characterize. ALS data provides detailed and accurate Digital Elevation Models (DEMs) under forest canopies, which in turn enable the characterization of detailed stream networks, as well as stream and terrain attributes important to salmonids. At the Nahmint watershed on Vancouver Island, British Columbia, Canada, we sampled six, 200 m long stream reaches, describing a range of terrain and stream features following standard data collection protocols. Our objective in this research was to use ALS data to estimate three attributes from the 3D point cloud and DEM that are known to be important for salmonids, including bankfull width,instream wood and discrete stream morphological units. Results indicate that ALS-based estimates had strong, significant, correlations with field-measured attributes (with Pearson's correlation of 0.80 and 0.81 for bankfull width and instream wood, respectively). Bankfull width was slightly underestimated using the ALS data (Bias = −1.01 m; MAD = 1.89 m; RMSD = 2.05 m) and 80% of instream wood pieces were detected. Using ALS-derived predictors in a Random Forest model, discrete stream morphological units (i.e. pools, riffles, glides, cascades) were classified with an overall accuracy of 85%, with pools having the highest user's class accuracy at 96%. Results presented herein indicate that ALS data can be used to provide a fine scale characterization of stream attributes that are required to identify salmonid habitat, providing critical information for sustainable forest management decision making, and providing a foundation for advanced salmonid habitat modeling. Numéro de notice : A2022-283 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.112948 Date de publication en ligne : 24/02/2022 En ligne : https://doi.org/10.1016/j.rse.2022.112948 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100301 [article]

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)  

Public [article]  inJournal of geodesy > vol 96 n° 4 (April 2022) . - n° 17 Titre : Coastal observation of sea surface tide and wave height using opportunity signal from Beidou GEO satellites: analysis and evaluation Type de document : Article/Communication Auteurs : Feng Wang, Auteur ; Dongkai Yang, Auteur ; Guodong Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 17 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] Chine [Termes IGN] données altimétriques [Termes IGN] données marégraphiques [Termes IGN] hauteurs de mer [Termes IGN] méthode de Monte-Carlo [Termes IGN] modèle géométrique [Termes IGN] rapport signal sur bruit [Termes IGN] récepteur GNSS [Termes IGN] réflectométrie par GNSS [Termes IGN] signal BeiDou [Termes IGN] surface de la mer [Termes IGN] vague Résumé : (auteur) In this paper, the methods retrieving tide and SWH using reflected BeiDou GEO satellite signals are proposed, and a data-driven method is proposed to calibrate sea state bias of the retrieved tide. In addition, an estimator combining multi-satellite observation based on linear unbiased minimum variance (LUMV) is developed to improve the retrieved precision. The B1I signal experiments in Qingdao and Shenzhen show after calibrating sea state influence using the proposed method, the root-mean-square error (RMSE) could fall to 0.40 m from 0.45 m, and compared to the single-satellite observation, the multi-satellite observation based on the LUMV estimator could significantly reduce the RMSE of the retrieved tide to 0.16 m. Shenzhen experiment is also used to evaluate the performance of retrieving SWH and the determination coefficient of 0.60 is obtained. This paper also conducts Monte Carlo simulation and experiment to evaluate the altimetry and measuring SWH precision using reflected B3I signal. The simulated results when SNR is over 5 dB, incoherent averaging number is 10000, and the receiver bandwidth is over 45 MHz, the estimated precision of the delay can reach up ∼0.15 m, and the precision of the normalized area ranges from 0.2 to 0.3 m. The B3I experiment show that compared to B1I signal, when the reflected signal from individual satellite is used, the better precision with the RMSE of 0.25 can be obtained, and when combining the measurements from the three satellites using LUMV estimator, the RMSE reduces to 0.16 m. Further, the precision of 0.12 m can be obtained by calibrating the sea state influence. Numéro de notice : A2022-213 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-022-01605-0 Date de publication en ligne : 06/03/2022 En ligne : https://doi.org/10.1007/s00190-022-01605-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100050 [article]

Determination of building flood risk maps from LiDAR mobile mapping data / Yu Feng in Computers, Environment and Urban Systems, vol 93 (April 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 93 (April 2022) . - n° 101759 Titre : Determination of building flood risk maps from LiDAR mobile mapping data Type de document : Article/Communication Auteurs : Yu Feng, Auteur ; Qing Xiao, Auteur ; Claus Brenner, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101759 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] bâtiment [Termes IGN] cartographie d'urgence [Termes IGN] cartographie des risques [Termes IGN] classification semi-dirigée [Termes IGN] détection d'objet [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction de traits caractéristiques [Termes IGN] façade [Termes IGN] infiltration [Termes IGN] inondation [Termes IGN] modèle de simulation [Termes IGN] prévention des risques [Termes IGN] risque naturel [Termes IGN] segmentation sémantique Résumé : (auteur) With increasing urbanization, flooding is a major challenge for many cities today. Based on forecast precipitation, topography, and pipe networks, flood simulations can provide early warnings for areas and buildings at risk of flooding. Basement windows, doors, and underground garage entrances are common places where floodwater can flow into a building. Some buildings have been prepared or designed considering the threat of flooding, but others have not. Therefore, knowing the heights of these facade openings helps to identify places that are more susceptible to water ingress. However, such data is not yet readily available in most cities. Traditional surveying of the desired targets may be used, but this is a very time-consuming and laborious process. Instead, mobile mapping using LiDAR (light detection and ranging) is an efficient tool to obtain a large amount of high-density 3D measurement data. To use this method, it is required to extract the desired facade openings from the data in a fully automatic manner. This research presents a new process for the extraction of windows and doors from LiDAR mobile mapping data. Deep learning object detection models are trained to identify these objects. Usually, this requires to provide large amounts of manual annotations. In this paper, we mitigate this problem by leveraging a rule-based method. In a first step, the rule-based method is used to generate pseudo-labels. A semi-supervised learning strategy is then applied with three different levels of supervision. The results show that using only automatically generated pseudo-labels, the learning-based model outperforms the rule-based approach by 14.6% in terms of F1-score. After five hours of human supervision, it is possible to improve the model by another 6.2%. By comparing the detected facade openings' heights with the predicted water levels from a flood simulation model, a map can be produced which assigns per-building flood risk levels. Thus, our research provides a new geographic information layer for fine-grained urban emergency response. This information can be combined with flood forecasting to provide a more targeted disaster prevention guide for the city's infrastructure and residential buildings. To the best of our knowledge, this work is the first attempt to achieve such a large scale, fine-grained building flood risk mapping. Numéro de notice : A2022-196 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101759 Date de publication en ligne : 01/02/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101759 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99964 [article]

Flood mapping using multi-temporal Sentinel-1 SAR images: A case study—Inaouene watershed from Northeast of Morocco / Brahim Benzougagh in Iranian Journal of Science and Technology - Transactions of Civil Engineering, vol 46 n° 2 (April 2022)  

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Multi-parameter risk mapping of Qazvin aquifer by classic and fuzzy clustering techniques / Saman Javadi in Geocarto international, vol 37 n° 4 (April 2022)  

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Application of catastrophe theory to spatial analysis of groundwater potential in a sub-humid tropical region: a hybrid approach / Laishram Kanta Singh in Geocarto international, vol 37 n° 3 ([01/03/2022])  

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Flood monitoring by integration of remote sensing technique and multi-criteria decision making method / Hadi Farhadi in Computers & geosciences, vol 160 (March 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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Assessment and mapping soil water erosion using RUSLE approach and GIS tools: Case of Oued el-Hai watershed, Aurès West, Northeastern of Algeria / Aida Bensekhria in ISPRS International journal of geo-information, vol 11 n° 2 (February 2022)  

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Fast local adaptive multiscale image matching algorithm for remote sensing image correlation / Niccolò Dematteis in Computers & geosciences, vol 159 (February 2022)  

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Possibilities for assessment and geovisualization of spatial and temporal water quality data using a webGIS application / Daniel Balla in ISPRS International journal of geo-information, vol 11 n° 2 (February 2022)  

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Using vertices of a triangular irregular network to calculate slope and aspect / Guanghui Hu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)  

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