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Reclaimed-airport surface-deformation monitoring by improved permanent-scatterer interferometric synthetic-aperture radar: a case study of Shenzhen Bao'an international airport, China / Lu Miao in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 2 (February 2021)
[article]
Titre : Reclaimed-airport surface-deformation monitoring by improved permanent-scatterer interferometric synthetic-aperture radar: a case study of Shenzhen Bao'an international airport, China Type de document : Article/Communication Auteurs : Lu Miao, Auteur ; Kailiang Deng, Auteur ; Guangcai Feng, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 105-116 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] aéroport
[Termes IGN] coin réflecteur
[Termes IGN] déformation d'édifice
[Termes IGN] déformation de surface
[Termes IGN] données spatiotemporelles
[Termes IGN] image Envisat-ASAR
[Termes IGN] image Sentinel-SAR
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] piste d'aéroport
[Termes IGN] Shenzhen
[Termes IGN] surveillance d'ouvrage
[Termes IGN] surveillance géologiqueRésumé : (Auteur) Reclaimed airports usually have fragile geological structures and are susceptible to the uneven ground settlements caused by filling-material consolidation, underground construction, and dynamic loading from takeoff and landing of aircrafts. Therefore, deformation monitoring is of great significance to the safe operation of reclaimed airports. This study adopts an improved permanent-scatterer interferometric synthetic-aperture radar strategy to map the spatiotemporal deformation of Shenzhen Bao'an International Airport in China using ascending and descending Envisat/ASAR data acquired from 2007 to 2010 and Sentinel-1 data from 2015 to 2019. The results show that uneven settlements of the airport concentrate in the new reclaimed land. Then we explore the settlement characteristics of each functional area. Furthermore, we separate out the dynamic-load settlement of runway No. 2 and confirm the settlements caused by dynamic load. This study provides new ideas for studying deformation in similar fields, and technical references for the future construction of Shenzhen Airport. Numéro de notice : A2021-098 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.2.105 Date de publication en ligne : 01/02/2021 En ligne : https://doi.org/10.14358/PERS.87.2.105 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97042
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 2 (February 2021) . - pp 105-116[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2021021 SL Revue Centre de documentation Revues en salle Disponible Impact of forest disturbance on InSAR surface displacement time series / Paula M. Bürgi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)
[article]
Titre : Impact of forest disturbance on InSAR surface displacement time series Type de document : Article/Communication Auteurs : Paula M. Bürgi, Auteur ; Rowena B. Lohman, Auteur Année de publication : 2021 Article en page(s) : pp 128 - 138 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] changement d'occupation du sol
[Termes IGN] déboisement
[Termes IGN] détection de changement
[Termes IGN] détection du signal
[Termes IGN] erreur de phase
[Termes IGN] erreur systématique
[Termes IGN] image ALOS
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] retard ionosphèrique
[Termes IGN] retard troposphérique
[Termes IGN] série temporelle
[Termes IGN] Sumatra
[Termes IGN] surveillance géologiqueRésumé : (auteur) As interferometric synthetic aperture radar (InSAR) data improve in their global coverage and temporal sampling, studies of ground deformation using InSAR are becoming feasible even in heavily vegetated regions such as the American Pacific Northwest (PNW) and Sumatra. However, ongoing forest disturbance due to logging, wildfires, or disease can introduce time-variable signals which could be misinterpreted as ground displacements. This study constrains the error introduced into InSAR time series in the presence of time-variable forest disturbance using synthetic data. For satellite platforms with randomly distributed orbital positions in time (e.g., Sentinel-1), mid-time series forest disturbance results in random error on the order of 0.2 and 10 cm/year for 1-year secular and time-variable velocities, respectively. If the orbital positions are not randomly distributed in time (e.g., ALOS-1), a biased error on the order of 10 cm/year is introduced to the inferred secular velocity. A time series using real ALOS-1 data near Eugene, OR, USA, shows agreement with the bias estimated by synthetic models. Mitigation of time-variable land cover change effects can be achieved if their timing is known, either through independent observations of surface properties (e.g., Landsat/Sentinel-2) or through the use of more computationally expensive, nonlinear inversions with additional terms for the timing of height changes. Inclusion of these additional terms reduces the potential for misinterpretation of InSAR signals associated with land surface change as ground deformation. Numéro de notice : A2021-032 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2992938 Date de publication en ligne : 18/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2992938 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96727
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 1 (January 2021) . - pp 128 - 138[article]
Titre : Operational monitoring of gravitational movements with image time series Titre original : Surveillance opérationnelle de mouvements gravitaires par séries temporelles d’images Type de document : Thèse/HDR Auteurs : Mathilde Desrues, Auteur ; Jean-Philippe Malet, Directeur de thèse Editeur : Strasbourg : Université de Strasbourg Année de publication : 2021 Importance : 231 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse présentée en vue de l’obtention du grade en Géosciences - Géophysique de Docteur de l’Université de StrasbourgLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse diachronique
[Termes IGN] données géologiques
[Termes IGN] effondrement de terrain
[Termes IGN] état de l'art
[Termes IGN] Hautes-Alpes (05)
[Termes IGN] image RVB
[Termes IGN] image terrestre
[Termes IGN] Isère (38)
[Termes IGN] modèle stéréoscopique
[Termes IGN] prise de vues en accéléré
[Termes IGN] risque naturel
[Termes IGN] Savoie (73)
[Termes IGN] série temporelle
[Termes IGN] structure géologique
[Termes IGN] surveillance géologiqueIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) Understanding the dynamics and the behavior of gravitational slope movements is essential to anticipate catastrophic failures and thus to protect lives and infrastructures. Several geodetic techniques already bring some information on the displacement / deformation fields of the unstable slopes. These techniques allow the analysis of the geometrical properties of the moving masses and of the mechanical behavior of the slopes. By combining time series of passive terrestrial imagery and these classical techniques, the amount of collected information is densified and spatially distributed. Digital passive sensors are increasingly used for the detection and the monitoring of gravitational motion. They provide both qualitative information, such as the detection of surface changes, and a quantitative characterization, such as the quantification of the soil displacement by correlation techniques. Our approach consists in analyzing time series of terrestrial images from either a single fixed camera or pair-wise cameras, the latter to obtain redundant and additional information. The time series are processed to detect the areas in which the Kinematic behavior is homogeneous. The slope properties, such as the sliding volume and the thickness of the moving mass, are part of the analysis results to obtain an overview which is as complete as possible. This work is presented around the analysis of four landslides located in the French Alps. It is part of a CIFRE/ANRT agreement between the SAGE Society - Société Alpine de Géotechnique (Gières, France) and the IPGS - Institut de Physique du Globe de Strasbourg / CNRS UMR 7516 (Strasbourg, France). Note de contenu : 1. Remote sensing methods for the monitoring of gravitational movements
1.1 Gravitational movements: risk and challenges
1.2 Landslide monitoring: in-situ measurements and remote sensing
1.3 Time-lapse photography
1.4 Presentation of the use cases: technologies and data
Conclusions
2. Image time series analysis for a monoscopic model
2.1 Introduction
2.2 Methodology
2.3 Combination strategies for processing large image datasets
2.4 Application to use cases: the Chambon and the Pas de l’Ours landslides
2.5 Sensitivity analysis
2.6 Results and Discussion
2.7 Advantages and Limitations of TSM Pipeline
Conclusions
3. A stereoscopic model for landslide analysis: Application to the Montgombert landslide (Savoie, French Alps)
3.1 Foreword
3.2 Stereoscopic model
3.3 Landslide displacement estimation
3.4 Landslide deformation estimation
Conclusions
4. Pre- and post-event monitoring analysis: application to the Cliets rockslide (Savoie, French Alps)
4.1 Case study in the context of monitoring and early-warning
4.2 Time-lapse image analysis
Conclusions
5. Conclusions and perspectives
5.1 General conclusions
5.2 Perspectives
A Caractéristiques des caméras II
B Analyse de sensibilité des paramétres externes sur les résultats de TSM VII
C Série temporelle des déplacements détéctés post événement - glissement des ClietsNuméro de notice : 26767 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Géosciences - Géophysique : Strasbourg : 2021 Organisme de stage : Institut de physique du globe de Strasbourg IPGS nature-HAL : Thèse DOI : sans Date de publication en ligne : 13/10/2021 En ligne : https://hal.science/tel-03376927/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99864 Bayesian-deep-learning estimation of earthquake location from single-station observations / S. Mostafa Mousavi in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)
[article]
Titre : Bayesian-deep-learning estimation of earthquake location from single-station observations Type de document : Article/Communication Auteurs : S. Mostafa Mousavi, Auteur ; Gregory C. Beroza, Auteur Année de publication : 2020 Article en page(s) : pp 8211 - 8224 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] apprentissage profond
[Termes IGN] classification bayesienne
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection du signal
[Termes IGN] épicentre
[Termes IGN] estimation bayesienne
[Termes IGN] onde sismique
[Termes IGN] régression
[Termes IGN] séisme
[Termes IGN] station d'observation
[Termes IGN] surveillance géologique
[Termes IGN] temps de propagationRésumé : (auteur) We present a deep-learning method for a single-station earthquake location, which we approach as a regression problem using two separate Bayesian neural networks. We use a multitask temporal convolutional neural network to learn epicentral distance and P travel time from 1-min seismograms. The network estimates epicentral distance and P travel time with mean errors of 0.23 km and 0.03 s and standard deviations of 5.42 km and 0.66 s, respectively, along with their epistemic and aleatory uncertainties. We design a separate multi-input network using standard convolutional layers to estimate the back-azimuth angle and its epistemic uncertainty. This network estimates the direction from which seismic waves arrive at the station with a mean error of 1°. Using this information, we estimate the epicenter, origin time, and depth along with their confidence intervals. We use a global data set of earthquake signals recorded within 1° (~112 km) from the event to build the model and demonstrate its performance. Our model can predict epicenter, origin time, and depth with mean errors of 7.3 km, 0.4 s, and 6.7 km, respectively, at different locations around the world. Our approach can be used for fast earthquake source characterization with a limited number of observations and also for estimating the location of earthquakes that are sparsely recorded—either because they are small or because stations are widely separated. Numéro de notice : A2020-684 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2988770 Date de publication en ligne : 06/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2988770 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96209
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 11 (November 2020) . - pp 8211 - 8224[article]Macrozonation of seismic transient and permanent ground deformation of Iran / Saeideh Farahani in Natural Hazards and Earth System Sciences, vol 20 n° 11 (November 2020)
[article]
Titre : Macrozonation of seismic transient and permanent ground deformation of Iran Type de document : Article/Communication Auteurs : Saeideh Farahani, Auteur ; Behrouz Behnam, Auteur ; Ahmad Tahershamsi, Auteur Année de publication : 2020 Article en page(s) : pp 2889 – 2903 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] carte sismologique
[Termes IGN] déformation de la croute terrestre
[Termes IGN] effondrement de terrain
[Termes IGN] faille géologique
[Termes IGN] modèle numérique de surface
[Termes IGN] réflexion (rayonnement)
[Termes IGN] surveillance géologique
[Termes IGN] système d'information géographique
[Termes IGN] zone à risqueRésumé : (auteur) Iran is located on the Alpide earthquake belt, in the active collision zone between the Eurasian and Arabian plates. This issue makes Iran a country that suffers from geotechnical seismic hazards associated with frequent destructive earthquakes. Also, according to the rapid growth of population and demands for construction lifelines, risk assessment studies which should be carried out in order to reduce the probable damages are necessary. The most important destructive effects of earthquakes on lifelines are transient and permanent ground displacements. The availability of the map of the displacements caused by liquefaction, landslide, and surface fault rupture can be a useful reference for researchers and engineers who want to carry out a risk assessment project for each specific region of the country. In this study, these precise maps are produced and presented by using a considerable number of GIS-based analyses and by employing the HAZUS methodology. It is important to note that a required accuracy for risk assessment is approximately around the macro scale. So, in order to produce a suitable map for risk assessment goals, in terms of accuracy, the GIS-based analyses are employed to map all of Iran. Numéro de notice : A2020-712 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/POSITIONNEMENT Nature : Article DOI : 10.5194/nhess-20-2889-2020 Date de publication en ligne : 03/11/2020 En ligne : https://doi.org/10.5194/nhess-20-2889-2020 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96277
in Natural Hazards and Earth System Sciences > vol 20 n° 11 (November 2020) . - pp 2889 – 2903[article]Soil erosion assessment using RUSLE model and its validation by FR probability model / Amiya Gayen in Geocarto international, vol 35 n° 15 ([01/11/2020])PermalinkGeo-environment risk assessment in Zhengzhou City, China / Chuanming Ma in Geomatics, Natural Hazards and Risk, vol 11 n° 1 (2020)PermalinkEvaluation of single-frequency receivers for studying crustal deformation at the longitudinal Valley fault, eastern Taiwan / Horng-Yue Chen in Survey review, vol 52 n° 374 (August 2020)PermalinkAnalysis of dam deformation with robust weight functions / Berkant Konakoglu in Geodetski vestnik, vol 64 n° 2 (June - August 2020)PermalinkFusing adjacent-track InSAR datasets to densify the temporal resolution of time-series 3-D displacement estimation over mining areas with a prior deformation model and a generalized weighting least-squares method / Yuedong Wang in Journal of geodesy, vol 94 n° 5 (May 2020)PermalinkTephra mass eruption rate from ground-based X-band and L-band microwave radars during the November 23, 2013, Etna Paroxysm / Frank S. Marzano in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)PermalinkMonitoring of landslide activity at the Sirobagarh landslide, Uttarakhand, India, using LiDAR, SAR interferometry and geodetic surveys / Ashutosh Tiwari in Geocarto international, vol 35 n° 5 ([01/04/2020])PermalinkRadial interpolation of GPS and leveling data of ground deformation in a resurgent caldera: application to Campi Flegrei (Italy) / Andrea Bevilacqua in Journal of geodesy, vol 94 n°2 (February 2020)PermalinkVolcano-seismic transfer learning and uncertainty quantification with bayesian neural networks / Angel Bueno in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)PermalinkPoint cloud registration and mitigation of refraction effects for geomonitoring using long-range terrestrial laser scanning / Ephraim Friedli (2020)Permalink