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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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 1 (January 2021) . - pp 128 - 138 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 descripteurs IGN] changement d'occupation du sol [Termes descripteurs IGN] déboisement [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] détection du signal [Termes descripteurs IGN] erreur de phase [Termes descripteurs IGN] erreur systématique [Termes descripteurs IGN] image ALOS [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] retard ionosphèrique [Termes descripteurs IGN] retard troposphérique [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] Sumatra [Termes descripteurs IGN] surveillance géologique Résumé : (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 [article]

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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 11 (November 2020) . - pp 8211 - 8224 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 descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification bayesienne [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection du signal [Termes descripteurs IGN] épicentre [Termes descripteurs IGN] estimation bayesienne [Termes descripteurs IGN] onde sismique [Termes descripteurs IGN] régression [Termes descripteurs IGN] séisme [Termes descripteurs IGN] station d'observation [Termes descripteurs IGN] surveillance géologique [Termes descripteurs IGN] temps de propagation Résumé : (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 [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)  

Public [article]  inNatural Hazards and Earth System Sciences > vol 20 n° 11 (November 2020) . - pp 2889 – 2903 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 descripteurs IGN] carte sismologique [Termes descripteurs IGN] déformation de la croute terrestre [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] faille géologique [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] réflexion (rayonnement) [Termes descripteurs IGN] surveillance géologique [Termes descripteurs IGN] système d'information géographique [Termes descripteurs IGN] zone à risque Résumé : (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 [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])  

Public [article]  inGeocarto international > vol 35 n° 15 [01/11/2020] . - pp 1750 - 1768 Titre : Soil erosion assessment using RUSLE model and its validation by FR probability model Type de document : Article/Communication Auteurs : Amiya Gayen, Auteur ; Sunil Saha, Auteur ; Hamid Reza Pourghasemi, Auteur Année de publication : 2020 Article en page(s) : pp 1750 - 1768 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse de sensibilité [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] érosion [Termes descripteurs IGN] érosion hydrique [Termes descripteurs IGN] fréquence [Termes descripteurs IGN] Inde [Termes descripteurs IGN] modèle RUSLE [Termes descripteurs IGN] modèle stochastique [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] pente [Termes descripteurs IGN] surveillance géologique [Termes descripteurs IGN] utilisation du sol Résumé : (auteur) The objective of the current study is to estimate the annual average soil loss through RUSLE model and furthermore assess the soil erosion risk and its distribution using frequency ratio (FR) probability algorithm. At first, soil erosion risk zones were identified using FR model by the consideration 14 soil erosion conditioning factors such as land use (LU/LC), slope, slope aspect, normalized difference vegetation index (NDVI), altitude, plan curvature, stream power index, distance from river, road, and lineament, soil types, rainfall erosivity, slope length and lineament density. Secondly, the spatial pattern of annual average soil loss rates was estimated using RUSLE model with consideration of five factors such as, rainfall erosivity (R), cover management (C), slope length (LS), soil erodability (K), and conservation practice factors (P). In order to map soil erosion susceptibility by the FR model, dataset divided randomly into parts 70/30 percent for training and validation purposes, respectively. Based on the FR value, the susceptibility map was reclassified into five different critical erosion probability zones. Among this, the severe and high erosion zones occupy 13.69% and 16.26%, respectively, of the total area, where as low and very low susceptibility zones together constitute 32.98% of the River Basin. The assessed high amount of average annual soil erosion (more than 100 t/ha/year) is occupied 9.55% of the total study area. It is conclude that high soil erosion susceptibility and yearly average soil loss were performed in this study area. Therefore, the produced soil erosion susceptibility maps and annual average soil erosion map can be very useful for primary land use planning and soil erosion hazard mitigation purpose for prioritizing areas. Numéro de notice : A2020-660 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1581272 date de publication en ligne : 21/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1581272 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96134 [article]

Geo-environment risk assessment in Zhengzhou City, China / Chuanming Ma in Geomatics, Natural Hazards and Risk, vol 11 n° 1 (2020)  

Public [article]  inGeomatics, Natural Hazards and Risk > vol 11 n° 1 (2020) . - pp 40 - 70 Titre : Geo-environment risk assessment in Zhengzhou City, China Type de document : Article/Communication Auteurs : Chuanming Ma, Auteur ; Wu Yan, Auteur ; Xinjie Hu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 40 - 70 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] Chine [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] évaluation des données [Termes descripteurs IGN] gestion des risques [Termes descripteurs IGN] pollution des eaux [Termes descripteurs IGN] processus d'analyse hiérarchisée [Termes descripteurs IGN] risque environnemental [Termes descripteurs IGN] séisme [Termes descripteurs IGN] structure hiérarchique de données [Termes descripteurs IGN] surveillance géologique [Termes descripteurs IGN] urbanisme [Termes descripteurs IGN] zone urbaine dense Résumé : (auteur) The urban geological environment risk assessment is based on the research and analysis of the main geological environmental problems of the city, comprehensively assessing the risk of urban geological environment problems and the possible losses, and studying the degree of matching between the natural and social attributes of the geological environment. According to the urban planning of Zhengzhou City, the different types of functional areas of the city were used as evaluation objects, and the analytic hierarchy-composite index model was used to evaluate the geological environment risk and social economic vulnerability. The risk assessment model was used to evaluate the geological environment risk of Zhengzhou City. The evaluation results show that the area of high-risk area in Zhengzhou accounts for 4.05%; the area of medium-high risk area accounts for 12.89%; the area of medium-low and low-risk area accounts for 83.06%. According to the assessment results, suggestions are put forward to provide service for the urban planning, development and risk management. Highlights: * An urban geo-environment risk assessment technique system combining with the AHP - composite index assessment model is proposed. * Different types of functional zones in Zhengzhou City are taken as assessment units. * Geo-environment risk in Zhengzhou City is qualitatively and quantitatively evaluated. Numéro de notice : A2020-565 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475705.2019.1701571 date de publication en ligne : 27/12/2019 En ligne : https://doi.org/10.1080/19475705.2019.1701571 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95890 [article]

Evaluation 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)  

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Analysis of dam deformation with robust weight functions / Berkant Konakoglu in Geodetski vestnik, vol 64 n° 2 (June - August 2020)  

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Fusing 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)  

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Tephra 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)  

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Monitoring 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])  

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Radial 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)  

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Volcano-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)  

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Uncertainty analysis of remotely-acquired thermal infrared data to extract the thermal Properties of active lava surfaces / James A. Thompson in Remote sensing, vol 12 n° 1 (January 2020)  

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Rockfalls multi-methods detection and characterization / Gaëlle Le Roy (2020)  

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Combining thermal imaging with photogrammetry of an active volcano using UAV: an example from Stromboli, Italy / Zoë E. Wakeford in Photogrammetric record, vol 34 n° 168 (December 2019)  

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