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Seismic deformation in the Adriatic Sea region / B. Orecchio in Journal of geodynamics, vol 155 (March 2023)  

Public [article]  inJournal of geodynamics > vol 155 (March 2023) . - n°101956 Titre : Seismic deformation in the Adriatic Sea region Type de document : Article/Communication Auteurs : B. Orecchio, Auteur ; D. Presti, Auteur ; S. Scolaro, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n°101956 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] Adriatique, mer [Termes IGN] déformation de la croute terrestre [Termes IGN] faille géologique [Termes IGN] forme d'onde [Termes IGN] histogramme [Termes IGN] inversion [Termes IGN] sismologie [Termes IGN] surveillance géologique [Termes IGN] tectonique des plaques Résumé : (auteur) We present an overall analysis of the recent seismic activity occurred in the Adriatic Sea region, a strongly debated sector of the Mediterranean area, where several authors have proposed different models of plate configuration and kinematics. In the past, seismic investigations of this marine area have been strongly hampered by non-optimal network geometries, but data quality increase and recent methodological improvements lay the groundwork to attempt more accurate analyses including proper evaluations of result reliability. On these grounds, we investigated the seismic activity of the last decades by means of new hypocenter locations, waveform inversion focal mechanisms and seismogenic stress fields. We used the Bayloc non-linear probabilistic algorithm to compute hypocenter locations for the most relevant seismic sequences by carefully evaluating location quality and seismolineaments reliability. We also provided an updated database of waveform inversion focal mechanisms including original solutions estimated by applying the waveform inversion method Cut And Paste and data available from official catalogs. Then, focal mechanism solutions have been used to estimate seismogenic stress fields through different inversion algorithms. Seismic results indicate a relevant degree of fragmentation and different patterns of deformation in the Central Adriatic region. In particular, our analyses depicted two NW-SE oriented, adjacent volumes: (i) a pure compressive domain with NNE-trending axis of maximum compression characterizes the northeastern volume where the seismic activity occurs on W-to-NW oriented seismic sources; (ii) a transpressive domain with NW-trending axis of maximum compression characterizes the southwestern sector where thrust faulting preferentially occurs on ENE-to-NE oriented planes and strike-slip faulting on E-W ones. Joint evaluation of seismic findings of the present study and kinematic models proposed in the literature indicates just in the Central Adriatic region the presence of a broad deformation zone, accommodating a still evolving fragmentation of the Adriatic domain in two blocks rotating in opposite directions. On these grounds, the obtained results not only furnish new seismological evidence supporting the "two-blocks model" proposed by previous authors, but they also provide additional constraints, useful for better understanding and modeling the seismotectonic processes occurring in the Adriatic region. Numéro de notice : A2023-051 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1016/j.jog.2022.101956 Date de publication en ligne : 30/11/2022 En ligne : https://doi.org/10.1016/j.jog.2022.101956 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102379 [article]

Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles / Nico Lang in Remote sensing of environment, vol 268 (January 2022)  

Public [article]  inRemote sensing of environment > vol 268 (January 2022) . - n* 112760 Titre : Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles Type de document : Article/Communication Auteurs : Nico Lang, Auteur ; Nicolai Kalischek, Auteur ; John Armston, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n* 112760 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] biomasse aérienne [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] estimation bayesienne [Termes IGN] forme d'onde [Termes IGN] Global Ecosystem Dynamics Investigation lidar [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] semis de points Résumé : (auteur) NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. While GEDI is the first space-based LIDAR explicitly optimized to measure vertical forest structure predictive of aboveground biomass, the accurate interpretation of this vast amount of waveform data across the broad range of observational and environmental conditions is challenging. Here, we present a novel supervised machine learning approach to interpret GEDI waveforms and regress canopy top height globally. We propose a probabilistic deep learning approach based on an ensemble of deep convolutional neural networks (CNN) to avoid the explicit modelling of unknown effects, such as atmospheric noise. The model learns to extract robust features that generalize to unseen geographical regions and, in addition, yields reliable estimates of predictive uncertainty. Ultimately, the global canopy top height estimates produced by our model have an expected RMSE of 2.7 m with low bias. Numéro de notice : A2022-086 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112760 Date de publication en ligne : 03/11/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112760 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99495 [article]

A CNN-based approach for the estimation of canopy heights and wood volume from GEDI waveforms / Ibrahim Fayad in Remote sensing of environment, vol 265 (November 2021)  

Public [article]  inRemote sensing of environment > vol 265 (November 2021) . - n° 112652 Titre : A CNN-based approach for the estimation of canopy heights and wood volume from GEDI waveforms Type de document : Article/Communication Auteurs : Ibrahim Fayad, Auteur ; Dino Lenco, Auteur ; Nicolas Baghdadi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 112652 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Brésil [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Eucalyptus (genre) [Termes IGN] forme d'onde [Termes IGN] Global Ecosystem Dynamics Investigation lidar [Termes IGN] hauteur des arbres [Termes IGN] modèle de croissance végétale [Termes IGN] semis de points [Termes IGN] volume en bois Résumé : (auteur) Full waveform (FW) LiDAR systems have proven their effectiveness to map forest biophysical variables in the last two decades, owing to their ability of measuring, with high accuracy, forest vertical structures. The Global Ecosystem Dynamics Investigation (GEDI) system on board the International Space Station (ISS) is the latest FW spaceborne LiDAR instrument for the continuous observation of Earth's forests. FW systems rely on very sophisticated pre-processing steps to generate a priori metrics in order to leverage their capabilities for the accurate estimation of the aforementioned forest characteristics. The ever-expanding volume of acquired GEDI data, which to date comprises more than 25 billion acquired unfiltered shots, and along with the pre-processed data, amounting to more than 90 TB of data, raises new challenges in terms of adapted preprocessing methods for the suitable exploitation of such a huge and complex amount of LiDAR data. To overcome the issues related to the generation of relevant metrics from GEDI data, we propose a new metric-free approach to estimate canopy dominant heights (Hdom) and wood volume (V) of Eucalyptus plantations over five different regions in Brazil. To avoid metric computation, we leverage deep learning techniques and, more in detail, convolutional neural networks with the aim to analyze the GEDI Level 1B geolocated waveforms. Performance comparisons were conducted between four convolutional neural network (CNN) variants using GEDI waveform data (either untouched, or subsetted) and a metric based Random Forest regressor (RF). Additionally, we tested if our framework can improve the generalization of the models to different distant regions. First, the models were trained using data from all the study regions. Cross validated results showed that the CNN based models compared well against their RF counterpart for both Hdom and V. The RMSE on the estimation of Hdom from the CNN based models varied between 1.54 and 1.94 m with a coefficient of determination (R2) between 0.86 and 0.91, while the RF model produced an accuracy on Hdom estimates of 1.45 m (R2 = 0.92). For V, CNN based estimations ranged from 27.76 to 33.33 m3.ha−1 (R2 between 0.82 and 0.88), while for RF, the RMSE was 27.61 m3.ha−1 (R2 = 0.88). Next, model generalization was assessed by means of a spatial transfer experiment. For Hdom, both the CNN and RF approaches showed similar performances to a global model, however, the CNN based approach showed higher variability on the estimation accuracy, and the variability was related to the forest structure between the trained and tested data (similar tree heights yield better accuracies). For the estimation of V, considering both approaches, the accuracy was dependent on the allometric relationship between Hdom and V in the training and testing regions while lower accuracies on V were obtained when the testing and training regions exhibited a different allometric relationship. Numéro de notice : A2021-869 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112652 Date de publication en ligne : 31/08/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112652 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99118 [article]

Diffuse attenuation coefficient (Kd) from ICESat-2 ATLAS spaceborne Lidar using random-forest regression / Forrest Corcoran in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 11 (November 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 11 (November 2021) . - pp 831 - 840 Titre : Diffuse attenuation coefficient (Kd) from ICESat-2 ATLAS spaceborne Lidar using random-forest regression Type de document : Article/Communication Auteurs : Forrest Corcoran, Auteur ; Christopher E. Parrish, Auteur Année de publication : 2021 Article en page(s) : pp 831 - 840 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage automatique [Termes IGN] arbre de décision [Termes IGN] capteur spatial [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données ICEsat [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forme d'onde [Termes IGN] littoral [Termes IGN] modèle de régression [Termes IGN] semis de points [Termes IGN] turbidité des eaux Résumé : (Auteur) This study investigates a new method for measuring water turbidity—specifically, the diffuse attenuation coefficient of downwelling irradiance Kd —using data from a spaceborne, green-wavelength lidar aboard the National Aeronautics and Space Administration's ICESat-2 satellite. The method enables us to fill nearshore data voids in existing Kd data sets and provides a more direct measurement approach than methods based on passive multispectral satellite imagery. Furthermore, in contrast to other lidar-based methods, it does not rely on extensive signal processing or the availability of the system impulse response function, and it is designed to be applied globally rather than at a specific geographic location. The model was tested using Kd measurements from the National Oceanic and Atmospheric Administration's Visible Infrared Imaging Radiometer Suite sensor at 94 coastal sites spanning the globe, with Kd values ranging from 0.05 to 3.6 m –1 . The results demonstrate the efficacy of the approach and serve as a benchmark for future machine-learning regression studies of turbidity using ICESat-2. Numéro de notice : A2021-896 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00013R2 Date de publication en ligne : 01/11/2021 En ligne : https://doi.org/10.14358/PERS.21-00013R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99272 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2021111	SL	Revue	Centre de documentation	Revues en salle	Disponible

Validation of Sentinel-3A SRAL coastal sea level data at high posting rate: 80 Hz / Ana Aldarias in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 6 (June 2020) . - pp 3809 - 3821 Titre : Validation of Sentinel-3A SRAL coastal sea level data at high posting rate: 80 Hz Type de document : Article/Communication Auteurs : Ana Aldarias, Auteur ; Jesus Gomez-Enri, Auteur ; Irene Laiz, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 3809 - 3821 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] coefficient de corrélation [Termes IGN] correction troposphérique [Termes IGN] courbe de Pearson [Termes IGN] données altimétriques [Termes IGN] données marégraphiques [Termes IGN] eaux côtières [Termes IGN] erreur moyenne quadratique [Termes IGN] Espagne [Termes IGN] forme d'onde [Termes IGN] image Sentinel-SRAL [Termes IGN] niveau de la mer [Termes IGN] série temporelle Résumé : (auteur) Altimetry data of two and a half years (June 2016–November 2018) of Sentinel-3A SRAL (S3A-SRAL) were validated at the sampling frequency of 80 Hz. The data were obtained from the European Space Agency (ESA) Grid Processing On Demand (GPOD) service over three coastal sites in Spain: Huelva (HU) (Gulf of Cádiz), Barcelona (BA) (Western Mediterranean Sea), and Bilbao (BI) (Bay of Biscay). Two tracks were selected in each site: one ascending and one descending. Data were validated using in situ tide gauge (TG) data provided by the Spanish Puertos del Estado. The altimetry sea level anomaly time series were obtained using the corrections available in GPOD with the exception of the sea state bias (SSB) correction, not available at 80 Hz. Hence, the SSB was approximated to 5% of the significant wave height (SWH). The validation was performed using two statistical parameters, the Pearson correlation coefficient (r) and the root mean square error (rmse). In the 5–20-km segment with respect to the coastline, the results were 6–8 cm (rmse) and 0.7–0.8 (r) for all the tracks. The 0–5-km segment was also analyzed in detail to study the land effect on the altimetry data quality. The results showed that the track orientation, the angle of intersection with the coast, and the land topography concur to determine the nearest distance to the coast at which the data retain a similar level of accuracy than in the 5–20-km segment. This “distance of good quality” to shore reaches a minimum of 3 km for the tracks at HU and the descending track at BA. Numéro de notice : A2020-281 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2957649 Date de publication en ligne : 01/01/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2957649 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95102 [article]

Discrimination of different sea ice types from CryoSat-2 satellite data using an Object-based Random Forest (ORF) / Su Shu in Marine geodesy, Vol 43 n° 3 (May 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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Arctic sea ice thickness retrievals from CryoSat-2: seasonal and interannual comparisons of three different products / Mengmeng Li in International Journal of Remote Sensing IJRS, vol 41 n° 1 (01 - 08 janvier 2020)  

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Real-time capturing of seismic waveforms using high-rate BDS, GPS and GLONASS observations: the 2017 Mw 6.5 Jiuzhaigou earthquake in China / Xingxing Li in GPS solutions, vol 23 n° 1 (January 2019)  

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Gold – A novel deconvolution algorithm with optimization for waveform LiDAR processing / Tan Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 129 (July 2017)  

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Pulse compression waveform and filter optimization for spaceborne cloud and precipitation radar / Robert M. Beauchamp in IEEE Transactions on geoscience and remote sensing, vol 55 n° 2 (February 2017)  

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Télédétection pour l'observation des surfaces continentales, ch. 6. Méthodes de traitement de données lidar / Clément Mallet (2017)

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Télédétection pour l'observation des surfaces continentales, Volume 1. Observation des surfaces continentales par télédétection optique / Nicolas Baghdadi (2017)

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Is waveform worth it? A comparison of LiDAR approaches for vegetation and landscape characterization / Karen Anderson in Remote sensing in ecology and conservation, vol 2 n° 1 (February 2016)  

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A wavelet-based echo detector for waveform LiDAR data / Cheng-Kai Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)  

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