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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]

Le radar révèle des montagnes cachées / Laurent Polidori in Géomètre, n° 2198 (janvier 2022)

Public [article]  inGéomètre > n° 2198 (janvier 2022) . - pp 17 - 17 Titre : Le radar révèle des montagnes cachées Type de document : Article/Communication Auteurs : Laurent Polidori, Auteur Année de publication : 2022 Article en page(s) : pp 17 - 17 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] altimétrie satellitaire par radar [Termes IGN] Biomass [Termes IGN] longueur d'onde [Termes IGN] représentation du relief [Termes IGN] télédétection en hyperfréquence [Termes IGN] traitement d'image radar Résumé : (Auteur) Un radar embarqué sur satellite peut voir des reliefs cachés par l’eau, la forêt, le sable ou la glace. Numéro de notice : A2022-060 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Date de publication en ligne : 01/01/2022 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99401 [article]

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063-2022011	SL	Revue	Centre de documentation	Revues en salle	En circulation Exclu du prêt

Generating GPS decoupled clock products for precise point positioning with ambiguity resolution / Shuai Liu in Journal of geodesy, vol 96 n° 1 (January 2022)  

Public [article]  inJournal of geodesy > vol 96 n° 1 (January 2022) . - n° 6 Titre : Generating GPS decoupled clock products for precise point positioning with ambiguity resolution Type de document : Article/Communication Auteurs : Shuai Liu, Auteur ; Yunbin Yan, Auteur Année de publication : 2021 Article en page(s) : n° 6 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] erreur de phase [Termes IGN] horloge du récepteur [Termes IGN] modèle stochastique [Termes IGN] onde porteuse [Termes IGN] positionnement cinématique [Termes IGN] positionnement ponctuel précis [Termes IGN] résolution d'ambiguïté Résumé : (auteur) Carrier-phase integer ambiguity resolution (AR) is the key to improving the positioning accuracy of precise point positioning (PPP). However, in theory, the integer property of ambiguities in PPP are destroyed due to the absorption of phase biases. In this study, we analyzed a set of clock products consisting of a code clock, phase clock and wide-lane (WL) bias based on the decoupled clock (DCK) model, to facilitate PPP AR. The determination of the datum of the receiver clock as well as ambiguity were analyzed in detail to arrive at ways to eliminate rank deficiency. To fix ambiguity at the server end, we propose an approach by rounding directly with “fixing solution” (FS) and “partial ambiguity hold” (PAH) strategies, to increase the fixing rate and avoid the biased solution resulting from ambiguity datum loss. With respect to the International GNSS Service (IGS) legacy clocks, the mean standard deviations (STDs) of the phase clock and code clock were about 0.02 and 1.05 ns respectively, while the WL bias was about 0.12 cycles. Additionally, the convergence speed and stability of the decoupled phase clock are significantly improved compared with the conventional PPP model. Experiments on PPP positioning performance were conducted using 1 week of GPS data from more than 100 stations, considering the IGS weekly solutions as a benchmark. The ambiguity-fixed PPP with decoupled clocks had almost the same accuracy as the integer-recovered clock model, but the average accuracy improvements compared with the conventional PPP model in the east, north, and up components were 59.2, 32.4, and 20.3%, respectively, in the static mode, and approximately 38.0, 26.2, and 19.2% in the kinematic mode. These results demonstrate that users can achieve ambiguity-fixed solutions and obtain high-precision positioning coordinates with our decoupled clock products. Numéro de notice : A2022-093 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01593-7 En ligne : https://doi.org/10.1007/s00190-021-01593-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99517 [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] random-motion-over-ground model [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 aléatoires [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]

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Real-time GNSS precise point positioning using improved robust adaptive Kalman filter / Abdelsatar Elmezayen in Survey review, Vol 53 n° 381 (November 2021)  

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Footprint size design of large-footprint full-waveform LiDAR for forest and topography applications: A theoretical study / Xuebo Yang in IEEE Transactions on geoscience and remote sensing, vol 59 n° 11 (November 2021)  

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Python software to transform GPS SNR wave phases to volumetric water content / Angel Martín in GPS solutions, vol 26 n° 1 (January 2022)  

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Ionospheric tomographic common clock model of undifferenced uncombined GNSS measurements / German Olivares-Pulido in Journal of geodesy, vol 95 n° 11 (November 2021)  

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Visualization of GNSS multipath effects and its potential application in IGS data processing / Weiming Tang in Journal of geodesy, vol 95 n° 9 (September 2021)  

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Comparison of polar ionospheric behavior at Arctic and Antarctic regions for improved satellite-based positioning / Arun Kumar Singh in Journal of applied geodesy, vol 15 n° 3 (July 2021)  

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Geometric calibration of satellite laser altimeters based on waveform matching / Shaoning Li in Photogrammetric record, vol 36 n° 174 (June 2021)  

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GNSS-based statistical analysis of ionospheric anomalies during typhoon landings in Taiwan/Japan / Hai Peng in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)  

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Integer phase clock method with single-satellite ambiguity fixing and its application in LEO satellite orbit determination / Kai Shao in Acta Geodaetica et Cartographica Sinica, vol 50 n° 4 ([20/04/2021])  

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Impact of the third frequency GNSS pseudorange and carrier phase observations on rapid PPP convergences / Jiang Guo in GPS solutions, vol 25 n° 2 (April 2021)  

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