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Two-phase forest inventory using very-high-resolution laser scanning / Henrik J. Persson in Remote sensing of environment, vol 271 (March- 2 2022)  

Public [article]  inRemote sensing of environment > vol 271 (March- 2 2022) . - n° 112909 Titre : Two-phase forest inventory using very-high-resolution laser scanning Type de document : Article/Communication Auteurs : Henrik J. Persson, Auteur ; Kenneth Olofsson, Auteur ; Johan Holmgren, Auteur Année de publication : 2022 Article en page(s) : n° 112909 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse comparative [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] échantillonnage [Termes IGN] forêt boréale [Termes IGN] hauteur des arbres [Termes IGN] inférence statistique [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] lasergrammétrie [Termes IGN] modélisation de la forêt [Termes IGN] peuplement forestier [Termes IGN] Suède [Termes IGN] télémétrie laser terrestre [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) In this study, we compared a two-phase laser-scanning-based forest inventory of stands versus a traditional field inventory using sample plots. The two approaches were used to estimate stem volume (VOL), Lorey's mean height (HL), Lorey's stem diameter (DL), and VOL per tree species in a study area in Sweden. The estimates were compared at the stand level with the harvested reference values obtained using a forest harvester. In the first phase, a helicopter acquired airborne laser scanning (ALS) data with >500 points/m2 along 50-m wide strips across the stands. These strips intersected systematic plots in phase two, where terrestrial laser scanning (TLS) was used to model DL for individual trees. In total, phase two included 99 plots across 10 boreal forest stands in Sweden (lat 62.9° N, long 16.9° E). The single trees were segmented in both the ALS and TLS data and linked to each other. The very-high-resolution ALS data enabled us to directly measure tree heights and also classify tree species using a convolutional neural network. Stem volume was predicted from the predicted DBH and the estimated height, using national models, and aggregated at the stand level. The study demonstrates a workflow to derive forest variables and stand-level statistics that has potential to replace many manual field inventories thanks to its time efficiency and improved accuracy. To evaluate the inventories, we estimated bias, RMSE, and precision, expressed as standard error. The laser-scanning-based inventory provided estimates with an accuracy considerably higher than the field inventory. The RMSE was 17 m3/ha (7.24%), 0.9 m (5.63%), and 16 mm (5.99%) for VOL, HL, and DL respectively. The tree species classification was generally successful and improved the three species-specific VOL estimates by 9% to 74%, compared to field estimates. In conclusion, the demonstrated laser-scanning-based inventory shows potential to replace some future forest inventories, thanks to the increased accuracy demonstrated empirically in the Swedish forest study area. Numéro de notice : A2022-249 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.112909 Date de publication en ligne : 22/01/2022 En ligne : https://doi.org/10.1016/j.rse.2022.112909 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100201 [article]

Robust GNSS phase tracking using variational bayesian inference / Fabio Fabozzi (2022)  

Public Titre : Robust GNSS phase tracking using variational bayesian inference Titre original : Méthodes de poursuite robuste de phase pour signaux GNSS basées sur l’inférence bayésienne variationnelle Type de document : Thèse/HDR Auteurs : Fabio Fabozzi, Auteur ; Stéphanie Bidon, Auteur Editeur : Toulouse : Université de Toulouse Année de publication : 2022 Importance : 173 p. Format : 21 x 30 cm Note générale : bibliographie Thèse en vue de l'obtention du Doctorat de l'Université de Toulouse, délivré par l'Institut Supérieur de l’Aéronautique et de l’Espace, Spécialité Signal, Image, acoustique et optimisation Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes IGN] données GNSS [Termes IGN] filtrage bayésien [Termes IGN] filtre de Kalman [Termes IGN] glissement de cycle [Termes IGN] inférence statistique [Termes IGN] méthode robuste [Termes IGN] phase [Termes IGN] rapport signal sur bruit [Termes IGN] récepteur GNSS [Termes IGN] signal GNSS Index. décimale : THESE Thèses et HDR Résumé : (auteur) In this Ph.D. thesis, we are interested in robust carrier-phase estimation by using Variational Bayesian filtering. Carrier-phase measurement has become a fundamental task in many various engineering applications such as precise point positioning in GNSS (Global Navigation Satellite System). Unfortunately, phase measurements obtained by traditional phase tracking techniques may be strongly affected by the presence of ambiguous phase jumps, known as cycle slips. The latter may strongly impact the performance of the considered phase tracking algorithm leading to, in the worst case, a permanent loss of lock (drop-lock) of the signal. A re-acquistion process is then necessary which afflicts the tracking performance. Therefore, to address this problem, we propose a robust nonlinear filter for carrier-phase tracking based on Restricted Variational Bayes inference. This methodology gives us a closed-form and easy-to-implement expression of the estimator. First, the algorithm is developed only for slow phase dynamics (i.e., first-order loop), then, its order is augmented by estimating a state vector formed by the carrier-phase and its derivatives. The performance of the proposed algorithm is compared with that of conventional techniques such as DPLL (Digital Phase Lock Loop) and KF (Kalman Filter)-based DPLL in terms of precision of estimation (root-mean-square error) and cycle slipping occurrence (mean-time-to-first-slip and cycle slip rate). The comparison is firstly conducted using synthetic data, and then, real GNSS data into a GNSS software-defined-radio receiver. Results show that the proposed method outperforms the conventional linear filters, when the signal-to-noise ratio is low. Note de contenu : Introduction 1- Introduction to GNSS 2- DPLL and robust phase tracking techniques 3- RVB algorithm in case of slow dynamics 4- RVB algorithm in case of high-order dynamics 5- RVB algorithm using real GNSS data Conclusion Numéro de notice : 60731 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Thèse française Note de thèse : thèse de Doctorat : Signal, Image, acoustique et optimisation : Toulouse : 2022 Organisme de stage : ISAE-ONERA SCANR DOI : sans En ligne : https://depozit.isae.fr/theses/2022/2022_Fabozzi_Fabio.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100684

Performance investigation of LAMBDA and bootstrapping methods for PPP narrow-lane ambiguity resolution / Omer Faruk Atiz in Geo-spatial Information Science, vol 24 n° 4 (October 2021)  

Public [article]  inGeo-spatial Information Science > vol 24 n° 4 (October 2021) . - ppp 604 - 614 Titre : Performance investigation of LAMBDA and bootstrapping methods for PPP narrow-lane ambiguity resolution Type de document : Article/Communication Auteurs : Omer Faruk Atiz, Auteur ; Sermet Ogutcu, Auteur ; Salih Alcay, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : ppp 604 - 614 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] Bootstrap (statistique) [Termes IGN] compensation Lambda [Termes IGN] coordonnées GNSS [Termes IGN] positionnement ponctuel précis [Termes IGN] résolution d'ambiguïté [Termes IGN] traitement de données GNSS Résumé : (auteur) Precise point positioning with ambiguity resolution (PPP-AR) is a powerful tool for geodetic and time-constrained applications that require high precision. The performance of PPP-AR highly depends on the reliability of the correct integer carrier-phase ambiguity estimation. In this study, the performance of narrow-lane ambiguity resolution of PPP using the Least-squares AMBiguity Decorrelation (LAMBDA) and bootstrapping methods is extensively investigated using real data from 55 IGS stations over one-month in 2020. Static PPP with 24-, 12-, 8-, 4-, 2-, 1- and ½-h sessions using two different cutoff angles (7° and 30°) was conducted with three PPP modes: i.e. ambiguity-float and two kinds of ambiguity-fixed PPP using the LAMBDA and bootstrapping methods for narrow-lane AR, respectively. The results show that the LAMBDA method can produce more reliable results for 2 hour and shorter observation sessions compared with the bootstrapping method using a 7° cutoff angle. For a 30° cutoff angle, the LAMBDA method outperforms the bootstrapping method for observation sessions of 4 h and less. For long observation times, the bootstrapping method produced much more accurate coordinates compared with the LAMBDA method without considering the wrong fixes cases. The results also show that occurrences of fixing the wrong integer ambiguities using the bootstrapping method are higher than that of the LAMBDA method. Numéro de notice : A2021-968 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1080/10095020.2021.1942236 En ligne : https://doi.org/10.1080/10095020.2021.1942236 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100384 [article]

3D map creation using crowdsourced GNSS data / Terence Lines in Computers, Environment and Urban Systems, vol 89 (September 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 89 (September 2021) Titre : 3D map creation using crowdsourced GNSS data Type de document : Article/Communication Auteurs : Terence Lines, Auteur ; Anahid Basiri, Auteur Année de publication : 2021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] approche participative [Termes IGN] Bootstrap (statistique) [Termes IGN] cartographie 3D [Termes IGN] données GNSS [Termes IGN] données localisées 2,5D [Termes IGN] hauteur du bâti [Termes IGN] interface de programmation [Termes IGN] régression logistique [Termes IGN] signal GNSS [Termes IGN] trajet multiple [Termes IGN] vision par ordinateur Résumé : (auteur) 3D maps are increasingly useful for many applications such as drone navigation, emergency services, and urban planning. However, creating 3D maps and keeping them up-to-date using existing technologies, such as laser scanners, is expensive. This paper proposes and implements a novel approach to generate 2.5D (otherwise known as 3D level-of-detail (LOD) 1) maps for free using Global Navigation Satellite Systems (GNSS) signals, which are globally available and are blocked only by obstacles between the satellites and the receivers. This enables us to find the patterns of GNSS signal availability and create 3D maps. The paper applies algorithms to GNSS signal strength patterns based on a boot-strapped technique that iteratively trains the signal classifiers while generating the map. Results of the proposed technique demonstrate the ability to create 3D maps using automatically processed GNSS data. The results show that the third dimension, i.e. height of the buildings, can be estimated with below 5 metre accuracy, which is the benchmark recommended by the CityGML standard. Numéro de notice : A2021-535 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101671 Date de publication en ligne : 19/06/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101671 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97998 [article]

Regularized regression: A new tool for investigating and predicting tree growth / Stuart I. Graham in Forests, vol 12 n° 9 (September 2021)  

Public [article]  inForests > vol 12 n° 9 (September 2021) . - n° 1283 Titre : Regularized regression: A new tool for investigating and predicting tree growth Type de document : Article/Communication Auteurs : Stuart I. Graham, Auteur ; Ariel Rokem, Auteur ; Claire Fortunel, Auteur ; Nathan J.B. Kraft, Auteur ; Janneke Hille Ris Lambers, Auteur Année de publication : 2021 Article en page(s) : n° 1283 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] croissance des arbres [Termes IGN] inférence statistique [Termes IGN] interpolation [Termes IGN] modèle de simulation [Termes IGN] modélisation de la forêt [Termes IGN] placette d'échantillonnage [Termes IGN] régression Résumé : (auteur) Neighborhood models have allowed us to test many hypotheses regarding the drivers of variation in tree growth, but require considerable computation due to the many empirically supported non-linear relationships they include. Regularized regression represents a far more efficient neighborhood modeling method, but it is unclear whether such an ecologically unrealistic model can provide accurate insights on tree growth. Rapid computation is becoming increasingly important as ecological datasets grow in size, and may be essential when using neighborhood models to predict tree growth beyond sample plots or into the future. We built a novel regularized regression model of tree growth and investigated whether it reached the same conclusions as a commonly used neighborhood model, regarding hypotheses of how tree growth is influenced by the species identity of neighboring trees. We also evaluated the ability of both models to interpolate the growth of trees not included in the model fitting dataset. Our regularized regression model replicated most of the classical model’s inferences in a fraction of the time without using high-performance computing resources. We found that both methods could interpolate out-of-sample tree growth, but the method making the most accurate predictions varied among focal species. Regularized regression is particularly efficient for comparing hypotheses because it automates the process of model selection and can handle correlated explanatory variables. This feature means that regularized regression could also be used to select among potential explanatory variables (e.g., climate variables) and thereby streamline the development of a classical neighborhood model. Both regularized regression and classical methods can interpolate out-of-sample tree growth, but future research must determine whether predictions can be extrapolated to trees experiencing novel conditions. Overall, we conclude that regularized regression methods can complement classical methods in the investigation of tree growth drivers and represent a valuable tool for advancing this field toward prediction. Numéro de notice : A2021-720 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f12091283 En ligne : https://doi.org/10.3390/f12091283 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98636 [article]

Variational bayesian compressive multipolarization indoor radar imaging / Van Ha Tang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)  

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Vectorial integer bootstrapping: flexible integer estimation with application to GNSS / Peter J.G. Teunissen in Journal of geodesy, vol 95 n° 9 (September 2021)  

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Data science: Measuring uncertainties / Carlos Alberto De Bragança Pereira (2021)  

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Inferencing hourly traffic volume using data-driven machine learning and graph theory / Zhiyan Yi in Computers, Environment and Urban Systems, vol 85 (January 2021)  

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Model based signal processing techniques for nonconventional optical imaging systems / Daniele Picone (2021)  

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Quantification probabiliste des taux de déformation crustale par inversion bayésienne de données GPS / Colin Pagani (2021)  

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Integrated Kalman filter of accurate ranging and tracking with wideband radar / Shaopeng Wei in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)  

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Mapping uncertain geographical attributes: incorporating robustness into choropleth classification design / Wangshu Mu in International journal of geographical information science IJGIS, vol 34 n° 11 (November 2020)  

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Ensemble learning for hyperspectral image classification using tangent collaborative representation / Hongjun Su in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)  

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Extracting activity patterns from taxi trajectory data: a two-layer framework using spatio-temporal clustering, Bayesian probability and Monte Carlo simulation / Shuhui Gong in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)  

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