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Transferability and calibration of airborne laser scanning based mixed-effects models to estimate the attributes of sawlog-sized Scots pines / Lauri Korhonen in Silva fennica, vol 53 n° 3 (2019)  

Public [article]  inSilva fennica > vol 53 n° 3 (2019) Titre : Transferability and calibration of airborne laser scanning based mixed-effects models to estimate the attributes of sawlog-sized Scots pines Type de document : Article/Communication Auteurs : Lauri Korhonen, Auteur ; Jaakko Repola, Auteur ; Tomi Karjalainen, Auteur ; Petteri Packalen, Auteur ; Matti Maltamo, Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] détection d'arbres [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] erreur systématique [Termes descripteurs IGN] estimation statistique [Termes descripteurs IGN] étalonnage de modèle [Termes descripteurs IGN] hauteur à la base du houppier [Termes descripteurs IGN] houppier [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] lasergrammétrie [Termes descripteurs IGN] Pinus sylvestris [Termes descripteurs IGN] placette d'échantillonnage [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Airborne laser scanning (ALS) data is nowadays often available for forest inventory purposes, but adequate field data for constructing new forest attribute models for each area may be lacking. Thus there is a need to study the transferability of existing ALS-based models among different inventory areas. The objective of our study was to apply ALS-based mixed models to estimate the diameter, height and crown base height of individual sawlog sized Scots pines (Pinus sylvestrisL.) at three different inventory sites in eastern Finland. Different ALS sensors and acquisition parameters were used at each site. Multivariate mixed-effects models were fitted at one site and the models were validated at two independent test sites. Validation was carried out by applying the fixed parts of the mixed models as such, and by calibrating them using 1–3 sample trees per plot. The results showed that the relative RMSEs of the predictions were 1.2–6.5 percent points larger at the test sites compared to the training site. Systematic errors of 2.4–6.2 percent points also emerged at the test sites. However, both the RMSEs and the systematic errors decreased with calibration. The results showed that mixed-effects models of individual tree attributes can be successfully transferred and calibrated to other ALS inventory areas in a level of accuracy that appears suitable for practical applications. Numéro de notice : A2019-379 Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.10179 date de publication en ligne : 31/07/2019 En ligne : https://doi.org/10.14214/sf.10179 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93446 [article]

Decomposition of geodetic time series: A combined simulated annealing algorithm and Kalman filter approach / Feng Ming in Advances in space research, vol 64 n°5 (1 September 2019)  

Public [article]  inAdvances in space research > vol 64 n°5 (1 September 2019) . - pp Titre : Decomposition of geodetic time series: A combined simulated annealing algorithm and Kalman filter approach Type de document : Article/Communication Auteurs : Feng Ming, Auteur ; Yuanxi Yang, Auteur ; Anmin Zeng, Auteur ; Bin Zhao, Auteur Année de publication : 2019 Article en page(s) : pp Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] algorithme du recuit simulé [Termes descripteurs IGN] bruit (théorie du signal) [Termes descripteurs IGN] bruit blanc [Termes descripteurs IGN] bruit rose [Termes descripteurs IGN] données GPS [Termes descripteurs IGN] filtre de Kalman [Termes descripteurs IGN] méthode du maximum de vraisemblance (estimation) [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] variation saisonnière [Vedettes matières IGN] Traitement de données GNSS Numéro de notice : A2019-399 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.asr.2019.05.049 date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1016/j.asr.2019.05.049 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93519 [article]

Robust M–M unscented Kalman filtering for GPS/IMU navigation / Cheng Yang in Journal of geodesy, vol 93 n° 8 (August 2019)  

Public [article]  inJournal of geodesy > vol 93 n° 8 (August 2019) . - pp 1093 - 1104 Titre : Robust M–M unscented Kalman filtering for GPS/IMU navigation Type de document : Article/Communication Auteurs : Cheng Yang, Auteur ; Wenzhong Shi, Auteur ; Wu Chen, Auteur Année de publication : 2019 Article en page(s) : pp 1093 - 1104 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes descripteurs IGN] coefficient de corrélation [Termes descripteurs IGN] erreur de mesure [Termes descripteurs IGN] erreur de modèle [Termes descripteurs IGN] filtre de Kalman [Termes descripteurs IGN] GNSS-INS [Termes descripteurs IGN] matrice de covariance [Termes descripteurs IGN] méthode du maximum de vraisemblance (estimation) [Termes descripteurs IGN] méthode robuste [Termes descripteurs IGN] modèle non linéaire Résumé : (auteur) In this paper, a robust unscented Kalman filter (UKF) based on the generalized maximum likelihood estimation (M-estimation) is proposed to improve the robustness of the integrated navigation system of Global Navigation Satellite System and Inertial Measurement Unit. The UKF is a variation of Kalman filter by which the Jacobian matrix calculation in a nonlinear system state model is not necessary. The proposed robust M–M unscented Kalman filter (RMUKF) applies the M-estimation principle to both functional model errors and measurement errors. Hence, this robust filter attenuates the influences of disturbances in the dynamic model and of measurement outliers without linearizing the nonlinear state space model. In addition, an equivalent weight matrix, composed of the bi-factor shrink elements, is proposed in order to keep the original correlation coefficients of the predicted state unchanged. Furthermore, a nonlinear error model is used as the dynamic equation to verify the performance of the proposed RMUKF with a simulation and field test. Compared with the conventional UKF, the impacts of measurement outliers and system disturbances on the state estimation are both controlled by RMUKF. Numéro de notice : A2019-370 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-018-01227-5 date de publication en ligne : 22/01/2019 En ligne : https://doi.org/10.1007/s00190-018-01227-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93455 [article]

Influence of stochastic modeling for inter-system biases on multi-GNSS undifferenced and uncombined precise point positioning / Feng Zhou in GPS solutions, vol 23 n° 3 (July 2019)  

Public [article]  inGPS solutions > vol 23 n° 3 (July 2019) Titre : Influence of stochastic modeling for inter-system biases on multi-GNSS undifferenced and uncombined precise point positioning Type de document : Article/Communication Auteurs : Feng Zhou, Auteur ; Danan Dong, Auteur ; Xin Li, Auteur ; Harald Schuh, Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes descripteurs IGN] bruit blanc [Termes descripteurs IGN] décalage d'horloge [Termes descripteurs IGN] erreur systématique inter-systèmes [Termes descripteurs IGN] estimation statistique [Termes descripteurs IGN] modèle stochastique [Termes descripteurs IGN] positionnement par GNSS [Termes descripteurs IGN] positionnement ponctuel précis Résumé : (auteur) The focus of this study is on proper modeling of the dynamics for inter-system biases (ISBs) in multi-constellation Global Navigation Satellite System (GNSS) precise point positioning (PPP) processing. First, the theoretical derivation demonstrates that the ISBs originate from not only the receiver-dependent hardware delay differences among different GNSSs but also the receiver-independent time differences caused by the different clock datum constraints among different GNSS satellite clock products. Afterward, a comprehensive evaluation of the influence of ISB stochastic modeling on undifferenced and uncombined PPP performance is conducted, i.e., random constant, random walk process, and white noise process are considered. We use data based on a 1-month period (September 2017) Multi-GNSS Experiment (MGEX) precise orbit and clock products from four analysis centers (CODE, GFZ, CNES, and WHU) and 160 MGEX tracking stations. The results demonstrate that generally, the positioning performance of PPP in terms of convergence time and positioning accuracy with the final products from CODE, CNES, and WHU is comparable among the three ISB handling schemes. However, estimating ISBs as random walk process or white noise process outperforms that as the random constant when using the GFZ products. These results indicate that the traditional estimation of ISBs as the random constant may not always be reasonable in multi-GNSS PPP processing. To achieve more reliable positioning results, it is highly recommended to consider the ISBs as random walk process or white noise process in multi-GNSS PPP processing. Numéro de notice : A2019-199 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-019-0852-0 date de publication en ligne : 09/04/2019 En ligne : https://doi.org/10.1007/s10291-019-0852-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92654 [article]

Two contemporary and efficient two-stage sampling methods for estimating the volume of forest stands: a brief overview and unified mathematical description / Aristeidis Georgakis in Open journal of forestry, vol 9 n° 3 (July 2019)  

Public [article]  inOpen journal of forestry > vol 9 n° 3 (July 2019) . - pp 241 - 254 Titre : Two contemporary and efficient two-stage sampling methods for estimating the volume of forest stands: a brief overview and unified mathematical description Type de document : Article/Communication Auteurs : Aristeidis Georgakis, Auteur ; Georgios Stamatellos, Auteur Année de publication : 2019 Article en page(s) : pp 241 - 254 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] estimation statistique [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] peuplement forestier [Termes descripteurs IGN] surface terrière [Termes descripteurs IGN] volume en bois [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Big Basal Area Factor (Big BAF) and Point-3P are two-stage sampling methods. In the first stage the sampling units, in both methods, are Bitterlich points where the selection of the trees is proportional to their basal area. In the second stage, sampling units are trees which are a subset of the first stage trees. In the Big BAF method, the probability of selecting trees in the second stage is made proportional to the two BAFs’ ratio, with a basal area factor larger than that of the first stage. In the Point-3P method the probability of selecting trees, in the second stage, is based on the height prediction and use of a specific random number table. Estimates of the forest stands’ volume and their sampling errors are based on the theory of the product of two random variables. The increasing error in the second stage is small, but the total cost of measuring the trees is much smaller than simply using the first stage, with all the trees measured. In general, the two sampling methods are modern and cost-effective approaches that can be applied in forest stand inventories for forest management purposes and are receiving the growing interest of researchers in the current decade. Numéro de notice : A2019-381 Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.4236/ojf.2019.93013 En ligne : https://doi.org/10.4236/ojf.2019.93013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93460 [article]

Real-time GPS satellite orbit and clock estimation based on OpenMP / Kaifa Kuang in Advances in space research, vol 63 n° 8 (15 April 2019)  

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Vertical ionospheric delay estimation for single-receiver operation / Ahmed Elsayed in Journal of applied geodesy, vol 13 n° 2 (April 2019)  

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Application de la loi de Benford au contrôle de qualité des modèles numériques de terrain / Laurent Polidori in XYZ, n° 158 (mars 2019)

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Estimating net biomass production and loss from repeated measurements of trees in forests and woodlands: Formulae, biases and recommendations / Takashi S. Kohyama in Forest ecology and management, vol 433 (15 February 2019)  

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Tree cover mapping using hybrid fuzzy C-means method and multispectral satellite images / Linda Gulbe in Baltic forestry, vol 25 n° 1 (2019)  

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Correcting for nondetection in estimating forest characteristics from single-scan terrestrial laser measurements / Mikko Kuronen in Canadian Journal of Forest Research, vol 49 n° 1 (janvier 2019)  

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Developing allometric equations for estimating shrub biomass in a Boreal Fen / Annie He in Forests, vol 9 n° 9 (September 2018)  

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Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data / P. Kumar in Geocarto international, vol 33 n° 9 (September 2018)  

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A two-stage estimation method with bootstrap inference for semi-parametric geographically weighted generalized linear models / Dengkui Li in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)  

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EPLA: efficient personal location anonymity / Dapeng Zhao in Geoinformatica [en ligne], vol 22 n° 1 (January 2018)  

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