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Using LiDAR-modified topographic wetness index, terrain attributes with leaf area index to improve a single-tree growth model in south-eastern Finland / Cheikh Mohamedou in Forestry, an international journal of forest research, vol 92 n° 3 (July 2019)
[article]
Titre : Using LiDAR-modified topographic wetness index, terrain attributes with leaf area index to improve a single-tree growth model in south-eastern Finland Type de document : Article/Communication Auteurs : Cheikh Mohamedou, Auteur ; Lauri Korhonen, Auteur ; Kalle Eerikäinen, Auteur ; Timo Tokola, Auteur Année de publication : 2019 Article en page(s) : pp 253 - 263 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] croissance des arbres
[Termes IGN] diamètre des arbres
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] erreur systématique
[Termes IGN] Finlande
[Termes IGN] humidité du sol
[Termes IGN] indice d'humidité
[Termes IGN] Leaf Area Index
[Termes IGN] modèle de croissance végétale
[Termes IGN] Perceptron multicoucheRésumé : (Auteur) Tree growth information is crucial in forest management and planning. Terrain-derived attributes such as the topographic wetness index (TWI), in addition to leaf area index (LAI) are closely related to tree growth, but are not commonly used in empirical growth models. In this study, we examined if modified TWI and LAI estimated from airborne light detection and ranging (LiDAR) data could be used to improve the predictions of a national single-tree diameter growth model. Altogether 1118 sample trees were selected within 197 subjectively placed plots in randomly selected forest stands in south-eastern Finland. Linear mixed effect (LME) and multilayer perceptron models were used to model the bias of 5-year growth predictions of the model and thus ultimately improve its predictions. The root mean square error (RMSE) of the national model was 0.604 cm. LME modelling reduced this value to 0.404 cm and MLP to 0.568 cm. The predictors included in the best-performing LME model were modified TWI, LAI estimated from LiDAR intensities, and elevation. Without an LAI estimate, the best RMSE was 0.436 cm. When applied as such, original and modified TWIs produced similar accuracy. We conclude that both TWI and LAI obtained from LiDAR data improve the diameter growth predictions of the national model. Numéro de notice : A2019-293 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/forestry/cpz010 Date de publication en ligne : 28/02/2019 En ligne : https://doi.org/10.1093/forestry/cpz010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93184
in Forestry, an international journal of forest research > vol 92 n° 3 (July 2019) . - pp 253 - 263[article]Simulation and analysis of photogrammetric UAV image blocks: influence of camera calibration error / Yilin Zhou in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2/W5 (May 2019)
[article]
Titre : Simulation and analysis of photogrammetric UAV image blocks: influence of camera calibration error Type de document : Article/Communication Auteurs : Yilin Zhou , Auteur ; Ewelina Rupnik , Auteur ; Christophe Meynard , Auteur ; Christian Thom , Auteur ; Marc Pierrot-Deseilligny , Auteur Année de publication : 2019 Projets : 1-Pas de projet / Conférence : ISPRS 2019, Geospatial Week 10/06/2019 14/06/2019 Enschede Pays-Bas ISPRS OA Annals Article en page(s) : pp 195 - 200 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] bloc d'images
[Termes IGN] compensation par faisceaux
[Termes IGN] effet thermique
[Termes IGN] erreur systématique
[Termes IGN] estimation de pose
[Termes IGN] étalonnage de chambre métrique
[Termes IGN] étalonnage en vol
[Termes IGN] image aérienne oblique
[Termes IGN] image captée par drone
[Termes IGN] longueur focale
[Termes IGN] photogrammétrie aérienne
[Termes IGN] point d'appui
[Termes IGN] points homologuesRésumé : (auteur) Unmanned aerial vehicles (UAV) are increasingly used for topographic mapping. The camera calibration for UAV image blocks can be performed a priori or during the bundle block adjustment (self-calibration). For an area of interest with flat, corridor configuration, the focal length of camera is highly correlated with the height of camera. Furthermore, systematic errors of camera calibration accumulate on the longer dimension and cause deformation. Therefore, special precautions must be taken when estimating camera calibration parameters. In this paper, a simulated, error-free aerial image block is generated. error is then added on camera calibration and given as initial solution to bundle block adjustment. Depending on the nature of the error and the investigation purpose, camera calibration parameters are either fixed or re-estimated during the bundle block adjustment. The objective is to investigate how certain errors in the camera calibration impact the accuracy of 3D measurement without the influence of other errors. All experiments are carried out with Fraser camera calibration model being employed. When adopting a proper flight configuration, an error on focal length for the initial camera calibration can be corrected almost entirely during bundle block adjustment. For the case where an erroneous focal length is given for pre-calibration and not re-estimated, the presence of oblique images limits the drift on camera height hence gives better camera pose estimation. Other than that, the error on focal length when neglecting its variation during the acquisition (e.g., due to camera temperature increase) is also investigated; a bowl effect is observed when one focal length is given in camera pre-calibration to the whole image block. At last, a local error is added in image space to simulate camera flaws; this type of error is more difficult to be corrected with the Fraser camera model and the accuracy of 3D measurement degrades substantially. Numéro de notice : A2019-591 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-IV-2-W5-195-2019 Date de publication en ligne : 29/05/2019 En ligne : https://doi.org/10.5194/isprs-annals-IV-2-W5-195-2019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94551
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol IV-2/W5 (May 2019) . - pp 195 - 200[article]Understanding demographic and socioeconomic biases of geotagged Twitter users at the county level / Jiang Juqin in Cartography and Geographic Information Science, vol 46 n° 3 (May 2019)
[article]
Titre : Understanding demographic and socioeconomic biases of geotagged Twitter users at the county level Type de document : Article/Communication Auteurs : Jiang Juqin, Auteur ; Zhenlong Li, Auteur ; Xinyue Ye, Auteur Année de publication : 2019 Article en page(s) : pp 228 - 242 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] agrégation spatiale
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] données démographiques
[Termes IGN] données massives
[Termes IGN] données socio-économiques
[Termes IGN] erreur systématique
[Termes IGN] Etats-Unis
[Termes IGN] géobalise
[Termes IGN] régression géographiquement pondérée
[Termes IGN] TwitterRésumé : (Auteur) Massive social media data produced from microblog platforms provide a new data source for studying human dynamics at an unprecedented scale. Meanwhile, population bias in geotagged Twitter users is widely recognized. Understanding the demographic and socioeconomic biases of Twitter users is critical for making reliable inferences on the attitudes and behaviors of the population. However, the existing global models cannot capture the regional variations of the demographic and socioeconomic biases. To bridge the gap, we modeled the relationships between different demographic/socioeconomic factors and geotagged Twitter users for the whole contiguous United States, aiming to understand how the demographic and socioeconomic factors relate to the number of Twitter users at county level. To effectively identify the local Twitter users for each county of the United States, we integrate three commonly used methods and develop a query approach in a high-performance computing environment. The results demonstrate that we can not only identify how the demographic and socioeconomic factors relate to the number of Twitter users, but can also measure and map how the influence of these factors vary across counties. Numéro de notice : A2019-093 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2018.1434834 Date de publication en ligne : 09/02/2018 En ligne : https://doi.org/10.1080/15230406.2018.1434834 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92338
in Cartography and Geographic Information Science > vol 46 n° 3 (May 2019) . - pp 228 - 242[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-2019031 RAB Revue Centre de documentation En réserve L003 Disponible A new relationship between the quality criteria for geodetic networks / Ivandro Klein in Journal of geodesy, vol 93 n° 4 (April 2019)
[article]
Titre : A new relationship between the quality criteria for geodetic networks Type de document : Article/Communication Auteurs : Ivandro Klein, Auteur ; Marcelo Tomio Matsuoka, Auteur ; Matheus Pereira Guzatto, Auteur ; Felipe Geremia-Nievinski, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 529 - 544 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux
[Termes IGN] erreur systématique
[Termes IGN] fiabilité des données
[Termes IGN] incertitude géométrique
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] réseau géodésique planimétrique
[Termes IGN] valeur aberranteRésumé : (Auteur) The goal of this paper is to present a new relationship between the quality criteria for geodetic networks. The quality criteria described here are fourfold: positional uncertainty of network points, considering both bias and precision (at a given confidence level); the maximum allowable number of undetected outliers; the level of reliability and its homogeneity for the observations; and the minimum power of the data snooping test procedure for multiple alternative hypotheses. The highlights consist of the use of advanced concepts, such as reliability measures for multiple outliers and the power of the test for multiple alternative hypotheses (instead of the single outlier and/or the single alternative hypothesis case); and a sequential computational procedure, wherein the quality criteria are mathematically related, instead of being treated as separate criteria. Its practical application is demonstrated numerically in the design of a real horizontal network. A satisfactory performance was achieved by means of simulations. Furthermore, Monte Carlo experiments were conducted to verify the power of the test and the positional uncertainty following the approach proposed. Results provide empirical evidence that the quality criteria present realistic outputs. Numéro de notice : A2019-156 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-018-1181-8 Date de publication en ligne : 04/08/2018 En ligne : https://doi.org/10.1007/s00190-018-1181-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92495
in Journal of geodesy > vol 93 n° 4 (April 2019) . - pp 529 - 544[article]Hyperspectral image classification with squeeze multibias network / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)
[article]
Titre : Hyperspectral image classification with squeeze multibias network Type de document : Article/Communication Auteurs : Leyuan Fang, Auteur ; Guangyun Liu, Auteur ; Shutao Li, Auteur ; Pedram Ghamisi, Auteur ; Jon Atli Benediktsson, Auteur Année de publication : 2019 Article en page(s) : pp 1291 - 1301 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] erreur systématique
[Termes IGN] image hyperspectraleRésumé : (Auteur) A convolutional neural network (CNN) has recently demonstrated its outstanding capability for the classification of hyperspectral images (HSIs). Typical CNN-based methods usually adopt image patches as inputs to the network. However, a fixed-size image patch in HSI with complex spatial contexts may contain multiple ground objects of different classes, which will deteriorate the classification performance of the CNN. In addition, traditional convolutional layers adopted in the CNN have a huge amount of parameters needed to be tuned, which will cause high computational cost. To address the above-mentioned issues, a novel squeeze multibias network (SMBN) is proposed for HSI classification. Specifically, the proposed SMBN first introduces the multibias module (MBM), which incorporates multibias into the rectified linear unit layers. The MBM can decouple the feature maps of input patches into multiple response maps (corresponding to different ground objects) and adaptively select the meaningful maps for classification. Furthermore, the proposed SMBN replaces the traditional convolutional layer with a squeeze convolution module, which can greatly reduce the number of parameters in the network, thus saving the running time, while still maintaining high classification accuracy. Experimental results on three real HSIs demonstrate the superiority of the proposed SMBN method over several state-of-the-art classification approaches. Numéro de notice : A2019-113 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2865953 Date de publication en ligne : 13/09/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2865953 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92453
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 3 (March 2019) . - pp 1291 - 1301[article]Thinking outside the square: Evidence that plot shape and layout in forest inventories can bias estimates of stand metrics / Thomas S. H. Paul in Methods in ecology and evolution, vol 10 n° 3 (March 2019)PermalinkUtilisation d’infrastructures géodésiques mondiales pour la réalisation nationale / Raphaël Legouge in XYZ, n° 158 (mars 2019)PermalinkEstimating 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)PermalinkFFT swept filtering: a bias-free method for processing fringe signals in absolute gravimeters / Petr Křen in Journal of geodesy, vol 93 n° 2 (February 2019)PermalinkImpact of humidity biases on light precipitation occurrence: observations versus simulations / Sophie Bastin in Atmospheric chemistry and physics, vol 19 n° 3 (February 2019)PermalinkInfluence of subdaily model for polar motion on the estimated GPS satellite orbits / Natalia Panafidina in Journal of geodesy, vol 93 n° 2 (February 2019)PermalinkCorrecting 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)PermalinkEstimating and assessing Galileo satellite fractional cycle bias for PPP ambiguity resolution / Guorui Xiao in GPS solutions, vol 23 n° 1 (January 2019)PermalinkPermalinkImproving the spatial bias correction algorithm in SMOS image reconstruction processor : validation of soil moisture retrievals with in situ data / Ali Khazaal in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)Permalink