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Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas / Xiaoqian Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 117 (July 2016)
[article]
Titre : Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas Type de document : Article/Communication Auteurs : Xiaoqian Zhao, Auteur ; Qinghua Guo, Auteur ; Yanjun Su, Auteur ; Baolin Xue, Auteur Année de publication : 2016 Article en page(s) : pp 79 – 91 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse comparative
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] filtrage numérique d'image
[Termes IGN] forêt
[Termes IGN] semence
[Termes IGN] test de performance
[Termes IGN] Triangulated Irregular NetworkRésumé : (auteur) Filtering of light detection and ranging (LiDAR) data into the ground and non-ground points is a fundamental step in processing raw airborne LiDAR data. This paper proposes an improved progressive triangulated irregular network (TIN) densification (IPTD) filtering algorithm that can cope with a variety of forested landscapes, particularly both topographically and environmentally complex regions. The IPTD filtering algorithm consists of three steps: (1) acquiring potential ground seed points using the morphological method; (2) obtaining accurate ground seed points; and (3) building a TIN-based model and iteratively densifying TIN. The IPTD filtering algorithm was tested in 15 forested sites with various terrains (i.e., elevation and slope) and vegetation conditions (i.e., canopy cover and tree height), and was compared with seven other commonly used filtering algorithms (including morphology-based, slope-based, and interpolation-based filtering algorithms). Results show that the IPTD achieves the highest filtering accuracy for nine of the 15 sites. In general, it outperforms the other filtering algorithms, yielding the lowest average total error of 3.15% and the highest average kappa coefficient of 89.53%. Numéro de notice : A2016-582 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.03.016 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.03.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81723
in ISPRS Journal of photogrammetry and remote sensing > vol 117 (July 2016) . - pp 79 – 91[article]Lidar imagery and InSAR for digital forestry / Benoît Saint-Onge in GIM international, vol 30 n° 7 (July 2016)
[article]
Titre : Lidar imagery and InSAR for digital forestry Type de document : Article/Communication Auteurs : Benoît Saint-Onge, Auteur Année de publication : 2016 Article en page(s) : pp 14 - 17 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] données lidar
[Termes IGN] hauteur des arbres
[Termes IGN] image 3D
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] sous-étageRésumé : (éditeur) L'auteur is a professor at the university at Quebec in Montréal, Canada. He works at the department of geography with a major research interest in 3D remote sening for forest analysis. Gim International recently interviewed professor Saint-Onge to gain a better understanding of the challenges and innovations in this field. Numéro de notice : A2016-489 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81507
in GIM international > vol 30 n° 7 (July 2016) . - pp 14 - 17[article]Nationwide airborne laser scanning based models for volume, biomass and dominant height in Finland / Eetu Kotivuori in Silva fennica, vol 50 n° 4 (2016)
[article]
Titre : Nationwide airborne laser scanning based models for volume, biomass and dominant height in Finland Type de document : Article/Communication Auteurs : Eetu Kotivuori, Auteur ; Lauri Korhonen, Auteur ; Petteri Packalen, Auteur Année de publication : 2016 Article en page(s) : 280 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse aérienne
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Finlande
[Termes IGN] hauteur des arbres
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modèle de simulation
[Termes IGN] régression
[Termes IGN] volume en bois
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) The aim of this study was to examine how well stem volume, above-ground biomass and dominant height can be predicted using nationwide airborne laser scanning (ALS) based regression models. The study material consisted of nine practical ALS inventory projects taken from different parts of Finland. We used field sample plots and airborne laser scanning data to create nationwide and regional models for each response variable. The final models had one or two ALS predictors, which were chosen based on the root mean square error (RMSE), and cross-validated. Finally, we tested how much predictions would improve if the nationwide models were calibrated with a small number of regional sample plots. Although forest structures differ among different parts of Finland, the nationwide volume and biomass models performed quite well (leave-inventory-area-out RMSE 22.3% to 33.8%, mean difference [MD] –13.8% to 18.7%) compared with regional models (leave-plot-out RMSE 20.2% to 26.8%). However, the nationwide dominant height model (RMSE 5.4% to 7.7%, MD –2.0% to 2.8%, with the exception of the Tornio region – RMSE 11.4%, MD –9.1%) performed nearly as well as the regional models (RMSE 5.2% to 6.7%). The results show that the nationwide volume and biomass models provided different means than real means at regional level, because forest structure and ALS device have a considerable effect on the predictions. Large MDs appeared especially in northern Finland. Local calibration decreased the MD and RMSE of volume and biomass models. However, the nationwide dominant height model did not benefit much from calibration. Numéro de notice : A2016--113 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.14214/sf.1567 En ligne : https://doi.org/10.14214/sf.1567 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84766
in Silva fennica > vol 50 n° 4 (2016) . - 280 p.[article]A new adaptive method to filter terrestrial laser scanner point clouds using morphological filters and spectral information to conserve surface micro-topography / Emilio Rodríguez-Caballero in ISPRS Journal of photogrammetry and remote sensing, vol 117 (July 2016)
[article]
Titre : A new adaptive method to filter terrestrial laser scanner point clouds using morphological filters and spectral information to conserve surface micro-topography Type de document : Article/Communication Auteurs : Emilio Rodríguez-Caballero, Auteur ; A. Afana, Auteur ; S. Chamizo, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 141 – 148 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] filtrage numérique d'image
[Termes IGN] microtopographie
[Termes IGN] modèle numérique de terrain
[Termes IGN] semis de points
[Termes IGN] télémétrie laser terrestreRésumé : (auteur) Terrestrial laser scanning (TLS), widely known as light detection and ranging (LiDAR) technology, is increasingly used to provide highly detailed digital terrain models (DTM) with millimetric precision and accuracy. In order to generate a DTM, TLS data has to be filtered from undesired spurious objects, such as vegetation, artificial structures, etc., Early filtering techniques, successfully applied to airborne laser scanning (ALS), fail when applied to TLS data, as they heavily smooth the terrain surface and do not retain their real morphology. In this article, we present a new methodology for filtering TLS data based on the geometric and radiometric properties of the scanned surfaces. This methodology was built on previous morphological filters that select the minimum point height within a sliding window as the real surface. However, contrary to those methods, which use a fixed window size, the new methodology operates under different spatial scales represented by different window sizes, and can be adapted to different types and sizes of plants. This methodology has been applied to two study areas of differing vegetation type and density. The accuracy of the final DTMs was improved by ∼30% under dense canopy plants and over ∼40% on the open spaces between plants, where other methodologies drastically underestimated the real surface heights. This resulted in more accurate representation of the soil surface and microtopography than up-to-date techniques, eventually having strong implications in hydrological and geomorphological studies. Numéro de notice : A2016-583 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.04.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.04.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81724
in ISPRS Journal of photogrammetry and remote sensing > vol 117 (July 2016) . - pp 141 – 148[article]A novel computer-aided tree species identification method based on burst wind segmentation of 3D bark textures / Alice Ahlem Othmani in Machine Vision and Applications, vol 27 n° 5 (July 2016)
[article]
Titre : A novel computer-aided tree species identification method based on burst wind segmentation of 3D bark textures Type de document : Article/Communication Auteurs : Alice Ahlem Othmani, Auteur ; Cansen Jiang, Auteur ; Nicolas Lomenie, Auteur ; Jean-Marie Favreau, Auteur ; Alexandre Piboule, Auteur ; Lew F. C. Lew Yan Voon, Auteur Année de publication : 2016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse comparative
[Termes IGN] arbre (flore)
[Termes IGN] classification
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] écorce
[Termes IGN] extraction d'arbres
[Termes IGN] forêt tempérée
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] reconnaissance de formes
[Termes IGN] segmentation
[Termes IGN] texture d'image
[Termes IGN] zone saillante 3DRésumé : (auteur) Terrestrial Laser Scanning (TLS) systems have gained increasing popularity in the forestry domain and are today widely used for the automatic measurement of forest inventory attributes. Nevertheless, to the best of our knowledge the problem of tree species recognition from TLS data has received very little attention from the scientific community. It is in this context that we present a novel Computer-Aided Tree Species Identification method based on 3D bark texture analysis. The novelty of our approach resides in the following three key points: (1) 3D salient regions extraction using a new morphological segmentation method that we have called Burst Wind Segmentation, (2) the extraction and pre-annotation of a collection of typical 3D bark patterns, known as scars, from each of the tree species. The pre-annotated scars are stored in a dictionary that we have called ScarBook and they are used as a reference for the comparison of the 3D salient segmented regions, (3) a wide variety of advanced shape, saliency, curvature and roughness features are extracted from the 3D salient segmented regions. To study the performance of our method, an experiment has been carried out on a dataset composed of 969 patches which correspond to 30 cm long segments of the trunk at breast height. Six species among the most dominant species in European forests have been tested with patches of different diameter at breast height values so as to study the identification accuracy with respect to age. The results obtained are very encouraging and promising and they confirm the possibility of identifying tree species using TLS data. Numéro de notice : A2016--134 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s00138-015-0738-2 Date de publication en ligne : 28/11/2015 En ligne : https://doi.org/10.1007/s00138-015-0738-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85267
in Machine Vision and Applications > vol 27 n° 5 (July 2016)[article]OpenBIM framework for a collaborative historic preservation system / Shawn E. O'Keeffe in International journal of 3-D information modeling, vol 5 n° 4 (October - December 2016)PermalinkWildlife management using aiborne Lidar / Joan Hagar in GIM international, vol 30 n° 7 (July 2016)PermalinkSimultaneous detection and tracking of pedestrian from panoramic laser scanning data / Wen Xiao in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol III-3 (July 2016)PermalinkLe 12ème Forum de la topographie : topographie et BIM / Tania Landes in XYZ, n° 147 (juin - août 2016)PermalinkAn intelligent geospatial processing unit for image classification based on geographic vector agents (GVAs) / Kambiz Borna in Transactions in GIS, vol 20 n° 3 (June 2016)PermalinkBoresight and lever arm calibration of a mobile terrestrial Lidar system / M. Leslar in Geomatica, vol 70 n° 2 (June 2016)PermalinkComparison of quality measures for building outline extraction / Markéta Potůčková in Photogrammetric record, vol 31 n° 154 (June - August 2016)PermalinkContext-dependent detection of non-linearly distributed points for vegetation classification in airborne LiDAR / Denis Horvat in ISPRS Journal of photogrammetry and remote sensing, vol 116 (June 2016)PermalinkEstimations dendrométriques pour l’aménagement forestier à l’aide de LiDAR aéroporté : premier démonstrateur en forêts littorales dunaires / Alain Munoz in Rendez-vous techniques, n° 50 (Hiver 2016)PermalinkExpérience pratique de la réalisation du projet démonstrateur « LiDAR forestier » / Didier Canteloup in Rendez-vous techniques, n° 50 (Hiver 2016)Permalink