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Comparison of high-density LiDAR and satellite photogrammetry for forest inventory / Grant D. Pearse in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)
[article]
Titre : Comparison of high-density LiDAR and satellite photogrammetry for forest inventory Type de document : Article/Communication Auteurs : Grant D. Pearse, Auteur ; Jonathan P. Dash, Auteur ; Henrik J. Persson, Auteur ; Michael S. Watt, Auteur Année de publication : 2018 Article en page(s) : pp 257 - 267 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] densité de la végétation
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt
[Termes IGN] hauteur des arbres
[Termes IGN] image multibande
[Termes IGN] image Pléiades-HR
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] Nouvelle-Zélande
[Termes IGN] photogrammétrie numérique
[Termes IGN] Pinus radiata
[Termes IGN] semis de points
[Termes IGN] surface terrière
[Termes IGN] sylviculture
[Termes IGN] volume en bois
[Vedettes matières IGN] Inventaire forestierRésumé : (Auteur) Point cloud data derived from stereo satellite imagery has the potential to provide large-scale forest inventory assessment but these methods are known to include higher error than airborne laser scanning (ALS). This study compares the accuracy of forest inventory attributes estimated from high-density ALS (21.1 pulses m−2) point cloud data (PCD) and PCD derived from photogrammetric methods applied to stereo satellite imagery obtained over a Pinus radiata D. Don plantation forest in New Zealand. The statistical and textural properties of the canopy height models (CHMs) derived from each point cloud were included alongside standard PCD metrics as a means of improving the accuracy of predictions for key forest inventory attributes. For mean top height (a measure of dominant height in a stand), ALS data produced better estimates (R2 = 0.88; RMSE = 1.7 m) than those obtained from satellite data (R2 = 0.81; RMSE = 2.1 m). This was attributable to a general over-estimation of canopy heights in the satellite PCD. ALS models produced poor estimates of stand density (R2 = 0.48; RMSE = 112.1 stems ha−1), as did the satellite PCD models (R2 = 0.42; RMSE = 118.4 stems ha−1). ALS models produced accurate estimates of basal area (R2 = 0.58; RMSE = 12 m2 ha−1), total stem volume (R2 = 0.72; RMSE = 107.5 m3 ha−1), and total recoverable volume (R2 = 0.74; RMSE = 92.9 m3 ha−1). These values differed little from the estimates of basal area (R2 = 0.57; RMSE = 12.2 m2 ha−1), total stem volume (R2 = 0.70; RMSE = 112.6 m3 ha−1), and total recoverable volume (R2 = 0.73; RMSE = 96 m3 ha−1) obtained from satellite PCD models. The statistical and textural metrics computed from the CHMs were important variables in all of the models derived from both satellite and ALS PCD, nearly always outranking the standard PCD metrics in measures of importance. For the satellite PCD models, the CHM-derived metrics were nearly exclusively identified as important variables. These results clearly show that point cloud data obtained from stereo satellite imagery are useful for prediction of forest inventory attributes in intensively managed forests on steeper terrain. Furthermore, these data offer forest managers the benefit of obtaining both inventory data and high-resolution multispectral imagery from a single product. Numéro de notice : A2018-295 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.06.006 Date de publication en ligne : 22/06/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.06.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90413
in ISPRS Journal of photogrammetry and remote sensing > vol 142 (August 2018) . - pp 257 - 267[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2018081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018083 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018082 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt A deep neural network with spatial pooling (DNNSP) for 3-D point cloud classification / Zhen Wang in IEEE Transactions on geoscience and remote sensing, vol 56 n° 8 (August 2018)
[article]
Titre : A deep neural network with spatial pooling (DNNSP) for 3-D point cloud classification Type de document : Article/Communication Auteurs : Zhen Wang, Auteur ; Liqiang Zhang, Auteur ; Liang Zhang, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 4594 - 4604 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] arbre aléatoire
[Termes IGN] classification par réseau neuronal
[Termes IGN] données hétérogènes
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] méthode robuste
[Termes IGN] Perceptron multicouche
[Termes IGN] pondération
[Termes IGN] précision de la classification
[Termes IGN] scène urbaine
[Termes IGN] semis de pointsMots-clés libres : deep neural network with spatial pooling (DNNSP) Résumé : (Auteur) The large number of object categories and many overlapping or closely neighboring objects in large-scale urban scenes pose great challenges in point cloud classification. Most works in deep learning have achieved a great success on regular input representations, but they are hard to be directly applied to classify point clouds due to the irregularity and inhomogeneity of the data. In this paper, a deep neural network with spatial pooling (DNNSP) is proposed to classify large-scale point clouds without rasterization. The DNNSP first obtains the point-based feature descriptors of all points in each point cluster. The distance minimum spanning tree-based pooling is then applied in the point feature representation to describe the spatial information among the points in the point clusters. The max pooling is next employed to aggregate the point-based features into the cluster-based features. To assure the DNNSP is invariant to the point permutation and sizes of the point clusters, the point-based feature representation is determined by the multilayer perception (MLP) and the weight sharing for each point is retained, which means that the weight of each point in the same layer is the same. In this way, the DNNSP can learn the features of points scaled from the entire regions to the centers of the point clusters, which makes the point cluster-based feature representations robust and discriminative. Finally, the cluster-based features are input to another MLP for point cloud classification. We have evaluated qualitatively and quantitatively the proposed method using several airborne laser scanning and terrestrial laser scanning point cloud data sets. The experimental results have demonstrated the effectiveness of our method in improving classification accuracy. Numéro de notice : A2018-471 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2829625 Date de publication en ligne : 22/05/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2829625 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91253
in IEEE Transactions on geoscience and remote sensing > vol 56 n° 8 (August 2018) . - pp 4594 - 4604[article]Incorporating crown shape information for identifying ash tree species / Haijian Liu in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 8 (août 2018)
[article]
Titre : Incorporating crown shape information for identifying ash tree species Type de document : Article/Communication Auteurs : Haijian Liu, Auteur ; Changshan Wu, Auteur Année de publication : 2018 Article en page(s) : pp 495 - 503 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Fraxinus (genre)
[Termes IGN] fusion de données
[Termes IGN] hauteur des arbres
[Termes IGN] houppier
[Termes IGN] image aérienne
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] Milwaukee
[Termes IGN] photo-interprétation assistée par ordinateur
[Termes IGN] zone urbaineRésumé : (Auteur) Identifying ash trees from other common deciduous trees is challenging due to subtle spectral differences of foliage among species. Although many researchers have integrated lidar-derived tree height and crown size metrics to improve tree species classification accuracy, these simple biophysical attributes provide inadequate explanatory power in distinguishing ash trees (Fraxinus, spp.) in urban ecosystems. To address this issue, shape-related features, including crown shape index (SI) and coefficient of variation (CV) of crown height, were extracted from lidar data, and fused with treetopbased spectra for ash tree species identification in Milwaukee City, Wisconsin, United States. Analysis results indicate shape features including SI and CV play a big role in improving the accuracy for ash tree identification. Specifically, Fusion of CV and treetop-based spectra improved the overall accuracy from 81.9 percent to 89 percent, and McNemar tests indicated the differences in accuracy between CV fusion and tree height fusion was statistically significant (p = 0.016). Numéro de notice : A2018-360 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.8.495 Date de publication en ligne : 01/08/2018 En ligne : https://doi.org/10.14358/PERS.84.8.495 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90600
in Photogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 8 (août 2018) . - pp 495 - 503[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2018081 RAB Revue Centre de documentation En réserve L003 Disponible Incorporating tree- and stand-level information on crown base height into multivariate forest management inventories based on airborne laser scanning / Matti Maltamo in Silva fennica, vol 52 n° 3 ([01/08/2018])
[article]
Titre : Incorporating tree- and stand-level information on crown base height into multivariate forest management inventories based on airborne laser scanning Type de document : Article/Communication Auteurs : Matti Maltamo, Auteur ; Tomi Karjalainen, Auteur ; Jaakko Repola, Auteur ; Jari Vauhkonen, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] classification barycentrique
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] hauteur à la base du houppier
[Termes IGN] hauteur des arbres
[Termes IGN] houppier
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] modèle de simulation
[Termes IGN] Pinus (genre)
[Termes IGN] placette d'échantillonnage
[Termes IGN] régression
[Vedettes matières IGN] Inventaire forestierRésumé : (Auteur) This study examines the alternatives to include crown base height (CBH) predictions in operational forest inventories based on airborne laser scanning (ALS) data. We studied 265 field sample plots in a strongly pine-dominated area in northeastern Finland. The CBH prediction alternatives used area-based metrics of sparse ALS data to produce this attribute by means of: 1) Tree-level imputation based on the k-nearest neighbor (k-nn) method and full field-measured tree lists including CBH observations as reference data; 2) Tree-level mixed-effects model (LME) prediction based on tree diameter (DBH) and height and ALS metrics as predictors of the models; 3) Plot-level prediction based on analyzing the computational geometry and topology of the ALS point clouds; and 4) Plot-level regression analysis using average CBH observations of the plots for model fitting. The results showed that all of the methods predicted CBH with an accuracy of 1–1.5 m. The plot-level regression model was the most accurate alternative, although alternatives producing tree-level information may be more interesting for inventories aiming at forest management planning. For this purpose, k-nn approach is promising and it only requires that field measurements of CBH is added to the tree lists used as reference data. Alternatively, the LME-approach produced good results especially in the case of dominant trees. Numéro de notice : A2018-509 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14214/sf.10006 Date de publication en ligne : 27/07/2018 En ligne : https://doi.org/10.14214/sf.10006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91190
in Silva fennica > vol 52 n° 3 [01/08/2018][article]Surface reconstruction of incomplete datasets: A novel Poisson surface approach based on CSRBF / Jules Morel in Computers and graphics, vol 74 (August 2018)
[article]
Titre : Surface reconstruction of incomplete datasets: A novel Poisson surface approach based on CSRBF Type de document : Article/Communication Auteurs : Jules Morel, Auteur ; Alexandra Bac, Auteur ; Cédric Vega , Auteur Année de publication : 2018 Projets : DIABOLO / Packalen, Tuula Conférence : SMI 2018, Shape Modelling International 06/06/2018 08/06/2018 Lisbonne Portugal https://www.sciencedirect.com/journal/computers-and-graphics/special-issue/10RZ9DXPNK4 Article en page(s) : pp 44 - 55 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] approximation
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] équation de Poisson
[Termes IGN] fonction de base radiale
[Termes IGN] jeu de données localisées
[Termes IGN] reconstruction d'objet
[Termes IGN] semis de pointsRésumé : (auteur) This paper introduces a novel surface reconstruction method based on unorganized point clouds, which focuses on offering complete and closed mesh models of partially sampled object surfaces. To accomplish this task, our approach builds upon a known a priori model that coarsely describes the scanned object to guide the modeling of the shape based on heavily occluded point clouds. In the region of space visible to the scanner, we retrieve the surface by following the resolution of a Poisson problem: the surface is modeled as the zero level-set of an implicit function whose gradient is the closest to the vector field induced by the 3D sample normals. In the occluded region of space, we consider the a priori model as a sufficiently accurate descriptor of the shape. Both models, which are expressed in the same basis of compactly supported radial functions to ensure computation and memory efficiency, are then blended to obtain a closed model of the scanned object. Our method is finally tested on traditional testing datasets to assess its accuracy and on simulated terrestrial LiDAR scanning (TLS) point clouds of trees to assess its ability to handle complex shapes with occlusions. Numéro de notice : A2018-530 Affiliation des auteurs : LIF+Ext (2012-2019) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.cag.2018.05.004 Date de publication en ligne : 17/05/2018 En ligne : https://doi.org/10.1016/j.cag.2018.05.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91400
in Computers and graphics > vol 74 (August 2018) . - pp 44 - 55[article]Modélisation 3D de la végétation sur le territoire de Rennes Métropole (Partie 1) / Coralie Leblan in Géomatique expert, n° 123 (juillet - août 2018)PermalinkAdaptive stopping criterion for top-down segmentation of ALS point clouds in temperate coniferous forests / Nina Amiri in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)PermalinkConstruction control and documentation of facade elements using terrestrial laser scanning / Ján Erdélyi in Applied geomatics, vol 10 n° 2 (June 2018)PermalinkGenève 1850, du plan-relief Magnin à la visite virtuelle / David Desbuisson in XYZ, n° 155 (juin - août 2018)PermalinkModeling diameter distributions in radiata pine plantations in Spain with existing countrywide LiDAR data / Manuel Arias-Rodil in Annals of Forest Science, vol 75 n° 2 (June 2018)PermalinkRange-image: Incorporating sensor topology for lidar point cloud processing / Pierre Biasutti in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 6 (juin 2018)PermalinkSpatially sensitive statistical shape analysis for pedestrian recognition from LIDAR data / Michalis A. Savelonas in Computer Vision and image understanding, vol 171 (June 2018)PermalinkWeighted simplicial complex reconstruction from mobile laser scanning using sensor topology / Stéphane Guinard in Revue Française de Photogrammétrie et de Télédétection, n° 217-218 (juin - septembre 2018)PermalinkLarge scale textured mesh reconstruction from mobile mapping images and LIDAR scans / Mohamed Boussaha in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2 (June 2018)PermalinkSensor-topology based simplicial complex reconstruction from mobile laser scanning / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2 (June 2018)PermalinkAn object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery / Luis Angel Ruiz in Geocarto international, vol 33 n° 5 (May 2018)PermalinkClassifying airborne LiDAR point clouds via deep features learned by a multi-scale convolutional neural network / Ruibin Zhao in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)PermalinkComparison of the performances of ground filtering algorithms and DTM generation from a UAV-based point cloud / Cigdem Serifoglu Yilmaz in Geocarto international, vol 33 n° 5 (May 2018)PermalinkFrom point cloud to BIM: an integrated workflow for documentation, research and modelling of architectural heritage / C. Rodríguez-Moreno in Survey review, vol 50 n° 360 (May 2018)PermalinkLarge-scale supervised learning for 3D Point cloud labeling : Semantic3d.Net / Timo Hackel in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 5 (mai 2018)PermalinkLiDAR, a technology to assist with smart cities and climate change resilience: a case study in an urban metropolis / Ryan Garnett in ISPRS International journal of geo-information, vol 7 n° 5 (May 2018)PermalinkEffects of terrain slope and aspect on the error of ALS-based predictions of forest attributes / Hans Ole Ørka in Forestry, an international journal of forest research, vol 91 n° 2 (April 2018)PermalinkRevue des descripteurs tridimensionnels (3D) pour la catégorisation des nuages de points acquis avec un système LiDAR de télémétrie mobile / Sylvie Daniel in Geomatica, vol 72 n° 1 (March 2018)PermalinkA spatio-temporal index for aerial full waveform laser scanning data / Debra F. Laefer in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)PermalinkUse of LiDAR for calculating solar irradiance on roofs and façades of buildings at city scale: Methodology, validation, and analysis / Liang Cheng in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)PermalinkUsing terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: A comparison with traditional destructive approach / Stéphane Momo Takoudjou in Methods in ecology and evolution, vol 9 n° 4 (April 2018)Permalink3D micro-mapping : Towards assessing the quality of crowdsourcing to support 3D point cloud analysis / Benjamin Herfort in ISPRS Journal of photogrammetry and remote sensing, vol 137 (March 2018)PermalinkAnalyse du risque végétation dans les emprises ferroviaires à partir de données LiDAR acquises par drones / Luc Perrin in XYZ, n° 154 (mars - mai 2018)PermalinkImportant LiDAR metrics for discriminating forest tree species in Central Europe / Yifang Shi in ISPRS Journal of photogrammetry and remote sensing, vol 137 (March 2018)PermalinkLarge off-nadir scan angle of airborne LiDAR can severely affect the estimates of forest structure metrics / Jing Liu in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)PermalinkLittoral, "Ricochet" ausculte / Marielle Mayo in Géomètre, n° 2155 (février 2018)PermalinkMultisource remote sensing data classification based on convolutional neural network / Xiaodong Xu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 2 (February 2018)PermalinkPredicting temperate forest stand types using only structural profiles from discrete return airborne lidar / Melissa Fedrigo in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)PermalinkRobust interpolation of DEMs from lidar-derived elevation data / Chuanfa Chen in IEEE Transactions on geoscience and remote sensing, vol 56 n° 2 (February 2018)PermalinkValue of airborne laser scanning and digital aerial photogrammetry data in forest decision making / Annika S. Kangas in Silva fennica, vol 52 n° 1 ([01/02/2018])PermalinkPermalinkAirborne laser scanning for tree diameter distribution modelling: a comparison of different modelling alternatives in a tropical single-species plantation / Matti Maltamo in Forestry, an international journal of forest research, vol 91 n° 1 (January 2018)PermalinkAssessing forest windthrow damage using single-date, post-event airborne laser scanning data / Gherardo Chirici in Forestry, an international journal of forest research, vol 91 n° 1 (January 2018)PermalinkPermalinkPermalinkPermalinkPermalinkUn inventaire forestier multisource pour la gestion des territoires / Dinesh Babu Irulappa-Pillai-Vijayakumar (2018)PermalinkPermalinkMise en place d’un outil de classification et d’utilisation des données LiDAR pour l’étude du couvert arboré à Florence / Florian Thill (2018)PermalinkOn the production of semantic and textured 3D meshes of large scale urban environments from mobile mapping images and LIDAR scans / Mohamed Boussaha (2018)PermalinkPermalinkSuivi et conservation du patrimoine historique et culturel / Jocelyn Le Maître (2018)PermalinkSuivi des impacts d’un arasement de barrage sur la végétation riveraine par télédétection à très haute résolution spatiale et temporelle / Marianne Laslier (2018)PermalinkSuperPoint Graph : segmentation sémantique de nuages de points LiDAR à grande échelle / Loïc Landrieu (2018)Permalink