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A novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds / Xiaoqiang Liu in Remote sensing of environment, vol 282 (December 2022)
[article]
Titre : A novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds Type de document : Article/Communication Auteurs : Xiaoqiang Liu, Auteur ; Qin Ma, Auteur ; Xiaoyong wu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 113280 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] canopée
[Termes IGN] Chine
[Termes IGN] couvert forestier
[Termes IGN] densité des points
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] échantillonnage
[Termes IGN] écosystème forestier
[Termes IGN] entropie
[Termes IGN] estimation par noyau
[Termes IGN] image captée par drone
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] semis de pointsRésumé : (auteur) Forest canopy structural complexity (CSC) describes the three-dimensional (3D) arrangement of canopy elements, and has become an emergent forest attribute mediating forest ecosystem functioning along with species diversity. Light detection and ranging (lidar), especially the emerging near-surface lidar platforms (e.g., terrestrial laser scanning/TLS, backpack laser scanning/BLS, unmanned aerial vehicle laser scanning/ULS), can depict 3D canopy information with high efficiency and accuracy, providing an ideal data source for forest CSC quantification. However, current existing lidar-based CSC quantification indices may share common limitations of getting saturated in structurally complex forest stands and not fully capturing within-canopy structural variations. In this study, we introduced the concept of entropy into forest CSC quantification, and proposed a new forest CSC index, namely canopy entropy (CE). Two major bottlenecks were addressed in the CE calculation procedure, including (1) using a Mann-Kendall (MK) test-based resampling strategy to address the issue of incongruent sampling chances of canopy elements at different locations from different lidar systems, and (2) using a kernel density estimation (KDE)-based method to reduce its dependence on point density. The effectiveness and generality of CE were evaluated by simulating TLS and ULS point clouds from nine forest stands and collecting TLS, BLS, and ULS point clouds from 110 field plots distributed in five forest sites, covering a large variety of forest types and forest CSC conditions. The results showed that CE was an effective forest CSC quantification index that successfully captured CSC variations caused by both tree density and the number of vertical canopy layers. It had significant positive correlations with four widely used CSC indices (i.e., canopy cover, foliage height diversity, canopy top rugosity, and fractal dimension; R2: 0.32 to 0.67), but outperformed them by overcoming their common limitations. CE estimates from multiplatform lidar point clouds agreed well with each other (R2 ≥ 0.70, RMSE ≤0.10), indicating it has generality in cross-platform forest CSC quantification practices. We believe the proposed CE index has great potential to help us unravel the correlations among forest CSC, species diversity, and forest ecosystem functions, and therefore improve our understanding on forest ecosystem processes. Numéro de notice : A2022-795 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113280 Date de publication en ligne : 26/09/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113280 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101930
in Remote sensing of environment > vol 282 (December 2022) . - n° 113280[article]Relevé 2D & 3D du marégraphe de Marseille / Emmanuel Clédat in XYZ, n° 173 (décembre 2022)
[article]
Titre : Relevé 2D & 3D du marégraphe de Marseille Type de document : Article/Communication Auteurs : Emmanuel Clédat , Auteur ; Clovis Bergeret, Auteur ; Marius Dahuron, Auteur ; Lilian Wecker, Auteur ; Frédéric Ye, Auteur Année de publication : 2022 Article en page(s) : pp 55 - 62 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] géoréférencement
[Termes IGN] image captée par drone
[Termes IGN] marégraphe
[Termes IGN] Marseille
[Termes IGN] modélisation 2D
[Termes IGN] modélisation 3D
[Termes IGN] semis de pointsRésumé : (Editeur) Le marégraphe de Marseille est un monument historique de l’IGN. Pour permettre au plus grand nombre de le visiter (virtuellement), et pour préparer d’éventuels travaux de restauration, l’association des amis du marégraphe a commandité une modélisation 3D. Effectués par les élèves de l’ENSG-Géomatique en utilisant les méthodes de photogrammétrie et de scanner laser terrestre, ces relevés ont permis de produire un modèle 3D intérieur et extérieur, mais aussi des produits 2D : coupes, plans, écorchés. Numéro de notice : A2022-912 Affiliation des auteurs : ENSG (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102270
in XYZ > n° 173 (décembre 2022) . - pp 55 - 62[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 112-2022041 RAB Revue Centre de documentation En réserve L003 Disponible GCPs-free photogrammetry for estimating tree height and crown diameter in Arizona cypress plantation using UAV-mounted GNSS RTK / Morteza Pourreza in Forests, vol 13 n° 11 (November 2022)
[article]
Titre : GCPs-free photogrammetry for estimating tree height and crown diameter in Arizona cypress plantation using UAV-mounted GNSS RTK Type de document : Article/Communication Auteurs : Morteza Pourreza, Auteur ; Fardin Moradi, Auteur ; Mohammad Khosravi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1905 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] cupressus (genre)
[Termes IGN] diamètre des arbres
[Termes IGN] hauteur de vol
[Termes IGN] hauteur des arbres
[Termes IGN] image captée par drone
[Termes IGN] Iran
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] point d'appui
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] structure-from-motionRésumé : (auteur) One of the main challenges of using unmanned aerial vehicles (UAVs) in forest data acquisition is the implementation of Ground Control Points (GCPs) as a mandatory step, which is sometimes impossible for inaccessible areas or within canopy closures. This study aimed to test the accuracy of a UAV-mounted GNSS RTK (real-time kinematic) system for calculating tree height and crown height without any GCPs. The study was conducted on a Cupressus arizonica (Greene., Arizona cypress) plantation on the Razi University Campus in Kermanshah, Iran. Arizona cypress is commonly planted as an ornamental tree. As it can tolerate harsh conditions, this species is highly appropriate for afforestation and reforestation projects. A total of 107 trees were subjected to field-measured dendrometric measurements (height and crown diameter). UAV data acquisition was performed at three altitudes of 25, 50, and 100 m using a local network RTK system (NRTK). The crown height model (CHM), derived from a digital surface model (DSM), was used to estimate tree height, and an inverse watershed segmentation (IWS) algorithm was used to estimate crown diameter. The results indicated that the means of tree height obtained from field measurements and UAV estimation were not significantly different, except for the mean values calculated at 100 m flight altitude. Additionally, the means of crown diameter reported from field measurements and UAV estimation at all flight altitudes were not statistically different. Root mean square error (RMSE Numéro de notice : A2022-838 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f13111905 Date de publication en ligne : 12/11/2022 En ligne : https://doi.org/10.3390/f13111905 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102039
in Forests > vol 13 n° 11 (November 2022) . - n° 1905[article]A joint deep learning network of point clouds and multiple views for roadside object classification from lidar point clouds / Lina Fang in ISPRS Journal of photogrammetry and remote sensing, vol 193 (November 2022)
[article]
Titre : A joint deep learning network of point clouds and multiple views for roadside object classification from lidar point clouds Type de document : Article/Communication Auteurs : Lina Fang, Auteur ; Zhilong You, Auteur ; Guixi Shen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 115 - 136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification orientée objet
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] fusion d'images
[Termes IGN] image captée par drone
[Termes IGN] reconnaissance d'objets
[Termes IGN] route
[Termes IGN] scène urbaine
[Termes IGN] semis de pointsRésumé : (auteur) Urban management and survey departments have begun investigating the feasibility of acquiring data from various laser scanning systems for urban infrastructure measurements and assessments. Roadside objects such as cars, trees, traffic poles, pedestrians, bicycles and e-bicycles describe the static and dynamic urban information available for acquisition. Because of the unstructured nature of 3D point clouds, the rich targets in complex road scenes, and the varying scales of roadside objects, finely classifying these roadside objects from various point clouds is a challenging task. In this paper, we integrate two representations of roadside objects, point clouds and multiview images to propose a point-group-view network named PGVNet for classifying roadside objects into cars, trees, traffic poles, and small objects (pedestrians, bicycles and e-bicycles) from generalized point clouds. To utilize the topological information of the point clouds, we propose a graph attention convolution operation called AtEdgeConv to mine the relationship among the local points and to extract local geometric features. In addition, we employ a hierarchical view-group-object architecture to diminish the redundant information between similar views and to obtain salient viewwise global features. To fuse the local geometric features from the point clouds and the global features from multiview images, we stack an attention-guided fusion network in PGVNet. In particular, we quantify and leverage the global features as an attention mask to capture the intrinsic correlation and discriminability of the local geometric features, which contributes to recognizing the different roadside objects with similar shapes. To verify the effectiveness and generalization of our methods, we conduct extensive experiments on six test datasets of different urban scenes, which were captured by different laser scanning systems, including mobile laser scanning (MLS) systems, unmanned aerial vehicle (UAV)-based laser scanning (ULS) systems and backpack laser scanning (BLS) systems. Experimental results, and comparisons with state-of-the-art methods, demonstrate that the PGVNet model is able to effectively identify various cars, trees, traffic poles and small objects from generalized point clouds, and achieves promising performances on roadside object classifications, with an overall accuracy of 95.76%. Our code is released on https://github.com/flidarcode/PGVNet. Numéro de notice : A2022-756 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.08.022 Date de publication en ligne : 22/09/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.08.022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101759
in ISPRS Journal of photogrammetry and remote sensing > vol 193 (November 2022) . - pp 115 - 136[article]A deep 2D/3D Feature-Level fusion for classification of UAV multispectral imagery in urban areas / Hossein Pourazar in Geocarto international, vol 37 n° 23 ([15/10/2022])
[article]
Titre : A deep 2D/3D Feature-Level fusion for classification of UAV multispectral imagery in urban areas Type de document : Article/Communication Auteurs : Hossein Pourazar, Auteur ; Farhad Samadzadegan, Auteur ; Farzaneh Dadrass Javan, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 6695 - 6712 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] alignement des données
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image captée par drone
[Termes IGN] image multibande
[Termes IGN] image proche infrarouge
[Termes IGN] image RVB
[Termes IGN] modèle numérique de surface
[Termes IGN] orthophotoplan numérique
[Termes IGN] zone urbaineRésumé : (auteur) In this paper, a deep convolutional neural network (CNN) is developed to classify the Unmanned Aerial Vehicle (UAV) derived multispectral imagery and normalized digital surface model (DSM) data in urban areas. For this purpose, a multi-input deep CNN (MIDCNN) architecture is designed using 11 parallel CNNs; 10 deep CNNs to extract the features from all possible triple combinations of spectral bands as well as one deep CNN dedicated to the normalized DSM data. The proposed method is compared with the traditional single-input (SI) and double-input (DI) deep CNN designations and random forest (RF) classifier, and evaluated using two independent test datasets. The results indicate that increasing the CNN layers parallelly augmented the classifier’s generalization and reduced overfitting risk. The overall accuracy and kappa value of the proposed method are 95% and 0.93, respectively, for the first test dataset, and 96% and 0.94, respectively, for the second test data set. Numéro de notice : A2022-749 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1959655 Date de publication en ligne : 04/08/2021 En ligne : https://doi.org/10.1080/10106049.2021.1959655 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101741
in Geocarto international > vol 37 n° 23 [15/10/2022] . - pp 6695 - 6712[article]Incremental road network update method with trajectory data and UAV remote sensing imagery / Jianxin Qin in ISPRS International journal of geo-information, vol 11 n° 10 (October 2022)PermalinkRiparian ecosystems mapping at fine scale: a density approach based on multi-temporal UAV photogrammetric point clouds / Elena Belcore in Remote sensing in ecology and conservation, vol 8 n° 5 (October 2022)PermalinkDiscontinuity interpretation and identification of potential rockfalls for high-steep slopes based on UAV nap-of-the-object photogrammetry / Wei Wang in Computers & geosciences, vol 166 (September 2022)PermalinkFeux de forêt : un drone traque les risques de reprise / Nathalie Da Cruz in Géomètre, n° 2205 (septembre 2022)PermalinkAssessing structural complexity of individual scots pine trees by comparing terrestrial laser scanning and photogrammetric point clouds / Noora Tienaho in Forests, Vol 13 n° 8 (August 2022)PermalinkIntegrating post-processing kinematic (PPK) structure-from-motion (SfM) with unmanned aerial vehicle (UAV) photogrammetry and digital field mapping for structural geological analysis / Daniele Cirillo in ISPRS International journal of geo-information, vol 11 n° 8 (August 2022)PermalinkTransfer learning from citizen science photographs enables plant species identification in UAV imagery / Salim Soltani in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 5 (August 2022)PermalinkDetection of diseased pine trees in unmanned aerial vehicle images by using deep convolutional neural networks / Gensheng Hu in Geocarto international, vol 37 n° 12 ([01/07/2022])PermalinkInvestigating the ability to identify new constructions in urban areas using images from unmanned aerial vehicles, Google Earth, and Sentinel-2 / Fahime Arabi Aliabad in Remote sensing, vol 14 n° 13 (July-1 2022)PermalinkRecent advances in forest insect pests and diseases monitoring using UAV-based data: A systematic review / André Duarte in Forests, vol 13 n° 6 (June 2022)Permalink