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Integrating terrestrial laser scanning and unmanned aerial vehicle photogrammetry to estimate individual tree attributes in managed coniferous forests in Japan / Katsuto Shimizu in International journal of applied Earth observation and geoinformation, vol 106 (February 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 106 (February 2022) . - n° 102658 Titre : Integrating terrestrial laser scanning and unmanned aerial vehicle photogrammetry to estimate individual tree attributes in managed coniferous forests in Japan Type de document : Article/Communication Auteurs : Katsuto Shimizu, Auteur ; Tomohiro Nishizono, Auteur ; Fumiaki Kitahara, Auteur ; Keiko Fukumoto, Auteur ; Hideki Saito, Auteur Année de publication : 2022 Article en page(s) : n° 102658 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] estimation statistique [Termes IGN] hauteur des arbres [Termes IGN] image captée par drone [Termes IGN] Japon [Termes IGN] Pinophyta [Termes IGN] volume en bois Résumé : (auteur) The accurate estimation of tree attributes is essential for sustainable forest management. Terrestrial Laser Scanning (TLS) is a viable remote sensing technology suitable for estimating under canopy structure. However, TLS measurements generally underestimate tree height in taller trees, which leads to the underestimation of other tree attributes (e.g., stem volume). The integration of information derived from TLS and Unmanned Aerial Vehicle (UAV) photogrammetry could potentially improve tree height estimation. This study investigated the applicability of integrating TLS and UAV photogrammetry to estimate individual tree attributes in managed coniferous forests of Japan. Diameter at breast height (DBH), tree height, and stem volume were estimated by (1) TLS data only, (2) integrating TLS and UAV data with TLS tree locations, and (3) integrating TLS and UAV data with treetop detections of the tree canopy. The TLS data only approach achieved high accuracy for DBH estimations with a root mean squared error (RMSE) of 2.36 cm (RMSE% 5.6%); however, tree height was greatly underestimated, with an RMSE of 8.87 m (28.9%) and a bias of −8.39 m. Integrating TLS and UAV photogrammetric data improved tree height estimation accuracy for both the TLS tree location (RMSE of 1.89 m and a bias of −0.46 m) and the treetop detection (RMSE of 1.77 m and a bias of 0.36 m) approaches. Integrating TLS and UAV photogrammetric data also improved the accuracy of the stem volume estimations with RMSEs of 0.21 m3 (10.8%) and 0.21 m3 (10.5%) for the TLS tree location and treetop detection approaches, respectively. Although the tree height of suppressed trees tended to be overestimated by TLS and UAV photogrammetric data integration, a good performance was obtained for dominant trees. The results of this study indicate that the integration of TLS and UAV photogrammetry is beneficial for the accurate estimation of tree attributes in coniferous forests. Numéro de notice : A2022-071 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2021.102658 En ligne : https://doi.org/10.1016/j.jag.2021.102658 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99423 [article]

Quantifying the shape of urban street trees and evaluating its influence on their aesthetic functions based on mobile lidar data / Tianyu Hu in ISPRS Journal of photogrammetry and remote sensing, vol 184 (February 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 184 (February 2022) . - pp 203 - 214 Titre : Quantifying the shape of urban street trees and evaluating its influence on their aesthetic functions based on mobile lidar data Type de document : Article/Communication Auteurs : Tianyu Hu, Auteur ; Dengjie Wei, Auteur ; Yanjun Su, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 203 - 214 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] arbre urbain [Termes IGN] canopée [Termes IGN] Chine [Termes IGN] couvert végétal [Termes IGN] distribution spatiale [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Green area index [Termes IGN] image panoramique [Termes IGN] semis de points [Termes IGN] système de numérisation mobile Résumé : (auteur) Street trees are important components of an urban green space and understanding and measuring their ecological and cultural services is crucial for assessing the quality of streets and managing urban environments. Currently, most studies mainly focus on evaluating the ecological services of street trees by measuring the amount of greenness, but how to evaluate their aesthetic functions through quantitative measurements of street trees remain unclear. To address this problem, we propose a method to assess the aesthetic functions of street trees by quantifying the shape of greenness inspired by assessments of skyline aesthetics. Using a state-of-the-art mobile mapping system, we collected downtown-wide lidar data and panoramic images in Jinzhou City, Hebei Province, China. We developed a method for extracting the canopy line from the mobile lidar data, and then identified two basic elements, peaks and gaps, from street canopy lines and extracted six indexes (i.e., richness of peaks, evenness of peaks, frequency of peaks, total length of gaps, evenness of gaps and frequency of gaps) to describe the fluctuations and continuities of street canopy lines. We analyzed the abundance and spatial distribution of these indexes together with survey responses on the streets’ aesthetics and found that most of them were significantly correlated with human perception of streets. Compared to indexes of amount of greenness (e.g., green volume and green view index), these shape indexes have stronger influences on the physical aesthetic beauty of street trees. These findings suggest that a comprehensive assessment of the aesthetic function of street trees should consider both shape and amount of greenness. This study provides a new perspective for the assessment of urban green spaces and can assist future urban greening planning and urban landscape management. Numéro de notice : A2022-105 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.01.002 Date de publication en ligne : 15/01/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.01.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99602 [article]

3D modeling of urban area based on oblique UAS images—An end-to-end pipeline / Valeria-Ersilia Oniga in Remote sensing, vol 14 n° 2 (January 2 2022)  

Public [article]  inRemote sensing > vol 14 n° 2 (January 2 2022) . - n° 422 Titre : 3D modeling of urban area based on oblique UAS images—An end-to-end pipeline Type de document : Article/Communication Auteurs : Valeria-Ersilia Oniga, Auteur ; Ana-Ioana Breaban, Auteur ; Norbert Pfeifer, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 422 Note générale : biliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] apprentissage automatique [Termes IGN] Bâti-3D [Termes IGN] CityGML [Termes IGN] classification par forêts aléatoires [Termes IGN] données lidar [Termes IGN] image aérienne oblique [Termes IGN] image captée par drone [Termes IGN] indice de végétation [Termes IGN] lasergrammétrie [Termes IGN] modèle numérique de surface [Termes IGN] modélisation 3D [Termes IGN] point d'appui [Termes IGN] Roumanie [Termes IGN] segmentation [Termes IGN] semis de points [Termes IGN] zone urbaine Résumé : (auteur) 3D modelling of urban areas is an attractive and active research topic, as 3D digital models of cities are becoming increasingly common for urban management as a consequence of the constantly growing number of people living in cities. Viewed as a digital representation of the Earth’s surface, an urban area modeled in 3D includes objects such as buildings, trees, vegetation and other anthropogenic structures, highlighting the buildings as the most prominent category. A city’s 3D model can be created based on different data sources, especially LiDAR or photogrammetric point clouds. This paper’s aim is to provide an end-to-end pipeline for 3D building modeling based on oblique UAS images only, the result being a parametrized 3D model with the Open Geospatial Consortium (OGC) CityGML standard, Level of Detail 2 (LOD2). For this purpose, a flight over an urban area of about 20.6 ha has been taken with a low-cost UAS, i.e., a DJI Phantom 4 Pro Professional (P4P), at 100 m height. The resulting UAS point cloud with the best scenario, i.e., 45 Ground Control Points (GCP), has been processed as follows: filtering to extract the ground points using two algorithms, CSF and terrain-mark; classification, using two methods, based on attributes only and a random forest machine learning algorithm; segmentation using local homogeneity implemented into Opals software; plane creation based on a region-growing algorithm; and plane editing and 3D model reconstruction based on piece-wise intersection of planar faces. The classification performed with ~35% training data and 31 attributes showed that the Visible-band difference vegetation index (VDVI) is a key attribute and 77% of the data was classified using only five attributes. The global accuracy for each modeled building through the workflow proposed in this study was around 0.15 m, so it can be concluded that the proposed pipeline is reliable. Numéro de notice : A2022-101 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.3390/rs14020422 Date de publication en ligne : 17/01/2022 En ligne : https://doi.org/10.3390/rs14020422 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99566 [article]

Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles / Nico Lang in Remote sensing of environment, vol 268 (January 2022)  

Public [article]  inRemote sensing of environment > vol 268 (January 2022) . - n* 112760 Titre : Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles Type de document : Article/Communication Auteurs : Nico Lang, Auteur ; Nicolai Kalischek, Auteur ; John Armston, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n* 112760 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] biomasse aérienne [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] estimation bayesienne [Termes IGN] forme d'onde [Termes IGN] Global Ecosystem Dynamics Investigation lidar [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] semis de points Résumé : (auteur) NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. While GEDI is the first space-based LIDAR explicitly optimized to measure vertical forest structure predictive of aboveground biomass, the accurate interpretation of this vast amount of waveform data across the broad range of observational and environmental conditions is challenging. Here, we present a novel supervised machine learning approach to interpret GEDI waveforms and regress canopy top height globally. We propose a probabilistic deep learning approach based on an ensemble of deep convolutional neural networks (CNN) to avoid the explicit modelling of unknown effects, such as atmospheric noise. The model learns to extract robust features that generalize to unseen geographical regions and, in addition, yields reliable estimates of predictive uncertainty. Ultimately, the global canopy top height estimates produced by our model have an expected RMSE of 2.7 m with low bias. Numéro de notice : A2022-086 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112760 Date de publication en ligne : 03/11/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112760 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99495 [article]

Detecting and visualizing observation hot-spots in massive volunteer-contributed geographic data across spatial scales using GPU-accelerated kernel density estimation / Guiming Zhang in ISPRS International journal of geo-information, vol 11 n° 1 (January 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 1 (January 2022) . - n° 55 Titre : Detecting and visualizing observation hot-spots in massive volunteer-contributed geographic data across spatial scales using GPU-accelerated kernel density estimation Type de document : Article/Communication Auteurs : Guiming Zhang, Auteur Année de publication : 2022 Article en page(s) : n° 55 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] données localisées des bénévoles [Termes IGN] données massives [Termes IGN] estimation par noyau [Termes IGN] exploration de données géographiques [Termes IGN] géovisualisation [Termes IGN] processeur graphique [Termes IGN] qualité des données [Termes IGN] réseau social [Termes IGN] tâche claire Résumé : (auteur) Volunteer-contributed geographic data (VGI) is an important source of geospatial big data that support research and applications. A major concern on VGI data quality is that the underlying observation processes are inherently biased. Detecting observation hot-spots thus helps better understand the bias. Enabled by the parallel kernel density estimation (KDE) computational tool that can run on multiple GPUs (graphics processing units), this study conducted point pattern analyses on tens of millions of iNaturalist observations to detect and visualize volunteers’ observation hot-spots across spatial scales. It was achieved by setting varying KDE bandwidths in accordance with the spatial scales at which hot-spots are to be detected. The succession of estimated density surfaces were then rendered at a sequence of map scales for visual detection of hot-spots. This study offers an effective geovisualization scheme for hierarchically detecting hot-spots in massive VGI datasets, which is useful for understanding the pattern-shaping drivers that operate at multiple spatial scales. This research exemplifies a computational tool that is supported by high-performance computing and capable of efficiently detecting and visualizing multi-scale hot-spots in geospatial big data and contributes to expanding the toolbox for geospatial big data analytics. Numéro de notice : A2022-091 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11010055 Date de publication en ligne : 12/01/2022 En ligne : https://doi.org/10.3390/ijgi11010055 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99507 [article]

Airborne LiDAR and high resolution multispectral data integration in Eucalyptus tree species mapping in an Australian farmscape / Niva Kiran Verma in Geocarto international, vol 37 n° 1 ([03/01/2022])  

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Attributing pedestrian networks with semantic information based on multi-source spatial data / Xue Yang in International journal of geographical information science IJGIS, vol 36 n° 1 (January 2022)  

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Urban geospatial information acquisition mobile mapping system based on close-range photogrammetry and IGS site calibration / Ming Guo in Geo-spatial Information Science, vol 24 n° 4 (December 2021)  

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AlpineBends – A benchmark for deep learning-based generalisation / Azelle Courtial (2022)  

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An approach for multi-scale urban building data integration and enrichment through geometric matching and semantic web / Abdulkadir Memduhoglu in Cartography and Geographic Information Science, vol 49 n° 1 (January 2022)  

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A comparison of linear-mode and single-photon airborne LiDAR in species-specific forest inventories / Janne Raty in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)  

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ReBankment: displacing embankment lines from roads and rivers with a least squares adjustment / Guillaume Touya in International journal of cartography, vol 8 n° inconnu ([01/01/2022])  

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Utilisation de l’apprentissage profond dans la modélisation 3D urbaine : partie 2, post-traitement et évaluation / Hamza Ben Addou in Géomatique expert, n° 136 (novembre - décembre 2021)

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Tidal flood area mapping in the face of climate change scenarios: case study in a tropical estuary in the Brazilian semi-arid region / Paulo Victor N. Araújo in Natural Hazards and Earth System Sciences, vol 21 n° 11 (November 2021)  

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Assessing the agreement of ICESat-2 terrain and canopy height with airborne lidar over US ecozones / Lonesome Malambo in Remote sensing of environment, vol 266 (December 2021)  

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