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Evaluating the 3D integrity of underwater structure from motion workflows / Ian M. Lochhead in Photogrammetric record, vol 37 n° 177 (March 2022)  

Public [article]  inPhotogrammetric record > vol 37 n° 177 (March 2022) . - pp 35 - 60 Titre : Evaluating the 3D integrity of underwater structure from motion workflows Type de document : Article/Communication Auteurs : Ian M. Lochhead, Auteur Année de publication : 2022 Article en page(s) : pp 35 - 60 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] auscultation d'ouvrage [Termes IGN] chaîne de traitement [Termes IGN] étalonnage d'instrument [Termes IGN] fond marin [Termes IGN] image sous-marine [Termes IGN] modélisation 3D [Termes IGN] Pacifique nord [Termes IGN] récif corallien [Termes IGN] semis de points [Termes IGN] semis de points clairsemés [Termes IGN] structure-from-motion Résumé : (auteur) Structure from motion (SfM) is an accessible and non-intrusive method of three-dimensional (3D) data capture popular for tropical coral reef surveying. In the north-east Pacific Ocean, where there are many environmentally sensitive benthic organisms whose morphology and function are equally important, SfM surveys are less commonly studied. Temperate waters pose unique challenges to SfM workflows, which must be systematically unpacked to understand their impact on data quality and veracity. This uncertainty raises broader questions concerning SfM as a spatial data-acquisition and ecological characterisation method in temperate waters, and whether a systematic workflow assessment reveals vital relationships between SfM implementation parameters, 3D data products and their implications for underwater SfM surveys. This paper, the second of two empirical assessments, reports on a series of wet-lab and dryland tests quantifying the impact that temperate waters, underwater cameras, and photograph quantity and configuration have on SfM accuracy. These tests provided crucial accuracy benchmarks informing subsequent field-based surveys and revealed that underwater SfM workflows can generate highly accurate 3D models in temperate waters. Numéro de notice : A2022-253 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : doi.org/10.1111/phor.12399 Date de publication en ligne : 07/03/2022 En ligne : https://doi.org/10.1111/phor.12399 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100216 [article]

Monitoring coastal vulnerability by using DEMs based on UAV spatial data / Antonio Minervino Amodio in ISPRS International journal of geo-information, vol 11 n° 3 (March 2022)  

Public [article]  inISPRS International journal of geo-information > vol 11 n° 3 (March 2022) . - n° 155 Titre : Monitoring coastal vulnerability by using DEMs based on UAV spatial data Type de document : Article/Communication Auteurs : Antonio Minervino Amodio, Auteur ; Gianluigi Di Paola, Auteur ; Carmen Maria Rosskopf, Auteur Année de publication : 2022 Article en page(s) : n° 155 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] Adriatique, mer [Termes IGN] détection de changement [Termes IGN] érosion côtière [Termes IGN] géoréférencement [Termes IGN] image captée par drone [Termes IGN] Italie [Termes IGN] littoral méditerranéen [Termes IGN] modèle numérique de surface [Termes IGN] orthophotographie [Termes IGN] point d'appui [Termes IGN] structure-from-motion [Termes IGN] surveillance du littoral [Termes IGN] trait de côte [Termes IGN] vulnérabilité Résumé : (auteur) The use of Unmanned Aerial Vehicles (UAVs) represents a rather innovative, quick, and low-cost methodological approach offering applications in several fields of investigation. The present study illustrates the developed method using Digital Elevation Models (DEMs) based on UAV-derived data for evaluating short-term morphological-topographic changes of the beach system and related implications for coastal vulnerability assessment. UAV surveys were performed during the summers of 2019 and 2020 along a beach stretch affected by erosion, located along the central Adriatic coast. Acquired high-resolution aerial photos were used to generate large-scale DEMs as well as orthophotos of the beach using the Structure from Motion (SfM) image processing tool. Comparison of the generated 2019 and 2020 DEMs highlighted significant morphological changes and a sediment volume loss of about 780 m3 within a surface area of about 4400 m2. Based on 20 m spaced beach profiles derived from the DEMs, a coastal vulnerability assessment was performed using the CVA approach that highlighted some significant variations in the CVA index between 2019 and 2020. Results evidence that UAV surveys provide high-resolution topographic data, suitable for specific beach monitoring activities and the updating of some parameters that enter in the CVA model contributing to its correct application. Numéro de notice : A2022-185 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11030155 Date de publication en ligne : 22/02/2022 En ligne : https://doi.org/10.3390/ijgi11030155 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99895 [article]

Ultrahigh-resolution boreal forest canopy mapping: Combining UAV imagery and photogrammetric point clouds in a deep-learning-based approach / Linyuan Li in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 107 (March 2022) . - n° 102686 Titre : Ultrahigh-resolution boreal forest canopy mapping: Combining UAV imagery and photogrammetric point clouds in a deep-learning-based approach Type de document : Article/Communication Auteurs : Linyuan Li, Auteur ; Xihan Mu, Auteur ; Francesco Chianucci, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102686 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] algorithme SLIC [Termes IGN] apprentissage profond [Termes IGN] canopée [Termes IGN] carte forestière [Termes IGN] Chine [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] couvert forestier [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] données lidar [Termes IGN] faisceau laser [Termes IGN] forêt boréale [Termes IGN] image captée par drone [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] modèle numérique de terrain [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] sous-étage [Termes IGN] structure-from-motion Résumé : (auteur) Accurate wall-to-wall estimation of forest crown cover is critical for a wide range of ecological studies. Notwithstanding the increasing use of UAVs in forest canopy mapping, the ultrahigh-resolution UAV imagery requires an appropriate procedure to separate the contribution of understorey from overstorey vegetation, which is complicated by the spectral similarity between the two forest components and the illumination environment. In this study, we investigated the integration of deep learning and the combined data of imagery and photogrammetric point clouds for boreal forest canopy mapping. The procedure enables the automatic creation of training sets of tree crown (overstorey) and background (understorey) data via the combination of UAV images and their associated photogrammetric point clouds and expands the applicability of deep learning models with self-supervision. Based on the UAV images with different overlap levels of 12 conifer forest plots that are categorized into “I”, “II” and “III” complexity levels according to illumination environment, we compared the self-supervised deep learning-predicted canopy maps from original images with manual delineation data and found an average intersection of union (IoU) larger than 0.9 for “complexity I” and “complexity II” plots and larger than 0.75 for “complexity III” plots. The proposed method was then compared with three classical image segmentation methods (i.e., maximum likelihood, Kmeans, and Otsu) in the plot-level crown cover estimation, showing outperformance in overstorey canopy extraction against other methods. The proposed method was also validated against wall-to-wall and pointwise crown cover estimates using UAV LiDAR and in situ digital cover photography (DCP) benchmarking methods. The results showed that the model-predicted crown cover was in line with the UAV LiDAR method (RMSE of 0.06) and deviate from the DCP method (RMSE of 0.18). We subsequently compared the new method and the commonly used UAV structure-from-motion (SfM) method at varying forward and lateral overlaps over all plots and a rugged terrain region, yielding results showing that the method-predicted crown cover was relatively insensitive to varying overlap (largest bias of less than 0.15), whereas the UAV SfM-estimated crown cover was seriously affected by overlap and decreased with decreasing overlap. In addition, canopy mapping over rugged terrain verified the merits of the new method, with no need for a detailed digital terrain model (DTM). The new method is recommended to be used in various image overlaps, illuminations, and terrains due to its robustness and high accuracy. This study offers opportunities to promote forest ecological applications (e.g., leaf area index estimation) and sustainable management (e.g., deforestation). Numéro de notice : A2022-192 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102686 Date de publication en ligne : 05/02/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102686 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99951 [article]

Comparing methods to extract crop height and estimate crop coefficient from UAV imagery using structure from motion / Nitzan Malachy in Remote sensing, vol 14 n° 4 (February-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 4 (February-2 2022) . - n° 810 Titre : Comparing methods to extract crop height and estimate crop coefficient from UAV imagery using structure from motion Type de document : Article/Communication Auteurs : Nitzan Malachy, Auteur ; Imri Zadak, Auteur ; Offer Rozenstein, Auteur Année de publication : 2022 Article en page(s) : n° 810 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse spectrale [Termes IGN] covariance [Termes IGN] cultures [Termes IGN] données lidar [Termes IGN] hauteur de la végétation [Termes IGN] hétérogénéité spatiale [Termes IGN] image captée par drone [Termes IGN] modèle de croissance végétale [Termes IGN] régression linéaire [Termes IGN] série temporelle [Termes IGN] structure-from-motion [Termes IGN] zone d'intérêt Résumé : (auteur) Although it is common to consider crop height in agricultural management, variation in plant height within the field is seldom addressed because it is challenging to assess from discrete field measurements. However, creating spatial crop height models (CHMs) using structure from motion (SfM) applied to unmanned aerial vehicle (UAV) imagery can easily be done. Therefore, looking into intra- and inter-season height variability has the potential to provide regular information for precision management. This study aimed to test different approaches to deriving crop height from CHM and subsequently estimate the crop coefficient (Kc). CHMs were created for three crops (tomato, potato, and cotton) during five growing seasons, in addition to manual height measurements. The Kc time-series were derived from eddy-covariance measurements in commercial fields and estimated from multispectral UAV imagery in small plots, based on known relationships between Kc and spectral vegetation indices. A comparison of four methods (Mean, Sample, Median, and Peak) was performed to derive single height values from CHMs. Linear regression was performed between crop height estimations from CHMs against manual height measurements and Kc. Height was best predicted using the Mean and the Sample methods for all three crops (R2 = 0.94, 0.84, 0.74 and RMSE = 0.056, 0.071, 0.051 for cotton, potato, and tomato, respectively), as was the prediction of Kc (R2 = 0.98, 0.84, 0.8 and RMSE = 0.026, 0.049, 0.023 for cotton, potato, and tomato, respectively). The Median and Peak methods had far less success in predicting both, and the Peak method was shown to be sensitive to the size of the area analyzed. This study shows that CHMs can help growers identify spatial heterogeneity in crop height and estimate the crop coefficient for precision irrigation applications. Numéro de notice : A2022-139 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14040810 Date de publication en ligne : 09/02/2022 En ligne : https://doi.org/10.3390/rs14040810 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99774 [article]

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]

Symbolic regression-based allometric model development of a mangrove forest LAI using structural variables and digital hemispherical photography / Somnath Paramanik in Applied Geography, vol 139 (February 2022)  

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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)  

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A 3D segments based algorithm for heterogeneous data registration / Rahima Djahel (2022)  

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3D stem modelling in tropical forest: towards improved biomass and biomass change estimates / Sébastien Bauwens (2022)  

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Classification of mediterranean shrub species from UAV point clouds / Juan Pedro Carbonell-Rivera in Remote sensing, vol 14 n° 1 (January-1 2022)  

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EuroSDR annual report 2021 / European Spatial Data Research EuroSDR (2022)  

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Learning multi-view aggregation in the wild for large-scale 3D semantic segmentation / Damien Robert (2022)  

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Learning surface reconstruction from point clouds in the wild / Raphaël Sulzer (2022)  

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Levé et numérisation du château de Lichtenberg en vue d’une proposition de visite virtuelle du site à des périodes remarquables / Maxime Rocha (2022)  

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Modalités et rythmes d'évolution des falaises des Vaches Noires (Normandie, France) : caractérisation et quantification des dynamiques hydrogravitaires par approches multi-scalaires / Thomas Roulland (2022)  

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