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scientifique

Vine canopy reconstruction and assessment with terrestrial Lidar and aerial imaging / Igor Petrovic in Remote sensing, vol 14 n° 22 (November-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 22 (November-2 2022) . - n° 5894 Titre : Vine canopy reconstruction and assessment with terrestrial Lidar and aerial imaging Type de document : Article/Communication Auteurs : Igor Petrovic, Auteur ; Matej Sečnik, Auteur ; Marko Hočevar, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5894 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] analyse comparative [Termes IGN] couvert végétal [Termes IGN] défoliation [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] échantillonnage de données [Termes IGN] épandage [Termes IGN] lasergrammétrie [Termes IGN] pas d'échantillonnage au sol [Termes IGN] photogrammétrie aérienne [Termes IGN] Slovénie [Termes IGN] viticulture Résumé : (auteur) For successful dosing of plant protection products, the characteristics of the vine canopies should be known, based on which the spray amount should be dosed. In the field experiment, we compared two optical experimental methods, terrestrial lidar and aerial photogrammetry, with manual defoliation of some selected vines. Like those of other authors, our results show that both terrestrial lidar and aerial photogrammetry were able to represent the canopy well with correlation coefficients around 0.9 between the measured variables and the number of leaves. We found that in the case of aerial photogrammetry, significantly more points were found in the point cloud, but this depended on the choice of the ground sampling distance. Our results show that in the case of aerial UAS photogrammetry, subdividing the vine canopy segments to 5 × 5 cm gives the best representation of the volume of vine canopies. Numéro de notice : A2022-881 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14225894 Date de publication en ligne : 21/11/2022 En ligne : https://doi.org/10.3390/rs14225894 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102203 [article]

Spatial variability of suspended sediments in San Francisco Bay, California / Niky C. Taylor in Remote sensing, vol 13 n° 22 (November-2 2021)  

Public [article]  inRemote sensing > vol 13 n° 22 (November-2 2021) . - n° 4625 Titre : Spatial variability of suspended sediments in San Francisco Bay, California Type de document : Article/Communication Auteurs : Niky C. Taylor, Auteur ; Raphael M. Kudela, Auteur Année de publication : 2021 Article en page(s) : n° 4625 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] baie [Termes IGN] échantillonnage [Termes IGN] estuaire [Termes IGN] image Sentinel-MSI [Termes IGN] pas d'échantillonnage au sol [Termes IGN] qualité des eaux [Termes IGN] réflectance [Termes IGN] San Francisco [Termes IGN] sédiment [Termes IGN] spectroradiométrie [Termes IGN] surface de l'eau [Termes IGN] surveillance du littoral [Termes IGN] turbidité des eaux [Termes IGN] variabilité Résumé : (auteur) Understanding spatial variability of water quality in estuary systems is important for making monitoring decisions and designing sampling strategies. In San Francisco Bay, the largest estuary system on the west coast of North America, tracking the concentration of suspended materials in water is largely limited to point measurements with the assumption that each point is representative of its surrounding area. Strategies using remote sensing can expand monitoring efforts and provide a more complete view of spatial patterns and variability. In this study, we (1) quantify spatial variability in suspended particulate matter (SPM) concentrations at different spatial scales to contextualize current in-water point sampling and (2) demonstrate the potential of satellite and shipboard remote sensing to supplement current monitoring methods in San Francisco Bay. We collected radiometric data from the bow of a research vessel on three dates in 2019 corresponding to satellite overpasses by Sentinel-2, and used established algorithms to retrieve SPM concentrations. These more spatially comprehensive data identified features that are not picked up by current point sampling. This prompted us to examine how much variability exists at spatial scales between 20 m and 10 km in San Francisco Bay using 10 m resolution Sentinel-2 imagery. We found 23–80% variability in SPM at the 5 km scale (the scale at which point sampling occurs), demonstrating the risk in assuming limited point sampling is representative of a 5 km area. In addition, current monitoring takes place along a transect within the Bay’s main shipping channel, which we show underestimates the spatial variance of the full bay. Our results suggest that spatial structure and spatial variability in the Bay change seasonally based on freshwater inflow to the Bay, tidal state, and wind speed. We recommend monitoring programs take this into account when designing sampling strategies, and that end-users account for the inherent spatial uncertainty associated with the resolution at which data are collected. This analysis also highlights the applicability of remotely sensed data to augment traditional sampling strategies. In sum, this study presents ways to supplement water quality monitoring using remote sensing, and uses satellite imagery to make recommendations for future sampling strategies. Numéro de notice : A2021-839 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13224625 Date de publication en ligne : 17/11/2021 En ligne : https://doi.org/10.3390/rs13224625 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99022 [article]

Homogeneous tree height derivation from tree crown delineation using Seeded Region Growing (SRG) segmentation / Muhamad Farid Ramli in Geo-spatial Information Science, vol 23 n° 3 (September 2020)  

Public [article]  inGeo-spatial Information Science > vol 23 n° 3 (September 2020) . - pp 195 - 208 Titre : Homogeneous tree height derivation from tree crown delineation using Seeded Region Growing (SRG) segmentation Type de document : Article/Communication Auteurs : Muhamad Farid Ramli, Auteur ; Khairul Nizam Tahar, Auteur Année de publication : 2020 Article en page(s) : pp 195 - 208 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] Arecaceae [Termes IGN] croissance des arbres [Termes IGN] diamètre des arbres [Termes IGN] hauteur des arbres [Termes IGN] houppier [Termes IGN] image captée par drone [Termes IGN] Malaisie [Termes IGN] modèle numérique de surface [Termes IGN] orthoimage [Termes IGN] pas d'échantillonnage au sol [Termes IGN] point d'appui [Termes IGN] positionnement cinématique en temps réel [Termes IGN] QGIS [Termes IGN] SAGA GIS [Termes IGN] segmentation en régions Résumé : (auteur) The demand for tree height derivation is increasing year by year, especially for large plantation and forest area. The conventional method needs a long time to complete tree measurement for large forest area, especially when using a pole, measuring tape, rangefinder, clinometer, and tree climbing. This study aims to evaluate the height of oil palm tree based on crown diameter by using a multi-rotor Unmanned Aerial Vehicle (UAV). Digital Elevation Model (DEM) and orthophoto were generated by using Agisoft software, while oil palm tree crown diameter was delineated by using seed generation with Quantum Geographic Information System (QGIS) and Seeded Region Growing (SRG) segmentation methods in the System for Automated Geoscientific Analysis (SAGA). The study validates the results between the actual tree height and tree height estimated from UAV. The results showed that the orthophoto was successfully generated with a Ground Sampling Distance (GSD) of 2.95 cm and 129 tree crowns were successfully analyzed. The accuracy of the tree height as compared to the actual measurement was 57.7 cm. In conclusion, UAV images are capable of determining the tree height after going through the correct procedure to help foresters in their daily task. Numéro de notice : A2020-562 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2020.1805366 Date de publication en ligne : 24/08/2020 En ligne : https://doi.org/10.1080/10095020.2020.1805366 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95878 [article]

Inside the ice shelf: using augmented reality to visualise 3D lidar and radar data of Antarctica / Alexandra L. Boghosian in Photogrammetric record, vol 34 n° 168 (December 2019)  

Public [article]  inPhotogrammetric record > vol 34 n° 168 (December 2019) . - pp 346 - 364 Titre : Inside the ice shelf: using augmented reality to visualise 3D lidar and radar data of Antarctica Type de document : Article/Communication Auteurs : Alexandra L. Boghosian, Auteur ; Martin J. Pratt, Auteur ; Maya A. Becker, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 346 - 364 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Antarctique [Termes IGN] banquise [Termes IGN] couplage GNSS-INS [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] glace de mer [Termes IGN] image radar [Termes IGN] Matlab [Termes IGN] modèle numérique de surface [Termes IGN] pas d'échantillonnage au sol [Termes IGN] réalité augmentée [Termes IGN] semis de points [Termes IGN] travail coopératif [Termes IGN] VRML Résumé : (auteur) From 2015 to 2017, the ROSETTA‐Ice project comprehensively mapped Antarctica's Ross Ice Shelf using IcePod, a newly developed aerogeophysical platform. The campaign imaged the ice‐shelf surface with lidar and its internal structure with ice‐penetrating radar. The ROSETTA‐Ice data was combined with pre‐existing ice surface and bed topography digital elevation models to create the first augmented reality (AR) visualisation of the Antarctic Ice Sheet, using the Microsoft HoloLens. The ROSETTA‐Ice datasets support cross‐disciplinary science that aims to understand 4D processes, namely the change of 3D ice‐shelf structures over time. The work presented here uses AR to visualise this dataset in 3D and highlights how AR can be simultaneously a useful research tool for interdisciplinary geoscience as well as an effective device for science communication education. Numéro de notice : A2019-575 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12298 Date de publication en ligne : 23/12/2019 En ligne : https://doi.org/10.1111/phor.12298 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94455 [article]

Super-resolution of Sentinel-2 images : Learning a globally applicable deep neural network / Charis Lanaras in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 146 (December 2018) . - pp 305 - 319 Titre : Super-resolution of Sentinel-2 images : Learning a globally applicable deep neural network Type de document : Article/Communication Auteurs : Charis Lanaras, Auteur ; José Bioucas-Dias, Auteur ; Silvano Galliani, Auteur ; Emmanuel P. Baltsavias, Auteur ; Konrad Schindler, Auteur Année de publication : 2018 Article en page(s) : pp 305 - 319 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] bande spectrale [Termes IGN] échantillonnage de données [Termes IGN] erreur moyenne quadratique [Termes IGN] image à basse résolution [Termes IGN] image Sentinel-MSI [Termes IGN] pansharpening (fusion d'images) [Termes IGN] pas d'échantillonnage au sol [Termes IGN] pouvoir de résolution spectrale [Termes IGN] réseau neuronal convolutif Résumé : (Auteur) The Sentinel-2 satellite mission delivers multi-spectral imagery with 13 spectral bands, acquired at three different spatial resolutions. The aim of this research is to super-resolve the lower-resolution (20 m and 60 m Ground Sampling Distance – GSD) bands to 10 m GSD, so as to obtain a complete data cube at the maximal sensor resolution. We employ a state-of-the-art convolutional neural network (CNN) to perform end-to-end upsampling, which is trained with data at lower resolution, i.e., from 40  20 m, respectively 360  60 m GSD. In this way, one has access to a virtually infinite amount of training data, by downsampling real Sentinel-2 images. We use data sampled globally over a wide range of geographical locations, to obtain a network that generalises across different climate zones and land-cover types, and can super-resolve arbitrary Sentinel-2 images without the need of retraining. In quantitative evaluations (at lower scale, where ground truth is available), our network, which we call DSen2, outperforms the best competing approach by almost 50% in RMSE, while better preserving the spectral characteristics. It also delivers visually convincing results at the full 10 m GSD. Numéro de notice : A2018-540 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.09.018 Date de publication en ligne : 21/10/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.09.018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91554 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018131	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018133	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2018132	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Toward automatic georeferencing of archival aerial photogrammetric surveys / Sébastien Giordano in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2 (June 2018)  

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Foveon VS Bayer: Comparison of 3D reconstruction performances / Pierre Bodin (2018) 

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Precision estimation of the angular resolution of terrestrial laser scanners / Xijiang Chen in Photogrammetric record, vol 32 n° 159 (September 2017)  

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Development of a SGM-based multi-view reconstruction framework for aerial imagery / Mathias Rothermel (2016)  

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Evaluation of Lidar-derived DEMs through terrain analysis and field / Cody P. Gillin in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 5 (May 2015)  

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Estimation of the mean tree height of forest stands by photogrammetric measurement using digital aerial images of high spatial resolution / Ivan Balenović in Annals of forest research, vol 58 n° 1 (January 2015)  

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A volumetric approach to change in satellite images / T. Pollard in Photogrammetric Engineering & Remote Sensing, PERS, vol 76 n° 7 (July 2010)  

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Réalité Terrain Étendue / A. Gademer (2010) 

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Testing aerial digital cameras / R. Kathofer in GIM international, vol 24 n° 1 (January 2010)

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Quality assessment for GPS-supported bundle block adjustment based on aerial digital frame imagery / X. Yuan in Photogrammetric record, vol 24 n° 126 (June - August 2009)  

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