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Auteur Stuart Phinn |
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Optimising drone flight planning for measuring horticultural tree crop structure / Yu-Hsuan Tu in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)
[article]
Titre : Optimising drone flight planning for measuring horticultural tree crop structure Type de document : Article/Communication Auteurs : Yu-Hsuan Tu, Auteur ; Stuart Phinn, Auteur ; Kasper Johansen, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 83 - 96 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] correction d'image
[Termes IGN] détection d'arbres
[Termes IGN] distorsion d'image
[Termes IGN] étalonnage de chambre métrique
[Termes IGN] horticulture
[Termes IGN] image captée par drone
[Termes IGN] MicMac
[Termes IGN] obturateur
[Termes IGN] photogrammétrie aérienne
[Termes IGN] plan de vol
[Termes IGN] point d'appui
[Termes IGN] qualité d'image
[Termes IGN] Queensland (Australie)
[Termes IGN] semis de pointsRésumé : (Auteur) In recent times, multi-spectral drone imagery has proved to be a useful tool for measuring tree crop canopy structure. In this context, establishing the most appropriate flight planning variable settings is an essential consideration due to their controls on the quality of the imagery and derived maps of tree and crop biophysical properties. During flight planning, variables including flight altitude, image overlap, flying direction, flying speed and solar elevation, require careful consideration in order to produce the most suitable drone imagery. Previous studies have assessed the influence of individual variables on image quality, but the interaction of multiple variables has yet to be examined. This study assesses the influence of several flight variables on measures of data quality in each processing step, i.e. photo alignment, point cloud densification, 3D model building, and ortho-mosaicking. The analysis produced a drone flight planning and image processing workflow that delivers accurate measurements of tree crops, including the tie point quality, densified point cloud density, and the measurement accuracy of height and plant projective cover derived from individual trees within a commercial avocado orchard. Results showed that flying along the hedgerow, at high solar elevation and with low image pitch angles improved the data quality. Optimal flying speed needs to be set to achieve the required forward overlap. The impacts of each image acquisition variable are discussed in detail and protocols for flight planning optimisation for three scenarios with different drone settings are suggested. Establishing protocols that deliver optimal image acquisitions for the collection of drone data over horticultural tree crops, will create greater confidence in the accuracy of subsequent algorithms and resultant maps of biophysical properties. Numéro de notice : A2020-044 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.12.006 Date de publication en ligne : 18/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.12.006 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94524
in ISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 83 - 96[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020021 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020023 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020022 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Modelling the effects of fundamental UAV flight parameters on LiDAR point clouds to facilitate objectives-based planning / Jeremy J. Sofonia in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)
[article]
Titre : Modelling the effects of fundamental UAV flight parameters on LiDAR point clouds to facilitate objectives-based planning Type de document : Article/Communication Auteurs : Jeremy J. Sofonia, Auteur ; Stuart Phinn, Auteur ; Chris Roelfsema, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 105 - 118 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] balayage laser
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image captée par drone
[Termes IGN] modèle de simulation
[Termes IGN] plan de vol
[Termes IGN] Queensland (Australie)
[Termes IGN] semis de pointsRésumé : (Auteur) Utilised globally across a wide range of applications, the ability to assess and understand LiDAR system capabilities represents an essential component in developing informed decisions on instrument selection and the logistical planning processes associated with site-specific limitations, project objectives and UAV operations. This study employed the new SLAM-based CSIRO “Hovermap” LiDAR system within a purpose-built environment as a testbed to experimentally investigate the interactive effects of fundamental UAV flight parameters on key metrics of LiDAR point clouds. Assessed within a full factorial design at both Site- and Target-levels, the UAV input variables of Pattern, ground Speed and above ground Altitude (AGL) were tested against the point cloud response variables Density, GSD and Accuracy as measured by RMSE and cloud-to-mesh Euclidian distance (‘Deviation’). A novel approach is described wherein the trajectory file of each flight was examined to determine the observed values of the input and response variables, remove noise and facilitate a standardised basis of comparison. Several new terms are introduced including Sampling Effort Variable (SEV, s⋅m−2), Effective Scan Rate (ESR, pts⋅s−1) and Effective Density Rate (EDR, pts⋅m−2⋅s−1) as well as an alternate approach to describe Pattern (s⋅m−1) as a scalar quantity. Reporting significant effects with all response variables at both Site- and Target-levels, the Range of the LiDAR sensor, closely associated with Altitude, presented as the single most important factor. Interestingly, the combination of the independent variables as SEV and EDRpred (‘predicted’ EDR) showed the highest coefficient of determination in the Site-level prediction of Density (AdjR2 = 0.894) and GSD (AdjR2 = 0.978,), respectively, whilst Range best correlated with observed RMSE (AdjR2 = 0.948) and Deviation (AdjR2 = 0.963). Predictive models returned mixed results when evaluated at the Target-level and highlights the need for further investigation to achieve the maximum benefit of high-resolution UAV LiDAR. The results presented here confirm that the CSIRO Hovermap performance is robust and, although variable depending on UAV flight parameters, is predictable and demonstrates the potential value in understanding system performance in harmonised flight planning to achieve project-specific objectives. Numéro de notice : A2019-116 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.020 Date de publication en ligne : 28/01/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.020 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92443
in ISPRS Journal of photogrammetry and remote sensing > vol 149 (March 2019) . - pp 105 - 118[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2019031 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019033 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019032 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt