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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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 83 - 96 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) Résumé : (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 [article]

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Using a U-net convolutional neural network to map woody vegetation extent from high resolution satellite imagery across Queensland, Australia / Neil Flood in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 82 (October 2019) . - 15 p. Titre : Using a U-net convolutional neural network to map woody vegetation extent from high resolution satellite imagery across Queensland, Australia Type de document : Article/Communication Auteurs : Neil Flood, Auteur ; Fiona Watson, Auteur ; Lisa Collett, Auteur Année de publication : 2019 Article en page(s) : 15 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] arbre (flore) [Termes IGN] arbuste [Termes IGN] bois sur pied [Termes IGN] carte de la végétation [Termes IGN] données d'apprentissage [Termes IGN] image à haute résolution [Termes IGN] image satellite [Termes IGN] méthode de Monte-Carlo [Termes IGN] mosaïque d'images [Termes IGN] Queensland (Australie) [Termes IGN] réseau neuronal convolutif [Termes IGN] texture d'image Résumé : (auteur) Convolutional neural networks offer a new approach to classifying high resolution imagery. We use the U-net neural network architecture to map the presence or absence of trees and large shrubs across the Australian state of Queensland. From a state-wide mosaic of 1 m resolution 3-band Earth-i imagery, a selection of 827 squares (1 km2) are manually labeled for the presence of trees or large shrubs, and these are used to train the neural network. The training is intended to capture the textures which are primary visual cues of such vegetation. The trained neural network has an accuracy on independent data of around 90%. The resulting map over the whole of Queensland (1.73 million km2) is intended to be manually checked, and edited where necessary, to provide a high quality map of woody vegetation extent to serve a range of government policy objectives. Numéro de notice : A2019-474 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.101897 date de publication en ligne : 28/06/2019 En ligne : https://doi.org/10.1016/j.jag.2019.101897 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93635 [article]

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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 149 (March 2019) . - pp 105 - 118 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 points Résumé : (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 [article]

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La propriété en 3D : état des lieux / Anonyme in Géomatique expert, n° 123 (juillet - août 2018)

Public [article]  inGéomatique expert > n° 123 (juillet - août 2018) Titre : La propriété en 3D : état des lieux Type de document : Article/Communication Auteurs : Anonyme, Auteur Année de publication : 2018 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Cadastre [Termes IGN] base de données foncières [Termes IGN] base de données localisées 3D [Termes IGN] Colombie-Britannique (Canada) [Termes IGN] Espagne [Termes IGN] France (administrative) [Termes IGN] Genève [Termes IGN] Norvège [Termes IGN] Pologne [Termes IGN] propriété foncière [Termes IGN] Queensland (Australie) [Termes IGN] Suède Résumé : (éditeur) En Suisse comme partout ailleurs, la densification du tissu urbain implique de construire des bâtiments élevés qui sont ensuite cogérés par plusieurs propriétaires. Se pose alors le problème de la représentation cadastrale, et SIG, de ces divisions en volumes. Numéro de notice : A2018-269 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/POSITIONNEMENT Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90329 [article]

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Long term land cover and seagrass mapping using Landsat and object-based image analysis from 1972 to 2010 in the coastal environment of South East Queensland, Australia / M. Lyons in ISPRS Journal of photogrammetry and remote sensing, vol 71 (July 2012)

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 71 (July 2012) . - pp 34 - 46 Titre : Long term land cover and seagrass mapping using Landsat and object-based image analysis from 1972 to 2010 in the coastal environment of South East Queensland, Australia Type de document : Article/Communication Auteurs : M. Lyons, Auteur ; S. Phinn, Auteur ; C. Roelfsema, Auteur Année de publication : 2012 Article en page(s) : pp 34 - 46 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] distribution spatiale [Termes IGN] herbier marin [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-MSS [Termes IGN] image Landsat-TM [Termes IGN] littoral [Termes IGN] occupation du sol [Termes IGN] Queensland (Australie) [Termes IGN] répartition géographique [Termes IGN] série temporelle Résumé : (Auteur) Long term global archives of high-moderate spatial resolution, multi-spectral satellite imagery are now readily accessible, but are not being fully utilised by management agencies due to the lack of appropriate methods to consistently produce accurate and timely management ready information. This work developed an object-based remote sensing approach to map land cover and seagrass distribution in an Australian coastal environment for a 38 year Landsat image time-series archive (1972–2010). Landsat Multi-Spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) imagery were used without in situ field data input (but still using field knowledge) to produce land and seagrass cover maps every year data were available, resulting in over 60 map products over the 38 year archive. Land cover was mapped annually using vegetation, bare ground, urban and agricultural classes. Seagrass distribution was also mapped annually, and in some years monthly, via horizontal projected foliage cover classes, sand and deep water. Land cover products were validated using aerial photography and seagrass maps were validated with field survey data, producing several measures of accuracy. An average overall accuracy of 65% and 80% was reported for seagrass and land cover products respectively, which is consistent with other studies in the area. This study is the first to show moderate spatial resolution, long term annual changes in land cover and seagrass in an Australian environment, created without the use of in situ data; and only one of a few similar studies globally. The land cover products identify several long term trends; such as significant increases in South East Queensland’s urban density and extent, vegetation clearing in rural and rural-residential areas, and inter-annual variation in dry vegetation types in western South East Queensland. The seagrass cover products show that there has been a minimal overall change in seagrass extent, but that seagrass cover level distribution is extremely dynamic; evidenced by large scale migrations of higher seagrass cover levels and several sudden and significant changes in cover level. These mapping products will allow management agencies to build a baseline assessment of their resources, understand past changes and help inform implementation and planning of management policy to address potential future changes. Numéro de notice : A2012-346 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31792 [article]

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Solutions for 4D cadastre - with a case study on utility networks / F. Döner in International journal of geographical information science IJGIS, vol 25 n° 6-7 (june - july 2011)  

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AAM modelling aids Brisbane floods crisis / J. Blackburn in GEO: Geoconnexion international, vol 10 n° 4 (april 2011)

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Technological aspects of a full 3D cadastral registration / Jantien E. Stoter in International journal of geographical information science IJGIS, vol 19 n° 6 (july 2005)

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Reflectance modeling of semiarid woodlands / R.P. Pech in Remote sensing of environment, vol 23 n° 2 (01/11/1987)

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Updating maps of climax vegetation cover with Landsat MSS data in Queensland, Australia / G.D. Kelly in Photogrammetric Engineering & Remote Sensing, PERS, vol 53 n° 6 Tome 1 (june 1987)

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Habitat mapping by Landsat for aerial census of kangaroos / G.J.E. Hill in Remote sensing of environment, vol 21 n° 1 (01/02/1987)

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