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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 descripteurs IGN] correction d'image [Termes descripteurs IGN] détection d'arbres [Termes descripteurs IGN] distorsion d'image [Termes descripteurs IGN] étalonnage de chambre métrique [Termes descripteurs IGN] horticulture [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] MicMac [Termes descripteurs IGN] obturateur [Termes descripteurs IGN] photogrammétrie aérienne [Termes descripteurs IGN] plan de vol [Termes descripteurs IGN] point d'appui [Termes descripteurs IGN] qualité d'image [Termes descripteurs 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 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]

Deep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery / Yuri Shendryk in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 157 (November 2019) . - pp 124 - 136 Titre : Deep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery Type de document : Article/Communication Auteurs : Yuri Shendryk, Auteur ; Yannik Rist, Auteur ; Catherine Ticehurst, Auteur ; Peter Thorburn, Auteur Année de publication : 2019 Article en page(s) : pp 124 - 136 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] Amazonie [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] Australie [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection d'ombre [Termes descripteurs IGN] état de l'art [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image PlanetScope [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] nuage [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] zone tropicale humide Résumé : (Auteur) With the increasing availability of high-resolution satellite imagery it is important to improve the efficiency and accuracy of satellite image indexing, retrieval and classification. Furthermore, there is a need for utilizing all available satellite imagery in identifying general land cover types and monitoring their changes through time irrespective of their spatial, spectral, temporal and radiometric resolutions. Therefore, in this study, we developed deep learning models able to efficiently and accurately classify cloud, shadow and land cover scenes in different high-resolution ( Numéro de notice : A2019-494 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.08.018 date de publication en ligne : 17/09/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.08.018 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93727 [article]

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The influence of sampling design on spatial data quality in a geographic citizen science project / Greg Brown in Transactions in GIS, Vol 23 n° 6 (novembre 2019)  

Public [article]  inTransactions in GIS > Vol 23 n° 6 (novembre 2019) . - pp 1184 - 1203 Titre : The influence of sampling design on spatial data quality in a geographic citizen science project Type de document : Article/Communication Auteurs : Greg Brown, Auteur ; Jonathan Rhodes, Auteur ; Daniel Lunney, Auteur ; Ross Goldingay, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1184 - 1203 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes descripteurs IGN] Australie [Termes descripteurs IGN] base de données localisées [Termes descripteurs IGN] cartographie collaborative [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] fiabilité des données [Termes descripteurs IGN] habitat animal [Termes descripteurs IGN] migration animale [Termes descripteurs IGN] précision des données [Termes descripteurs IGN] SIG participatif Résumé : (auteur) Geographic citizen science has much potential to assist in wildlife research and conservation, but the quality of observation data is a key concern. We examined the effects of sampling design on the quality of spatial data collected for a koala citizen science project in Australia. Data were collected from three samples—volunteers (n = 454), an Internet panel (n = 103), and landowners (n = 35)—to assess spatial data quality, a dimension of citizen science projects rarely considered. The locational accuracy of koala observations among the samples was similar when benchmarked against authoritative data (i.e., an expert‐derived koala distribution model), but there were differences in the quantity of data generated. Fewer koala location data were generated per participant by the Internet panel sample than the volunteer or landowner samples. Spatial preferences for land uses affecting koala conservation were also mapped, with landowners more likely to map locations for residential and tourism development and volunteers less likely. These spatial preferences have the potential to influence the social acceptability of future koala conservation proposals. With careful sampling design, both citizen observations and land use preferences can be included within the same project to augment scientific assessments and identify conservation opportunities and constraints. Numéro de notice : A2019-566 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12568 date de publication en ligne : 11/07/2019 En ligne : https://onlinelibrary.wiley.com/doi/10.1111/tgis.12568 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94417 [article]

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 descripteurs IGN] apprentissage profond [Termes descripteurs IGN] arbre (flore) [Termes descripteurs IGN] bois sur pied [Termes descripteurs IGN] Buisson [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] données d'apprentissage [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] méthode de Monte-Carlo [Termes descripteurs IGN] mosaïque d'images [Termes descripteurs IGN] Queensland (Australie) [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs 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 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 discontinuous terrain from DSMs using segment labelling, outlier removal and thin-plate splines / Kassel Hingee in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 155 (September 2019) . - pp 159 - 171 Titre : Modelling discontinuous terrain from DSMs using segment labelling, outlier removal and thin-plate splines Type de document : Article/Communication Auteurs : Kassel Hingee, Auteur ; Peter Caccetta, Auteur ; Louis Caccetta, Auteur Année de publication : 2019 Article en page(s) : pp 159 - 171 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] Australie [Termes descripteurs IGN] discontinuité [Termes descripteurs IGN] étiquetage sémantique [Termes descripteurs IGN] filtrage de points [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] modèle numérique de terrain [Termes descripteurs IGN] valeur aberrante Résumé : (Auteur) Models of ground surface elevations are crucial to many applications of remotely sensed data, including estimates of the height relative to ground of non-ground objects, such as buildings and vegetation. In highly engineered regions, such as cities, there are many discontinuities in both the ground surface and the surface of non-ground objects. Sub-metre resolution elevation data for these regions are increasingly available. At these resolutions there is sufficient information and a growing need to improve model accuracies by incorporating discontinuities. Here we provide a new method for generating high resolution models of discontinuous ground surfaces from breakline data and digital surface models derived from remotely sensed data. The method uses segment based filtering, outlier removal and multiresolution thin-plate spline surface fitting. Breaklines are included in the fitted surface using partial derivatives and a breakline-aware method for transferring between different resolutions. We demonstrate our method using elevation data derived from photogrammetry for suburban regions of Perth, Western Australia, and Vaihingen, Germany. We produced ground surface models with noticeable qualitative and quantitative improvements when breaklines are included, at an increased computational cost of approximately 10% when all other parameters remained the same. For LiDAR derived elevations, we report our residual error against a number of other methods recorded using the ISPRS Ground Filtering Test Sites. Numéro de notice : A2019-314 Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.07.004 date de publication en ligne : 24/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.07.004 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93340 [article]

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081-2019092	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Sea level variation around Australia and its relation to climate indices / Armin Agha Karimi in Marine geodesy, vol 42 n° 5 (September 2019)  

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Error propagation for the Molodensky G1 term / Jack C. McCubbine in Journal of geodesy, vol 93 n°6 (June 2019)  

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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)  

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Testing the generality of below-ground biomass allometry across plant functional types / Keryn I. Paul in Forest ecology and management, vol 432 (15 January 2019)  

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AUSGeoid2020 combined gravimetric–geometric model : location-specific uncertainties and baseline-length-dependent error decorrelation / Nicholas J. Brown in Journal of geodesy, vol 92 n° 12 (December 2018)  

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

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Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar / Melissa Fedrigo in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)  

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The first Australian gravimetric quasigeoid model with location-specific uncertainty estimates / Will E. Featherstone in Journal of geodesy, vol 92 n° 2 (February 2018)  

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Remote sensing scene classification by unsupervised representation learning / Xiaoqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)  

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Automatic spatial metadata systems – the case of Australian urban research infrastructure network / Moshen Kalantari in Cartography and Geographic Information Science, Vol 44 n° 4 (July 2017)  

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