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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 (November 2019)
[article]
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 IGN] Australie
[Termes IGN] base de données localisées
[Termes IGN] cartographie collaborative
[Termes IGN] données localisées des bénévoles
[Termes IGN] échantillonnage
[Termes IGN] fiabilité des données
[Termes IGN] habitat animal
[Termes IGN] migration animale
[Termes IGN] précision des données
[Termes IGN] SIG participatifRésumé : (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 Affiliation des auteurs : non IGN 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
in Transactions in GIS > Vol 23 n° 6 (November 2019) . - pp 1184 - 1203[article]Segmenting mangrove ecosystems drone images using SLIC superpixels / Edward Zimudzi in Geocarto international, vol 34 n° 14 ([30/10/2019])
[article]
Titre : Segmenting mangrove ecosystems drone images using SLIC superpixels Type de document : Article/Communication Auteurs : Edward Zimudzi, Auteur ; Ian Sanders, Auteur ; Nicholas Rollings, Auteur ; Christian Omlin, Auteur Année de publication : 2019 Article en page(s) : pp 1648 - 1662 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme SLIC
[Termes IGN] classification par nuées dynamiques
[Termes IGN] classification pixellaire
[Termes IGN] écosystème
[Termes IGN] Fidji
[Termes IGN] image captée par drone
[Termes IGN] mangrove
[Termes IGN] modèle numérique de surface
[Termes IGN] orthophotoplan numérique
[Termes IGN] segmentation d'image
[Termes IGN] superpixelRésumé : (auteur) Mangrove ecosystems play a very important ecological role on land–ocean interfaces in tropical regions. These ecosystems comprise of various tree species and aquatic animals, protecting the environment and providing a habitat that supports many living organisms including humans. The identification of image regions in mangrove ecosystems plays a significant role in ecosystem monitoring and conservation. Recent studies have suggested oversegmentation of colour images using superpixels as a solution to the segmentation of image regions. This study used the SLIC superpixel algorithm and k-means clustering to segment images taken from a camera mounted on a drone from a mangrove ecosystem in Fiji. The SLIC superpixel algorithm performed well to demarcate image regions with similar colour and texture information into patches and to use k-means for the segmentation of the whole image. These results lend support to the use of superpixel algorithms for the segmentation of mangrove ecosystems. Understanding how superpixels can be used for the segmentation of drone images will assist conservation efforts in mangrove ecosystems. Numéro de notice : A2019-539 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1497093 Date de publication en ligne : 22/10/2018 En ligne : https://doi.org/10.1080/10106049.2018.1497093 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94114
in Geocarto international > vol 34 n° 14 [30/10/2019] . - pp 1648 - 1662[article]Optimal segmentation of high spatial resolution images for the classification of buildings using random forests / James Bialas in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)
[article]
Titre : Optimal segmentation of high spatial resolution images for the classification of buildings using random forests Type de document : Article/Communication Auteurs : James Bialas, Auteur ; Thomas Oommen, Auteur ; Timothy C. Havens, Auteur Année de publication : 2019 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage automatique
[Termes IGN] bâtiment
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] dommage matériel
[Termes IGN] image à haute résolution
[Termes IGN] image aérienne
[Termes IGN] Nouvelle-Zélande
[Termes IGN] précision de la classification
[Termes IGN] qualité du processus
[Termes IGN] segmentation d'image
[Termes IGN] séisme
[Termes IGN] zone urbaineRésumé : (auteur) In the application of machine learning to geographic object based image analysis, several parameters influence overall classifier performance. One of the first parameters is segmentation size—for example, how many pixels should be grouped together to form an image object. Often, trial and error methods are used to obtain segmentation parameters that best delineate the borders of real world objects. Several attempts at automated methods have produced promising results, but manual intervention is still necessary. Meanwhile, numerous measures of segmentation quality have been defined, but their relationship to classifier performance is not then directly shown. For example, as measures of segmentation quality improve, do classification results improve as well? Our work considers the problem of building classification in high resolution aerial imagery of urban areas. Based on user defined training polygons generated with or without a reference segmentation, we have found several measures of segmentation quality and feature performance that can help users narrow the range of appropriate segmentations. Furthermore, our work finds that given this range, performance of machine learning algorithms remains relatively constant for any given segmentation as long as features used for classification are chosen correctly. We find that the range of scale parameters capable of producing an accurate classification is much broader than typically assumed and trial and error methods for finding this parameter may be an acceptable approach. Numéro de notice : A2019-472 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.06.005 Date de publication en ligne : 08/06/2019 En ligne : https://doi.org/https://doi.org/10.1016/j.jag.2019.06.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93632
in International journal of applied Earth observation and geoinformation > vol 82 (October 2019) . - pp[article]Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata / Tingting Xu in International journal of geographical information science IJGIS, vol 33 n° 10 (October 2019)
[article]
Titre : Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata Type de document : Article/Communication Auteurs : Tingting Xu, Auteur ; Jay Gao, Auteur ; Giovanni Coco, Auteur Année de publication : 2019 Article en page(s) : pp 1960 - 1983 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse diachronique
[Termes IGN] Auckland
[Termes IGN] automate cellulaire
[Termes IGN] base de données d'occupation du sol
[Termes IGN] chaîne de Markov
[Termes IGN] classification par réseau neuronal
[Termes IGN] croissance urbaine
[Termes IGN] étalement urbain
[Termes IGN] Kappa de Cohen
[Termes IGN] modèle de simulation
[Termes IGN] morphologie urbaine
[Termes IGN] optimisation (mathématiques)Résumé : (auteur) Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone. Numéro de notice : A2019-428 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1600701 Date de publication en ligne : 05/04/2019 En ligne : https://doi.org/10.1080/13658816.2019.1600701 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93561
in International journal of geographical information science IJGIS > vol 33 n° 10 (October 2019) . - pp 1960 - 1983[article]Unmanned aerial vehicles (UAVs) for monitoring macroalgal biodiversity: comparison of RGB and multispectral imaging sensors for biodiversity assessments / Leigh Tait in Remote sensing, vol 11 n° 19 (October-1 2019)
[article]
Titre : Unmanned aerial vehicles (UAVs) for monitoring macroalgal biodiversity: comparison of RGB and multispectral imaging sensors for biodiversity assessments Type de document : Article/Communication Auteurs : Leigh Tait, Auteur ; Jochen Bind, Auteur ; Hannah Charan-Dixon, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algue
[Termes IGN] biodiversité
[Termes IGN] changement climatique
[Termes IGN] estran
[Termes IGN] habitat (nature)
[Termes IGN] image captée par drone
[Termes IGN] image multibande
[Termes IGN] image RVB
[Termes IGN] Kappa de Cohen
[Termes IGN] Nouvelle-Zélande
[Termes IGN] orthophotoplan numérique
[Termes IGN] réflectance spectrale
[Termes IGN] surveillance du littoralRésumé : (auteur) Developments in the capabilities and affordability of unmanned aerial vehicles (UAVs) have led to an explosion in their use for a range of ecological and agricultural remote sensing applications. However, the ubiquity of visible light cameras aboard readily available UAVs may be limiting the application of these devices for fine-scale, high taxonomic resolution monitoring. Here we compare the use of RGB and multispectral cameras deployed aboard UAVs for assessing intertidal and shallow subtidal marine macroalgae to a high taxonomic resolution. Our results show that the diverse spectral profiles of marine macroalgae naturally lend themselves to remote sensing and habitat classification. Furthermore, we show that biodiversity assessments, particularly in shallow subtidal habitats, are enhanced using six-band discrete wavelength multispectral sensors (81% accuracy, Cohen’s Kappa) compared to three-band broad channel RGB sensors (79% accuracy, Cohen’s Kappa) for 10 habitat classes. Combining broad band RGB signals and narrow band multispectral sensing further improved the accuracy of classification with a combined accuracy of 90% (Cohen’s Kappa). Despite notable improvements in accuracy with multispectral imaging, RGB sensors were highly capable of broad habitat classification and rivaled multispectral sensors for classifying intertidal habitats. High spatial scale monitoring of turbid exposed rocky reefs presents a unique set of challenges, but the limitations of more traditional methods can be overcome by targeting ideal conditions with UAVs. Numéro de notice : A2019-553 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11192332 Date de publication en ligne : 08/10/2019 En ligne : https://doi.org/10.3390/rs11192332 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94206
in Remote sensing > vol 11 n° 19 (October-1 2019) . - 18 p.[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)PermalinkModelling 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)PermalinkSea level variation around Australia and its relation to climate indices / Armin Agha Karimi in Marine geodesy, vol 42 n° 5 (September 2019)PermalinkError propagation for the Molodensky G1 term / Jack C. McCubbine in Journal of geodesy, vol 93 n°6 (June 2019)PermalinkModelling 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)PermalinkThinking outside the square: Evidence that plot shape and layout in forest inventories can bias estimates of stand metrics / Thomas S. H. Paul in Methods in ecology and evolution, vol 10 n° 3 (March 2019)PermalinkTesting the generality of below-ground biomass allometry across plant functional types / Keryn I. Paul in Forest ecology and management, vol 432 (15 January 2019)PermalinkPermalinkPermalinkAUSGeoid2020 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)PermalinkSea-land interdependence in the global maritime network: the case of Australian port cities / Justin Berli in Networks and Spatial Economics, vol 18 n° 3 (September 2018)PermalinkComparison of high-density LiDAR and satellite photogrammetry for forest inventory / Grant D. Pearse in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)PermalinkThe New Zealand gravimetric quasigeoid model 2017 that incorporates nationwide airborne gravimetry / Jack C. McCubbine in Journal of geodesy, vol 92 n° 8 (August 2018)PermalinkLa propriété en 3D : état des lieux / Anonyme in Géomatique expert, n° 123 (juillet - août 2018)PermalinkModeling of inland flood vulnerability zones through remote sensing and GIS techniques in the highland region of Papua New Guinea / Porejane Harley in Applied geomatics, vol 10 n° 2 (June 2018)PermalinkPredicting 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)PermalinkThe first Australian gravimetric quasigeoid model with location-specific uncertainty estimates / Will E. Featherstone in Journal of geodesy, vol 92 n° 2 (February 2018)PermalinkAugmented reality and maps : new possibilities for engaging with geographic data / Gabriel Henrique de Almeida Pereira in Cartographic journal (the), Vol 54 n° 4 (November 2017)PermalinkAn investigation into the performance of real-time GPS + GLONASS Precise Point Positioning (PPP) in New Zealand / Ken Harima in Journal of applied geodesy, vol 11 n° 3 (September 2017)PermalinkRemote sensing scene classification by unsupervised representation learning / Xiaoqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)PermalinkGeospatial big data and archaeology: Prospects and problems too great to ignore / Mark D. McCoy in Journal of archaeological science, vol 84 (August 2017)PermalinkAutomatic 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)PermalinkExtending a BIM-based data model to support 3D digital management of complex ownership spaces / Behnam Atazadeh in International journal of geographical information science IJGIS, vol 31 n° 3-4 (March-April 2017)PermalinkA hybrid genetic algorithm with local optimiser improves calibration of a vegetation change cellular automata model / Rachel Whitsed in International journal of geographical information science IJGIS, vol 31 n° 3-4 (March-April 2017)PermalinkDéveloppement d'un outil de lecture et de traitement des observations satellitaires des capteurs "Ocean & Land Colour Imager" et "Multi-Spectral Imager" / Gabriel Calassou (2017)PermalinkMapping individual tree health using full-waveform airborne laser scans and imaging spectroscopy: A case study for a floodplain eucalypt forest / Iurii Shendryk in Remote sensing of environment, vol 187 (15 December 2016)PermalinkL'Australie bouge : pourquoi ce nouveau "buzz" dans nos médias / Françoise Duquenne in XYZ, n° 149 (décembre 2016 - février 2017)PermalinkA spatial data infrastructure approach for the characterization of New Zealand's groundwater systems / Alexander Kmoch in Transactions in GIS, vol 20 n° 4 (August 2016)PermalinkApplication of satellite navigation system for emergency warning and alerting / Suelynn Choy in Computers, Environment and Urban Systems, vol 58 (July 2016)PermalinkEmergency management perspectives on volunteered geographic information: Opportunities, challenges and change / Billy Haworth in Computers, Environment and Urban Systems, vol 57 (May 2016)PermalinkSurveying graffiti, an emerging culture / Anonyme in Position, n° 81 (February - March 2016)PermalinkAssessment of forest canopy vertical structure with multi - scale remote sensing : from the plot to the large area / Phil Wilkes (2016)PermalinkEmpirical determination of geometric parameters for selective omission in a road network / Qi Zhou in International journal of geographical information science IJGIS, vol 30 n° 1-2 (January - February 2016)PermalinkTrouver le Nord / Olivier Le Carrer (2016)PermalinkPermalinkOcular robotics : The world's most dynamic eye / Ocular robotics in GIM international [en ligne], vol 29 n° 12 (December 2015)PermalinkSocio-economic benefits from protected areas in southeastern Australia / E.C. Heagney in Conservation biology, vol 29 n° 6 (December 2015)PermalinkUsing integrated visualization techniques to investigate associations between cardiovascular health outcomes and residential migration in Auckland, New Zealand / Jinfeng Zhao in Cartography and Geographic Information Science, Vol 42 n° 5 (November 2015)PermalinkDriving intelligent transport / Danielle Mulligan in Position, n° 79 (October - November 2015)PermalinkFlying high with a UAS adventurer / Anonyme in Position, n° 79 (October - November 2015)PermalinkUpdated best practice for EDM calibrations in New South Wales / Volker Janssen in Position, n° 78 (August - September 2015)PermalinkHow good is AUSGeoid09 in the Blue Mountains ? / Joseph Allerton in Position, n° 77 (June - July 2015)PermalinkValidation of canopy height profile methodology for small-footprint full-waveform airborne LiDAR data in a discontinuous canopy environment / Karolina D. Fieber in ISPRS Journal of photogrammetry and remote sensing, vol 104 (June 2015)PermalinkMulti-GNSS enabling Australia's positioning infrastructure / Matt Higgins in Position, n° 76 (April - May 2015)PermalinkPredicting floods with GPS / Paul Grad in Position, n° 76 (April - May 2015)Permalink