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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)  

Public [article]  inRemote sensing > vol 11 n° 19 (October-1 2019) . - 18 p. 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 littoral Résumé : (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 [article]