Centre de documentation
scientifique

Dépouillements

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]

sUAS-based remote rensing of river discharge using thermal particle image velocimetry and bathymetric lidar / Paul J. Kinzel in Remote sensing, vol 11 n° 19 (October-1 2019)  

Public [article]  inRemote sensing > vol 11 n° 19 (October-1 2019) . - 19 p. Titre : sUAS-based remote rensing of river discharge using thermal particle image velocimetry and bathymetric lidar Type de document : Article/Communication Auteurs : Paul J. Kinzel, Auteur ; Carl J. Legleiter, Auteur Année de publication : 2019 Article en page(s) : 19 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] bathymétrie laser [Termes IGN] Colorado (Etats-Unis) [Termes IGN] débit [Termes IGN] données lidar [Termes IGN] image captée par drone [Termes IGN] image thermique [Termes IGN] lidar bathymétrique [Termes IGN] Matlab [Termes IGN] rivière [Termes IGN] série temporelle [Termes IGN] surveillance hydrologique [Termes IGN] vitesse Résumé : (auteur) This paper describes a non-contact methodology for computing river discharge based on data collected from small Unmanned Aerial Systems (sUAS). The approach is complete in that both surface velocity and channel geometry are measured directly under field conditions. The technique does not require introducing artificial tracer particles for computing surface velocity, nor does it rely upon the presence of naturally occurring floating material. Moreover, no prior knowledge of river bathymetry is necessary. Due to the weight of the sensors and limited payload capacities of the commercially available sUAS used in the study, two sUAS were required. The first sUAS included mid-wave thermal infrared and visible cameras. For the field evaluation described herein, a thermal image time series was acquired and a particle image velocimetry (PIV) algorithm used to track the motion of structures expressed at the water surface as small differences in temperature. The ability to detect these thermal features was significant because the water surface lacked floating material (e.g., foam, debris) that could have been detected with a visible camera and used to perform conventional Large-Scale Particle Image Velocimetry (LSPIV). The second sUAS was devoted to measuring bathymetry with a novel scanning polarizing lidar. We collected field measurements along two channel transects to assess the accuracy of the remotely sensed velocities, depths, and discharges. Thermal PIV provided velocities that agreed closely ( R2 = 0.82 and 0.64) with in situ velocity measurements from an acoustic Doppler current profiler (ADCP). Depths inferred from the lidar closely matched those surveyed by wading in the shallower of the two cross sections ( R2 = 0.95), but the agreement was not as strong for the transect with greater depths ( R2 = 0.61). Incremental discharges computed with the remotely sensed velocities and depths were greater than corresponding ADCP measurements by 22% at the first cross section and Numéro de notice : A2019-554 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11192317 Date de publication en ligne : 05/10/2019 En ligne : https://doi.org/10.3390/rs11192317 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94207 [article]