Centre de documentation
scientifique

Dépouillements

Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery / Teja Kattenborn in Remote sensing in ecology and conservation, vol 6 n° 4 (December 2020)  

Public [article]  inRemote sensing in ecology and conservation > vol 6 n° 4 (December 2020) . - pp 472 - 486 Titre : Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery Type de document : Article/Communication Auteurs : Teja Kattenborn, Auteur ; Jana Eichel, Auteur ; Susan Wiser, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 472 - 486 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte forestière [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] espèce exotique envahissante [Termes IGN] image à très haute résolution [Termes IGN] image captée par drone [Termes IGN] image RVB Résumé : (auteur) Unmanned Aerial Vehicles (UAV) greatly extended our possibilities to acquire high resolution remote sensing data for assessing the spatial distribution of species composition and vegetation characteristics. Yet, current pixel- or texture-based mapping approaches do not fully exploit the information content provided by the high spatial resolution. Here, to fully harness this spatial detail, we apply deep learning techniques, that is, Convolutional Neural Networks (CNNs), on regular tiles of UAV-orthoimagery (here 2–5 m) to identify the cover of target plant species and plant communities. The approach was tested with UAV-based orthomosaics and photogrammetric 3D information in three case studies, that is, (1) mapping tree species cover in primary forests, (2) mapping plant invasions by woody species into forests and open land and (3) mapping vegetation succession in a glacier foreland. All three case studies resulted in high predictive accuracies. The accuracy increased with increasing tile size (2–5 m) reflecting the increased spatial context captured by a tile. The inclusion of 3D information derived from the photogrammetric workflow did not significantly improve the models. We conclude that CNN are powerful in harnessing high resolution data acquired from UAV to map vegetation patterns. The study was based on low cost red, green, blue (RGB) sensors making the method accessible to a wide range of users. Combining UAV and CNN will provide tremendous opportunities for ecological applications. Numéro de notice : A2020-852 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1002/rse2.146 Date de publication en ligne : 05/02/2020 En ligne : https://doi.org/10.1002/rse2.146 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98681 [article]

Mapping of land cover with open-source software and ultra-high-resolution imagery acquired with unmanned aerial vehicles / Ned Horning in Remote sensing in ecology and conservation, vol 6 n° 4 (December 2020)  

Public [article]  inRemote sensing in ecology and conservation > vol 6 n° 4 (December 2020) . - pp 487 - 497 Titre : Mapping of land cover with open-source software and ultra-high-resolution imagery acquired with unmanned aerial vehicles Type de document : Article/Communication Auteurs : Ned Horning, Auteur ; Erika Fleishman, Auteur ; Peter J. Ersts, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 487 - 497 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] carte d'occupation du sol [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] image aérienne [Termes IGN] image captée par drone [Termes IGN] Orfeo Tool Box [Termes IGN] orthoimage [Termes IGN] R (langage) Résumé : (auteur) The use of unmanned aerial vehicles (UAVs) to map and monitor the environment has increased sharply in the last few years. Many individuals and organizations have purchased consumer-grade UAVs, and commonly acquire aerial photographs to map land cover. The resulting ultra-high-resolution (sub-decimeter-resolution) imagery has high information content, but automating the extraction of this information to create accurate, wall-to-wall land-cover maps is quite difficult. We introduce image-processing workflows that are based on open-source software and can be used to create land-cover maps from ultra-high-resolution aerial imagery. We compared four machine-learning workflows for classifying images. Two workflows were based on random forest algorithms. Of these, one used a pixel-by-pixel approach available in ilastik, and the other used image segments and was implemented with R and the Orfeo ToolBox. The other two workflows used fully connected neural networks and convolutional neural networks implemented with Nenetic. We applied the four workflows to aerial photographs acquired in the Great Basin (western USA) at flying heights of 10 m, 45 m and 90 m above ground level. Our focal cover type was cheatgrass (Bromus tectorum), a non-native invasive grass that changes regional fire dynamics. The most accurate workflow for classifying ultra-high-resolution imagery depends on diverse factors that are influenced by image resolution and land-cover characteristics, such as contrast, landscape patterns and the spectral texture of the land-cover types being classified. For our application, the ilastik workflow yielded the highest overall accuracy (0.82–0.89) as assessed by pixel-based accuracy. Numéro de notice : A2020-853 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1002/rse2.144 Date de publication en ligne : 13/01/2020 En ligne : https://doi.org/10.1002/rse2.144 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98682 [article]