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Detection of aspen in conifer-dominated boreal forests with seasonal multispectral drone image point clouds / Alwin A. Hardenbol in Silva fennica, vol 55 n° 4 (September 2021)  

Public [article]  inSilva fennica > vol 55 n° 4 (September 2021) . - n° 10515 Titre : Detection of aspen in conifer-dominated boreal forests with seasonal multispectral drone image point clouds Type de document : Article/Communication Auteurs : Alwin A. Hardenbol, Auteur ; Anton Kuzmin, Auteur ; Lauri Korhonen, Auteur ; Pasi Korpelainen, Auteur ; Timo Kumpula, Auteur ; Matti Maltamo, Auteur ; Jari Kouki, Auteur Année de publication : 2021 Article en page(s) : n° 10515 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] aire protégée [Termes IGN] analyse discriminante [Termes IGN] Betula (genre) [Termes IGN] détection d'arbres [Termes IGN] forêt boréale [Termes IGN] image captée par drone [Termes IGN] image multibande [Termes IGN] orthoimage couleur [Termes IGN] peuplement mélangé [Termes IGN] Picea abies [Termes IGN] Pinus sylvestris [Termes IGN] Populus tremula [Termes IGN] semis de points [Termes IGN] variation saisonnière Résumé : (auteur) Current remote sensing methods can provide detailed tree species classification in boreal forests. However, classification studies have so far focused on the dominant tree species, with few studies on less frequent but ecologically important species. We aimed to separate European aspen (Populus tremula L.), a biodiversity-supporting tree species, from the more common species in European boreal forests (Pinus sylvestris L., Picea abies [L.] Karst., Betula spp.). Using multispectral drone images collected on five dates throughout one thermal growing season (May–September), we tested the optimal season for the acquisition of mono-temporal data. These images were collected from a mature, unmanaged forest. After conversion into photogrammetric point clouds, we segmented crowns manually and automatically and classified the species by linear discriminant analysis. The highest overall classification accuracy (95%) for the four species as well as the highest classification accuracy for aspen specifically (user’s accuracy of 97% and a producer’s accuracy of 96%) were obtained at the beginning of the thermal growing season (13 May) by manual segmentation. On 13 May, aspen had no leaves yet, unlike birches. In contrast, the lowest classification accuracy was achieved on 27 September during the autumn senescence period. This is potentially caused by high intraspecific variation in aspen autumn coloration but may also be related to our date of acquisition. Our findings indicate that multispectral drone images collected in spring can be used to locate and classify less frequent tree species highly accurately. The temporal variation in leaf and canopy appearance can alter the detection accuracy considerably. Numéro de notice : A2021-735 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.10515 Date de publication en ligne : 14/07/2021 En ligne : https://doi.org/10.14214/sf.10515 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98691 [article]