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Multitemporal hyperspectral tree species classification in the Białowieza Forest World Heritage site

Modzelewska, Aneta; Kamińska, Agnieszka; Fassnacht, Fabian Ewald ORCID iD icon 1; Stereńczak, Krzysztof
1 Institut für Geographie und Geoökologie (IFGG), Karlsruher Institut für Technologie (KIT)


Tree species composition maps derived from hyperspectral data have been found to be accurate but it is still unclear whether an optimal time window exists to acquire the images. Trees in temperate forests are subject to phenological changes that are species-specific and can have an impact on species recognition. Our study examined the performance of a multitemporal hyperspectral dataset to classify tree species in the Polish part of the Białowieża Forest. We classified seven tree species including spruce (Picea abies (L.) H.Karst), pine (Pinus sylvestris L.), alder (Alnus glutinosa Gaertn.), oak (Quercus robur L.), birch (Betula pendula Roth), hornbeam (Carpinus betulus L.) and linden (Tilia cordata Mill.), using Support Vector Machines. We compared the results for three data acquisitions—early and late summer (2–4 July and 24–27 August), and autumn (1–2 October) as well as a classification based on an image stack containing all three acquisitions. Furthermore, the sizes (height and crown diameter) of misclassified and correctly classified trees of the same species were compared. The early summer acquisition reached the highest accuracies with an Overall Accuracy (OA) of 83–94 per cent and Kappa (κ) of 0.80–0.92. ... mehr

DOI: 10.1093/forestry/cpaa048
Zitationen: 17
Zitationen: 17
Zugehörige Institution(en) am KIT Institut für Geographie und Geoökologie (IFGG)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 03.07.2021
Sprache Englisch
Identifikator ISSN: 0015-752X, 1464-3626
KITopen-ID: 1000129314
Erschienen in Forestry
Verlag Oxford University Press (OUP)
Band 94
Heft 3
Seiten 464-476
Vorab online veröffentlicht am 03.02.2021
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