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

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 Białowie˙za 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
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Kappa (κ) of 0.80–0.92. The classification based on the stacked multitemporal dataset resulted in slightly higher
accuracies (84–94 per centOA and 0.81–0.92 κ). For some species, e.g. birch and oak, tree size differed notably for
correctly and incorrectly classified trees.We conclude that implementing multitemporal hyperspectral data can
improve the classification result as compared with a single acquisition. However, the obtained accuracy of the
multitemporal image stack was in our case comparable to the best single-date classification and investing more
time in identifying regionally optimal acquisition windows may be worthwhile as long hyperspectral acquisitions
are still sparse.