Multitemporal hyperspectral tree species classification in the Biatowieta 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 Biatowieza 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 (kappa) of 0.80-0.92. The classification based on the stacked multitemporal dataset resulted in slightly higher accuracies (84-94 per cent OA and 0.81-0.92 kappa). 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.

Automated summary

Tree species composition maps derived from hyperspectral data are accurate, but the optimal time window for image acquisition remains unclear. Our study in the Polish part of the Biatowieza Forest used multitemporal hyperspectral data to classify tree species, with early summer acquisition achieving the highest accuracies. Comparison of different data acquisitions showed slightly better results for the stacked multitemporal dataset, indicating the potential benefits of using multiple acquisitions for classification.