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. The classification based on the stacked multitemporal dataset resulted in slightly higher accuracies (84–94 per cent OA 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.

中文翻译：

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BiałowieForesta森林世界遗产站点中的多时相高光谱树种分类

已经发现从高光谱数据得出的树种组成图是准确的，但仍不清楚是否存在获取图像的最佳时间窗口。温带森林中的树木会发生因物而异的物候变化，并可能影响物种识别。我们的研究检查了多时相高光谱数据集对Białowieża森林的波兰部分树种进行分类的性能。我们对7种树种进行了分类，包括云杉（云杉（Picea abies（L.）H.Karst），松树（Pinus sylvestris L。），der木（Alnus glutinosa Gaertn。），橡树（Quercus robur L.），桦木（Betula pendula Roth），角树（Carpinus betulusL.）和菩提树（Tilia cordataMill。），使用支持向量机。我们比较了三个数据采集的结果-初夏和夏末（7月2日至4日和8月24-27日）和秋季（10月1-2日），以及基于包含所有三个采集的图像堆栈的分类。此外，比较了同一物种的错误分类和正确分类的树木的大小（高度和树冠直径）。初夏获得的精度最高，总体准确度（OA）为83-94％，卡伯（κ）为0.80-0.92。基于堆叠的多时间数据集的分类产生了更高的准确度（OA的百分比为84-94％，κ的值为0.81-0.92κ）。对于某些树种，例如桦木和橡树，树木的大小在正确分类和错误分类的树木上明显不同。我们得出的结论是，与单次采集相比，实现多时相高光谱数据可以改善分类结果。但是，在我们的情况下，获得的多时相图像叠层的精度可与最佳单日分类相媲美，并且由于长期以来仍很少有高光谱采集，因此花更多的时间来确定区域最优的采集窗口可能是值得的。