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Multitemporal hyperspectral tree species classification in the Białowieża Forest World Heritage site
- Source :
- Forestry: An International Journal of Forest Research. 94:464-476
- Publication Year :
- 2021
- Publisher :
- Oxford University Press (OUP), 2021.
-
Abstract
- 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.
- Subjects :
- Carpinus betulus
010504 meteorology & atmospheric sciences
biology
0211 other engineering and technologies
Hyperspectral imaging
Forestry
Picea abies
02 engineering and technology
biology.organism_classification
01 natural sciences
Alder
Quercus robur
Alnus glutinosa
Hornbeam
Betula pendula
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Subjects
Details
- ISSN :
- 14643626 and 0015752X
- Volume :
- 94
- Database :
- OpenAIRE
- Journal :
- Forestry: An International Journal of Forest Research
- Accession number :
- edsair.doi...........d53295bdec23977b4ab3a3c2b1257d46