Using UAV multispectral photography to discriminate plant species in a seep wetland of the fynbos biome
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Date
2024
Authors
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Journal ISSN
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Publisher
Springer Science and Business Media B.V.
Abstract
Wetlands harbour a wide range of vital ecosystems. Hence, mapping wetlands is essential to conserving the ecosystems that depend on them. However, the physical nature of wetlands makes fieldwork difficult and potentially erroneous. This study used multispectral UAV aerial photography to map ten wetland plant species in the Fynbos Biome in the Steenbras Nature Reserve. We developed a methodology that used K-Nearest Neighbour (KNN), Support Vector Machine (SVM), and Random Forest (RF) machine learning algorithms to classify ten wetland plant species using the preselected bands and spectral indices. The study identified Normalized green red difference index (NGRDI), Red Green (RG) index, Green, Log Red Edge (LogRE), Normalized Difference Red-Edge (NDRE), Chlorophyll Index Red-Edge (CIRE), Green Ratio Vegetation Index (GRVI), Normalized Difference Water Index (NDWI), Green Normalized Difference Vegetation Index (GNDVI) and Red as pertinent bands and indices for classifying wetland plant species in the Proteaceae, Iridaceae, Restionaceae, Ericaceae, Asteraceae and Cyperaceae families.
Description
Keywords
Fynbos, Indices, Machine learning, Pigments, Unmanned aerial vehicles
Citation
Musungu, K., Dube, T., Smit, J. and Shoko, M., 2024. Using UAV multispectral photography to discriminate plant species in a seep wetland of the Fynbos Biome. Wetlands Ecology and Management, 32(2), pp.207-227.