An in situ hyperspectral dataset for typical aquatic vegetation
The heterogeneity of wetland habitats promotes aquatic vegetation diversity. The large number of plant species creates challenges in classifying wetland habitats. In-situ hyperspectral data directly relate vegetation species to the spectral response of the canopy, which serves as a foundation for in...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-01-01
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| Series: | Geo-spatial Information Science |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2024.2440653 |
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| Summary: | The heterogeneity of wetland habitats promotes aquatic vegetation diversity. The large number of plant species creates challenges in classifying wetland habitats. In-situ hyperspectral data directly relate vegetation species to the spectral response of the canopy, which serves as a foundation for interpreting and validating remote sensing data, estimating nutrient estimates, and biomass inversions. An in-situ hyperspectral reflectance dataset (spectral range: 350–2500 nm with 300 bands) of typical aquatic vegetation is described in this paper. The dataset includes 134 effective original spectral curves of 124 aquatic vegetation species, including 122 healthy canopy spectra, 7 spectra of withered leaves of aquatic plants, and 5 spectra of flowers. The first-order differential and continuum removal curves are obtained by calculating the original data. To provide a reliable reference for extracting plant spectral features, we describe the spectral curve differences of different plant families, genera, and species. Kruskal–Wallis tests and paired comparison validation were also used to determine the optimal wavelengths for distinguishing different plant types, families, and genera. We expect that this hyperspectral dataset can facilitate ecological remote sensing applications and thus can support remote sensing assessment of the carbon sink capacity of wetland vegetation from multiple perspectives. |
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| ISSN: | 1009-5020 1993-5153 |