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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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-01-01
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| Series: | Geo-spatial Information Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2024.2440653 |
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| author | Yaqin Fang Liqiao Tian Jing Xia Fang Chen Cong Shen Liqiong Liu Zixiao Liu Yuan Zhang |
| author_facet | Yaqin Fang Liqiao Tian Jing Xia Fang Chen Cong Shen Liqiong Liu Zixiao Liu Yuan Zhang |
| author_sort | Yaqin Fang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5fafadb475314976a383666dd68d372a |
| institution | Kabale University |
| issn | 1009-5020 1993-5153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj-art-5fafadb475314976a383666dd68d372a2025-02-04T15:06:46ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532025-01-0113810.1080/10095020.2024.2440653An in situ hyperspectral dataset for typical aquatic vegetationYaqin Fang0Liqiao Tian1Jing Xia2Fang Chen3Cong Shen4Liqiong Liu5Zixiao Liu6Yuan Zhang7The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaThe State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaChangjiang Spatial Information Technology Engineering Co. Ltd, Wuhan, ChinaThe State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaThe State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaKey Laboratory for Environment and Disaster Monitoring and Evaluation of Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, ChinaTianjin Institute of Surveying and Mapping Co. Ltd, Tianjin, ChinaEcology Environment Monitoring Center of Hubei Province, Wuhan, ChinaThe 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.https://www.tandfonline.com/doi/10.1080/10095020.2024.2440653In situ hyperspectralaquatic vegetationsreflectance datasetKruskal–Wallis testbands identification |
| spellingShingle | Yaqin Fang Liqiao Tian Jing Xia Fang Chen Cong Shen Liqiong Liu Zixiao Liu Yuan Zhang An in situ hyperspectral dataset for typical aquatic vegetation Geo-spatial Information Science In situ hyperspectral aquatic vegetations reflectance dataset Kruskal–Wallis test bands identification |
| title | An in situ hyperspectral dataset for typical aquatic vegetation |
| title_full | An in situ hyperspectral dataset for typical aquatic vegetation |
| title_fullStr | An in situ hyperspectral dataset for typical aquatic vegetation |
| title_full_unstemmed | An in situ hyperspectral dataset for typical aquatic vegetation |
| title_short | An in situ hyperspectral dataset for typical aquatic vegetation |
| title_sort | in situ hyperspectral dataset for typical aquatic vegetation |
| topic | In situ hyperspectral aquatic vegetations reflectance dataset Kruskal–Wallis test bands identification |
| url | https://www.tandfonline.com/doi/10.1080/10095020.2024.2440653 |
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