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Identification for the species of aquatic higher plants in the Taihu Lake basin based on hyperspectral remote sensing

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Abstract

Aquatic plants are crucial for aquatic ecosystems and their species and distribution reflect aquatic ecosystem health. Remote sensing technology has been used to monitor plant distributions over large scales. However, the fine identification of the species of aquatic higher plants is challenging due to large temporal-spatial changes in optical water body properties and small spectral differences among plant species. Here, an aquatic plant identification method was developed by constructing a decision tree file in the C4.5 algorithm based on the canopy spectra of eight plants in the Changguangxi Wetland water area from hyperspectral remote sensing technology. The method was used to monitor the distribution of different plants in the Changguangxi Wetland area and two other water areas. The results showed that the spectral characteristics of plants were enhanced by calculating their spectral index, thereby improving the comparability among different species. The total recognition accuracy of the constructed decision tree file for eight types of plants was 85.02%. Nymphaea tetragona, Pontederia cordata, and Nymphoides peltatum had the highest recognition accuracy and Eichhornia crassipes was the lowest. The specific species and distributions of aquatic plants were consistent with the water quality in the area. The results can provide a reference for the accurate identification of aquatic plants in the same type of water area.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by the National Water Pollution Control and Treatment Science and Technology Major Project of China (No. 2017ZX07204004) and the National Natural Science Foundation of China (21501068).

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Authors and Affiliations

  1. Jiangsu Key Laboratory of Anaerobic Biotechnology, College of Environment and Civil Engineering, Jiangnan University, Wuxi, 214122, China

    Shichen Mu, Kai You & Lihong Wang

  2. Wuxi Environmental Monitoring Central Station, Wuxi, 214121, China

    Ting Song & Junzhe Shi

  3. School of Environmental Science and Engineering Jiangsu Provincial Key Laboratory of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, Jiangsu Province, 215009, China

    Yajie Li

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  1. Shichen Mu
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Contributions

Shichen Mu: formal analysis; methodology; data analysis; writing—original draft preparation. Kai You: methodology, data curation. Ting Song: methodology. Yajie Li: writing—review and editing. Lihong Wang: conceptualization; methodology; supervision; writing—review and editing. Junzhe Shi: supervision, funding acquisition.

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Correspondence to Lihong Wang.

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Mu, S., You, K., Song, T. et al. Identification for the species of aquatic higher plants in the Taihu Lake basin based on hyperspectral remote sensing. Environ Monit Assess 195, 989 (2023). https://doi.org/10.1007/s10661-023-11523-z

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