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

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.

Automated summary

This article introduces a method for identifying aquatic plants based on remote sensing technology. By constructing a decision tree file, the canopy spectra of eight plants in the Changguangxi Wetland water area were monitored using hyperspectral remote sensing technology, and the effectiveness of this method was demonstrated. The results showed that the spectral characteristics of aquatic plants can be enhanced by calculating spectral indices, thereby improving comparability among different species. The overall recognition accuracy of the constructed decision tree file for eight types of plants reached 85.02%.