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Research on remote sensing automatic identification algorithms for different communities of lacuscular grass algae has progressed


Under the dual pressure of human activities and climate warming, lake water environment problems have become increasingly prominent. More than 60% of the world’s lakes are eutrophic, and 8.8% of lakes have algal blooms. The evolution and transformation of different steady-state types of grass and algae is one of the hot spots in lake ecology research, but there is no obvious breakthrough due to the lack of long-term grass algae data support.

The Landsat series of satellite data can quickly obtain high-resolution information on the current state of the land surface and reconstruct historical information since the 1980s; The development of Landssat-based automatic recognition algorithms for algal blooms, emergent/floating leaves and submerged vegetation is the key to the evolution and transformation of grass algae. At present, a series of remote sensing extraction algorithms have been developed around lake algal blooms or aquatic vegetation, but there is no algorithm that can fully realize the automatic identification and large-scale application of grass and algae.

Luo Juhua, an associate researcher in the team of Duan Hongtao, a researcher at the Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, developed an automatic identification algorithm for remote sensing of algal blooms, emergent/floating leaves and submerged vegetation (VBI algorithm) around the above problems (Figure 1). Recently, the relevant research results were published in Remote Sensing of Environment under the title A new technique for quantifying algal bloom, floating/emergent and submerged vegetation in eutrophic shallow lakes using Landsat imagery.

Figure 1.Aquatic vegetation and algal bloom (VBI) remote sensing recognition algorithm process (a) and Taihu Lake case (b); Note: AB: algal bloom; FEAV: emergent/floating leaf vegetation; SAV: Submerged vegetation.

The VBI algorithm constructs the aquatic vegetation index (AVI) through the tasseled cap transform, which solves two problems in the remote sensing recognition of grass algae, namely, the shape of emergent/floating leaf vegetation and algal bloom spectrum is similar and difficult to identify. Submerged vegetation is located below the water surface, the spectral signal is weak, and it is difficult to distinguish from the water spectrum. The VBI algorithm has been widely verified in the middle and lower reaches of the Yangtze River, and the average classification accuracy is higher than 80%, which has high accuracy, robustness and applicability. At the same time, the VBI algorithm and the existing algorithms have been compared in many lakes around the world, and it is found that the VBI algorithm has obvious advantages, and it is the only remote sensing classification algorithm that can simultaneously identify algal blooms, emergent/floating leaf vegetation and submerged vegetation.

The eutrophication of lakes in the middle and lower reaches of the Yangtze River is serious, and the long-term changes of grass and algae are unknown. Based on the VBI algorithm, the researchers reconstructed the dataset of large lakes (area >50km2) in the middle and lower reaches of the Yangtze River from 1985 to 2021, and found that the large lakes all had aquatic vegetation distribution, and the average vegetation coverage was 59%, of which the average coverage of submerged vegetation was 36.98%, and the average coverage of floating leaf vegetation was 22.06%. About 81% of the dominant vegetation groups of large lakes were submerged vegetation, and algal blooms occurred in 12 lakes to varying degrees. Since the 1980s, the aquatic vegetation coverage of lakes in the middle and lower reaches of the Yangtze River has decreased significantly, especially the submerged vegetation has decreased significantly, and the floating leaf vegetation has not changed significantly (Figures 2 and 3). In addition, the number of lakes where algal blooms occur has increased significantly, especially in the lower reaches of the Yangtze River.

Fig. 2.Spatial-temporal distribution and change trend of aquatic vegetation in lakes (> 50km2) in the middle and lower reaches of the Yangtze River

Fig. 3.Spatial-temporal distribution frequency and variation of algal blooms in lakes (> 50 km2) in the middle and lower reaches of the Yangtze River

The research work is supported by the National Natural Science Foundation of China, the Nanjing Institute of Geography independently deployed scientific research projects, and the Jiangsu Province Carbon Peaking and Carbon Neutrality Science and Technology Innovation Special Fund Project. (Source: Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences)

Related paper information:https://doi.org/10.1016/j.rse.2023.113480

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