A Novel Convolutional Neural Network for the Extraction of Algal Bloom and Aquatic Vegetation in Typical Eutrophic Shallow Lakes

Abstract

Under the hybrid impact of regional climate change and extensive human activities, lake eutrophication has become an increasingly serious problem, which causes a dramatic reduction in the area of aquatic vegetation (AV) and poses huge challenges to the balance of regional lake ecosystems. As an important freshwater resource, shallow lakes play an important role in balancing water resources, adjusting regional climate, and retaining clean water supply. However, in view of the complexity and variability of shallow lake environment, especially the similarity of spectral characteristics between algal bloom (AB) and AV in shallow lakes, the extraction results of AB and AV using most algorithms are not satisfactory. In response to these problems, this study utilized Landsat images as the dataset to accurately differentiate AB and AV by developing a new extraction network (AAENet) aiming at eutrophic shallow lakes. Next, the AAENet model was compared with three classic semantic segmentation models (UNet, Deeplab v3, and PSPNet) and the vegetation and bloom indices algorithm. Finally, the spatiotemporal distribution and area change in typical shallow lakes were analyzed based on the extraction results of the AAENet model. The results showed that: 1) the AAENet model achieved the highest accuracy in distinguishing AB and AV, with an overall accuracy of 87.85%, an F1 score of 0.85, and a Frequency Weighted Intersection-over-Union of 0.76 in the testing lakes. 2) In terms of improving the performance of the AAENet model, the ConvNeXt encoder played the most significant role. 3) During 2013–2023, the area of AB in Chaohu Lake and Taihu Lake decreased annually by 0.73 km2 and 3.29 km2, respectively. In particular, the area of AV in Chaohu Lake steadily increased at a rate of 0.27 km2/year, whereas the area of AV in Taihu Lake exhibited an initial decline followed by an increase. This study can provide important technical support for monitoring the dynamics of AB and AV in lakes.

Keywords

• Algal bloom (AB)
• aquatic vegetation (AV)
• deep learning
• eutrophic shallow lake
• Landsat