Monitoring the Spatial and Temporal Variations in The Water Surface and Floating Algal Bloom Areas in Dongting Lake Using a Long-Term MODIS Image Time Series

Mengmeng Cao,Xinyi Shen,Tongren Xu,Zijin Yuan,Yibo Yan,Kebiao Mao

doi:10.3390/rs12213622

Abstract

Significant water quality changes have been observed in the Dongting Lake region due to environmental changes and the strong influence of human activities. To protect and manage Dongting Lake, the long-term dynamics of the water surface and algal bloom areas were systematically analyzed and quantified for the first time based on 17 years of Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The traditional methods (index-based threshold algorithms) were optimized by a dynamic learning neural network (DL-NN) to extract and identify the water surface area and algal bloom area while reducing the extraction complexity and improving the extraction accuracy. The extraction accuracy exceeded 94.5% for the water and algal bloom areas, and the analysis showed decreases in the algal bloom and water surface areas from 2001–2017. Additionally, the variations in the water surface and algal bloom areas are greatly affected by human activities and climatic factors. The results of these analyses can help us better monitor human contamination in Dongting Lake and take measures to control the water quality during certain periods, which is crucial for future management. Moreover, the traditional methods optimized by the DL-NN used in this study can be extended to other inland lakes to assess and monitor long-term temporal and spatial variations in algal bloom areas and can also be used to acquire baseline information for future assessments of the water quality of lakes.

Highlights

Lakes, which enable shipping and supply water resources for human consumption and improve the local microclimate and ecological environment, represent one of the most important components of the hydrosphere [1,2]
For extracting the areas of surface water and algal blooms were greater than 94.53% and 94.66%, respectively, and the corresponding root mean square error (RMSE) values were 3.60% and 0.71%, respectively
The optimized methods can improve the extraction accuracy of surface water and algal blooms

Summary

Introduction

Lakes, which enable shipping and supply water resources for human consumption and improve the local microclimate and ecological environment, represent one of the most important components of the hydrosphere [1,2]. The first type of method is the threshold approach, which is based on a related water body index, such as the normalized difference water index (NDWI) [22], the modified normalized difference water index (MNDWI) [23], and the automated water extraction index (AWEI) [24]. The third type of method involves a water-specific classification; these methods include expert systems [29] and spectrum matching based on discrete particle swarm optimization (SMDPSO) [30]. This approach is more automated and accurate than general supervised classification [4]. Some studies have used synthetic aperture radar (SAR) data to monitor the dynamics of surface water because these data are insensitive to clouds and independent of the illumination conditions [31,32,33,34,35]; the extent of surface water can be extracted from SAR data based mainly on textural analysis [36], change detection [37], automatic segmentation [38] and classification [39]

Methods

Results

Conclusion