期刊
REMOTE SENSING
卷 12, 期 21, 页码 -出版社
MDPI
DOI: 10.3390/rs12213622
关键词
water surface area; floating algal blooms; DL-NN; MODIS; Dongting Lake
类别
资金
- National Key Project of China [2018YFC1506502, 2018YFC1506602]
- Fundamental Research Funds for Central Non-profit Scientific Institution [1610132020014]
- Open Fund of State Key Laboratory of Remote Sensing Science [OFSLRSS201910]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据