基于光谱特征参数的官厅水库富营养化及污染分析

Analysis of Eutrophication and Pollution in Guanting Reservoir Based on Spectral Features

官厅水库水环境保护对积极推进京津冀协同发展具有重要意义。文章根据2018年官厅水库联合试验中获取的卫星影像数据、水体光谱数据和实验室分析数据,探讨了官厅水库水体光谱特征与叶绿素、浊度和溶解氧等富营养化和污染指标之间的相关关系。基于最优光谱特征参数,建立了适用于官厅水库水体的富营养化和污染遥感定量模型,并对官厅水库的富营养化和污染程度进行了分析。结果表明:最优光谱特征参数与叶绿素、浊度和溶解氧浓度之间的相关系数分别为0.8053、0.813和0.7475,较高的相关度表明所选的光谱特征参数能够准确反映富营养化程度和污染程度;反演叶绿素、浊度、溶解氧的平均相对误差分别为23.2%、31.74%、23.75%,误差在二类水体反演可接受范围之内。文章基于现场可信数据建立的模型,得到的结论可在后续应用中推广。

Water environment protection of Guanting Reservoir is of great significance for actively promoting the coordinated development of Beijing-Tianjin-Hebei.Based on the in situ data including satellite data,spectral data and analytical data in lab,the correlation between the spectral features of the water body in Guanting Reservoir and the eutrophication and pollution indicators such as chlorophyll,turbidity and dissolved oxygen was discussed in this paper.The optimal spectral features were used to reconstruct the neural network model of chlorophyll,turbidity and dissolved oxygen from the remote sensing reflectivity.The inversion model was applied to the high-resolution satellite imagery to analyze the eutrophication and pollution degree of Guanting Reservoir.The results show that the correlation coefficients between the optimal spectral features and chlorophyll,turbidity and dissolved oxygen are 0.8053,0.813 and 0.7475,respectively,indicating that the selected spectral features can accurately reflect the eutrophication and pollution degree.The error of the inversion model is 23.2%for chlorophyll,31.74%for turbidity,and 23.75%for dissolved oxygen respectively.The inversion error in the case II water is within the acceptable range.The models presented in this paper are based on reliable situ data,and the results can be useful in other applications.