Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China.
Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China.
Water Res. 2017 Oct 1;122:455-470. doi: 10.1016/j.watres.2017.06.022. Epub 2017 Jun 10.
The occurrence and related risks from cyanobacterial blooms have increased world-wide over the past 40 years. Information on the abundance and distribution of cyanobacteria is fundamental to support risk assessment and management activities. In the present study, an approach based on Empirical Orthogonal Function (EOF) analysis was used to estimate the concentrations of chlorophyll a (Chla) and the cyanobacterial biomarker pigment phycocyanin (PC) using data from the MODerate resolution Imaging Spectroradiometer (MODIS) in Lake Chaohu (China's fifth largest freshwater lake). The approach was developed and tested using fourteen years (2000-2014) of MODIS images, which showed significant spatial and temporal variability of the PC:Chla ratio, an indicator of cyanobacterial dominance. The results had unbiased RMS uncertainties of <60% for Chla ranging between 10 and 300 μg/L, and unbiased RMS uncertainties of <65% for PC between 10 and 500 μg/L. Further analysis showed the importance of nutrient and climate conditions for this dominance. Low TN:TP ratios (<29:1) and elevated temperatures were found to influence the seasonal shift of phytoplankton community. The resultant MODIS Chla and PC products were then used for cyanobacterial risk mapping with a decision tree classification model. The resulting Water Quality Decision Matrix (WQDM) was designed to assist authorities in the identification of possible intake areas, as well as specific months when higher frequency monitoring and more intense water treatment would be required if the location of the present intake area remained the same. Remote sensing cyanobacterial risk mapping provides a new tool for reservoir and lake management programs.
在过去的 40 年中，蓝藻水华的发生及其相关风险在全球范围内有所增加。有关蓝藻丰度和分布的信息是支持风险评估和管理活动的基础。在本研究中，使用基于经验正交函数（EOF）分析的方法，利用 MODerate resolution Imaging Spectroradiometer（MODIS）在巢湖（中国第五大淡水湖）的数据来估算叶绿素 a（Chla）和蓝藻生物标志物藻蓝蛋白（PC）的浓度。该方法是在使用 MODIS 图像的十四年（2000-2014 年）中开发和测试的，这些图像显示了 PC：Chla 比值的显著时空变异性，PC：Chla 比值是蓝藻优势的指标。结果表明，Chla 范围在 10 至 300μg/L 时，无偏均方根不确定性 <60%，PC 范围在 10 至 500μg/L 时，无偏均方根不确定性 <65%。进一步的分析表明，营养物质和气候条件对这种优势具有重要影响。低 TN：TP 比值（<29:1）和升高的温度被发现会影响浮游植物群落的季节性变化。然后，使用 MODIS Chla 和 PC 产品进行蓝藻风险制图，采用决策树分类模型。所得水质决策矩阵（WQDM）旨在协助当局识别可能的摄入区，以及如果当前摄入区的位置保持不变，则需要更频繁监测和更强化水疗的特定月份。遥感蓝藻风险制图为水库和湖泊管理计划提供了新工具。
