MODIS observations of cyanobacterial risks in a eutrophic lake: Implications for long-term safety evaluation in drinking-water source

环境科学 富营养化 优势(遗传学) 叶绿素a 浮游植物 中分辨率成像光谱仪 水质 水华 藻蓝蛋白 蓝藻 营养物 生态学 生物 卫星 航空航天工程 工程类 细菌 基因 植物 生物化学 遗传学
作者
Hongtao Duan,Min Tao,Steven Loiselle,Wei Zhao,Zhigang Cao,Ronghua Ma,Xiaoxian Tang
出处
期刊:Water Research [Elsevier BV]
卷期号:122: 455-470 被引量:141
标识
DOI:10.1016/j.watres.2017.06.022
摘要

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.
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