Ground-based remote sensing provides alternative to satellites for monitoring cyanobacteria in small lakes
Ground-based remote sensing provides alternative to satellites for monitoring cyanobacteria in small lakes
复制标题
地面遥感为小湖泊蓝藻监测提供了卫星替代方案
DOI：
10.1016/j.watres.2023.120076
复制
发表时间：
2023
期刊：
影响因子：
12.8
通讯作者：
Hambright, K. David
中科院分区：
文献类型：
--
作者：
Cook, Katherine V.;Beyer, Jessica E.;Xiao, Xiangming;Hambright, K. David
蓝藻是世界各地淡水系统中最普遍的水华形成有害藻类。受影响系统的充分采样在空间、时间和空间上都是有限的。利用空间或地面系统在空间和时间尺度上对大型水体进行遥感，对于标准的水质监测来说，这是成本高昂的，但事实证明，这种遥感在探测和量化蓝藻有害藻华方面是有用的。本研究的目的是确定一个区域性的“通用”多光谱反射率模型，可用于快速，远程检测和定量的氰基有害生物在中小型生产水库，如典型的俄克拉荷马州，美国。我们的目标是将这些小型水体纳入我们的研究中，因为它们通常在更大的大陆范围内的研究中被忽视，但分布广泛并用于娱乐和饮用水供应。我们使用Landsat卫星反射率和in-situpigment数据跨越16年，从38个水库在俄克拉荷马州构建经验线性模型预测浓度的叶绿素a和藻蓝蛋白，两个关键的藻类色素通常用于评估总的和蓝藻藻类丰度，分别。我们还使用了地面高光谱反射和in-situpigment数据从七个水库在五年内在俄克拉荷马州建立多光谱模型预测藻类色素从新定义的反射带。我们的俄克拉何马州衍生的陆地卫星和地面为基础的模型优于建立反射色素模型对俄克拉荷马州水库。重要的是，我们的研究结果表明，基于地面的多光谱模型是远远上级的陆地卫星为基础的模型和蓝藻指数（CI）检测氰有害生物在高产，小型到中型水库在俄克拉荷马州，水管理和公共卫生提供了一个有价值的工具。虽然基于卫星的遥感方法已被证明是有效的相对较大的系统，我们的新的研究结果表明，地面遥感可以提供更好的cyanohabs监测小型或高度树枝状混浊的湖泊，如那些在整个南部大平原，从而证明有利于努力，旨在尽量减少与cyanoHABs在供应和娱乐沃茨的公共卫生风险。
Cyanobacteria are the most prevalent bloom-forming harmful algae in freshwater systems around the world. Adequate sampling of affected systems is limited spatially, temporally, and fiscally. Remote sensing using space- or ground-based systems in large water bodies at spatial and temporal scales that are cost-prohibitive to standard water quality monitoring has proven to be useful in detecting and quantifying cyanobacterial harmful algal blooms. This study aimed to identify a regional ‘universal’ multispectral reflectance model that could be used for rapid, remote detection and quantification of cyanoHABs in small- to medium-sized productive reservoirs, such as those typical of Oklahoma, USA. We aimed to include these small waterbodies in our study as they are typically overlooked in larger, continental wide studies, yet are widely distributed and used for recreation and drinking water supply. We used Landsat satellite reflectance andin-situpigment data spanning 16 years from 38 reservoirs in Oklahoma to construct empirical linear models for predicting concentrations of chlorophyll-aand phycocyanin, two key algal pigments commonly used for assessing total and cyanobacterial algal abundances, respectively. We also used ground-based hyperspectral reflectance andin-situpigment data from seven reservoirs across five years in Oklahoma to build multispectral models predicting algal pigments from newly defined reflectance bands. Our Oklahoma-derived Landsat- and ground-based models outperformed established reflectance-pigment models on Oklahoma reservoirs. Importantly, our results demonstrate that ground-based multispectral models were far superior to Landsat-based models and the Cyanobacteria Index (CI) for detecting cyanoHABs in highly productive, small- to mid-sized reservoirs in Oklahoma, providing a valuable tool for water management and public health. While satellite-based remote sensing approaches have proven effective for relatively large systems, our novel results indicate that ground-based remote sensing may offer better cyanoHAB monitoring for small or highly dendritic turbid lakes, such as those throughout the southern Great Plains, and thus prove beneficial to efforts aimed at minimizing public health risks associated with cyanoHABs in supply and recreational waters.