Oak Ridge Institute for Science and Education, U.S. Environmental Protection Agency, Durham, NC 27709, USA.
U.S. Environmental Protection Agency, Office of Research and Development, Durham, NC 27709, USA.
Harmful Algae. 2022 Jun;115:102191. doi: 10.1016/j.hal.2022.102191. Epub 2022 May 12.
Cyanobacteria harmful algal blooms (cyanoHABs) negatively affect ecological, human, and animal health. Traditional methods of validating satellite algorithms with data from water samples are often inhibited by the expense of quantifying cyanobacteria indicators in the field and the lack of public data. However, state recreation advisories and other recorded events of cyanoHAB occurrence reported by local authorities can serve as an independent and publicly available dataset for validation. State recreation advisories were defined as a period delimited by a start and end date where a warning was issued due to detections of cyanoHABs over a state's risk threshold. State reported events were defined as any event that was documented with a single date related to cyanoHABs. This study examined the presence-absence agreement between 160 state reported cyanoHAB advisories and 1,343 events and cyanobacteria biomass estimated by a satellite algorithm called the Cyanobacteria Index (CI). The true positive rate of agreement with state recreation advisories was 69% and 60% with state reported events. CI detected a reduction or absence in cyanobacteria after 76% of the recreation advisories ended. CI was used to quantify the magnitude, spatial extent, and temporal frequency of cyanoHABs; each of these three metrics were greater (r > 0.2) during state recreation advisories compared to non-advisory times with effect sizes ranging from small to large. This is the first study to quantitatively evaluate satellite algorithm performance for detecting cyanoHABs with state reported events and advisories and supports informed management decisions with satellite technologies that complement traditional field observations.
蓝藻有害藻华(cyanoHABs)对生态、人类和动物健康产生负面影响。传统的方法是使用水样数据来验证卫星算法,但这种方法往往受到限制,因为在现场量化蓝藻指标既昂贵又缺乏公共数据。然而,州立娱乐建议和地方当局报告的其他蓝藻藻华发生事件可以作为独立的、公开可用的数据集进行验证。州立娱乐建议被定义为一个时期,其开始和结束日期由州风险阈值以上的蓝藻藻华检测引起的警告所限定。州报告的事件被定义为任何与蓝藻藻华有关的记录在案的单一日期的事件。本研究检验了 160 个州报告的蓝藻藻华建议和 1343 个事件以及卫星算法(称为蓝藻指数(CI))估计的蓝藻生物量之间的存在-缺失一致性。与州立娱乐建议的真实阳性率一致的有 69%,与州报告的事件一致的有 60%。在 76%的娱乐建议结束后,CI 检测到蓝藻减少或消失。CI 用于量化蓝藻藻华的幅度、空间范围和时间频率;与非咨询时间相比,这三个指标中的每一个在州立娱乐建议期间都更大(r > 0.2),其效应大小从小到大不等。这是第一项使用州报告的事件和建议来定量评估卫星算法检测蓝藻藻华性能的研究,支持使用卫星技术做出明智的管理决策,这些技术补充了传统的现场观测。