Development of novel detection and prediction algorithms for Microcystis blooms

描述(由申请人提供) 拟议的研究有两个主要目标：1)通过改进和增强现有的定性算法，基于遥感图像开发一种新的五大湖有害藻华定量检测算法；2)利用遥感数据产品和最新的现场环境测量数据，开发一个生态位模型来预测五大湖有害藻华的发生。 以蓝藻为主的赤潮目前对五大湖的居民和使用者构成严重的健康和经济威胁，经常产生足够数量的毒素，给人类和动物造成严重的健康问题。研究人员建议的研究将有利于人类的健康和生计，提供工具，在更好地了解赤潮的潜在生态和改进的天气探测方法的基础上，更好地管理未来与赤潮有关的人类健康风险。调查人员研究的一个组成部分将包括实施一项监测计划，该计划包括对西部伊利湖和萨吉诺湾的环境参数进行自主测量--与船舶测量相结合--这对HAB的启动和推广至关重要。这些测量将用于开发新的卫星产品，以提供对这些有毒藻华的环境特征的天气观测。研究人员将把这些新的卫星产品与其他天气卫星环境产品和现有的现场环境测量相结合，以构建一个历史数据集，在孕育水华的相关环境条件的背景下描述赤潮事件的特征。该数据集将指导基于多变量分析的创新生态位模型的开发，该模型将根据实时环境输入预测即将发生的赤潮事件的时空分布。 研究团队由经验丰富的海洋学家组成，在仪器、生物光学、遥感、浮游植物和赤潮生态以及算法开发方面拥有多年经验，为赤潮检测和预测问题带来了一套免费和全面的专业知识。他们的最终目标是开发一套关键的工具，有效地管理与全球水体蓝藻水华有关的人类健康风险。

DESCRIPTION (provided by applicant) There are two main objectives to the proposed research: 1) to develop a new quantitative detection algorithm for Harmful Algal Blooms (HABs) in the Great Lakes based on remote sensing imagery by refining and augmenting currently qualitative algorithms; and 2) to develop an ecological niche model to predict the onset of HABs in the Great Lakes with inputs from remotely-sensed data products and state-of-the-art in-situ environmental measurements. Cyanobacteria-dominated HABs currently pose a serious health and economic risk to inhabitants and users of the Great Lakes, often producing toxins in sufficient quantity to cause severe health problems to humans and animals. The investigators' proposed research will benefit the human health and livelihood by providing tools to better manage the human health risks associated with HABs in the future based on a better understanding of the underlying ecology of HABs and improved synoptic detection methods. One component of the investigators' research will consist of implementing a monitoring program consisting of autonomous measurements - combined with ship measurements - of environmental parameters in western Lake Erie and Saginaw Bay critical to HAB initiation and promotion. These measurements will be used in the development of new satellite products that can provide synoptic observation of environmental signatures of these toxic algal blooms. The investigators will combine these new satellite products with other synoptic satellite environmental products and available in-situ environmental measurements to construct an historical data set that characterizes HAB events in the context of associated environmental conditions that cultivate the blooms. This data set will guide the development of an innovative ecological niche model based on multivariate analysis that will predict the spatial and temporal distribution of imminent HAB events from real-time environmental inputs. The investigators' team consists of seasoned oceanographers with many years of experience in instrumentation, bio-optics, remote sensing, phytoplankton and HAB ecology, and algorithm development, bringing a complimentary and comprehensive set of expertise to the problem of HAB detection and prediction. Their ultimate aim is to develop a critical set of tools for effectively managing human health risks associated with cyanobacteria blooms in water bodies around the globe.