Development of novel detection and prediction algorithms for Microcystis blooms
Development of novel detection and prediction algorithms for Microcystis blooms
批准号:
8387923
负责人:
Timothy Moore
金额:
$6.37万
依托单位国家:
美国
项目类别:
财政年份:
2012
资助国家:
美国
项目状态:
已结题
起止时间:
2012-09-24 至 2015-07-31
关键词:
AddressAlgal BloomsAlgorithmsAnimalsCoupledCyanobacteriumDataData CollectionData SetDatabasesDetectionDevelopmentDisease OutbreaksEcologyEconomicsEventEvolutionFresh WaterFutureGoalsGreat Lakes RegionHealthHealth SciencesHot SpotHumanImageryIn SituLeadLifeMapsMeasurementMeasuresMethodsMetricMicrocystisModelingMonitorMultivariate AnalysisNutrientOpticsOrganismPhytoplanktonPoliciesProbabilityPropertyPublic HealthResearchResearch PersonnelRiskSeasonsShippingShipsSpatial DistributionStreamSurfaceSystemTechnologyTimeToxinWaterWater SupplyWorkbasecostdrinking waterenvironmental health economicsexperiencefallsharmful algal bloomshealth economicsimprovedindexinginnovationinstrumentationnovelprogramspublic health relevanceremote sensingskillstooltoxic bloomwater quality
中文摘要
描述(由申请人提供)
本研究的主要目标有两个:1)通过对现有定性算法的改进和扩充,提出一种新的基于遥感图像的五大湖赤潮定量检测算法;以及2)开发一个生态小生境模型,利用遥感数据产品和最新技术的投入,预测五大湖中有害生物的发生。现场环境测量。
目前,以蓝细菌为主的有害藻华对五大湖的居民和使用者构成了严重的健康和经济风险,往往产生的毒素足以对人类和动物造成严重的健康问题。 研究人员提出的研究将通过提供工具来更好地管理未来与赤潮相关的人类健康风险,从而有利于人类健康和生计,这是基于对赤潮潜在生态的更好理解和改进的天气检测方法。调查人员研究的一个组成部分将包括实施一个监测方案,该方案包括对伊利湖西部和萨吉诺湾的环境参数进行自主测量-结合船舶测量-这对赤潮的启动和推广至关重要。这些测量结果将用于开发新的卫星产品,以便对这些有毒藻华的环境特征进行天气观测。 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,在培养水华的相关环境条件的背景下描述HAB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空间和时间分布。
调查小组由经验丰富的海洋学家组成,他们在仪器、生物光学、遥感、浮游植物和赤潮生态学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全面的专业知识。他们的最终目标是开发一套关键的工具,用于有效管理与地球仪水体中蓝藻水华相关的人类健康风险。
公共卫生相关性:发生在五大湖的蓝细菌有害生物可产生各种毒素,对人类和动物的健康产生严重影响,并造成严重的经济破坏。在赤潮事件期间,五大湖饮用水供应中的毒素浓度限制经常被超过。研究人员提出的研究将提高五大湖地区目前对有害生物的检测和预测能力,并将直接有益于生活在该地区的许多美国公民的健康和经济方面。 调查人员带来了遥感、原位观测技术和浮游植物生态学方面的广泛经验,这些经验可以帮助解决与五大湖有毒水华有关的健康和科学问题。
英文摘要
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.
Public Health Relevance: Cyanobacteria HABs that occur in the Great Lakes can produce a wide range of toxins with severe health impacts on humans and animals, and also cause significant economic disruption. During HAB events, toxin concentration limits in the Great Lakes drinking water supply are often exceeded. The investigators' proposed research will improve current detection and prediction capabilities of HABs in the Great Lakes region, and will be directly beneficial to health and economic aspects of the many U.S. citizens who live in the region. The investigators bring a broad range of experience in remote sensing, in situ observational technology, and phytoplankton ecology that can help address the health and science issues related to toxic blooms in the Great Lakes.
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会议论文
Development of novel detection and prediction algorithms for Microcystis blooms
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批准号:8554363
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项目类别:
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资助金额:$6.65万
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财政年份:2012
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负责人:Timothy Moore
-
依托单位:
Development of novel detection and prediction algorithms for Microcystis blooms
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批准号:8705519
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项目类别:
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资助金额:$5.61万
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财政年份:2012
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负责人:Timothy Moore
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依托单位:
Research Infrastructure Core
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批准号:10589035
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项目类别:
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资助金额:$70.86万
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财政年份:1997
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负责人:Timothy Moore
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依托单位:
Research Infrastructure Core
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批准号:10361203
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项目类别:
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资助金额:$50.25万
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财政年份:1997
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负责人:Timothy Moore
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依托单位:
海外基金