WATERS Network: Observing and Predicting Freshwater Eutrophication-Algal Bloom Dynamics Using Local Hyperspectral Imaging

WATERS Network：利用局部高光谱成像观测和预测淡水富营养化-藻华动态

• 批准号: 0854566
• 负责人: Thomas Harmon
• 金额: $ 39.5万
• 依托单位: University of California - Merced
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0854566&HistoricalAwards=false
• 关键词: WATERS; Network; Observing; Predicting; Freshwater

WATERS Network: Observing and Predicting Nutrient-Algae Dynamics Using Ground-Based and Underwater Hyperspectral ImagingThomas C. Harmon and Qinghua GuoSchool of Engineering, University of California, MercedThis award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). ABSTRACT - Problems associated with primary production include eutrophication, hypoxia and harmful algal blooms, all of which can negatively impact human health and ecosystem services in the critical zone. The proposed research will explore expanded use of a commonly implemented remote sensing tool, hyperspectral imaging (HSI), in the realm of water quality observation. HSI has proven invaluable when deployed both aerially and on-the-ground for noninvasively classifying vegetative type and other properties and, to a lesser yet significant extent, for mapping water quality properties (e.g., chlorophyll, suspended solids, dissolved organic matter) in lakes, relatively large rivers, estuaries and coastal water bodies. Underwater application of HSI is as yet unproven, but recent evidence points to the potential of HSI to reveal complex spatiotemporal patterns of a vast array of chemicals, including nitrogen and phosphorous species critical to many aquatic ecosystems. The main objective of the proposed work is to test a strategy for expanding the application of hyperspectral imaging (HSI) to a broad array of coupled physical, chemical, and biological processes in aquatic systems. The work will emphasize the use of ground-based and underwater (as opposed to airborne) HSI. Experiments will begin in the laboratory, and extend to reservoirs and streams in the San Joaquin Valley and the Southern Sierra Critical Zone Observatory. The approach will not only provide a powerful noninvasive and real-time sensor for the simultaneous mapping of multiple water quality parameters, but the results of this work will provide a transformative sensor for the broad range of large scale observatories, such as the CZO, NEON, and proposed WATERS Network efforts.

沃茨网络：利用地基和水下高光谱成像观测和预测营养藻类动态。哈蒙和Qinghua Guo工程学院，加州大学，梅赛德斯该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助。摘要-与初级生产相关的问题包括富营养化、缺氧和有害藻华，所有这些都会对关键区的人类健康和生态系统服务产生负面影响。 拟议的研究将探讨在水质观测领域扩大使用一种常用的遥感工具，高光谱成像（HSI）。 HSI已被证明是非常宝贵的，当部署在空中和地面上的非侵入性分类植被类型和其他属性，并在较小但重要的程度上，为映射水质属性（例如，叶绿素、悬浮固体、溶解有机物）。 HSI的水下应用尚未得到证实，但最近的证据表明，HSI有可能揭示大量化学品的复杂时空模式，包括对许多水生生态系统至关重要的氮和磷物种。 拟议的工作的主要目标是测试的战略，扩大应用的高光谱成像（HSI）的耦合的物理，化学和生物过程在水生系统中的广泛阵列。 这项工作将强调使用地面和水下（而不是空中）的HSI。 实验将开始在实验室，并扩大到水库和溪流在圣华金河谷和南部塞拉利昂临界区观测站。 该方法不仅将提供一个强大的非侵入性和实时传感器的多个水质参数的同时映射，但这项工作的结果将提供一个变革性的传感器的大范围的大型观测站，如CZO，氖，并建议沃茨网络的努力。