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A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans

机译:海洋颜色遥感方法和统计技术综述,用于沿海和公海浮游植物开花的检测,制图和分析

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摘要

The need for more effective environmental monitoring of the open and coastal ocean has recently led to notable advances in satellite ocean color technology and algorithm research. Satellite ocean color sensors' data are widely used for the detection, mapping and monitoring of phytoplankton blooms because earth observation provides a synoptic view of the ocean, both spatially and temporally. Algal blooms are indicators of marine ecosystem health; thus, their monitoring is a key component of effective management of coastal and oceanic resources. Since the late 1970s, a wide variety of operational ocean color satellite sensors and algorithms have been developed. The comprehensive review presented in this article captures the details of the progress and discusses the advantages and limitations of the algorithms used with the multi-spectral ocean color sensors CZCS, SeaWiFS, MODIS and MERIS. Present challenges include overcoming the severe limitation of these algorithms in coastal waters and refining detection limits in various oceanic and coastal environments. To understand the spatio-temporal patterns of algal blooms and their triggering factors, it is essential to consider the possible effects of environmental parameters, such as water temperature, turbidity, solar radiation and bathymetry. Hence, this review will also discuss the use of statistical techniques and additional datasets derived from ecosystem models or other satellite sensors to characterize further the factors triggering or limiting the development of algal blooms in coastal and open ocean waters.
机译:最近,需要对开放海洋和沿海海洋进行更有效的环境监控,这导致了卫星海洋色彩技术和算法研究的显着进步。卫星海洋颜色传感器的数据被广泛用于浮游植物浮游生物的检测,制图和监测,因为地球观测可在空间和时间上提供海洋的概要视图。藻华是海洋生态系统健康的指标;因此,对它们的监测是有效管理沿海和海洋资源的关键组成部分。自1970年代后期以来,已经开发了多种可操作的海洋彩色卫星传感器和算法。本文中提出的全面综述捕获了进展的细节,并讨论了与多光谱海洋颜色传感器CZCS,SeaWiFS,MODIS和MERIS配合使用的算法的优点和局限性。当前的挑战包括克服这些算法在沿海水域中的严重局限性,以及完善各种海洋和沿海环境中的检测极限。要了解藻华的时空格局及其触发因素,必须考虑环境参数的可能影响,例如水温,浊度,太阳辐射和测深。因此,本篇综述还将讨论使用统计技术和从生态系统模型或其他卫星传感器获得的其他数据集,以进一步表征触发或限制沿海和开放海域藻华生长的因素。

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  • 来源
    《Progress in Oceanography》 |2014年第4期|123-144|共22页
  • 作者单位

    North Australian Marine Research Alliance (NAMRA), The Research Institute for the Environment and Livelihoods (RIEL), Charles Darwin University, Darwin, Australia;

    Fisheries and Oceans Canada, Institute of Ocean Sciences, Sidney, Canada;

    CSIRO Land and Water, Aquatic Remote Sensing Group, Canberra, Australia,School of Geography, Planning and Environmental Management, The University of Queensland, Brisbane, Australia;

    School of Geography, Planning and Environmental Management, The University of Queensland, Brisbane, Australia;

    CSIRO Land and Water, Aquatic Remote Sensing Group, Canberra, Australia,School of Geography, Planning and Environmental Management, The University of Queensland, Brisbane, Australia;

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