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Examining change and long-term trends in the marine environment using satellite-based time series

机译:使用基于卫星的时间序列检查海洋环境的变化和长期趋势

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The oceans and coastal areas are dynamic environments in which variability occurs at a wide range of temporal scales, from seconds to years to decades and longer. Some very good time series now exist at specific locations, that permit characterization of this variability as well as of longer-term trends, but for much of the world ocean the in situ data is sparse and such characterization is not possible, or possible only by extrapolation. For these less well-studied areas, satellite imagery and gridded products created from a combination of satellite and in situ data are the only available sources of continuous historical information. Satellite imagery provides regular, spatially synoptic, global information, at spatial resolutions on the order of 1 km and temporal resolutions typically measured in days. Some sensors offer higher spatial resolutions but at lower temporal resolution. With accumulated time series of up to 30 years or longer for some satellites and other gridded datasets, we can now begin to use this technology to identify and track long-term change, as well as to characterize shorter-term variability. In this paper we will discuss the analysis of long-term change and trends in marine and coastal environments using examples from recent projects. We will illustrate vegetation changes in and around the Anderson River delta on the Beaufort Sea coast of the Arctic Ocean using 30 m spatial resolution Landsat imagery acquired over a 31-year period from 1972 to 2003. Using a temporal classification approach, we map both interannual variability and long-term losses on the mudflats of the delta itself, and gains on the nearby tundra. In a second example, we illustrate how chlorophyll and sea surface temperature (SST) vary locally within the northeast Pacific, and how temporal patterns and trends also vary regionally, using 24 years of weekly 4 km resolution sea surface temperature from the weather satellites, and 11 years of weekly 9 km chlorophyll composite da- ta products from ocean colour satellites. Finally, we show examples of trends and anomalies calculated from 41 years of sea ice distribution, concentrations and ice type in the Beaufort Sea, using ice chart maps produced by the Canadian Ice Service from a combination of satellite, airborne and in situ observations. The weekly ice charts provide coverage of the entire western hemisphere of the Arctic Ocean over the continental shelf, slope and rise areas at a spatial resolution of 4 km for a total of 26 distinct ice parameters spanning the years 1968 to the present. Using this rich data source, the analysis of long-term trends in relation to seasonal and interannual variability can be used to estimate potential changes due to climate change for local and regional areas, which are shown to vary considerably from one region to another. The availability of data from all of the sources discussed over extended time periods of 30 years or longer provides a means of assessing the potential degree of climate change that has been occurring over the present and earlier decades. This information can also be useful in calibrating and validating models that can be applied to anticipate and quantify the effects of climate change which may occur in future decades.
机译:海洋和沿海地区是动态环境,在广泛的时间尺度上发生变化,从几十年到几十年和更长的时间。现在存在一些非常好的时序在特定地点存在,允许对这种可变性的表征以及长期趋势,但对于大部分世界海洋而言,原位数据是稀疏的,并且不可能这样的表征,或者仅可能推断。对于这些较少学习的领域,卫星图像和从卫星组合创建的网格产品和原位数据是唯一的连续历史信息的可用来源。卫星图像在空间分辨率下提供定期,空间舞台,全球信息,大约一公里和通常在几天内测量的时间分辨率。一些传感器提供更高的空间分辨率,但在较低的时间分辨率下。对于某些卫星和其他网格数据集的累计时间序列长达30年或更长时间,我们现在可以开始使用这项技术来识别和跟踪长期变化,以及表征短期变化。在本文中,我们将讨论近期项目的例子的海洋和沿海环境的长期变化和趋势分析。我们将使用30米空间分辨率的Landsat图像从1972年至2003年获得31年期间获得的北极海洋的Beaufort海岸和Anderson河三角洲和周围地区的植被变化。使用时间分类方法,我们将互际在达美德节本身的泥滩上的变异性和长期损失,以及附近的苔原上涨。在第二个例子中,我们说明了叶绿素和海上表面温度(SST)如何在东北太平洋地区局部地差异,以及时间模式和趋势如何在天气卫星的每周4公里分辨率的海上表面温度下,以及11年每周9公里的叶绿素复合DA-TA产品来自海洋彩色卫星。最后,我们展示了Beaufort海中的41岁海冰分布,浓度和冰型计算的趋势和异常的例子,使用加拿大冰服务从卫星,空中和原位观察的组合生产的冰图地图。每周冰图表提供北极海洋的整个西半球,在大陆架,坡度和上升地区,4公里的空间分辨率,总共26年的1968年跨越现在。使用这种丰富的数据源,与季节性和际变量相关的长期趋势分析可用于估计由于本地和区域区域的气候变化导致的潜在变化,这显示出从一个地区到另一个地区的差异很大。来自30年或更长的延长时间段讨论的所有来源的数据提供了一种评估目前和早些时候发生的潜在气候变化程度的方法。该信息在校准和验证模型方面也可用于预测和量化可能发生在未来数十年中可能发生的气候变化的影响。

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