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Quantifying total suspended matter (TSM) in waters using Landsat images during 1984-2018 across the Songnen Plain, Northeast China

机译:使用Landsat影像在1984-2018年间在中国东北的松嫩平原上量化水中的总悬浮物(TSM)

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摘要

Understanding the spatiotemporal dynamics of total suspended matter (TSM) in waters is necessary to promote efficient water resource management. In our study, we have estimated the spatiotemporal pattern of TSM with the combination of time-series Landsat images and field survey. Among various remote sensing-derived parameters, the red/blue band turns to be robust and the most sensitive to the TSM from field measurements. In Songnen Plain, the mean annual TSM in 60.5% of the water bodies decreased from 1984 to 2018. The decreasing of TSM is likely due to the increasing of vegetation in the area. The TSM concentration in waters declined from April to July, and then increased from September onwards. We also found the TSM in water bodies in Songnen Plain has very high spatial variation. Our results indicated that the meteorological factors such as wind and precipitation may affect the variation of TSM. Our results demonstrate that long-term Landsat data are useful to examine TSM in inland waters. Our findings can support for water resource management under human activities and climate change.
机译:了解水中总悬浮物(TSM)的时空动态对于促进有效的水资源管理是必要的。在我们的研究中,我们结合时间序列Landsat影像和野外调查估计了TSM的时空格局。在各种遥感参数中,红/蓝波段变得更健壮,并且对现场测量的TSM最敏感。在松嫩平原,60.5%的水体年平均TSM从1984年到2018年下降。TSM的下降很可能是由于该地区植被的增加。水体中TSM的浓度从4月到7月下降,然后从9月开始上升。我们还发现松嫩平原水体中的TSM具有很高的空间变化。我们的结果表明,诸如风和降水之类的气象因素可能会影响TSM的变化。我们的结果表明,长期的Landsat数据对于检查内陆水域的TSM非常有用。我们的发现可以支持人类活动和气候变化下的水资源管理。

著录项

  • 来源
    《Journal of Environmental Management》 |2020年第may15期|110334.1-110334.12|共12页
  • 作者单位

    Northeast Institute of Geography and Agroecology Chinese Academy of Sciences 4888 Shengbei Road Changchun Jilin Province 130102 China University of Chinese Academy of Sciences No.l9A Yuquan Road Beijing 100049 China;

    Northeast Institute of Geography and Agroecology Chinese Academy of Sciences 4888 Shengbei Road Changchun Jilin Province 130102 China School of Environment and Planning Liaocheng University Liaocheng 252000 China;

    Northeast Institute of Geography and Agroecology Chinese Academy of Sciences 4888 Shengbei Road Changchun Jilin Province 130102 China;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Inland water; Landsat images; Water quality parameters; TSM;

    机译:内陆水;Landsat图片;水质参数;TSM;

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