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首页> 外文期刊>Ecological indicators >Applying remote sensing techniques to monitoring seasonal and interannual changes of aquatic vegetation in Taihu Lake, China
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Applying remote sensing techniques to monitoring seasonal and interannual changes of aquatic vegetation in Taihu Lake, China

机译：运用遥感技术监测太湖水生植物的季节和年际变化

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摘要

Knowledge of the composition and areal distribution of aquatic vegetation types, as well as their seasonal and interannual variations, is crucial for managing and maintaining the balance of lake ecosystems. In this study, a series of remotely sensed images with a resolution of 30 m (HJ-CCD and Landsat TM) were collected and used to map the distribution of aquatic vegetation types in Taihu Lake, China. Seasonal and interannual dynamics of aquatic vegetation types were explored and analyzed. The distribution areas of Type I (emergent, floating-leaved and floating vegetation) and Type II (submerged vegetation) were used to model their growing season phenology by double logistic functions. The resulting double logistic models showed, the area of Type I reached its peak in mid-August, and the maximum area for Type II occurred in mid-September. From 1984 to 2013, Type I area increased continuously from 59.75 km(2) to 148.00 km(2) (R-2 = 0.84), whereas the area covered by Type II first increased and then decreased, with a trend conforming to a significant quadratic curve (R-2 = 0.83). The eutrophication and stable state of Taihu Lake was assessed using a simple indicator which was expressed as a ratio of Type II area to Type I area. The results showed that the eutrophication in the lake might have been increasing in the area studied since 2000. Additionally, the results showed that air temperature had likely a direct effect on the growth of Type I (R-2 = 0.66) and a significant, but delayed, effect on the growth of Type II. (C) 2015 Elsevier Ltd. All rights reserved.
机译：了解水生植被类型的组成和面积分布及其季节性和年际变化，对于管理和维持湖泊生态系统的平衡至关重要。在这项研究中，收集了一系列分辨率为30 m的遥感图像（HJ-CCD和Landsat TM），并用于绘制中国太湖水生植被类型的分布图。探索和分析了水生植被类型的季节性和年际动态。利用双重逻辑函数，将I型（紧急，浮叶和浮动植被）和II型（淹没植被）的分布区域用于模拟其生长季节物候。由此产生的双重逻辑模型表明，I型面积在8月中旬达到峰值，II型最大面积在9月中旬出现。从1984年到2013年，类型I的面积从59.75 km（2）连续增加到148.00 km（2）（R-2 = 0.84），而类型II的面积先增大然后减小，趋势是显着的二次曲线（R-2 = 0.83）。太湖的富营养化和稳定状态使用简单指标进行评估，该指标表示为II型面积与I型面积之比。结果表明，自2000年以来，该地区的湖泊富营养化程度可能一直在增加。此外，结果表明，气温可能直接影响I型细菌的生长（R-2 = 0.66），并且显着但延迟了对II型生长的影响。 （C）2015 Elsevier Ltd.保留所有权利。

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  • 来源
    《Ecological indicators》 |2016年第1期|503-513|共11页
  • 作者

    Luo Juhua; Li Xinchuan; Ma Ronghua; Li Fei; Duan Hongtao; Hu Weiping; Qin Boqiang; Huang Wenjiang;

  • 作者单位

    Chinese Acad Sci, Nanjing Inst Geog & Limnol, Key Lab Watershed Geog Sci, Nanjing 210008, Jiangsu, Peoples R China;

    Hohai Univ, Sch Earth Sci & Engn, Nanjing 210098, Jiangsu, Peoples R China;

    Chinese Acad Sci, Nanjing Inst Geog & Limnol, Key Lab Watershed Geog Sci, Nanjing 210008, Jiangsu, Peoples R China;

    Chinese Acad Sci, Nanjing Inst Geog & Limnol, Key Lab Watershed Geog Sci, Nanjing 210008, Jiangsu, Peoples R China;

    Chinese Acad Sci, Nanjing Inst Geog & Limnol, Key Lab Watershed Geog Sci, Nanjing 210008, Jiangsu, Peoples R China;

    Chinese Acad Sci, Nanjing Inst Geog & Limnol, Key Lab Watershed Geog Sci, Nanjing 210008, Jiangsu, Peoples R China;

    Chinese Acad Sci, Nanjing Inst Geog & Limnol, Key Lab Watershed Geog Sci, Nanjing 210008, Jiangsu, Peoples R China;

    Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China;

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  • 正文语种 eng
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  • 关键词

    Wetland; Remote sensing; Aquatic vegetation; Classification tree; Taihu Lake;

    机译：湿地遥感水生植被分类树太湖;

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