基于软分类的太湖水体叶绿素a浓度遥感反演与长时间序列分析
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摘要
近年来,中国的水体污染问题越来越严重,特别是内陆湖泊的富营养化问题最为突出。叶绿素是影响光合作用的最重要的色素,是水体富营养化的重要指示参量。卫星遥感具有成本低、范围广、速度快、连续性好等优势,在水环境监测中发挥了越来越重要的作用。叶绿素a浓度反演是水色遥感的重要研究内容,而浑浊富营养化内陆水体的叶绿素a浓度反演一直是个难点。本文主要研究内容就是利用遥感技术监测浑浊富营养化的太湖水体的叶绿素a浓度,在此基础上分析其空间分布特点和长时间变化趋势,并初步结合降水、气温、风速等因素解释了太湖叶绿素a浓度的变化规律。
本文在水体光学分类研究的基础上提出了两种基于软分类(模糊分类)的水体叶绿素a浓度反演策略,相应的将已有的叶绿素a浓度反演方法归结为传统反演策略和硬分类反演策略。本文以太湖为研究对象获取大量实测水面光谱数据和MERIS卫星图像,分别实现了基于实测水面光谱的和基于MERIS图像的软分类反演策略,通过对比传统反演策略和硬分类反演策略评价了软分类反演策略的精度。采用精度最高的软分类反演策略,建立了MERIS长时间序列批量数据叶绿素a浓度反演流程,解决了反演过程的关键技术难题。最后,反演了太湖2002～2012年的叶绿素a浓度,分析了十年来太湖叶绿素a浓度的空间分布特征以及年际变化、季节变化和月变化规律与发展趋势。
通过利用实测水面光谱和MERIS图像对不同反演策略的精度检验发现：硬分类反演策略比传统反演策略的精度有所提高,软分类反演策略取得了最好的精度,且普适性最强。十年来,太湖叶绿素a浓度的空间分布呈现从北向南递减的规律；年际变化呈现“W”字形的波动变化趋势；季节变化显著：冬季最低,春季升高,夏季最高,秋季开始降低；月变化规律总体呈现冬季月份低、夏季月份高的“Λ”字形,且以年为周期的波动变化。
本文的主要研究贡献有以下三个方面：
1)提出了4种叶绿素a浓度反演策略,全面评价了4种反演策略的精度,以及不同反演策略下5种基于水面光谱的和12种基于MERIS数据的叶绿素a浓度反演算法的精度,通过比较找到了太湖不同类型水体对应的最优算法和模型。
2)论证了MERIS2P离水辐射产品在太湖的适用性。通过MERIS2P遥感反射率数据与实测遥感反射率的对比分析,评估了其在太湖的适用性。
3)反演了太湖2002～2012年共1932景MERIS数据的叶绿素a浓度,并据此全面分析了十年来太湖叶绿素a浓度的年际变化、季节变化和月变化规律和发展趋势。
本文的创新点主要体现在以下两个方面：
1)创新性地提出了基于软分类的太湖水体叶绿素a浓度反演策略。该反演策略通过最优算法和距离权重的加权融合提高了算法的稳定性、可靠性以及结果的平滑性、连续性。软分类反演策略解决了叶绿素a浓度反演算法的区域性和季节性适用性问题,提高了算法的普适性。
2)创新性地提出了矢量边界辅助下的MERIS数据水体自动提取方法。该方法实现了MERIS数据水体提取阂值的自动化选择,提高了水体提取的精度和对大批量数据的处理效率。
Water pollution, especially eutrophication of inland waters, has become more and more serious in China in recent years. Chlorophyll-a (Chla) is the most important pigment in phytoplankton for photosynthesis, and its concentration is an important index of eutrophication. Satellite remote sensing plays a more and more important role in water environmental monitoring with the advantages of low cost, wide range, fast speed, good continuity, and so on. Chlorophyll-a concentration (Cchla) estimation is an important content of water color remote sensing, while it is always difficult in eutrophic turbid inland water. The main content of this study was to estimate Chla concentration of eutrophic turbid inland water, such as Taihu lake in eastern China, by using the technology of remote sensing. And the spatial distribution rules and long time trend of Cchla of Taihu Lake were further analyzed.
In this study, we presented a new Chla estimation strategy, which was called soft classification (fuzzy classification) based estimation strategy. In comparision, we renamed the existing algorithms as tradition estimation strategy and hard classification based estimation strategy. We selected the eutrophic turbid inland water Taihu lake as the research area, and obtained a large number of data in Taihu lake, including water surface spectra and MERIS satellite images. We actualized the estimation strategies used these data, and evaluated the soft classification based estimation strategy by comparing with tradition estimation strategy and hard classification based estimation strategy. Then, we developed a Chla estimation technological process for massive and long time MERIS data by soft classification based estimation strategy, and solved some key technical problems in the process. Finally, we estimated Cchla in Taihu lake from2002to2012by MERIS data, and analyzed the characteristics of space distribution and the annual, seasonal and monthly variation and trend of of Cchlas in Taihu lake.
We found the following phenomenon from the results of estimation strategies accuracy evaluation by water surface spectra and MERIS satellite images. The accuracy of hard classification based estimation strategy was better than tradition estimation strategy; the accuracy of soft classification based estimation strategy was the best, and its universality was also the best. Over the last decade, the spatial distribution of Chla decreased progressively from north to south in Taihu lake; the annual variation obeyed the shape of "W"; the seasonal variation was remarkable: lowest in winter, rising in spring, highest in summer, and reducing in autumn; the monthly variation obeyed the shape of "A" which showed low in the months of winter and high in the months of summer, and the fluctuant variation on one year cycle.
The mainly contributions of this study are as follows:
Firstly, we summarized and presented four Chla estimation strategy. Then, we comprehensively evaluated the accuracies of the four estimation strategy with five Chla estimation algorisms based on water surface spectra, and with twelve algorisms based on MERIS images. We found optimal algorisms and models of different type of waters by comparasion.
Secondly, We demonstrated the suitability of MERIS2P remote sensing reflectance product in Taihu lake by comparing the MERIS2P data and the field measured reflectance.
Thirdly, We estimated Cchla to1932scenes of MERIS images from2002to2012in Taihu lake, and then analyzed the spatial distribution, annual change, seasonal change and lunar change of Cchla in Taihu lake.
The main innovation of this study are as follows:
Firstly, We developed a soft classification based estimation strategy for Cchla estimation in Taihu lake. The strategy can improve the stability, reliability and smoothness of Cchla estimation results. This estimation strategy had solved the regional and seasonal limitations of the traditional Cchla estimation methods. Secondly, We developed an automatic water extraction methods assisted by water vector boundary data. This method greatly improved the water extraction accuracy and the ability to process mass data.
本文在水体光学分类研究的基础上提出了两种基于软分类(模糊分类)的水体叶绿素a浓度反演策略,相应的将已有的叶绿素a浓度反演方法归结为传统反演策略和硬分类反演策略。本文以太湖为研究对象获取大量实测水面光谱数据和MERIS卫星图像,分别实现了基于实测水面光谱的和基于MERIS图像的软分类反演策略,通过对比传统反演策略和硬分类反演策略评价了软分类反演策略的精度。采用精度最高的软分类反演策略,建立了MERIS长时间序列批量数据叶绿素a浓度反演流程,解决了反演过程的关键技术难题。最后,反演了太湖2002～2012年的叶绿素a浓度,分析了十年来太湖叶绿素a浓度的空间分布特征以及年际变化、季节变化和月变化规律与发展趋势。
通过利用实测水面光谱和MERIS图像对不同反演策略的精度检验发现：硬分类反演策略比传统反演策略的精度有所提高,软分类反演策略取得了最好的精度,且普适性最强。十年来,太湖叶绿素a浓度的空间分布呈现从北向南递减的规律；年际变化呈现“W”字形的波动变化趋势；季节变化显著：冬季最低,春季升高,夏季最高,秋季开始降低；月变化规律总体呈现冬季月份低、夏季月份高的“Λ”字形,且以年为周期的波动变化。
本文的主要研究贡献有以下三个方面：
1)提出了4种叶绿素a浓度反演策略,全面评价了4种反演策略的精度,以及不同反演策略下5种基于水面光谱的和12种基于MERIS数据的叶绿素a浓度反演算法的精度,通过比较找到了太湖不同类型水体对应的最优算法和模型。
2)论证了MERIS2P离水辐射产品在太湖的适用性。通过MERIS2P遥感反射率数据与实测遥感反射率的对比分析,评估了其在太湖的适用性。
3)反演了太湖2002～2012年共1932景MERIS数据的叶绿素a浓度,并据此全面分析了十年来太湖叶绿素a浓度的年际变化、季节变化和月变化规律和发展趋势。
本文的创新点主要体现在以下两个方面：
1)创新性地提出了基于软分类的太湖水体叶绿素a浓度反演策略。该反演策略通过最优算法和距离权重的加权融合提高了算法的稳定性、可靠性以及结果的平滑性、连续性。软分类反演策略解决了叶绿素a浓度反演算法的区域性和季节性适用性问题,提高了算法的普适性。
2)创新性地提出了矢量边界辅助下的MERIS数据水体自动提取方法。该方法实现了MERIS数据水体提取阂值的自动化选择,提高了水体提取的精度和对大批量数据的处理效率。
Water pollution, especially eutrophication of inland waters, has become more and more serious in China in recent years. Chlorophyll-a (Chla) is the most important pigment in phytoplankton for photosynthesis, and its concentration is an important index of eutrophication. Satellite remote sensing plays a more and more important role in water environmental monitoring with the advantages of low cost, wide range, fast speed, good continuity, and so on. Chlorophyll-a concentration (Cchla) estimation is an important content of water color remote sensing, while it is always difficult in eutrophic turbid inland water. The main content of this study was to estimate Chla concentration of eutrophic turbid inland water, such as Taihu lake in eastern China, by using the technology of remote sensing. And the spatial distribution rules and long time trend of Cchla of Taihu Lake were further analyzed.
In this study, we presented a new Chla estimation strategy, which was called soft classification (fuzzy classification) based estimation strategy. In comparision, we renamed the existing algorithms as tradition estimation strategy and hard classification based estimation strategy. We selected the eutrophic turbid inland water Taihu lake as the research area, and obtained a large number of data in Taihu lake, including water surface spectra and MERIS satellite images. We actualized the estimation strategies used these data, and evaluated the soft classification based estimation strategy by comparing with tradition estimation strategy and hard classification based estimation strategy. Then, we developed a Chla estimation technological process for massive and long time MERIS data by soft classification based estimation strategy, and solved some key technical problems in the process. Finally, we estimated Cchla in Taihu lake from2002to2012by MERIS data, and analyzed the characteristics of space distribution and the annual, seasonal and monthly variation and trend of of Cchlas in Taihu lake.
We found the following phenomenon from the results of estimation strategies accuracy evaluation by water surface spectra and MERIS satellite images. The accuracy of hard classification based estimation strategy was better than tradition estimation strategy; the accuracy of soft classification based estimation strategy was the best, and its universality was also the best. Over the last decade, the spatial distribution of Chla decreased progressively from north to south in Taihu lake; the annual variation obeyed the shape of "W"; the seasonal variation was remarkable: lowest in winter, rising in spring, highest in summer, and reducing in autumn; the monthly variation obeyed the shape of "A" which showed low in the months of winter and high in the months of summer, and the fluctuant variation on one year cycle.
The mainly contributions of this study are as follows:
Firstly, we summarized and presented four Chla estimation strategy. Then, we comprehensively evaluated the accuracies of the four estimation strategy with five Chla estimation algorisms based on water surface spectra, and with twelve algorisms based on MERIS images. We found optimal algorisms and models of different type of waters by comparasion.
Secondly, We demonstrated the suitability of MERIS2P remote sensing reflectance product in Taihu lake by comparing the MERIS2P data and the field measured reflectance.
Thirdly, We estimated Cchla to1932scenes of MERIS images from2002to2012in Taihu lake, and then analyzed the spatial distribution, annual change, seasonal change and lunar change of Cchla in Taihu lake.
The main innovation of this study are as follows:
Firstly, We developed a soft classification based estimation strategy for Cchla estimation in Taihu lake. The strategy can improve the stability, reliability and smoothness of Cchla estimation results. This estimation strategy had solved the regional and seasonal limitations of the traditional Cchla estimation methods. Secondly, We developed an automatic water extraction methods assisted by water vector boundary data. This method greatly improved the water extraction accuracy and the ability to process mass data.
引文
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