潮滩底栖微藻生物量遥感反演研究
李少朋
学位类型硕士
导师邢前国
2014-05
学位授予单位中国科学院研究生院
学位授予地点北京
学位专业环境工程
关键词底栖微藻 高光谱 遥感反演 叶绿素浓度 Microphytobenthos Hyperspectrum Remote Sensing Retrieval Chlorophyll Concentration
摘要本文以四十里湾潮滩作为主要研究区,以潮滩底栖微藻生物量的遥感反演和监测为目标,采用实测高光谱数据和实测叶绿素浓度数据,构建适用于潮滩的底栖微藻生物量遥感反演模式,同时尝试揭示四十里湾潮滩底栖微藻的时空变化规律并分析其影响机制。 首先,通过实测的高光谱遥感反射率和叶绿素浓度数据,建立底栖微藻叶绿素浓度的波段比值、波段差值及其他波段组合遥感估测模型,并利用同一区域的实测数据进行验证。结果表明,基于R700/R675比值模型的反演精度最高,其反演值和实测值的均方根误差和平均相对误差分别为4.01µg/cm3、14.6%。 其次,利用实测高光谱遥感数据分别模拟Hyperion、HICO、Rapid Eye波段,建立叶绿素浓度波段组合的估测模型,并利用实测数据进行验证。结果表明,基于波段比值建立的模型的反演精度较高。其中,验证结果最好的是Hyperion传感器的B35和B33的波段比值,平均相对误差为8.94%,均方根误差为4.14µg/cm3。另外,利用2013年4月12日的韩国西南海岸潮滩Rapid Eye卫星影像得到基于波段比值模型的底栖微藻生物量遥感反演结果,反演效果比较理想。 分别以3mm和1mm分层间隔对柱状沉积物进行分层,分析叶绿素垂直分布特征;模拟垂直衰减曲线斜率,分析潮滩叶绿素垂直分布特征对遥感反演模式的影响。结果表明,潮滩表层1cm和3cm内,分别以1mm和3mm分层的柱样底栖微藻叶绿素浓度均随着深度增加呈指数式衰减。 利用实测高光谱数据,基于模拟的Hyperion卫星传感器遥感反演模型,反演得到烟台大学东门断面2012年11月-2013年4月期间的潮滩表层底栖微藻生物量。 利用实测数据,重点分析了沉积物组分特征及脱镁叶绿素对潮滩底栖微藻生物量的影响,结果表明,不同底质条件的潮滩,其沉积物中细颗粒物质含量与叶绿素a浓度的关系不同,脱镁叶绿素的存在会影响底栖微藻生物量与光谱特征波段组合之间的相关性。
其他摘要In this paper, intertidal flat of Sishili Bay was the interested area, and the objective of this study was to retrieve and monitor biomass of microphytobenthos using in situ measurement data and remote sensing data. Firstly, retrieval algorithms for estimating chlorophyll-a concentration(Chl-a) of microphytobenthos were established using field data such as hyperspectral data and Chl-a(proxy of microphytobenthos biomass in this study) data. Results showed that ratio algorithm of 675nm to 700nm were better than other retrieval models. The MRE (Mean Relative Error) and the RMSE (Root Mean Square Error) of the retrieved and measured data on the basis of the ratio model were 4.01µg/cm3(n=10) and 14.6% (n=10). Secondly, retrieval algorithms of Chl-a were established on the basis of simulated bands of Hyperion, HICO and Rapid Eye. Moreover, field data which was collected from Muping tidal flat in August 2013 was used to test the retrieval models. Results showed that the precision of band ratio algorithms were better than other retrieval models. The MRE and the RMSE of the band ratio model of B35/B33 of Hyperion were 8.94% (n=10) and 4.14µg/cm3 (n=10). Furthermore, a remote sensing image of Rapid Eye was used to monitor the Chl-a in the tidal flat of southwest of Korea on the basis of Rapid Eye simulated band ratio model. Thirdly, Chl-a of microphytobenthos of tidal flat sediments core was measured with a resolution of 3 mm and 1 mm in day time at the Sishili Bay; and surface reflectance of tidal flat was simultaneously measured. Chl-a remote sensing retrieval algorithm was established on the regression with the NDI-MPB (Normalized Difference Index of Microphytobenthos) which is based on the reflectance at 675nm and 700nm. In situ data showed that Chl-a decreased in an exponential function with the increase in depth, and the vertical distribution of Chl-a might be characterized by the slop of the exponentially decreasing model of Chl-a. The impacts of variations in the vertical distribution of Chl-a on the remote sensing retrieval algorithms were simulated, and results showed that Chl-a retrieval algorithms changed with its vertical distribution. Finally, simulated band ratio model of Hyperion was developed to estimate Chl-a of microphytobenthos from November 2012 to April 2013 in the tidal flat of Yantai coast. Relationship between Chl-a and fine-grained sediment content as well as pheophytin were analyzed, and results showed that there was a remarkable correlation between Chl-a and sediment components; and the correlation between Chl-a and reflectance could be affected by pheophytin.
语种中文
文献类型学位论文
条目标识符http://ir.yic.ac.cn/handle/133337/6810
专题中国科学院烟台海岸带研究所知识产出_学位论文
推荐引用方式
GB/T 7714
李少朋. 潮滩底栖微藻生物量遥感反演研究[D]. 北京. 中国科学院研究生院,2014.
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