基于多源数据的黄海绿潮遥感监测研究

发布时间：2018-01-02 06:26

本文关键词：基于多源数据的黄海绿潮遥感监测研究　出处：《中国科学院烟台海岸带研究所》2017年硕士论文　论文类型：学位论文

【摘要】：近些年来,黄海海域每年都会暴发不同规模的绿潮(大型藻类——浒苔)灾害,对当地养殖业、旅游业、交通运输业、海洋生态环境等造成了严重的危害。针对绿潮暴发持续时间长、规模大、位置不固定的特点,利用遥感手段进行监测显得尤为重要,但目前监测所用的卫星遥感数据,或空间分辨率过低,监测精度得不到保障,或时间分辨率过低,监测的时间序列跨度过大。因此,为了弥补这类不足,本文以南黄海地区为研究区,综合使用多种卫星遥感数据(GF-1 WFV、HJ-1A/1B CCD、CBERS-04 WFI、Landsat-7 ETM+、Landsta-8 OLI、MODIS SST-8day)以及无人机和船测数据,结合遥感和GIS技术对2014至2016年黄海绿潮进行了监测,并在此基础上进行了相关研究。本文的主要研究内容为:(1)通过对同一影像分别采用不同的大气校正方法进行校正,并对多个变化量的进行统计与分析,确定当使用NDVI指数提取绿潮时,效果最好的大气校正方法。(2)分析2014年至2016年黄海绿潮的时空分布特征;对不同数据源的监测结果进行对比。(3)以2016年的黄海海表温度和绿潮监测结果为例,分析两者之间的相关性;根据本文的研究结果,从遥感角度研究绿潮灾害的防控策略。研究结果表明:(1)当采用NDVI阈值法提取绿潮信息时,COST大气校正后的影像绿潮提取效果最好,FLAASH、6S大气校正依次次之,但COST大气校正在其它绿潮提取算法中的适应性仍待进一步考证。(2)宏观上看,2014至2016年黄海绿潮的时空分布特征基本一致,四月底至五月初绿潮初生于江苏辐射沙脊群,五月份不断生长并持续向北推移,六月份开始暴发,7月份绿潮开始消亡,至八月份绿潮灾害基本结束;通过对多种数据源的监测结果进行对比,空间分辨率带来的混合像元效应是产生监测误差的主要原因。(3)黄海海表温度与绿潮的暴发具有很大的相关性;为了防控绿潮灾害,首先从长远来看,要从源头降低海水富营养化程度,对于近期暴发的绿潮,可采取前置打捞,及时预警的策略,降低绿潮带来的危害。综上分析,本研究利用多源数据对黄海绿潮进行动态监测,并比较了监测结果,提升了监测精度和置信度,具有一定的创新性。另外,本研究从影响绿潮提取的大气校正因素、时空分布特征、监测结果对比、温度因子与绿潮暴发的相关性、防控策略等方面对黄海绿潮展开研究,一定程度上丰富了对绿潮的认识,对防治绿潮、减少损失具有重要的现实意义。
[Abstract]:In recent years, Huang Hai sea area each year will break out different scale green tide (macroalgae-margin) disaster, to the local aquaculture, tourism, transportation. The marine ecological environment has caused serious harm. It is very important to use remote sensing to monitor the green tide outbreak because of its long duration large scale and irregular position. But at present, the satellite remote sensing data used in monitoring is too low spatial resolution, the monitoring accuracy is not guaranteed, or the time resolution is too low, monitoring time series span is too large. Therefore, in order to make up for this kind of deficiency. In this paper, the southern Huang Hai area is used as the research area, and various satellite remote sensing data are used synthetically, such as GF-1 WFVV HJ-1A / 1B CCDERS-04 WFI. Landsat-7 ETM Landsta-8 OLI MODIS SST-8day) and UAV and ship survey data. The green tide of Huang Hai from 2014 to 2016 was monitored by remote sensing and GIS technology. In this paper, the main content of this study is: 1) through the same image using different atmospheric correction methods, and the statistical analysis of a number of variables. When using NDVI index to extract the green tide, the most effective atmospheric correction method. 2) to analyze the spatial and temporal distribution characteristics of Huang Hai green tide from 2014 to 2016. The correlation between Huang Hai sea surface temperature and green tide in 2016 was analyzed by comparing the monitoring results of different data sources. According to the results of this paper, the prevention and control strategy of green tide disaster is studied from the view of remote sensing. The results show that the NDVI threshold method is used to extract the green tide information. After COST atmospheric correction, the effect of green tide extraction was the best, followed by FLAASHN 6S atmospheric correction. However, the adaptability of COST atmospheric correction in other green tide extraction algorithms still needs further research. (2) from 2014 to 2016, the spatial and temporal distribution of Huang Hai green tide is basically the same. From the end of April to the beginning of May, the green tide first occurred in the radiative sand ridge of Jiangsu Province, and it continued to grow and move northward in May. The green tide began to break out in June and the green tide began to die out in July, and the green tide disaster basically ended in June. By comparing the monitoring results of various data sources, the mixed pixel effect caused by spatial resolution is the main cause of monitoring error.) Huang Hai sea surface temperature has a great correlation with the outbreak of green tide. In order to prevent and control the green tide disaster, first of all, from the source to reduce the level of eutrophication of sea water, for the recent outbreak of green tide, can take forward salvage, timely early warning strategy. To reduce the harm caused by green tide. In summary analysis, this study uses multi-source data to dynamically monitor Huang Hai green tide, and compares the monitoring results, which improves the monitoring accuracy and confidence, and has certain innovation. This study studied Huang Hai green tide from the following aspects: atmospheric correction factors, temporal and spatial distribution characteristics, monitoring results, correlation between temperature factor and green tide outbreak, prevention and control strategy, etc. To some extent, it enriches the understanding of green tide, and has important practical significance for preventing green tide and reducing losses.
【学位授予单位】：中国科学院烟台海岸带研究所
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：X87;X55

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