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| 基于混合像元分解的东北地区时令水体提取及变化监测 | |
| 其他题名 | The temporary water bodies extraction and monitoring in Northeast China through spectral mixture analysis |
| 王铭 | |
| 出版年 | 2017 |
| 学位类型 | 硕士 |
| 导师 | 宋开山 |
| 学位授予单位 | 中国科学院大学 |
| 中文摘要 | 时令水体通常被划分为一种湿地类型，在水体和湿地之间循环发生着相态的转换，其形成与消失、扩张与收缩，及其所引起的生态环境的变化都反映了一定地域乃至全球的气候变化规律。时令水体在生态系统中的作用不容忽视，为众多生物资源提供了栖息环境，同时也是全球水循环、碳、氮循环的组成部分，具有重要的生态学意义。东北地区地域辽阔，气候类型多样，地貌形态差异明显，在平原区域水体发达、河网密布，湖泊数量众多，发育了众多盐碱湖泊、沼泽洼地、浅水湖泊等对气候变化较为敏感的水体。在全球气候变化的大背景之下，加之人类活动的影响，湿地面积萎缩、地下水资源超采、土地荒漠化以及盐碱化等生态问题，导致东北湖区的生态环境问题日益突出，成为生态环境较为脆弱的区域之一。本文采用高时间分辨率GOCI遥感影像作为数据源，以东北地区湖泊、沼泽集中分布的平原地区作为研究区域，选择了气候、水文条件差异较为明显的特殊年份(丰水年2013年与枯水年2016年)的非结冰期(5月至10月)，基于BP神经网络模型对各监测时段研究区的影像进行混合像元分解，进而分别提取了2013年与2016年非结冰期的水体组分信息，进行了高频次的水体组分变化监测，并且分别建立时间序列下的水体组分数据集，逐像元监测了水体组分值在时间序列下的变化，最终提取出在时间序列中发生相态变化的水体像元，即时令水体，同时，通过对时令水体的相态转换频次分析、像元状态的变化分析以及在监测时段内的变化规律等，从不同的角度对时令水体进行变化监测与分析，在研究过程中，主要得到以下结论：（1）基于BP神经网络的水体提取方法相比于传统的水体提取方法(波段比值法、NDWI、MNDWI、谱间关系法、单波段阈值法等)具有更高的提取精度，尤其是对于细小河渠、小型水体以及水陆边界的提取精度要明显优于传统的水体提取方法。（2）本文提出的基于混合像元分解的时令水体面积计算方法可以较为准确的计算出时令水体的面积，实现大面积的时令水体监测与面积计算，通过计算可得，研究区内2013年全年 (非结冰期)的时令水体总面积达5819.5km2，2016年全年(非结冰期)的时令水体面积为4398.55km2。（3）东北地区的绝大多数的时令性水体在监测时间段内，会发生1-3次相态转换(水体出现或者水体消失)，2013年研究区的时令水体像元发生1-3次相态转换的像元占所有时令水体像元的89.13%，2016年研究区的时令水体像元发生1-3次相态转换的像元占所有时令水体像元的88.41%。（4）统计在各个监测时段内发生水体相态变化(水体出现或者水体消失)的像元个数，根据统计结果，同时结合气温、降雨等气象水文数据，可发现时令水体对于气候条件的变化极为敏感，气候条件的改变会直接反映在时令水体的相态变化之上。（5）在时间序列下，考虑时令水体像元的状态(纯水体像元、混合像元、非水体像元)，其中，纯水体像元或者非水体像元的比重越大的像元，在时间序列下变化最为稳定，主要分布在水体的水陆交界处；反之，混合像元的比重越大的像元，在时间序列下变化最为剧烈，主要分布在地势偏低的洼地与河网密集地带。 |
| 英文摘要 | Temporary water, usually classified as one of wetland types, arises phase-state transformation between water and wetland cycle. The regional and global variation law can be reflected by the formation, expansion, contraction and triggered eco-environment change of temporary water. Simultaneously, the temporary water, which provides favorable habitat for a large number of biological resource, exerts such a profound effect on ecological system. In addition, the temporary water, the component of global hydrologic cycle, carbon cycle, and nitrogen cycle, performs a favorable role in ecology. There is vast territory, various climate types and differential landform in Northeast China. Many kinds of waters here, like saline lakes, marshes, shallow lakes and other waters sensitive to climate change, appear in plain area on account of developed water, dense waterway and more lakes. Under the background of global climate change and the effect of human activities, some serious ecological problems have been produced by reduced wetland area, overdraft groundwater, land desertization and salinization, which leads to vulnerable eco-environment in the Northeast China.Using GOCI remote-sensing images of high time resolution, a comparative study, selected in the concentrated-distribution plain of lakes and bogs, is carried out between 2013 (high flow year) and 2016 (low flow year) on non-ice season in the Northeast China. Based on BP neural network method, mixed-pixels decomposition are used to extract water component information in monitoring time images and the high frequency change monitoring of temporary water component is conducted. Besides, water component datasets of time series are created to monitor every pixel score of water component, and then to extract the water pixels of changeable phase-state (temporary water body). Furthermore, this thesis compares and analyzes the spatial distribution characteristic, frequency and spatio-temporal change law of different climatic temporary water in 2013 and 2016. The results indicate that:(1) BP neural network extraction method, constructed by the mixed-pixel decomposition, has higher extraction precision than traditional methods such as band ratio method, NDWI, MNDWI, spectrum-photometric method, single-band threshold method and so on. More specifically, the extraction results of fine canal, small water body and water-land boundary are better than those used traditional methods.(2) According to calculation, the total area of temporary water is 5819.5km2 during 2013, while the total area of temporary water is 4398.55km2in 2016.(3) Most varying phase-state temporary water has taken place 1to 3 times during May to November, and the percentage of varying temporary water pixel of total temporary water pixel is 89.13% in 2013, whereas the percentage of varying temporary water pixel of total temporary water pixel is 88.41% in 2016.(4) The number of changeable water phase-state pixels(the water appears or disappears)are summed up in every monitoring time. According to statistical results and other hydro-meteorological data like temperature and precipitation, the temporary water is sensitive to the change of climate conditions, which means the changes of climate conditions can be correspond to the changeable phase-state of temporary water.(5) The greater the proportion of pure water pixels and non-water pixels are, the more stable the changeable state is in time series, and these pure water pixels are mainly distributed in the water and land boundary. While the greater the proportion of the mixed pixels is, the more changeable the fluctuation is in time series, and these non- water pixels are mainly distributed in depression and dense waterway area. |
| 中文关键词 | 混合像元分解 ; BP神经网络 ; GOCI ; 时令水体 |
| 英文关键词 | mixed-pixel decomposition BP neural network GOCI temporary water |
| 语种 | 中文 |
| 国家 | 中国 |
| 来源学科分类 | 地图学与地理信息系统 |
| 来源机构 | 中国科学院东北地理与农业生态研究所 |
| 资源类型 | 学位论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/287880 |
| 推荐引用方式 GB/T 7714 | 王铭. 基于混合像元分解的东北地区时令水体提取及变化监测[D]. 中国科学院大学,2017. |
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