| 基于SAR影像后向散射特性的中俄黑龙江流域洪水监测 |
| |
| 引用本文： | 张文璇,王卷乐.基于SAR影像后向散射特性的中俄黑龙江流域洪水监测[J].地球信息科学,2022,24(4):802-813. |
| |
| 作者姓名： | 张文璇 王卷乐 |
| |
| 作者单位： | 1.中国矿业大学(北京)地球科学与测绘工程学院,北京 1000832.中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 1001013.江苏省地理信息资源开发与利用协同创新中心,南京 210023 |
| |
| 基金项目： | 中国科学院特别交流计划;中国科学院战略性先导科技专项;中国工程科技知识中心建设项目 |
| |
| 摘 要： | 黑龙江（又称为阿穆尔河）是中国和俄罗斯之间的国际界河,近年来洪水事件频发,给流域内中俄两国带来巨大的人口伤亡和经济损失,加强该流域的洪水监测是两国面临的共同紧迫需求。传统的光学遥感影像受制于洪水期间多云多雨的天气状况,难以及时获得无云影像。本文充分利用全天候雷达数据的优势,提出了一种基于哨兵1号（Sentinel-1）合成孔径雷达数据监测大面积区域洪水的方法。通过Gamma分布和高斯分布拟合SAR影像后向散射系数的概率密度分布,迭代后验概率差值,自动获得全局阈值来分割初始的水体,基于辅助数据细化去除了初始水体中与水体相似的误分类型,并由形态学操作后处理提高了提取的洪水的均匀性。结果表明：① 与传统的分割算法相比,本文提出的方法基于SAR影像后向散射系数的分布规律进行概率密度函数分段拟合,将全局统计划分为局部关系,显著地改善了常规分割算法在水体和非水体像素量级相差过大而表现不佳的情况;② 研究获得了2017—2020年逐年的洪水分布,结果总体精度在87.78%~94.89%之间,Kappa系数在0.76~0.89之间;③ 特别是对于大面积半干旱地区,本文结合了后向散射特性、地形和其他辅助信息的关系,使得能够有效地保留水体并去除与水体后向散射系数相似的地物;④ 结果显示黑龙江（阿穆尔河）中下游沿岸城市哈巴罗夫斯克、阿穆尔斯克等地区为经常性泛洪区域,洪水面积整体呈增加趋势。研究表明,基于雷达数据对洪水空间范围进行时间序列监测,可以为中俄黑龙江流域洪水发展动态监测提供科学支撑。
|
| 关 键 词： | 中俄黑龙江流域 中俄边境 洪水监测 SAR Sentinel-1 概率密度函数 散射特性 遥感解译 |
| 收稿时间： | 2021-01-13 |
Flood Monitoring of Heilongjiang River Basin in China and Russia Transboundary Region based on SAR Backscattering Characteristics |
| |
| ZHANG Wenxuan,WANG Juanle.Flood Monitoring of Heilongjiang River Basin in China and Russia Transboundary Region based on SAR Backscattering Characteristics[J].Geo-information Science,2022,24(4):802-813. |
| |
| Authors: | ZHANG Wenxuan WANG Juanle |
| |
| Institution: | 1. College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China |
| |
| Abstract: | In recent years, Heilongjiang (Amur) River, a border river between China and Russia, is frequently flooded, which brings huge population casualties and economic losses to China and Russia in the basin. Strengthening flood monitoring in the basin is an urgent need for both countries. Influenced by terrible cloudy and rainy weather during the flooding season, the function of traditional optical remote sensing images are restricted. Thus, it is difficult to obtain cloud-free images during the flood. In this paper, through making full use of the advantages of all-weather radar data, a method of monitoring flood in large area based on Sentinel-1 synthetic aperture radar data is proposed. The probability density distribution of backscattering coefficient of SAR image is fitted by Gamma distribution and Gaussian distribution. The global threshold is obtained automatically to segment the initial water by iterating a posteriori probability difference. The misclassification type that looks like water in the initial water classification is refined and removed based on auxiliary data. Moreover, the uniformity of the extracted flood is improved by the post-processing of morphological operation. It can be concluded from the results that, firstly, compared with the traditional segmentation algorithm, the method proposed in this paper carries out the piecewise fitting of the probability density function based on the distribution law of the backscattering coefficient of the SAR image. It also divides the global statistics into local relations, which significantly improves the poor performance, caused by the large difference between water and non-water pixels, of the conventional segmentation algorithm. The flood distribution year by year from 2017 to 2020 is obtained. The overall accuracy of the results is between 87.78% and 94.89% while the Kappa coefficient is between 0.76 and 0.89. Secondly, especially for large semi-arid areas, this paper uses the relationships among backscattering characteristics, topography, and other auxiliary information, to ensure that water can be effectively preserved while the non-water objects similar to the backscattering coefficient of water can be removed. Thirdly, the results reflect that areas along the middle and lower reaches of Heilongjiang (Amur) River, such as Khabarovsk and Amursk, have frequent floods. Meanwhile, the flood area shows an increasing trend as a whole. The research shows that the time series monitoring of flood spatial extent based on radar data can provide scientific support for flood development dynamic monitoring in Heilongjiang River basin in China and Russia. |
| |
| Keywords: | Heilongjiang River basin of China and Russia China and Russia transboundary flood monitoring SAR Sentinel-1 probability density function scattering characteristics remote sensing interpretation |
| 本文献已被 万方数据 等数据库收录！ |
| 点击此处可从《地球信息科学》浏览原始摘要信息 |
| 点击此处可从《地球信息科学》下载免费的PDF全文 |
|
