适应复杂区域的时序SAR影像洪水监测与分析
Flood monitoring and analysis based on time-series SAR image for complex area
- 2024年28卷第2期 页码：346-358
纸质出版日期： 2024-02-07
DOI： 10.11834/jrs.20211228
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摘要
洪涝灾害对中国沿湖沿江地带的国家经济和人民财产威胁巨大。针对洪涝灾害期间光学影像质量低，单幅SAR（Synthetic Aperture Radar）影像水体提取多依赖经验阈值且可靠性不足等问题，本文提出了一种适应复杂区域的时序SAR影像洪水监测方法。为获取完整的洪水淹没区域，首先结合影像序列的统计分析结果，设计了两个归一化差异指数——突出临时洪水的淹没范围提取指数SREI（Submerged Range Extraction Index）和突出矮植被覆盖的植被区淹没范围提取指数SRVEI（Submerged Range in Vegetation area Extraction Index）；然后，根据同一区域植被季候性分布较为稳定的前提，给出了阈值的自适应选取方式；最后，考虑中国湖泊周围地物特点，构建了适应的后处理过程，优化提取区域，形成了洪水淹没区域提取与监测的详细流程和通用框架。本文以东洞庭湖流域为主要研究区域验证了方法的提取精度，并在此基础上进行了2020年东洞庭湖流域洪涝灾害态势分析和洪水淹没地物分析，展示了方法在洪涝灾害监测评估上的应用性能。另外，本文还将该方法应用于东洞庭湖流域往年数据，进行了汛期洪水淹没范围年际分析，并添加了同年鄱阳湖洪水淹没区域的实验，验证了方法时空应用的稳定性。相关实验结果表明，本文方法对洪水淹没区域的提取精度高，用户依赖性低，可跨越时空地应用于不同洪水淹没区域监测场景，且可以初步区分不同属性的洪水淹没范围，能够为本文涉及区域及其他区域的洪涝灾害监测、评估和预警提供一定的参考。
Abstract
Flood disasters are a great threat to the national economy and people’s property along the lakes and rivers in China. Synthetic Aperture Radar (SAR) adopts active imaging methods that can realize all-weather imaging and ensure continuous observation of flood disaster areas under severe weather
such as heavy rains and clouds. The current flood-monitoring methods based on SAR images often have problems
such as difficulty in threshold selection
high computational cost
or inefficient use of time-series information. Aiming at the above problems
this paper makes full use of information from time-series SAR image sequence to design an effective and stable method of monitoring flood
which can be adapted to complex areas.
Through preprocessing and statistical analysis of the image sequence
two normalized difference indices including submerged range extraction index and submerged range in vegetation area extraction index are designed and applied to calculate the candidate area of flood inundation. Then
the adaptive selection method of threshold for flood extraction is given based on the stability assumption of vegetation seasonal distribution in the same area. Finally
considering the characteristics of the surrounding features of the lakes in China
a post-processing process is designed. The process involves removing spots and holes
excluding areas with large slopes and filtering out fragmented areas with large rectangular degrees. Post-processing is conducted to optimize the extraction area for the final results of flood-inundation range.
In the experiment
this paper takes the East Dongting Lake basin as the main research area to verify the effectiveness of the proposed method by comparing the extraction accuracy with the other four methods. Experimental results prove that the overall extraction accuracy of the proposed method is higher than that of all comparative methods. To achieve the purpose of flood-disaster monitoring and evaluation
an analysis of the flood-disaster situation in the East Dongting Lake Basin in 2020 and an analysis of flood submerged land cover types are conducted. The method has also been successfully applied to the data of the East Dongting Lake basin in previous years and the Poyang Lake basin in that the method can be applied across time and space.
Based on the time-series SAR image sequence
this paper proposes an effective method
forms a detailed process flow
and constructs a general framework for flood monitoring. The proposed method has advantages of simple parameter setting and low user dependence on threshold determination. Experiments show that the method has high extraction accuracy of submerged areas with good robustness and versatility. The proposed method can be applied to different flood-monitoring scenarios across time and space and can preliminarily distinguish different attributes of submerged areas. Thus
a certain reference is provided for flood-disaster monitoring
assessment
and early warning in other regions.
关键词
洪涝灾害合成孔径雷达时序监测水文遥感SAR洞庭湖
Keywords
flood disastersynthetic aperture radartime series monitoringhydrological remote sensingSARDongting Lake
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相关作者
李聪妤 湖南大学电气与信息工程学院
刘家奇 湖南大学电气与信息工程学院
刘欣鑫 湖南大学电气与信息工程学院
李树涛 湖南大学电气与信息工程学院
康旭东 湖南大学电气与信息工程学院
崔光曦 中国科学院空天信息创新研究院 遥感科学国家重点实验室;中国科学院大学
杜延磊 中国科学院空天信息创新研究院 遥感科学国家重点实验室;中国科学院空天信息创新研究院海南研究院 海南省地球观测重点实验室
杨晓峰 中国科学院空天信息创新研究院 遥感科学国家重点实验室;中国科学院空天信息创新研究院海南研究院 海南省地球观测重点实验室
相关机构
湖南大学电气与信息工程学院
中国科学院空天信息创新研究院 遥感科学国家重点实验室
中国科学院空天信息创新研究院海南研究院 海南省地球观测重点实验室
中国科学院大学
澳门大学
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