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国土资源遥感  2020, Vol. 32 Issue (2): 111-119    DOI: 10.6046/gtzyyg.2020.02.15
  技术方法 本期目录 | 过刊浏览 | 高级检索 |
3种时空融合算法在洪水监测中的适用性研究
石晨烈1, 王旭红1,2(), 张萌1, 刘状1, 祝新明3
1.西北大学城市与环境学院,西安 710127
2.陕西省地表系统与环境承载力重点实验室,西安 710127
3.中国科学院大学资源与环境学院,北京 100049
Analysis of the applicability of three remote sensing spatiotemporal fusion algorithms in flood monitoring
Chenlie SHI1, Xuhong WANG1,2(), Meng ZHANG1, Zhuang LIU1, Xinming ZHU3
1. College of Urban and Environmental Sciences, Northwest University, Xi’an 710127,China
2. Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi’an 710127, China
3. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing,100049,China
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摘要 

洪水灾害的遥感监测依赖于高时空分辨率影像,但目前中高空间分辨率的遥感影像受卫星回访周期及天气的影响,限制了在洪水监测中的应用。为此,提出融合MODIS和Landsat影像生成高时空分辨率影像来监测洪水灾害。以Gwydir和New Orleans 2地区为研究区,利用时空自适应反射率融合模型(spatial and temporal adaptive reflectance fusion model, STARFM)、时空反射率解混模型(spatial and temporal reflectance unmixing model, STRUM)和灵活的时空融合模型(flexible spatiotemporal data fusion, FSDAF)3种流行算法融合MODIS和Landsat影像,获得Landsat融合影像,采用支持向量机(support vector machine, SVM)对融合影像分类来提取洪水信息,并对其结果进行精度评估。实验结果表明,3种时空融合算法能够有效应用到洪水监测中,且FSDAF算法融合结果在2个研究区都优于STARFM和STRUM。在Gwydir研究区,STARFM,STRUM和FSDAF 3种算法洪水分类总体精度分别为0.89,0.90和0.91,Kappa系数分别为0.63,0.64和0.67; 在New Orleans研究区,3种融合算法洪水分类精度为0.90,0.89和0.91,Kappa系数分别为0.77,0.76和0.81。此研究表明时空融合算法能够有效应用到洪水监测中。

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石晨烈
王旭红
张萌
刘状
祝新明
关键词 时空融合洪水监测高时空分辨率STARFM模型STRUM模型FSDAF模型    
Abstract

Remote sensing images with high spatiotemporal resolution offer a reliable way to the monitoring of flood disasters. However, the application of high spatial resolution images is restricted by satellite revisit period and extreme weather. Therefore, this paper proposes a method that can blend Landsat and MODIS images to generate high spatiotemporal images for monitoring flood disaster. Selecting Gwydir and the New Orleans as study areas, the authors performed fusion of MODIS and Landsat TM based on three major spatiotemporal fusion algorithms, i.e., the spatial and temporal adaptive reflectance fusion model (STARFM), the spatial and temporal reflectance unmixing model (STRUM) and the flexible spatiotemporal data fusion (FSDAF), which led to the formation of a new TM image. Meanwhile, classified flood information was extracted by applying support vector machine (SVM) to the new TM image. The results show that three spatiotemporal fusion algorithms can monitor flood disasters effectively, with FSDAF playing a more superior role in fusion accuracy and flood information extraction. Evaluation of flood classification shows that, in Gwydir, the overall accuracy of STARFM, STRUM and FSDAF is 0.89, 0.90, 0.91, and the Kappa coefficients are 0.63, 0.64, 0.67, respectively. In the New Orleans, the overall accuracy of three fusion algorithms is 0.90, 0.89, 0.91, and the Kappa coefficients are 0.77, 0.76, 0.81, respectively. This study shows that spatiotemporal fusion algorithms can be effectively applied to flood monitoring.

Key wordsspatiotemporal fusion    flood monitoring    high spatiotemporal resolution    STARFM    STRUM    FSDAF
收稿日期: 2019-04-04      出版日期: 2020-06-18
:  TP79  
基金资助:中国科学院战略性先导科技专项资助项目“泛第三极环境变化与绿色丝绸之路建设子课题”(XDA 2004030201);国家自然科学基金面上项目“不同地貌类型区的遥感图像信息容量的差异性研究”(41071271);陕西省自然基金面上项目“基于遥感图像信息容量的城市热岛效应研究”(2015JM4132)
通讯作者: 王旭红
作者简介: 石晨烈(1995-),男,硕士研究生,主要从事环境遥感方面的研究。Email: max1995@stumail.nwu.edu.cn。
引用本文:   
石晨烈, 王旭红, 张萌, 刘状, 祝新明. 3种时空融合算法在洪水监测中的适用性研究[J]. 国土资源遥感, 2020, 32(2): 111-119.
Chenlie SHI, Xuhong WANG, Meng ZHANG, Zhuang LIU, Xinming ZHU. Analysis of the applicability of three remote sensing spatiotemporal fusion algorithms in flood monitoring. Remote Sensing for Land & Resources, 2020, 32(2): 111-119.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/gtzyyg.2020.02.15      或      https://www.gtzyyg.com/CN/Y2020/V32/I2/111
Fig.1  研究区Landsat B4(R),B3(G),B2(B)合成影像
Fig.2  研究区影像
Fig.3  研究方法流程
Fig.4  Gwydir研究区2004年12月12日验证影像和3种时空算法融合影像图
Fig.5  New Orleans研究区2005年9月7日验证影像和3种时空算法融合影像图
波段 STARFM STRUM FSDAF
AD RMSE CC SSIM AD RMSE CC SSIM AD RMSE CC SSIM
0.011 0.016 0.597 0.566 0.011 0.015 0.615 0.611 0.010 0.014 0.667 0.653
绿 0.014 0.022 0.622 0.565 0.015 0.022 0.628 0.618 0.013 0.019 0.689 0.661
0.017 0.026 0.606 0.546 0.017 0.026 0.619 0.611 0.016 0.023 0.681 0.653
近红外 0.025 0.035 0.799 0.777 0.026 0.036 0.789 0.782 0.024 0.033 0.828 0.814
短波红外1 0.047 0.062 0.766 0.754 0.053 0.069 0.736 0.720 0.045 0.058 0.766 0.754
短波红外2 0.053 0.054 0.751 0.708 0.049 0.061 0.718 0.662 0.042 0.053 0.744 0.723
平均值 0.028 0.036 0.690 0.652 0.029 0.038 0.684 0.667 0.025 0.033 0.729 0.710
Tab.1  Gwydir研究区3种时空融合算法融合结果精度评估
波段 STARFM STRUM FSDAF
AD RMSE CC SSIM AD RMSE CC SSIM AD RMSE CC SSIM
0.021 0.037 0.611 0.601 0.018 0.027 0.813 0.800 0.017 0.026 0.820 0.798
绿 0.024 0.040 0.659 0.641 0.022 0.031 0.805 0.793 0.021 0.030 0.818 0.795
0.026 0.042 0.655 0.635 0.024 0.034 0.784 0.777 0.022 0.032 0.805 0.786
近红外 0.038 0.056 0.862 0.848 0.033 0.049 0.903 0.903 0.032 0.046 0.912 0.912
短波红外1 0.034 0.050 0.848 0.844 0.035 0.054 0.844 0.843 0.031 0.046 0.878 0.879
短波红外2 0.047 0.057 0.754 0.765 0.049 0.087 0.770 0.781 0.048 0.085 0.800 0.812
平均值 0.032 0.047 0.731 0.722 0.030 0.047 0.819 0.816 0.028 0.044 0.839 0.830
Tab.2  New Orleans 研究区3种时空融合算法融合结果精度评估
Fig.6  Gwydir研究区2004年12月12日标准洪水分类图和3种算法融合影像的洪水分类
Fig.7  New Orleans 研究区2005年9月7日标准洪水分类图和3种算法融合影像的洪水分类
指标 Gwydir New Orleans
STARFM STRUM FSDAF STARFM STRUM FSDAF
用户精度 0.67 0.72 0.73 0.91 0.92 0.93
制图精度 0.73 0.68 0.74 0.81 0.78 0.83
总体精度 0.89 0.90 0.91 0.90 0.89 0.91
Kappa系数 0.63 0.64 0.67 0.77 0.76 0.81
Tab.3  基于3种时空融合算法融合影像洪水分类图的精度评估
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