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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (2) : 111-119     DOI: 10.6046/gtzyyg.2020.02.15
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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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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.
Keywords spatiotemporal fusion      flood monitoring      high spatiotemporal resolution      STARFM      STRUM      FSDAF     
:  TP79  
Corresponding Authors: Xuhong WANG     E-mail: jqy_wxh@nwu.edu.cn
Issue Date: 18 June 2020
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Chenlie SHI
Xuhong WANG
Meng ZHANG
Zhuang LIU
Xinming ZHU
Cite this article:   
Chenlie SHI,Xuhong WANG,Meng ZHANG, et al. Analysis of the applicability of three remote sensing spatiotemporal fusion algorithms in flood monitoring[J]. Remote Sensing for Land & Resources, 2020, 32(2): 111-119.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.02.15     OR     https://www.gtzyyg.com/EN/Y2020/V32/I2/111
Fig.1  Image of the study area combined with Landsat B4(R),B3(G),B2(B)
Fig.2  Data of the study area
Fig.3  Flow chart of research method
Fig.4  December 12, 2004 standard map and prediction map of three spatiotemporal fusion algorithms in Gwydir
Fig.5  September 7, 2005 standard map and prediction map of three spatiotemporal fusion algorithms in New Orleans
波段 STARFM STRUM FSDAF
AD RMSE CC SSIM AD RMSE CC SSIM AD RMSE CC SSIM
蓝 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
红 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  Accuracy assessment of synthesized Landsat-like images by STARFM,STRUM and FSDAF in Gwydir
波段 STARFM STRUM FSDAF
AD RMSE CC SSIM AD RMSE CC SSIM AD RMSE CC SSIM
蓝 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
红 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  Accuracy assessment of synthesized Landsat-like images by STARFM,STRUM and FSDAF in New Orleans
Fig.6  December 12, 2004 standard flood mapping and prediction flood mapping of three spatiotemporal fusion algorithms in Gwydir
Fig.7  September 7, 2005 standard flood mapping and prediction flood mapping of three spatiotemporal fusion algorithms in New Orleans
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  Accuracy assessment of flood mapping by STARFM, STRUM and FSDAF in Gwydir and New Orleans
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