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
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.
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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