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利用遥感技术获取过火区信息对生态环境监测具有重要意义，其中高分辨率数据更适合提取小范围过火区。目前已有多种利用国外火点产品结合遥感影像提取过火区的研究。为了增强国产遥感数据火情监测能力，提高小范围过火区的提取效率和精度，基于过火前后两幅GF-1 WFV影像和多时相FY-3D MERSI火点产品开展过火区提取研究。两处研究区分别位于四川省凉山彝族自治州木里藏族自治县和西昌市。根据火点与过火区形成的关系，结合火点的时间、空间和光谱特征，筛选并扩充火点像元，确定过火区粗略范围；确定每种地表类型的分割阈值，分类过火像元和非过火像元；剔除周边小斑块，得到过火区提取结果。以人机交互方式获得的过火区参考真值作验证，并与神经网络分类法提取过火区的结果作对比。结果表明，本文方法的过火区提取结果精度要明显高于神经网络分类法，Kappa系数达到0.82。该方法可以充分结合GF-1 WFV影像和FY-3D MERSI火点产品数据的优势，降低样本像元选择时间成本和不确定性，快速准确地提取小范围过火区。未来可考虑通过选择更高精度的火点产品，结合实地考察验证对该方法改进完善。
Objective: Using remote sensing technology to obtain information about burned areas is important for ecological environment monitoring, in which high-resolution data is more suitable for extracting small-scale burned areas. In order to develop the fire monitoring ability of domestic remote sensing data and improve the extraction efficiency and accuracy of the small-scale burned area, two GF-1 WFV images (before and after fires) and multi temporal FY-3D MERSI fire products are used to extract burned areas for two study areas, respectively located in Tibetan Autonomous County of Muli and Xichang City, Sichuan Province. Method: The method is mainly divided into two parts: rough extraction and fine extraction. In rough extraction, according to the relationship between fire points and the formation of burned areas, the fire point pixels are selected and expanded to the rough range of burned areas by combination of temporal, spatial and spectral characteristics. Temporal characteristic refers to fire points with concentrated occurrence time are easy to form burned areas; spatial characteristic refers to fire points with concentrated location are easy to form burned areas, and burned pixels are usually adjacent to fire point pixels; spectral characteristics refer to pixels with higher NDVI difference before and after fire which may be burned pixels. In fine extraction, the land cover types included in the burned area are determined according to the number of fire point pixels. The segmentation threshold is determined using the iterative threshold method for each land cover type. In addition, burned pixels and unburned pixels in each land cover type are classified using the segmentation threshold. The small patches are removed to get the result of burned area extraction. Result: The reference true values are obtained by human-computer interaction for verification. The results of burned areas extracted by neural network classification are compared with the result of the proposed method. The results show that the accuracy of burned areas detected by the proposed method is higher than that by neural network classification, and the Kappa coefficients in two study areas are 0.82 and 0.87, respectively. The region of commission and omission are usually distributed at the edge of the burned area patch. The distribution of burned area in Xichang is more compact than that in Muli, so the accuracy of burned area mapping in Xichang is higher. Conclusion: The method can fully combine the advantages of the two kinds of data, reduce the uncertainty and time cost caused by sample selection, and extract the small-scale burned area quickly and accurately. Making full use of temporal, spatial and spectral characteristics of fire points and burned areas can make up for the shortcomings of GF-1 WFV images in temporal and spectral resolution. Meanwhile, the method can fully combine the two kinds of data and minimize the impact of the difference of spatial resolution. In the future, the method can be improved by using a higher accuracy of fire point products. The accuracy of the reference true value of the burned area can be improved through field investigation.