利用GF-1 WFV影像和FY-3D MERSI火点产品提取过火区的方法
Extraction method of burned area using GF-1 WFV images and FY-3D MERSI fire-point products
- 2024年28卷第2期 页码：375-384
纸质出版日期： 2024-02-07
DOI： 10.11834/jrs.20221552
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摘要
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2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。2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。首先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；然后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；最后剔除周边小斑块，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。
Abstract
Using remote-sensing technology to obtain information about burned areas is important for ecological environment monitoring. High-resolution data are more suitable for extracting small-scale burned areas. To develop the fire monitoring ability of domestic remote-sensing data and improve the extraction efficiency and accuracy of a 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 the Tibetan Autonomous County of Muli and Xichang City
Sichuan Province.
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. Our 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 regions 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.
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. Fully exploiting the temporal
spatial
and spectral characteristics of fire points and burned areas can compensate 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 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.
The method is primarily divided into two parts
2
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 into the rough range of burned areas by combining temporal
spatial
and spectral characteristics. Temporal characteristic refers to fire points with concentrated occurrence time that easily form burned areas; spatial characteristic refers to fire points with concentrated location that easily 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. Burned pixels and unburned pixels in each land-cover type are classified using the segmentation threshold. The small patches are removed to obtain the result of burned-area extraction.
关键词
遥感过火区火点监测产品FY-3D MERSIGF-1 WFVNDVI阈值分割
Keywords
remote sensingburned areafire point productFY-3D MERSIGF-1 WFVNDVIsegmentation threshold
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