Extraction method of burned area using GF-1 WFV images and FY-3D MERSI fire-point products

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 method is primarily 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 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. 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. © 2024 Science Press. All rights reserved.