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黄云, 文军, 葛翔宇, 等. 基于风云气象和高分静止卫星数据的四川省凉山地区森林火灾遥感监测研究[J/OL]. 四川林业科技, 2024, 45[2024-05-16]. DOI: 10.12172/202401100002
引用本文: 黄云, 文军, 葛翔宇, 等. 基于风云气象和高分静止卫星数据的四川省凉山地区森林火灾遥感监测研究[J/OL]. 四川林业科技, 2024, 45[2024-05-16]. DOI: 10.12172/202401100002
HUANG Y, WEN J, * G, et al. A Study on Monitoring Forest Fires in Liangshan Region, Sichuan Province, Based on FY-4A and GF-4[J/OL]. Journal of Sichuan Forestry Science and Technology, 2024, 45[2024-05-16]. DOI: 10.12172/202401100002
Citation: HUANG Y, WEN J, * G, et al. A Study on Monitoring Forest Fires in Liangshan Region, Sichuan Province, Based on FY-4A and GF-4[J/OL]. Journal of Sichuan Forestry Science and Technology, 2024, 45[2024-05-16]. DOI: 10.12172/202401100002

基于风云气象和高分静止卫星数据的四川省凉山地区森林火灾遥感监测研究

A Study on Monitoring Forest Fires in Liangshan Region, Sichuan Province, Based on FY-4A and GF-4

  • 摘要: 森林火灾不仅会对区域生态环境造成严重破坏,还可能导致人类生命财产遭受巨大损失,因此研究有效的火灾监测算法至关重要。在此背景下,利用我国风云四号卫星数据开展研究,通过对比确定了适用于凉山地区的森林火灾监测算法。采用高分四号卫星中波红外扫描仪数据,成功确定了火点的大小和位置,并提取了火烧迹地面积,优选了最适宜于凉山地区的方法。研究结果表明,上下文算法在凉山地区的森林火灾监测中效果最佳,漏判率最低,可准确检测到小火点的位置,算法的综合指标达到了0.7以上,为凉山地区的森林火灾扑救工作提供了科学参考。通过将高分四号卫星红外扫描仪数据的阈值设置为330 K,进一步了确定火点的大小和位置,弥补了风云四号卫星空间分辨率的不足,为森林火灾监测提供更好的技术支持。基于高分四号卫星数据对比不同方法的灰度衰减图时,红外波段的衰减变化最为明显,其次为全球环境监测植被指数和归一化植被指数,过火区识别指数的衰减变化不明显,根据红外波段的灰度衰减图可准确提取出火烧迹地面积范围。

     

    Abstract: Forest fires not only cause serious damage to the regional ecological environment, but also lead to huge losses of lives and property. The forest fires happened in Muli County and Xichang City of Liangshan region of Sichuan Province in March 2020 had been investigated by using intercomparing data of China's FY-4 satellite to determine a suitable forest fire monitoring algorithm. The determination of the fire point size and location, extract of the burning area, and prefered selection of the most suitable method for the fire monitoring over Liangshan region have been conducted by using Middel Wave Infrared Scannerof Gaofen-4 satellite. The results show that contextual algorithms have the best performance in forest fire monitoring in the Liangshan Autonomous Prefecture, with the lowest missed detection rate and the ability to accurately detect the location of small fire points. The comprehensive index of the algorithm reaches 0.7 or above, providing a scientific reference for forest fire suppression serve in the Liangshan area. The threshold set for the Infrared Scanner data of Gaofen-4 satellite is 330 K, which can further determine the size and location of the fire point, filling the gap in spatial resolution of Fengyun-4 satellite and providing better technical support for forest fire monitoring. Based on the comparison of grayscale attenuation maps using different methods by using data from the Gaofen-4 satellite, the infrared band shows the most significant attenuation change, followed by the Global Environmental Monitoring Vegetation Index and Normalized Vegetation Index. The attenuation changes of the identification index for fire burning area is not significant, and the range of fire footprint area can be accurately extracted. The results provide satellite remote sensing data and effective monitoring algorithms suitable for the Liangshan area, successfully extracting the area of burned areas, providing strong support for forest fire monitoring, treatment, and post disaster reconstruction.

     

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