Center for Remote Sensing, Federal University of Minas Gerais, Belo Horizonte, Brazil.
Federal University of Brasília, Brasília, Brazil.
Sci Rep. 2023 Mar 24;13(1):4829. doi: 10.1038/s41598-023-30560-9.
Wildfires are aggravating due to climate change. Public policies need territorial intelligence to prevent and promptly fight fires, especially in vast regions like Brazil. To this end, we have developed a fire-spread prediction system for the Brazilian Cerrado, the biome most affected by wildfires in South America. The system automatically uploads hot pixels and satellite data to calculate maps of fuels loads, vegetation moisture, and probability of burning for simulating fire spread thrice a day for the entire Cerrado at 25 ha and for nine conservation units at 0.04 ha spatial resolution. In both versions, the model attains 65-89% of spatial match. Model results together with ancillary data, e.g., historical burned areas and annual CO emissions from fires, are available on an interactive web-platform that serves as a tool for fire prevention and fight, particularly in the selected conservation units where the platform is being used for daily operations.
由于气候变化，野火日益加剧。公共政策需要地域智慧来预防和及时扑灭火灾，特别是在巴西这样幅员辽阔的地区。为此，我们开发了一个用于巴西塞拉多的火灾蔓延预测系统，该生物群系是南美洲受野火影响最严重的地区。该系统自动上传热点像素和卫星数据，以计算燃料负荷、植被湿度和燃烧概率图，每天三次模拟整个塞拉多地区的 25 公顷和 9 个保护区的 0.04 公顷空间分辨率的火灾蔓延。在这两个版本中，模型的空间匹配度达到 65-89%。模型结果以及辅助数据，例如历史上的火灾面积和每年的 CO 排放量，都可以在一个交互式网络平台上获得，该平台可用作预防和扑灭火灾的工具，特别是在选定的保护区，该平台正在用于日常运营。
