Abstract The Brazilian Cerrado is considered to be the most species-rich savannah region in the world, covering ~2 million km2. Uncontrolled late season fires promote deforestation, produce greenhouse gases (~25% of Brazil's land-use related CO2 emissions between 2003 and 2005) and are a major threat to the conservation of biodiversity in protected areas. Governmental institutions therefore implemented early dry season (EDS) prescribed burnings as part of integrated fire management (IFM) in protected areas of the Cerrado, with the aim to reduce the area and severity of late dry season (LDS) fires. The planning and implementation of EDS prescribed burning is supported by satellite-based geo-information on fuel conditions, derived from Landsat 8 and Sentinel-2 data. The Mixture Tuned Matched Filtering algorithm was used to analyse the data, and the relationship between the resulting matched fractions (dry vegetation, green vegetation and soil) and in situ surface fuel samples was assessed. The linear regression of in situ data versus matched filter scores (MF scores) of dry vegetation showed an r2 of 0.81 (RMSE = 0.15) and in situ data versus MF scores of soil showed an r2 of 0.65 (RMSE = 0.38). To predict quantitative fuel load, a multiple linear regression analysis was carried out with MF scores of NPV and soil as predictors (adjusted r2 = 0.86; p

中文翻译：

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巴西塞拉多的燃料负荷测绘以支持综合火灾管理

摘要 巴西塞拉多被认为是世界上物种最丰富的大草原地区，面积约 200 万平方公里。不受控制的晚季火灾会促进森林砍伐，产生温室气体（2003 年至 2005 年间巴西土地使用相关二氧化碳排放量的约 25%），并且是对保护区生物多样性保护的主要威胁。因此，政府机构在塞拉多保护区实施了早期旱季 (EDS) 规定的燃烧，作为综合火灾管理 (IFM) 的一部分，旨在减少旱季晚期 (LDS) 火灾的面积和严重程度。EDS 规定燃烧的规划和实施得到基于卫星的燃料条件地理信息的支持，该信息源自 Landsat 8 和 Sentinel-2 数据。混合调谐匹配滤波算法用于分析数据，并且评估了所得匹配部分（干植被、绿色植被和土壤）与原位地表燃料样品之间的关系。干燥植被的原位数据与匹配过滤器分数（MF 分数）的线性回归显示 r2 为 0.81（RMSE = 0.15），原位数据与土壤 MF 分数的线性回归显示 r2 为 0.65（RMSE = 0.38）。为了预测定量燃料负荷，以 NPV 和土壤的 MF 分数作为预测因子进行了多元线性回归分析（调整后的 r2 = 0.86；p 65 (RMSE = 0.38)。为了预测定量燃料负荷，以 NPV 和土壤的 MF 分数作为预测因子进行了多元线性回归分析（调整后的 r2 = 0.86；p 65 (RMSE = 0.38)。为了预测定量燃料负荷，以 NPV 和土壤的 MF 分数作为预测因子进行了多元线性回归分析（调整后的 r2 = 0.86；p