Understanding the temporal patterns of fire occurrence and their relationships with fuel dryness is key to sound fire management, especially under increasing global warming. At present, no system for prediction of fire occurrence risk based on fuel dryness conditions is available in Mexico. As part of an ongoing national-scale project, we developed an operational fire risk mapping tool based on satellite and weather information. We demonstrated how differing monthly temporal trends in a fuel greenness index, dead ratio (DR), and fire density (FDI) can be clearly differentiated by vegetation type and region for the whole country, using MODIS satellite observations for the period 2003 to 2014. We tested linear and non-linear models, including temporal autocorrelation terms, for prediction of FDI from DR for a total of 28 combinations of vegetation types and regions. In addition, we developed seasonal autoregressive integrated moving average (ARIMA) models for forecasting DR values based on the last observed values. Most ARIMA models showed values of the adjusted coefficient of determination (R2 adj) above 0.7 to 0.8, suggesting potential to forecast fuel dryness and fire occurrence risk conditions. The best fitted models explained more than 70% of the observed FDI variation in the relation between monthly DR and fire density. These results suggest that there is potential for the DR index to be incorporated in future fire risk operational tools. However, some vegetation types and regions show lower correlations between DR and observed fire density, suggesting that other variables, such as distance and timing of agricultural burn, deserve attention in future studies.

中文翻译：

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2003-2014年墨西哥按植被类型和地区划分的主动火密度的时空分布及其与卫星燃料绿色度指数的关系

了解火灾发生的时间模式及其与燃料干燥的关系，是进行良好的火灾管理的关键，特别是在全球变暖加剧的情况下。目前，墨西哥没有基于燃料干燥条件的火灾发生风险预测系统。作为正在进行的国家级项目的一部分，我们基于卫星和天气信息开发了可操作的火灾风险制图工具。我们利用2003年至2014年的MODIS卫星观测资料，证明了整个国家如何通过植被类型和地区清楚地区分燃料绿色指数，空载率（DR）和火密度（FDI）的每月不同时空趋势。我们测试了线性和非线性模型，包括时间自相关项，用于从DR预测FDI的总共28种植被类型和区域组合。此外，我们开发了季节性自回归综合移动平均值（ARIMA）模型，用于根据最近观察到的值预测DR值。大多数ARIMA模型显示调整后的确定系数（R2 adj）的值在0.7到0.8之间，这表明有潜力预测燃料的干燥度和发生火灾的风险条件。最佳拟合模型解释了所观察到的外国直接投资变化中超过70％的变化，这些变化是每月DR与火灾密度之间的关系。这些结果表明，DR指数有可能被纳入未来的火灾风险操作工具中。但是，某些植被类型和区域显示出DR与实测火密度之间的相关性较低，这表明其他变量，