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Improving the prediction of wildfire potential in boreal Alaska with satellite imaging radar

机译：利用卫星成像雷达改进对阿拉斯加北部野火势的预测

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Alaska currently relies on the Canadian Fire Weather Index (FWI) System for the assessment of the potential for wildfire and although it provides invaluable information it is designed as a single system that does not account for the varied fuel types and drying conditions (day length, permafrost, decomposition rate, and soil type) that occur across the North American boreal forest. The FWI System is completely weather-based using noontime measurements of precipitation, relative humidity, temperature and wind speed. The most common problem observed with the FWI system is in the initialisation and need for calibration of one of the moisture codes that make up the FWI system, the Drought Code (DC), which is representative of the deeper organic soil layers and has a 53 day lag period. SAR data represent an innovative tool to improve the current weather-based fire danger system of interior Alaska by initialising the spring values of DC, calibrating the codes throughout the season and providing additional point-source data. Using radar backscatter values from several recently burned boreal forests, an algorithm was developed that related backscatter to DC. The authors then demonstrated the application and validation of this algorithm at independent test sites with good correlation to in situ soil moisture and rainfall variations.
机译：阿拉斯加目前依靠加拿大火灾天气指数（FWI）系统来评估野火的可能性，尽管它提供了宝贵的信息，但它被设计为一个单独的系统，无法说明变化的燃料类型和干燥条件（天长，北美寒带林中发生的多年冻土，分解速率和土壤类型）。 FWI系统完全基于天气，使用中午测量降水，相对湿度，温度和风速。 FWI系统最常见的问题是初始化和校准构成FWI系统的湿度代码之一干旱代码（DC），该代码代表较深的有机土壤层，具有53日滞后期。 SAR数据是一种创新工具，可通过初始化DC的弹簧值，在整个季节中校准代码并提供其他点源数据来改善阿拉斯加当前基于天气的火灾危险系统。利用最近被烧毁的北方森林的雷达反向散射值，开发了一种将反向散射与DC相关的算法。作者随后证明了该算法在独立测试现场的应用和验证，与原地土壤水分和降雨变化具有良好的相关性。

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