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Reasoning on the Evaluation of Wildfires Risk Using the Receiver Operating Characteristic Curve and MODIS Images

机译:利用接收器工作特征曲线和MODIS图像评估野火风险的推理

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This paper presents a method to evaluate the wildfires risk using the Receiver Operating Characteristic (ROC) curve and Terra moderate resolution imaging spectroradiometer (MODIS) images. To evaluate the wildfires risk fuel moisture content (FMC) was used, the relationship between satellite images and field collected FMC data was based on two methodologies; empirical relations and statistical models based on simulated reflectances derived from radiative transfer models (RTM). Both models were applied to the same validation data set to compare their performance. FMC of grassland and shrublands were estimated using a 5-year time series (2001-2005) of Terra moderate resolution imaging spectroradiometer (MODIS) images. The simulated reflectances were based on the leaf level PROSPECT coupled with the canopy level SAILH RTM. The simulated spectra were generated for grasslands and shrublands according to their biophysical parameters traits and FMC range. Both RTM-based models, empirical and statistical, offered similar accuracy with better determination coefficients for grasslands. In this work, we have evaluated the accuracy of (MODIS) images to discriminate between situations of high and low fire risk based on the FMC, by using the Receiver Operating Characteristic (ROC) curve. Our results show that none of the MODIS bands have a good discriminatory capacity (0.9984) when used separately, but the joint information provided by them offer very small misclassification errors.
机译:本文提出了一种使用接收器工作特征(ROC)曲线和Terra中分辨率成像光谱仪(MODIS)图像评估野火风险的方法。为了评估野火危险燃料的水分含量(FMC),卫星图像和现场收集的FMC数据之间的关系基于两种方法:基于从辐射传递模型(RTM)得出的模拟反射率的经验关系和统计模型。两种模型均应用于相同的验证数据集以比较其性能。草原和灌木丛的FMC使用Terra中分辨率成像光谱仪(MODIS)图像的5年时间序列(2001年至2005年)估算。模拟的反射率基于叶级PROSPECT和冠层级SAILH RTM的结合。根据草地和灌木的生物物理参数特征和FMC范围,生成了模拟光谱。两种基于RTM的模型,无论是经验模型还是统计模型,都提供了相似的准确性,并且草地具有更好的确定系数。在这项工作中,我们通过使用接收器工作特性(ROC)曲线评估了(MODIS)图像的准确性,以基于FMC区分高火险和低火险。我们的结果表明,当单独使用MODIS频段时,没有一个具有很好的辨别能力(0.9984),但是它们提供的联合信息提供了非常小的误分类误差。

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