Valparaiso, a central-southern region in Chile, has one of the highest rates of wildfire occurrence in the country. The constant threat of fires is mainly due to its highly flammable forest plantation, composed of 97.5% Pinus radiata and Eucalyptus globulus. Fuel moisture content is one of the most relevant parameters for studying fire spreading and risk, and can be estimated from the reflectance of leaves in the short wave infra-red (SWIR) range, not easily available in most vision-based sensors. Therefore, this work addresses the problem of estimating the water content of leaves from the two previously mentioned species, without any knowledge of their spectrum in the SWIR band. To this end, and for validation purposes, the reflectance of 90 leaves per species, at five dehydration stages, were taken between 350 nm and 2500 nm (full spectrum). Then, two machine-learning regressors were trained with 70% of the data set to determine the unknown reflectance, in the range 1000 nm–2500 nm. Results were validated with the remaining 30% of the data, achieving a root mean square error less than 9% in the spectrum estimation, and an error of 10% in spectral indices related to water content estimation.

中文翻译：

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从 SWIR 区域重建的叶片反射率估计辐射松和蓝桉的水分含量

瓦尔帕莱索是智利的中南部地区，是该国野火发生率最高的地区之一。火灾的持续威胁主要是由于其高度易燃的人工林，由 97.5% 的辐射松和蓝桉组成。燃料水分含量是研究火势蔓延和风险的最相关参数之一，可以通过短波红外 (SWIR) 范围内的树叶反射率进行估算，大多数基于视觉的传感器都不容易获得。因此，这项工作解决了在不了解它们在 SWIR 波段中的光谱的情况下估计前面提到的两个物种的叶子含水量的问题。为此，为了验证目的，在 350 nm 和 2500 nm（全光谱）之间获取了每个物种 90 片叶子在五个脱水阶段的反射率。然后，使用 70% 的数据集训练两个机器学习回归器以确定未知反射率，范围为 1000 nm–2500 nm。结果用剩余的 30% 的数据进行了验证，在光谱估计中实现了小于 9% 的均方根误差，在与含水量估计相关的光谱指数中实现了 10% 的误差。