基于卫星多光谱的广东亚热带森林FMC遥感反演

Remote Sensing Retrieval of FMC in Subtropical Forests of Guangdong Based on Satellite Multispectral Data

通过森林可燃物含水率(FMC)监测评估植被活叶的水分状况,探究可燃物水分含水率与植被本身的微观特征及植被的环境条件的关系,包括森林几何特征因子(如树高、冠幅宽度)和森林水分特征因子(NDVI、NDII)。除具有传统的线性关联外,森林的特征变量之间存在非线性关系。采用常规线性回归、XGboost、梯度提升回归3种经典的机器学习算法预测FMC。最后,通过分配权重整合这3种算法,构成新的“综合投票回归”方法。计算结果与样点初始值相关性最好,误差最低。该研究基于卫星遥感反演的低成本、准实时的FMC指数,可为制定森林火灾风险管理策略提供理论支撑和森林可燃物水分的时空分布数据。

Forest fires caused by climate warming and human activities are the main disasters of subtropical forests in Guangdong in autumn and winter.We selected 116 satellite ground synchronous sampling sites in the subtropical forest area of Conghua District,Guangzhou City,Guangdong Province,to monitor and evaluate the water status of vegetation living leaves by forest fuel moisture content(FMC),and to explore the relationship between fuel water content and vegetation micro-characteristics and vegetation environmental conditions,including forest geometric characteristics(such as tree height,crown width)and forest water characteristic factors(such as NDVI,NDII).In addition to the traditional linear correlation,we also find that there is a nonlinear relationship among the characteristic variables of the forest,and the determination coefficient R2 is 0.88.We have adopted three different classical machine learning algorithms,including conventional linear regression,XGboost and Gradient Boosting Regression algorithms to predict FMC.Finally,the new"comprehensive voting regression"method,which integrates the three algorithms by allocating weights,has the best correlation with the initial values of the sample points and the lowest error,so this method is used to predict the FMC of the whole region,and the prediction accuracy of the sample points can reach 86%~87%.The low-cost and quasi-real-time FMC index based on satellite remote sensing can provide solid theory and temporal and spatial distribution data of forest fuel moisture when formulating forest fire risk management strategies.