森林火灾的发生与植被冠层可燃物含水率的大小有着密切联系。利用高精度、大尺度、高效率的遥感影像反演获取植被冠层可燃物含水率对于有效防治森林火灾具有重要意义。油松由于其自身理化性质成为引发森林火灾的主要树种之一,以张家口崇礼区的油松为研究对象,基于Sentinel-2B遥感影像和油松含水率实测数据,建立了多个油松冠层可燃物含水率反演模型:一元线性回归模型、一元非线性回归模型和多元非线性回归模型,并利用决定系数(R²)和均方根误差(RMSE)进行模型精度评价。结果表明,非线性模型总体上要优于线性模型;通过多个自变量因子建立的多元非线性模型能够更好地反映油松冠层可燃物含水率情况,模型反演精度更高,可以为植被冠层可燃物含水率反演模型方法选择提供一定的理论依据。

The occurrence of forest fires is closely related to the moisture content of vegetation canopy combustibles.Using high-precision,large-scale,and high-efficiency remote sensing image inversion to obtain the moisture content of vegetation canopy combustibles is of great significance for effective prevention and control of forest fires.Pinus tabulaeformis is one of the main tree species causing forest fires due to its physical and chemical properties.This study takes Pinus tabulaeformis in Chongli District,Zhangjiakou as the research object.Based on Sentinel 2B remote sensing images and measured moisture content dataof Pinus tabulaeformis,multiple linear regression models,nonlinear regression models and multiple nonlinear regression models were established for the moisture content of Pinus tabulaeformis canopy combustibles.Using the coefficient of determination(R²)and root mean square error(RMSE)to evaluate model accuracy.The results indicated that the nonlinear model was generally superior to the linear model;The multivariate nonlinear model established through multiple independent variable factors better reflected the moisture content of Pinus tabulaeformis canopy combustibles,and the model had higher inversion accuracy,which provided a certain theoretical basis for the selection of vegetation canopy fuel moisture inversion model methods.