Estimation of Fuel Load in Coniferous Forests of Yuanling County Based on Sentinel Data
Forest combustible material,as one of the key factors contributing to forest fires,holds significant im-portance in formulating fire prevention and control strategies,enhancing fire warning capabilities,and safeguard-ing ecological environments.This study utilizes Sentinel-1A and Sentinel-2A imagery as data sources in Yuanling County,Hunan Province.Through the extraction of various types of remote sensing factors from multiple data sources and integrating ground survey data on combustible load,the study employs forward feature selection and four machine learning models(Multiple Linear Regression,MLR;k-Nearest Neighbor,kNN;Support Vector Re-gression,SVR;Random Forest,RF)to construct models for estimating combustible load in coniferous forests.The results indicate that:①The VH polarization backscatter coefficient extracted based on Sentinel-1A data has a high correlation with the fuel load of coniferous forests;②Compared to single data sources,the combination of Senti-nel-1 and Sentinel-2 data contributes to improved accuracy in estimating combustible load in coniferous forests.The optimal models exhibit an increase in R2 by 0.19 and 0.29,a decrease in r RMSE by 4.66 and 6.94 percentage points,a decrease in RMSE by 6.13 t/hm2 and 9.13 t/hm2,and an average decrease in rRMSE by 5.17 and 5.75 per-centage points,respectively.The best-performing model is the SVR model,with R2=0.5,rRMSE=27.71,and RMSE=36.47 t/hm2.The incorporation of Sentinel-1A data contributes to the enhancement of accuracy in esti-mating combustible load in coniferous forests.
万方数据
维普
