Remote Sensing Estimation of Forest Aboveground Biomass Based on Lasso-SVR

编号 010036801

推送时间 20221107

研究领域 森林生态 

年份 2022 

类型 期刊 

语种 英语

标题 Remote Sensing Estimation of Forest Aboveground Biomass Based on Lasso-SVR

来源期刊 forest

期 第368期

发表时间 20220929

关键词 aboveground biomass;  remote sensing estimation;  Lasso algorithm;  support vector regression model; 

摘要 With the Lutou Forest Farm as the research area, the Lasso algorithm was used for characteristic selection, and the optimal combination of variables was input into the support vector regression (SVR) model. The most suitable SVR model was selected to estimate the aboveground biomass of the forest through the comparison of the kernel function and optimal parameters, and the spatial distribution map of the aboveground biomass in the study area was drawn. The significance analysis of special variables showed good correlations between forest aboveground biomass and each vegetation index. There was a more significant correlation with some remote sensing bands, a less significant correlation with some texture features, and a strong correlation with DEM in the terrain features. When the parameters C is 2 and g is 0.01, the SVR model has the highest precision, which can illustrate 73% of the forest aboveground biomass, with the validation set R2 being 0.62. The statistical analysis of the results shows that the total aboveground biomass of the Lutou Forest Farm is 4.82 × 105 t. The combination of Lasso with the SVR model can improve the estimation accuracy of forest aboveground biomass, and the model has a strong generalization ability.

服务人员 王璐

服务院士 蒋有绪

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