Abstract: In order to explore the influence of different feature selection methods and estimation models on canopy closure estimation of natural secondary forests, 63（0.09 hm~2） sample plots in Maoershan Experimental Forest Farm were taken as the research area, and Airborne Laser Scanning（ALS） data was used to extract features. Three feature selection methods（Pearson correlation analysis, Random Forest（RF） and Boruta）) and three models（Partial Least Squares Regression（PLSR）, Random Forest Regression（RFR） and Support Vector Regression（SVR）） were employed to estimate canopy closure, and the accuracy evaluation and analysis of variance（ANOVA） of the model were carried out by fisheye photo method and sample method. The results showed that the accuracy of PLSR was the highest and SVR was the lowest. The estimation accuracy of RF and Boruta algorithm was higher than Pearson correlation analysis. The PLSR model after Boruta feature selection had the highest accuracy（R~2=0.451 1, RMSE=0.067 5）. The feature selection of fisheye photo and sample method, model fitting method of the fisheye photo had no significant effect, yet the model of sample method had a significant effect on estimating canopy closure. The results indicated that the accuracy of PLSR was better than others. The model of the sample method had a significant effect on canopy closure. ALS can effectively estimate the canopy closure of natural secondary forests, which provided a basis for large-scale estimation.