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Forest canopy closure y is an important parameter in forest resource surveys, and plays a crucial role in evaluating and monitoring the stability of forest ecosystems. With the continuous development of remote sensing technology, the estimation of forest canopy closure using multi-source remote sensing data has become a hot research topic. In this study, a regression model was constructed using machine learning algorithms based on laser point cloud and multi-source optical remote sensing data, for estimating the canopy density of large forest areas. Firstly, the true values of forest canopy closure were calculated from airborne laser scanning (ALS) point cloud data as the dependent variable of the regression model. Secondly, 18 features such as vegetation indices and textures were extracted from Sentinel-2 MSI, Landsat-8 OLI, and Sentinel-1 SAR image data as the independent variables of the regression model. Then, 14 forest sample plots in Guangxi region were used as examples to investigate the impact of different variable combinations and machine learning methods on the estimation of forest canopy closure using Random Forest Regression (RFR) and Support Vector Machine Regression (SVR) models. Finally, the best variable combination and machine learning method were selected to map the forest canopy closure of Laibin city, Guangxi. Experimental results showed that RFR had better inversion performance than SVR, and the combination of S2+S1 had the highest accuracy, with a correlation coefficient R2 of 0.703, root mean square error RMSE of 0.19, and mean absolute error MAE of 0.13. Additionally, polarization features significantly improved the accuracy of forest canopy closure estimation.
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