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The mobile single-layer laser detection and ranging (LiDAR) scanning (MSLS) method for estimating tree crown leaf area used a single-layer LiDAR sensor with a single perspective to collect point cloud data on crowns, but the obtained canopy information was not comprehensive enough, which limited the accuracy of crown leaf area estimation. A crown leaf area estimation method was proposed based on mobile multi-layer LiDAR scanning (MMLS), which used a multi-layer LiDAR sensor to collect crown point cloud data from multiple perspectives and improve the accuracy of crown leaf area estimation. Firstly, the point cloud data collected by multi-layer LiDAR was transformed into the world coordinate system. The crown point cloud was extracted through the region of interest (ROI). Then, an MMLS crown point cloud fusion method was proposed, which fused the crown point clouds collected by a single laser one by one, set a distance threshold to remove duplicate points, and added new points. Finally, an MMLS spatial resolution grid was constructed, and a crown leaf area estimation model was established based on the crown grid area. The experiment used a multi-layer LiDAR sensor VLP-16 to build an MMLS system. Two measurement distances of 1 m and 1.5 m, and eight measurement angles with an increment of 45°, were set to collect data from six tree crowns with different canopy densities. A total of 96 tree crown samples were obtained. Using the proposed method, the root mean square error (RMSE) of the linear estimation model for crown leaf area was 0.104 1 m2, which was 0.057 8 m2 lower than that of the MSLS model, and the coefficient of determination R2was 0.952 6, which was 0.067 5 higher than that of the MSLS model. The experimental results showed that the proposed method can effectively improve the accuracy of crown leaf area estimation through multi-layer LiDAR multi-perspective crown point cloud data collection, MMLS crown point cloud fusion, and spatial-resolution grid construction.