Abstract:

【Objective】Terrestrial laser scanning involves collecting dense laser point clouds of plants to finely characterize the structural parameters of a forest, such as tree skeletons and true leaf area. True leaf area is an important index for phenotypic studies in forestry and botany. At present, there are no well-evidenced methods for measuring phenotypic traits in plant science. Here we develop a novel approach for true leaf area retrieval from terrestrial laser scanned points to appraise key phenotypic parameters. 【Method】 First we designed an individual leaf segmentation algorithm based on the small plane locating and region growing for plant point clouds, to achieve an accurate single-leaf point cloud segmentation. Second, we input three parameters: the angle between the normal vector of a single leaf and the incident laser beam from the scanner, the distance between the scanner and leaf, and the number of point clouds of a single leaf. Training samples combined L1 and L2 regularized multiple regression methods to realize inverse calculations of the total leaf area of all leaf elements in a tree canopy. Finally, we chose four individual trees on our campus(crape myrtle, cherry, ginkgo and camphor) to verify the effectiveness of our results by comparison with field measurements. 【Result】Leaf area retrieval results indicated the superiority of our approach over existing least-square fitting methods. Compared with field measurements, we saw better performance for two small trees: the crape myrtle [coefficient of determination (R²) was 0.95 and root mean square error (RMSE) was 0.42 cm²] and the cherry (R² = 0.9 and RMSE was 0.42 cm²). Appreciable results were achieved for the ginkgo (R² = 0.83 and RMSE was 1.24 cm²) and camphor trees (R² = 0.86; RMSE was 1.10 cm²); these are larger trees with extended crowns and more vegetative elements in the canopy. 【Conclusion】 This method synergistically employed a computer vision and machine learning to accurately calculate the leaf area of canopies using scanned points, yielding novel perspectives for assessing the true leaf area of canopies.

Key words: terrestrial laser scanning (TLS), individual leaf segmentation, L1+L2 regularized multiple regression, leaf area retrieval