Direct estimation of leaf area index of tropical forests using LiDAR point cloud

Light Detection and Ranging (LiDAR) remote sensing is a promising method for accurate estimation of various forest structural parameters. A ground based LiDAR scanner, terrestrial laser scanner (TLS) has been used for the reference field measurements for the estimation of structural parameters such as tree height, DBH across large area forests sites. Leaf Area Index (LAI) is important forest structural parameter which has recently been estimated from TLS point cloud by geometrical/statistical modeling with tree height and DBH as proxy variables. In this work we present a method for direct estimation of LAI of tropical forests from TLS point cloud proposing a new algorithmic approach named as Point Spatial Density (PSD) algorithm. As an intermediate processing, the proposed method involves filtering and 3D reconstruction of individual trees using super voxel and min-cut segmentation approaches. Reconstructed trees are then extracted and geometrically modelled for estimating the LAI by the proposed PSD algorithm. The proposed PSD algorithm establishes a relationship between TLS point density, point spacing, and height of trees. Based on the number of points and the point spacing, the value of LAI found has been observed to be varying in sparse and dense canopy tree stands. Validation of the results with reference LAI measurements obtained using a LAI meter (LiCOR LAI Meter 2200) indicates a consistent correlation (R-2 = 0.96) between the estimated and reference LAI measurements. The results suggest that the LAI in dense heterogeneous forests could be accurately estimated by the proposed PSD algorithm based approach.