Normalization | The arguments of normalize | Defining the point cloud | Adjusting the algorithms applied in normalize function | The output data frame | Tree detection | Data from TLS single-scan approach | Defining the range of diameters and heights of possible trees | Resolution of the TLS | Including further information about the plots | Algorithm to distinguish stem points and foliage points | Algorithms for identification of trees | Estimation of tree attributes | Data from TLS multi-scan approach | Automatic normalization and tree detection of several plots

Estimating optimal plot size without field data | Validation with field data and optimizing plot design | Plot simultaion and estimation of metrics and variables | The input data frames | Specifying designs of simulated plots | Further adjustable arguments | Output of the simulations function | Calculation of relative bias | Functions facilitating model-based or model-assisted sampling approaches | Computing correlations | Visualizing correlations

Computing stand-level metrics and variables | Distance sampling (distance.sampling function) | Additional information about trees through field data | Selecting trees to be included in the calculations | Plot design and parameters | Output files | Stand-level metrics | Statistics of the z, rho and r | Stand density (N), volume (V) and basal area (G) | Occlusion correction | Mean and dominant heights (h) and diameters (d)