Automatic Processing of Terrestrial-Based Technologies Point Cloud Data for Forestry Purposes
Compute Metrics and Variables for Terrestrial-Based Technologies Point...
Calculate dominant diameters and heights for simulations for angle-cou...
Correlation Between Field Estimations and TLS Metrics
Distance Sampling Methods for Correcting Occlusions Effects
Assess Consistency of Metrics for Simulated TLS Plots
Calculate dominant diameters and heights for simulations for angle-cou...
FORTLS: Automatic Processing of Terrestrial-Based Technologies Point C...
Calculate dominant diameters and heights for simulations for angle-cou...
Calculate dominant diameters and heights for simulations for angle-cou...
Calculate dominant diameters and heights for simulations for angle-cou...
Relative Coordinates and Density Reduction for Terrestrial-Based Techn...
Optimize Plot Design Based on Optimal Correlations
Relative Bias Between Field Estimations and TLS metrics
Compute Metrics and Variables for Simulated TLS and Field Plots
Tree-Level Variables Estimation
Tree-Level Variables Estimation for Several Plots
Tree-Level Variables Estimation for TLS Single-Scan Approach
Calculate dominant diameters and heights for simulations for angle-cou...
Calculate weighted arithmetic mean.
Calculate weighted geometric mean.
Calculate weighted harmonic mean.
Calculate weighted quadratic mean.
Process automation of point cloud data derived from terrestrial-based technologies such as Terrestrial Laser Scanner (TLS) or Mobile Laser Scanner. 'FORTLS' enables (i) detection of trees and estimation of tree-level attributes (e.g. diameters and heights), (ii) estimation of stand-level variables (e.g. density, basal area, mean and dominant height), (iii) computation of metrics related to important forest attributes estimated in Forest Inventories at stand-level, and (iv) optimization of plot design for combining TLS data and field measured data. Documentation about 'FORTLS' is described in Molina-Valero et al. (2022, <doi:10.1016/j.envsoft.2022.105337>).