Estimating Diameter and Height Distributions from Airborne Lidar Via Copulas

Ting-Ru Yang, John A Kershaw


Estimates of quantitative variables in forest stands often are required. Light Detection and Ranging (LiDAR) can create three-dimensional point clouds of forest structures and ground surface
elevation maps. These features are useful for quantifying forest stand parameters such as volume and canopy height at broad scales. This study explores the potential of applying copulas and LiDAR metrics to obtain diameter and height estimates. Predicted values were compared with field measurements. Diameter and height distributions were obtained using moment–based parameter recovery and prediction of moments using nonlinear least squares from LiDAR attributes. We then used copula methods to link the diameter and height distributions. Using the diameter and height distributions, other attributes such as volume or carbon content can be estimated and summed to obtain area-based estimates.


stand structure, height - diameter relationships, bivariate distributions, copulas, momentbased parameter recovery

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