Estimating Tree Height Distribution Using Low-Density ALS Data With and Without Training Data

摘要

This study applies an approach based on stochastic geometry for retrieval of forest characteristics from airborne laser scanning (ALS) in two situations: 1) without ground-measured training data and 2) with training data. The applied model treats the ALS echo heights as an outcome of a random process, expressing the observed heights of canopy envelope as a function of stand density, the parameters of the tree height distribution, and the shape of the individual tree crown. The model was applied to a eucalyptus plantation dataset with known spacing, where the main interest was to estimate the plot-specific tree height distribution. Estimation without training data resulted in RMSEs of 2.9 and 0.9 m for mean and dominant heights, respectively. Estimation using training data resulted in RMSE’s of 1.4 and 0.8 m, respectively. In both cases, the estimates of dominant height were more accurate than with the reference method, but the estimates of mean height were less accurate (area-based approach; RMSEs 1.1 and 0.9 m, respectively). The model-based method was robust to substantial decrease in echo density from 1.4 echoes/ to 0.14 echoes/.