Refining and evaluating a Horvitz-Thompson-like stand density estimator in individual tree detection based on airborne laser scanning

Horvitz-Thompson-like stand density estimation is a method for estimating the stand density from tree crown objects extracted from airborne laser scanning data through individual tree detection. The estimator is based on stochastic geometry and mathematical morphology of the (planar) set formed by the detected tree crowns. This set is used to approximate the detection probabilities of trees. These probabilities are then used to calculate the estimate. The method includes a tuning parameter, which needs to be known to apply the method. We present a refinement of the method to allow more general detection conditions than those of previous papers. We also present and discuss the methods for estimating the tuning parameter of the estimator using a functional k-nearest neighbors method. We test the model fitting and prediction in two spatially separate data sets and examine the plot-level accuracy of estimation. The estimator produced a 13% lower RMSE (root-mean-square error) than the benchmark method in an external validation data set. We also analyze the effects of similarity and dissimilarity of training and validation data on the results.

Automated summary

Horvitz-Thompson-like stand density estimation is a method that estimates stand density using tree crown objects extracted from airborne laser scanning data. The method uses stochastic geometry and mathematical morphology to approximate the detection probabilities of trees based on the detected tree crowns. We refined the method to allow more general detection conditions and developed a method to estimate the tuning parameter of the estimator. Experimental results showed that the estimator outperformed the benchmark method in terms of accuracy.