Retrieval of canopy structure types for forest characterization using multi-temporal airborne laser scanning

We present a method to characterize forest canopy structure in space and time based on vertical echo distributions from airborne laser scanning (ALS). We developed a transferable, grid-based method using ALS data combined with an automatic determination of the best feasible spatial unit for canopy structure characterization. We derive canopy structure types (CSTs) using a hierarchical, multi-scale classification approach based on Bayesian robust mixture models (BRMMs), which satisfy structurally homogenous criteria without the use of in-situ calibration information. The validation shows promising results for the CSTs, particularly in terms of seasonal and horizontal variations in vertical canopy structure. We conclude that our method can improve the robustness and reliability of canopy structure characterization. Future work will include tests of transferability to a larger variety of forests and extensive testing using CSTs as a structural classification scheme.