Forest canopy-structure characterization: A data-driven approach

Abstract

Forest canopy structure influences and partitions the energy fluxes between the atmosphere and vegetation. It serves as an indicator of a variety of biophysical variables and ecosystem goods and services. Airborne laser scanning (ALS) can simultaneously provide horizontal and vertical information on canopy structure. Existing approaches to assess canopy structure often focus on in situ collected structural variables and require a substantial set of prior information about stand characteristics. They also rely on pre-defined spatial units and are usually dependent on site-specific model calibrations. We propose a method to provide quantitative canopy-structure descriptors on different scales, retrieved from ALS data. The approach includes (i) a sensitivity assessment and a quantification of ALS-derived canopy-structure information dependent on ALS data properties, (ii) an automatic determination of the most feasible spatial unit for canopy-structure characterization, and (iii) the derivation of canopy-structure types (CSTs) using a hierarchical, multi-scale classification approach based on Bayesian robust mixture models (BRMM), satisfying structurally homogenous criteria without the use of in situ calibration information. The CSTs resulted in retrievals of canopy layering (single-, two-, and multi-layered canopies) and canopy types (deciduous or evergreen canopies). Retrievals classified seven CSTs with accuracies ranging from 52% to 82% user accuracy (canopy layering) and 89–99% user accuracy (canopy type). The method supports a data-driven approach, allowing for an efficient monitoring of canopy structure.