Mapping the vertical forest structure in a large subtropical region using airborne LiDAR data

Abstract

Vertical forest structure (VFS) refers to the vertical stratification or layering of forest communities in space, which is a fundamental characteristic of a plant community. It plays a vital role in forest vitality and facilitates various ecological activities and processes. The mapping of VFS is of significant value for both ecological and forestry purposes. In this paper, we presented a novel approach for the automated mapping of VFS in a large subtropical region based on discrete airborne LiDAR data. Firstly, the LiDAR point clouds of a stand (plot or grid cell) were segmented into 100 height bins from the top to the ground, and a height-frequency histogram was obtained by calculating the proportion of the number of returns in the bins to the total number of returns, which objectively represented the vertical distribution of canopy material. Secondly, a univariate ten-order polynomial was used to fit the height-frequency histogram, enabling the generation of a continuous vertical canopy profile (pseudo-waveform) of the stand. Thirdly, a comprehensive set of vertical structure parameters was defined and extracted based on the pseudo-waveforms, which effectively characterized the vertical profile layer and the canopy layer. Fourthly, to construct a comprehensive framework, 43 model profiles were summarized from the field plots, taking into account the number of effective peaks in the pseudo-waveforms and other vertical structure parameters. Finally, 43 classification rules were developed and 18 judgment criteria were established using the vertical structure parameters of the mode profiles. They classified vertical forest structures into 24 classes with explicit spatial definitions. The classification of 1147 field plots resulted in an overall accuracy of 94.7% and a kappa coefficient of 0.937. The VFS mapping over a large area demonstrated an effective execution rate of 99.8% for both rules and criteria. The proposed approach exhibits high accuracy and excellent generalization ability across different forest types, species, and study sites, highlighting its ecological and forestry significance. It represents a significant advance in the automated classification and mapping of VFS in large subtropical regions using airborne LiDAR data. However, the proposed approach needs to be validated in other vegetation zones to assess its generalizability and extend its applicability.