On the compatibility of single-scan terrestrial LiDAR with digital photogrammetry and field inventory metrics of vegetation structure in forest and agroforestry landscapes

In tropical ecosystems, accurately quantifying vegetation structure is crucial to determining their capacity to deliver ecosystem services. Terrestrial laser scanning (TLS) and UAV-based digital aerial photogrammetry (DAP) are remote sensing tools used to assess vegetation structure, but are challenging to use with conventional methods. Single-Scan TLS and DTM-independent DAPs are alternative scanning approaches used to describe vegetation structure; however, it remains unclear to what extent they relate to each other and how accurately they can distinguish forest structural characteristics, including vertical structure, horizontal structure, vegetation density, and structural heterogeneity. First, we quantified bivariate and multivariate correlations between equivalent/analogous structural metrics from these data sources using principal component and Procrustes analysis. We then evaluated their ability to characterize the forest and agroforestry landscapes. DAP, TLS, and Field metrics were moderately aligned for vegetation density, canopy top height, and gap dynamics, but differed in height variability and surface heterogeneity, reflecting differences in data structure. DAP and TLS achieved the highest accuracy in classifying forests and agroforestry plots, with overall accuracies of 89% and 78%, respectively. Though the field metrics were unable to resolve 3D characteristics related to heterogeneity, their capacity to distinguish the stand structure at 69% accuracy was driven by the relative pattern of its suite of metrics. The results indicate that the single-scan TLS and DTM-independent DAP yield meaningful descriptors of vegetation structure, which, when combined, can provide a comprehensive representation of the structure in these tropical landscapes.