Fine-scale replication and quantitative assessment of forest vertical structure using LiDAR for forest avian habitat characterization

Abstract

Light detection and ranging (LiDAR) is an active remote sensing technology that can be used to directly acquire three-dimensional forest information, and it has great potential to replace traditional time-consuming field surveys. We aimed to determine the accuracy with which enhanced airborne LiDAR with high density (mean, 47 points m−2) can replicate detailed vertical foliage arrangement and three forest structural indices, i.e. number of layers (NSTRAT), sum of the percent vegetation cover (PCVC), and foliage height diversity (FHD), all of which are useful for characterizing avian habitats. Thirteen 40-m-long lines were set in an urban forest area, and LiDAR-based and field-based cross-sectional profiles with 1 × 1-m meshes were compared. In addition, we performed random LiDAR point thinning to determine whether the replications were possible with lower point densities. The 1 × 1-m mesh vertical foliage arrangements were replicated with high accuracy by using LiDAR (mean producer's accuracy, 0.756; mean user's accuracy, 0.810) before the point reduction. All the three indices were also replicated with high accuracy (<0.12 of the relative root mean square error for the 1:1 relationship). Even when the mean point density was reduced to 2.4 points m−2, the three indices could be quantitatively estimated with high accuracy by using simple linear regression analyses (>0.8 in R 2). For NSTRAT and FHD, numerical replication was possible with high accuracy (<0.2 and <0.1, respectively, of the relative root mean square error for the 1:1 relationship), even when the mean point density was reduced to 2.4 points m−2. In conclusion, enhanced high-density LiDAR seems to be highly suitable as a substitute for field surveys for delineating detailed vertical forest structure in order to characterize forest avian habitats. Moreover, lower-density LiDAR data could replicate the forest structural indices, suggesting its useful low-cost application in quantitative avian habitat models.