The diameter at breast height (DBH) of trees and stands is not only a widely used plant functional trait in ecology and biodiversity but also one of the most fundamental measurements in managing forests. However, systematically measuring the DBH of individual trees over large areas using conventional ground-based approaches is labour-intensive and costly. Here, we present an improved area-based approach to estimate plot-level tree DBH from airborne LiDAR data using the relationship between tree height and DBH, which is widely available for most forest types and many individual tree species. We first determined optimal functional forms for modelling height-DBH relationships using field-measured tree height and DBH. Then we estimated plot-level mean DBH by inverting the height-DBH relationships using the tree height predicted by LiDAR. Finally, we compared the predictive performance of our approach with a classical area-based method of DBH. The results showed that our approach significantly improved the prediction accuracy of tree DBH (R2 = 0.85–0.90, rRMSE = 9.57%–11.26%) compared to the classical area-based approach (R2 = 0.80–0.83, rRMSE = 11.98%–14.97%). Our study demonstrates the potential of using height-DBH relationships to improve the estimation of the plot-level DBH from airborne LiDAR data.
|Publication status||Published - 1 Jan 2023|