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.

树木和林分的胸径 (DBH) 不仅是生态学和生物多样性中广泛使用的植物功能性状，也是森林管理中最基本的测量指标之一。然而，使用传统的地面方法系统地测量大面积单棵树木的胸径是一项劳动密集型且成本高昂的工作。在这里，我们提出了一种改进的基于区域的方法，利用树高和 DBH 之间的关系，从机载 LiDAR 数据估计地块级树木 DBH，这广泛适用于大多数森林类型和许多个别树种。我们首先使用实地测量的树高和 DBH 确定了建模高度-DBH 关系的最佳函数形式。然后，我们通过使用 LiDAR 预测的树高反转高度-DBH 关系来估计地块级平均 DBH。最后，我们将我们的方法的预测性能与经典的基于区域的 DBH 方法进行了比较。结果表明，我们的方法显着提高了树木胸径的预测精度（R ²  = 0.85–0.90，rRMSE = 9.57%–11.26%）与经典的基于区域的方法（R ²  = 0.80–0.83，rRMSE = 11.98%–14.97%）相比。我们的研究证明了使用高度-DBH 关系来改进机载 LiDAR 数据的地块级 DBH 估计的潜力。