Improving Traffic-noise-mitigation Strategies with LiDAR-based 3D Tree-canopy Analysis

Abstract

The leaves on trees absorb road noise and serve as noise barriers. Tree structures such as tree belts and isolated trees have various methods for absorbing sounds. The depth, surface area, and noise-absorption coefficient of trees contribute to noise absorption. Therefore, this study aims to address this issue of traffic-noise pollution through the use of trees; in particular, by analyzing the noise-absorption coefficient of leaves, the surface area of the leaves, and the depths of the trees. However, the study stresses the need for 3D tree-canopy visualization to identify these factors. To achieve this, the study used LiDAR point clouds to provide accurate data for the convex hull visualizations of canopies. Additionally, a formulated equation for calculating traffic noise after absorption has been suggested by combining the traffic-noise absorption and Henk de Kluijver traffic-noise models. The study also compares the effectiveness of tree belts and isolated trees in reducing noise pollution, concluding that, below a canopy of trees, there is no noise reduction. Finally, the study has demonstrated that the number and sizes of leaves affect noise absorption, showing that noise pollution can be reduced by 1 to 3 dB(A) in the research area by using trees.