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A platform for research: civil engineering, architecture and urbanism
Assessing the Traffic Noise Reduction Effect of Roadside Green Space Using LiDAR Point Cloud Data in Shenzhen, China
The characteristics of vegetation in urban road side green spaces affect their noise reduction capacity. How to objectively, extensively, and accurately evaluate the noise reduction effect of these complex structures is challenging. In this study, we take urban roadside green space quadrats as the research object, use knapsack LiDAR to collect point cloud data of vegetation in the quadrats, and then construct and extract factor indices that can reflect the different vegetation characteristics based on LiDAR point cloud data with LiDAR360 software. We then combine the actual collected and calculate attenuation of traffic noise using correlation analysis and ordinary least square regression analysis to clarify the characteristic factors and correlation of noise attenuation in order to explore the influence of vegetation characteristics on the effect of noise reduction. The results show that a variety of factors affect the noise reduction effect of complex vegetation structures, and the importance degree is the following: horizontal occlusion degree > width > percentage of point cloud grid > leaf area index > coverage degree. By comparing the vegetation characteristic factors at different heights, we found that coverage degree, leaf area index, horizontal occlusion degree, and the percentage of the point cloud grid have the most significant positive correlation with the actual attenuation at a height of 5 m, but the coverage degree and leaf area index at absolute height have no correlation with the actual attenuation. The amount of vegetation near the road has a greater effect on noise reduction than that on the far side. The actual noise attenuation and the vegetation characteristic factors of green space have a non-linear relationship, and the interaction has a comprehensive influence on the noise reduction effect. These findings can provide a scientific basis for the reduction of traffic noise through the structural optimization of urban green space.
Assessing the Traffic Noise Reduction Effect of Roadside Green Space Using LiDAR Point Cloud Data in Shenzhen, China
The characteristics of vegetation in urban road side green spaces affect their noise reduction capacity. How to objectively, extensively, and accurately evaluate the noise reduction effect of these complex structures is challenging. In this study, we take urban roadside green space quadrats as the research object, use knapsack LiDAR to collect point cloud data of vegetation in the quadrats, and then construct and extract factor indices that can reflect the different vegetation characteristics based on LiDAR point cloud data with LiDAR360 software. We then combine the actual collected and calculate attenuation of traffic noise using correlation analysis and ordinary least square regression analysis to clarify the characteristic factors and correlation of noise attenuation in order to explore the influence of vegetation characteristics on the effect of noise reduction. The results show that a variety of factors affect the noise reduction effect of complex vegetation structures, and the importance degree is the following: horizontal occlusion degree > width > percentage of point cloud grid > leaf area index > coverage degree. By comparing the vegetation characteristic factors at different heights, we found that coverage degree, leaf area index, horizontal occlusion degree, and the percentage of the point cloud grid have the most significant positive correlation with the actual attenuation at a height of 5 m, but the coverage degree and leaf area index at absolute height have no correlation with the actual attenuation. The amount of vegetation near the road has a greater effect on noise reduction than that on the far side. The actual noise attenuation and the vegetation characteristic factors of green space have a non-linear relationship, and the interaction has a comprehensive influence on the noise reduction effect. These findings can provide a scientific basis for the reduction of traffic noise through the structural optimization of urban green space.
Assessing the Traffic Noise Reduction Effect of Roadside Green Space Using LiDAR Point Cloud Data in Shenzhen, China
Chao Xu (author) / Baolong Han (author) / Fei Lu (author) / Tong Wu (author)
2022
Article (Journal)
Electronic Resource
Unknown
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