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            Geoplanning: Journal of Geomatics and Planning, Vol. 11, No. 2, 2024, 149-164 

 

Original Research 

Traffic Noise Absorption and Propagation in A 

Three-Dimensional Spatial Environment 

Nevil V. Wickramathilaka1, 2, Uznir Ujang1*, Suhaibah Azri1, Tan 

Liat Choon1, Attygalage R. Rupasinghe2 

1. 3D GIS Research Lab, Faculty of Built Environment and Surveying, Universiti Teknologi 

Malaysia, 81310, Johor Bahru, Johor, Malaysia 

2. Southern Campus, General Sir John Kotelawala Defence University, Edison Hill, Nugegalayaya, 

Sewanagala, Sri Lanka 

 

DOI: 10.14710/geoplanning.11.2.149-164 

Abstract 

The impact of noise barriers on noise propagation is vital for traffic noise calculations and visualizations. Noise barriers 

create a major noise reduction. Green belts are the most common type of noise barrier to mitigate road traffic noise. The 

width, height, and surface area of leaves a green belt, as well as the noise absorption coefficient of leaves, are vital for noise 

absorption. This review aims to compare the characteristics and performance of green belts barriers built for traffic noise 

reduction. Individual tree canopies play the main role in absorbing noise in green belts. Therefore, identifying the canopy's 

properties is important. The side scan and nadir scan from the LiDAR survey were used to detect the tree canopy points 

cloud. The voxel-based, convex hull, and concave hull methods are used to visualize tree canopies in three-dimensional (3D). 

Concave hull provides an extract fitting surface than convex hull visualization. However, these hull surfaces do not provide 

accurate estimation of surface area of leaves. Further, voxel-based horizontal layers through the voxel-based profiling 

describes a significant method to calculate surface area of leaves in tree canopies. Establishing green belts as barriers is 

more cost-effective, making the former better for developing countries.  

Copyright © 2024 by Authors,  

Published by Universitas Diponegoro Publishing Group.  

This open access article is distributed under a  

Creative Commons Attribution 4.0 International license 

 

1. Introduction 

Road traffic noise is a major contributor to overall noise pollution (Subramani et al., 2012) and traffic noise 

creates 90% of urban noise pollution (Gilani & Mir, 2021; Halim et al., 2018; Islam et al., 2021; Kurakula & 

Kuffer, 2008). Noise barriers are the major form of noise reduction in sound propagation areas (Can et al., 2008; 

Guarnaccia et al., 2012). Traffic noise travels in all directions from the source point, so it affects a 360-degree 

range (Almansi et al., 2024; Huang et al., 2018; Wickramathilaka et al., 2023). Therefore, the three-dimensional 

visualization of noise barriers is vital for predicting the performance of noise barriers (Dubey et al., 2022; 

Pamanikabud & Tansatcha, 2009). Recently, traffic noise visualization scenarios have been implemented using 

noise-reduction barriers in urban areas (Jamrah et al., 2006; Murthy et al., 1970; Peng et al., 2021; Tobollik et 

al., 2019; Yang et al., 2020). Trees can be used as noise barriers along roads. Especially tree leaves absorb road 

traffic noise (Wickramathilaka et al., 2024). 

The structures of trees such as group of trees, isolated trees and tree belts act as different ways to block 

the noise (Wickramathilaka et al., 2024). Tree belts along roads have long been identified as having noise 

reduction potential than other tree structures (Van Renterghem, 2014; Wickramathilaka et al., 2024). The width 

e-ISSN: 2355-6544 
 
Received: 19 December 2023;  
Revised:  24 September 2024;  
Accepted: 28 October 2024; 
Available Online: 30 November 2024; 
Published: 04 December 2024. 

 
Keywords:  
Green Belts, Noise Barriers, Noise 
Propagation, Tree Canopy Detection, 
Tree Canopy Three-Dimensional 
Visualization  
 
*Corresponding author(s) 
email: mduznir@utm.my 
 

 
 
 

 

 

https://doi.org/10.14710/geoplanning.11.2.149-164
mailto:mduznir@utm.my


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and height of a tree belts are impact to reduce noise (Wickramathilaka et al., 2024). However, the performance 

of individual trees in tree belts is significant in terms of noise mitigation. In particular, tree leaves absorb road 

traffic noise (Karbalaei et al., 2015; Kowalska-Koczwara et al., 2021; Samara & Tsitsoni, 2011; Tang & Ong, 

1988), and the amount of noise absorption depends on the noise absorption coefficient of the leaves, their surface 

area of leaves, and the depth of tree (Watanabe & Yamada, 1996). The noise absorption coefficient of the leaves 

varies with the size, texture, and thickness of the leaves. In addition, as the leaves become wider and thicker, so 

does the amount of noise absorption (Joshi et al., 2013). The amount of greenery in a leaf improves its capacity 

to absorb sound (Jang, 2023). Because they usually have dense foliage all year round, evergreen trees are better 

suited to serve as a continuous sound barrier than trees with dried leaves (Jang, 2023; Samara & Tsitsoni, 2011). 

When the leaves dry out, some of their flexibility and density are lost. However, compared to evergreen foliage, 

dry leaves might not be as successful at reducing noise  (Jang, 2023). Younger leaves often have higher moisture 

content, are softer, and are more flexible than older leaves. Compared to older leaves, younger leaves are often 

softer, more flexible, and have higher moisture content. Thus, the noise absorption coefficient depends on several 

factors of the leaves. Therefore, several studies have suggested an experimental method of impedance tube to 

identify noise absorption coefficient of leaves. This review paper shows more information about identifying noise 

absorption coefficient of leaves.  

Furthermore, the surface area of the leaves is vital to absorb noise. It means that the canopy of the tree is 

prominent. The road traffic noise absorption equation describes the depth of tree is vital to absorb noise 

(Watanabe & Yamada, 1996),  Identifying surface area of leaves is not an easy process. Because leaves spread in 

a 3D space. Thus, this study tries to convey information about finding the surface area of leaves to identify noise 

absorption. To identify the noise-mitigation performance of tree belts, it is vital to construct individual trees in 

a three-dimensional space using their actual dimensions. 3D visualization of a canopy is essential to identify 

surface area of leaves, and depth of trees accurately. Therefore, this study demonstrates the visualization of the 

3D tree and their accuracy comparison for traffic noise absorption.  But the visualization of trees is still an issue. 

Recently, 3D points clouds have been widely used for the visualization of tree canopies (Itakura & Hosoi, 2018; 

Parmehr & Amati, 2021), and a combination of terrestrial laser scanning and drone survey techniques are vital 

for the detection of tree canopies (Shimizu et al., 2022). Furthermore, developments of terrestrial scanning survey 

to identify the surface area of leaves are described in this review. The depth of the tree can be found directly from 

the 3D point clouds. However, there are several methods to calculate the surface area of leaves through the 

surface generation of the point clouds. 

Furthermore, finding an exactly fitting surface with canopy point clouds enhances the accuracy of the 

canopy properties (Suwardhi et al., 2022). Recently, the convex hull, concave hull (Kempf et al., 2021)and voxel-

based methods have been widely used for surface fitting for point clouds (Suwardhi et al., 2022). Somehow, 

several studies have demonstrated the convex using the slice method to identify canopy properties. The 

visualization of a tree canopy using a convex hull and concave hull forms a surface mesh, and a cartographic 

generalization of the mesh demands that a tree canopy has a real three-dimensional appearance (Li & Nan, 2021). 

Although several noise studies are available on mitigating traffic noise pollution in urban areas, the problem 

remains the same. Green areas are vital for reducing traffic noise levels through their absorption properties. In 

particular, tree belts are more effective in terms of noise reduction than isolated trees. If green spaces are 

manipulated for noise mitigation, it is essential to extract the properties of trees into 3D space. Further, the 

detection of tree canopies using modern survey techniques and their visualization in a three-dimensional 

environment are important to identify the noise reduction by trees. The main objectives of this review are to 

address the problems mentioned above and find applicable solutions by reviewing previous research. 

2. Methodology 

For this paper, two hundred (200) research papers were collected to review traffic noise absorption of trees 

and 3D tree visualization to identify traffic noise absorption. One hundred (100) of the best research papers were 

selected to review under the subtopics; Traffic noise absorption and trees, Noise absorption coefficient of trees, 

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Tree canopy detection and visualization, Tree canopy 3D visualization, concave hull and convex hull, 3D tree 

visualization developments, and 3D Tree visualization object modeling. Figure 1 shows the flow chart of the 

methodology used in this review paper. 

 

Figure 1. Flow Chart of the Methodology  

3. Results and Discussion 

3.1 Traffic Noise Absorption and Trees 

Well-grown vegetation belts are an effective part of the mitigation of road traffic noise levels (Maleki et 

al., 2010). Therefore, the identification and recommendation of suitable plants for vegetation belts are vital 

(Pathak et al., 2011). Traffic noise is reduced by 50% when vegetation is improved from minimum to moderate 

vegetation density, and when vegetation barriers increase from moderate to dense, this can reduce noise by 9 

dB(A)-11 dB(A). A 5 m deep vegetation belt was identified to be the general depth needed to mitigate of traffic 

noise (Ow & Ghosh, 2017). Previous research has shown that trees can absorb 5 to 10 dB(A) of road noise, which 

10–24% of the pollution caused by traffic noise (Li & Xie, 2021). To determine the trees' ability to absorb traffic 

noise, comprehensive data about the trees is required (Kalansuriya et al., 2009). Furthermore, several studies 

have shown that road noise is reduced by tree belts.  

This study measured traffic noise levels at 5, 10, and 20 meters away from moving cars using three 

different planting schemes: minimal, medium, and dense. Table 1 shows the research findings. The findings 

showed how much noise reduction along the roadsides with and without trees. At 5 m, the width of the tree belt, 

the noise reduction was 2, 3, and 2 dB(A) in relation to the site. Furthermore, at a tree belt width of 10 m, the 

reduction in noise was 1, 2, and 2 dB(A) with respect to the site. Furthermore, at 20 m tree belt width, the site 

noise reduction was 4, 8, and 6 dB(A) (Ow & Ghosh, 2017) Table 1 shows the noise absorption of trees. 

In this paper, the identification of tree-based noise absorption is discussed.  However, determining noise 

absorption remains a challenge. Noise levels were measured in this investigation using a sound level meter. But 

there is no proper formula for figuring out whether trees in tree belts absorb noise or not from these kinds of 

investigation. Standard noise formulae should be used to measure noise levels from road traffic to ensure study 

accuracy. For leaves to absorb road noise, their noise absorption coefficient is essential. The sizes, thicknesses, 

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and textures of leaves are the main components that absorb noise (Joshi et al., 2013; Watanabe & Yamada, 1996). 

In addition, the depth of a tree's canopy and the surface area of its leaves are ideal for absorbing noise. 

Furthermore, compared to dry leaves, green leaves are more effective in absorbing noise (Safikhani et al., 2014). 

Surface area, depth, noise-absorption coefficient, and traffic noise frequency have an impact on the ability to 

absorb noise. An equation has been developed to determine the amount of noise absorption by trees. Equation 1 

shows the noise absorption of leaves. 

                                                   A = −10log (1 − (
𝐺 × 𝐹 × 𝐿 × 𝑓0.5

8
))……………. (Eq.1) 

where: G – coefficient (the frequency-absorption factor of leaves), F – the surface area of leaves for unit volume, 

L – the depth of the tree, f – the frequency of the road-traffic noise  

Table 1. Noise Absorption based on Different Sites 

Site 
 

At Source 
dB(A) 

Difference 
between 
source & at 
5m 

5 m from 
source 
dB(A) 

Difference 
between 
5 and 10m 

10m from 
source 
dB(A) 

Difference 
between 
I0 and 20 
m 

20 m 
from 
source 
dB(A) 

Total 
reduction 
dB(A) 

Minimal 
planting scheme 

78 1 77 2 75 1 74 4 

Sparse to 
medium planting 
scheme 

73 3 70 3 67 2 65 8 

Dense planting 
scheme 

67 2 65 2 63 2 61 6 

The size, thickness and texture of leaves are important factors in the absorption of noise; therefore, the 

noise absorption coefficient of the leaves depends on these attributes. The canopy of a tree affects noise absorption 

because the leaves cover a larger area than the bark and branches. Tree belts are a more effective way to absorb 

noise than isolated trees. When measuring noise absorption, factors such as leaf surface area and tree depth 

accuracy have a significant impact. The findings indicate that a tree belt's depth, width, and tree-to-tree spacing 

of a tree belt are the main determinants of how much road noise it blocks (Ow & Ghosh, 2017; Peng et al., 2014). 

Furthermore, tree structures such as tree belts and groupings of trees reduce traffic noise pollution better than 

single, isolated trees (Ow & Ghosh, 2017; Wickramathilaka et al., 2022). In addition to covering larger areas 

than tree bark and branches, leaves are essential to increase noise absorption; therefore, it is important to 

concentrate on leaves to absorb noise (Dobson & Ryan, 2000). This means that the canopy of a tree is vital for 

absorbing noise.  

Furthermore, traffic noise attenuation through 10 m to 20 m width of tree belts was found to be 2 dB to 3 

dB (with a tree spacing of less than 0.5 m), while it was up to 7 dB through 120 m tree belts of eucalyptus 

vegetation (with a tree spacing greater than 0.5 m) (Huddart, 1990; Ow & Ghosh, 2017; Peng et al., 2014). 

Moreover, researchers have found that a narrow belt of dense conifer vegetation reduced noise by 5 dB through 

3m (Kragh, 1981; Ow & Ghosh, 2017). The height, width and density of a tree belt are the most important factors 

for noise reduction, rather than characteristics of leaf sizes and branches. A confirmed width (at least 30 m) has 

a positive impact on noise reduction, while height provides greater opportunities for noise reduction because 

high tree belts consist of a greater surface area (Fang & Ling, 2003). Research by Fang & Ling (2003), found 

that shrubs offered greater noise reduction. Therefore, both trees and shrubs should be taken advantage of in the 

context of noise mitigation. 

3.2 Noise Absorption Coefficient of Trees 

The sound absorption coefficient refers to the acoustical effectiveness of a material and the incident sound 

energy absorption (Bohatkiewicz, 2016). A sound absorption coefficient value closer to zero means poor noise 

absorption (Joshi et al., 2013). Thicker, denser, and heavier materials are the main factors for noise absorption 

(Karlinasari et al., 2012; Tudor et al., 2020). The leaves of trees are more effective for noise absorption than their 

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trunks. Leaves have several acoustic properties like absorption, scattering and reflection, but noise absorption is 

a more effective acoustic property of leaves. The size, shape, thickness and texture of leaves are the factors 

affecting their noise absorption coefficient (Watanabe & Yamada, 1996). A study was carried out to estimate the 

noise absorption coefficient of leaves. Various leaf thicknesses (1 cm, 1.75 cm and 2 cm) and sizes (0.5 × 0.5 cm2, 

1.0 × 1.0 cm2 and 2.0 × 2.0 cm2) were used under different frequencies in the impedance tube (Jung et al., 2020). 

As a result of this study, the noise absorption coefficient of twenty-two (22) leaves is shown in Figure 2. 

Moreover, the acoustic properties of leaves depend on the frequency of the noise, and the noise absorption 

coefficient varies with different noise frequencies (Joshi et al., 2013). 

 
Source: Joshi et al., 2013 

Figure 2. Noise Absorption Coefficient of Leaves  

3.3 Tree Canopy Detection and Visualization  

Well-grown trees are vital for the mitigation of road traffic noise levels (Margaritis et al., 2018). The 

canopy of a tree is an effective biomass for noise absorption because it consists of leaves (Pathak et al., 2008). 

Therefore, detecting the tree canopy (Jichen et al., 2017) is very important for identifying the amount of noise 

absorption using the parameters of the surface area of leaves and tree depth (Watanabe & Yamada, 1996). LiDAR 

point clouds are widely used for decision-making processes in vegetation operations (Wulder et al., 2008). The 

distribution of LiDAR points in the canopy provides reliable information, based on individual trees (Wallace et 

al., 2012). Mini-Unmanned Aerial Vehicles (UAV) are a platform to use when conducting high spatial resolution 

surveys for tree canopy height detection (Wallace et al., 2012). In order to assess canopy height, a set of 

quantitative statistics was used for each point cloud (Donoghue et al., 2007; Lim & Treitz, 2004). However, the 

effect of flight conditions on measuring vegetation is still being examined. It is recommended to use a lower 

flying height, a small survey area and high point densities for mini-UAV surveying (Disney et al., 2010; Goodwin 

et al., 2006; Lovell et al., 2005). These studies described using the beam divergence, point density, scan angle 

and internal properties of scanners for the detection of individual trees. Lovell et al. (2005), found that the 

measurements of tree heights were formulated using a higher point density. Furthermore, this study described 

how the use of a large scan angle reduced the number of lower canopy returns and helped with a large canopy 

cover (Hao et al., 2021). The results obtained by Wallace et al. (2012), showed the points cloud of a tree canopy 

generated at an average flying height of 48.3 m; the first (blue), second (green), and third (red) return signal 

points were shown in point clouds. The multi-rotor drone with an Ibeo LUX laser scanner was used for this 

study. The histograms of the above-ground level of the LiDAR return signals from the point clouds were 

captured at different flight heights (30 m, 50 m, 70 m and 90 m). The point cloud density for an individual tree 

was significantly different from different flying heights (Wallace et al., 2012). Figure 3 shows an example of the 

point clouds of a tree canopy.       

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Source: Wallace et al., 2012 

Figure 3. An Example of the Point Clouds of a Tree Canopy 

The vegetation heights returned over a single plot for point clouds were captured at above-ground flying 

heights of a) 30 m, b) 50 m and c) 70 m. There were an obvious attenuation of the upper canopy returns due to 

the flight altitude. Figure 4 shows the canopy point clouds at various flying heights. 

 
Source: Wallace et al., 2012 

Figure 4. An Example of the Point Clouds of a Tree Canopy 

Terrestrial laser scanning (TLS) is used to obtain precise information about trees (Zhong et al., 2016). It 

enables the extraction of tree information - such as the crown size, tree height and crown base height - more 

easily than a UAV survey (Hillman et al., 2021). TLS systems can be adapted to measure the actual shape of a 

tree canopy accurately, and TLS data can be used to delineate the boundaries of the canopy. Typically, TLS does 

not measure the horizontal top view of the canopy or the top view of the canopy due to its side scanning. 

Therefore, embedding the nadir perspective and TLS approaches are vital for capturing tree canopy details (Paris 

et al., 2017). Figure 5 shows the side scanning and nadir scanning of a tree canopy. 

 
     Source: Paris et al., 2017 

Figure 5. Side and nadir scanning of tree canopies 

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3.4 Tree Canopy 3D Visualization  

The voxel-based method, the convex hull by slices and the 3D convex hull are the methods usually used 

for the 3D visualization of a tree canopy (Yan et al., 2019). Three-dimensional tree modelling is an indispensable 

part of real-world visualization (Zhang et al., 2022). However, due to the irregular and intricate structure of 

trees, large-scale 3D modelling is impossible (Xu et al., 2021). Nevertheless, to identify the traffic noise 

absorption of trees, accurate visualization is essential. A mesh surface is often used for vegetation modelling and 

this leads to a time-consuming modelling process (Tang et al., 2013). Unlike the triangular mesh model, the 

voxel model consists of a voxel grid model. Recently, voxel grid modelling has been used for individual tree 

modelling, and the voxel size is important for 3D modelling (Li et al., 2017). The voxel size can be defined 

according to the density of the point clouds and the user requirements (Hancock et al., 2017). However, 

researchers are searching for standardised modelling methods for voxels (Chakraborty et al., 2019; Zhang et al., 

2022). 

The study by Park et al. (2010) used K-Dimensional tree (KD-tree) algorithms for the voxelisation of point 

clouds. Traditionally, to estimate the canopy properties, the boundary box was used, which was a simpler method. 

Gaps in the canopy structure are not considered in the boundary box model (Smart & Robinson, 1991). 

Voxelization algorithms are usually based on classifying points into three-dimensional grid voxels (Fernández-

Sarría et al., 2013), and all analysis vox functions are provided in the VoxR package (Béland et al., 2014). A 

simple voxelisation can be fully filled by rounding the point coordinates of the three-dimensional Cartesian 

system: (coord (x, y and z) *res)/res, where res is the voxel resolution. The vox function output is discrete, and 

the centre of each voxel and the number of points is represented within each voxel (Fernández-Sarría et al., 2013; 

Lecigne et al., 2018). Figure 6 shows a voxel representation of tree canopies using voxel, resolutions of 0.1 m 

(A), 0.5 m (B), and 1 m (C). 

 
Source: Lecigne et al., 2018 

Figure 6. Voxel Representation and Resolution of a Tree Canopy 

In the convex hull by slices method, the point clouds are divided into several irregular planes. All the point 

clouds are formulated in the direction of the Z-axis, according to a certain interval. This interval depends on the 

density of the point clouds. If the interval is too large, the volume estimation is not accurate. However, if the 

interval is too small, the calculation may be too complicated. In processing, the interval is given as one to five 

times the point density. Finally, the overall canopy volume can be estimated by summing the volume of each 

slice (Li et al., 2016). Figure 7 illustrates the use of the convex hull by slices method for the volume estimation 

of a tree canopy. The 3D convex hull method is a mesh method used to visualise the canopy of a tree in 3D, 

including the creation of minimal vertex and points enclosed by external planes. The convex hull volume is 

calculated using the boundaries of planes, which consist of many Delaunay triangles (Yan et al., 2019). Figure 8 

illustrates the volume estimation using the 3D convex hull method. 

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Source: Lecigne et al., 2018 

 

Figure 7. Volume Estimation from the 3D Convex 
Hull, the Convex Hull by Slices 

 

Figure 8. Volume Estimation from the 3D 
Convex Hull 

 

3.5 Concave Hull and Convex Hull 

The estimation of tree canopy measurements depends on the raster and vector visualization (Dunbar et 

al., 2004). The raster visualization of point clouds depends on the voxel, but the canopy parameters may vary 

with the voxel size. By contrast, tree canopy point clouds from vectors use networks of irregular triangles with 

a higher level of accuracy (Soma et al., 2021). The well-known convex and concave hull methods, in particular, 

are used for tree canopy estimation (Colaço et al., 2017; Yan et al., 2019). Parmehr & Amati (2021), compared 

the convex hull and concave hull representations of tree canopy point clouds. The canopy visualization was 

projected onto a two-dimensional plane to calculate the maximum diameter and area of a tree canopy. Figure 

9(a) describes the surface fitting of a tree canopy convex hull in red and a concave hull in blue. Whereas a convex 

hull provides an overestimated volume, a concave hull provides an extract-fitting surface to the point clouds 

while managing accuracy and reliability (Parmehr & Amati, 2021). Figure 9(b) illustrates the 3D visualization 

of a tree canopy from a convex hull, and Figure 9(c) illustrates the 3D visualization of a tree canopy from a 

concave hull. 

(a) (b) (c) 

 
Source: Parmehr & Amati, 2021 

 
Figure 9. a) Describes the surface fitting of a tree canopy convex hull in red and a concave hull in blue; b) 

Illustrates a 3D visualization of a tree canopy from a convex hull; c) illustrates a 3D visualization of a tree 

canopy from a concave hull 

3.6 3D Tree Visualization Developments 

Convex hull, concave hull, and voxel-based are the methods to create a surface for the tree canopy. 

However, these methods are not shown a clear identification of the total surface area of leaves in the tree canopy. 

Therefore, tree visualization development is vital. Several studies have developed mathematical methods to 

identify the Leaf area density (LAD) of a tree canopy. The surface area of leaves in unit volume is prime to 

calculate, traffic noise absorption from the tree canopies. The studies of Hosoi & Omasa (2006), Kargar et al. 

(2019), and Gu et al. (2022) and, have conducted studies to identify the LAD of a tree canopy using Lidar point 

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clouds. Points cloud segmentation is required to select leaves' points cloud separately. Normal of returns 

associate the surface structure in unorganized point clouds. Figure 10 is illustrated the difference between normal 

distributions of leaves and branches. 

 
Source: Gu et al., 2022 

Figure 10. Difference between the Normal Distribution of Leaves and Branches 

The normal changing difference is small in the leaf points cloud. According to Equation (2), n (p) is the 

normal of the point cloud in pth point. r is the average distance between two leaves. The leaves points cloud can 

be extracted using the amount of Δn. 

Δn (𝑝, 𝑟) = (
1

𝑁
) ∑ (𝑛(𝑝) − 𝑛(𝑝𝑖))𝑁

𝑖=1  ………... (Eq. 2) 

Moreover, the points cloud is segmented into layers on the horizontal direction (x-axis), and LAD is 

calculated using voxel-based canopy profiling (VCP). VCP describes voxel-based profiling (see figure 11) 

between two horizontal layers in a points cloud.  

 
Source: Gu et al, 2022 

Figure 11. Horizontal Voxel Layer of Leaf Point Clouds 

𝐿𝐴𝐷(ℎ, Δℎ) =  𝛼(𝜃) (
1

Δh
) ∑

𝑛1(𝑘)

𝑛1(𝑘)+ 𝑛𝑝(𝑘)′

𝑚ℎ+Δh
𝑘=𝑚ℎ

 ………... (Eq. 3) 

According to equation (3), where Δh is the thickness of the layer, and mh and mh+Δh indicate the voxel 

coordinates on the vertical direction (y-axis), θ is the zenith angle of the lidar beam. The heights h and h + Δh, 

and n1(k) and np(k) represent a number of voxels consisting of leaf points and excluding. Respectively, n1(k) + 

np(k) denotes the total number of incident laser beams in the k-th layer. The α(θ) is a correction factor of the 

leaf inclination angle. 

3.7 3D Tree Visualization Object Modeling 

The concept of object modeling for a tree canopy is not widely used to estimate the volume of the canopy. 

Object modeling does not represent an extract fitting surface to canopy points cloud. But it derives some 

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geometric shapes from tree canopies. Therefore, volume estimation of tree canopies from object modeling is not 

an accurate method. But Object modeling is vital for enhancing the visual quality (geometric shapes). Computer 

programing methods are inserted to identify the area of the canopy. Canopy volume calculation is vital to find 

noise absorption by trees. Tree height, canopy diameter, and canopy height are the parameters for object 

modeling. Biomass Estimates from Canopy Volume (BECVOL), and Arbour Structure (ARBORSTRUQ) are the 

methods of corresponding object modeling for tree canopies (Melville et al., 1999). ARBORSTRUQ is a more 

user-friendly method than BECVOL, and a complex algorithm is used in BECVOL. There are several geometric 

shapes have been derived in the ARBORSTRUQ method (see figure 12), and corresponding equations of 

geometric shapes to find the volume is described in figure 13. Where, X is the total tree height in centimeters, 

Y is the height of the lowest leaves in centimeters, R is the greatest canopy diameter in centimeters, and H is the 

variable height. 

 
Source: Melville et al., 1999 

 
Figure 12. Geometric Shapes of the Tree Canopy Figure 13. Equation to Find the Volume of 

Geometric Shapes 
 

3.8 Discussion 

Increasing green spaces is a cost-effective method of mitigateing noise pollution from road traffic noise. 

According to the recent study of Gharibi & Shayesteh (2024), noise reduction of green areas is done by two 

approaches. The study has found the variation of traffic noise with and without tree noise barriers. According to 

the result of this study, green sound barriers reduce noise by 0 to 4.5 dB.  On average, the mean noise reduction 

by green barriers is 0.1–6.4 dB which is 9–11 dB in the study of Ow and Ghosh. However, this study does not 

describe the impact of green spaces for noise reduction in a 3D space.  In particular, moderate and high-thickness 

green belts along roads are more effective than isolated trees in introducing traffic noise. The study by Sultan et 

al. (2024), has found the quantity of sound absorption by a vegetation barrier is related to the width and height 

of the vegetation barrier.  

Although several studies have discussed the exact general depth (5 m) needed for minimizing traffic noise, 

the mitigation depends on the width and height of the tree belt and the type of tree. Accurately determining the 

width and height of a tree belt to mitigate highway noise pollution is challenging in practical scenarios. However, 

a suitable height and width for the tree belt can be determined after visualizing the trees in a 3D environment. 

According to the study of Wickramathilaka et al. (2024), a method has been demonstrated and developed to 

identify noise absorption by tree belts through a 3D visualization of trees. In addition, tree spacing and surface 

areas are vital for noise reduction. It is not possible to identify the impact of tree spacing for road traffic noise 

reductions, the study of variety of trees for sound attenuation, and it was succeeded in minimizing traffic noise 

pollution in urban cities (Martínez-Sala et al., 2006). The study of Yofianti & Usman (2021) has mentioned that 

not only trees but also shrubs offer greater noise reduction, so a combination of both trees and shrubs should be 

employed to achieve the maximum noise reduction through green areas. 

When considering individual tree properties, the size, thickness, and texture of the leaves are important 

for noise absorption. In particular, the noise absorption coefficient of the leaves directly relates to the noise 

absorption. A leaf will vibrate at a particular (resonant) frequency when the sound's wavelength is comparable 

to that of the leaf. Sound energy is converted to heat during this process. This means that the sound energy is 

reduced (Romanova et al., 2019). In conclusion, the optimal noise reduction effect would probably be achieved 

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by combining large, textured, and fairly thick leaves. For real-world uses, think about how these elements could 

be used into sound barriers or landscape architecture. Furthermore, compared to dry leaves, wet leaves often 

have a higher sound absorption coefficient. The density of the leaf material can increase due to the dampness, 

increasing its capacity to absorb sound energy (Ali et al., 2020). 

In general, the frequency of traffic noise is not constant and varies with the speed, amount, and composition 

of traffic. It indicates that in an urban setting, the frequency of road traffic noise is dynamic. The noise absorption 

coefficient of the leaves varies with the frequency of the noise. Therefore, different noise frequencies are needed 

to identify the average noise absorption coefficient of leaves (Adhika et al., 2023). Therefore, identifying the noise 

absorption coefficient of leaves using an impedance tube is important to determine the noise absorption of trees. 

In general, varied leaf thicknesses )1 cm, 1.75 cm, and 2 cm) and sizes (0.5 × 0.5 cm2, 1.0 × 1.0 cm2, and 2.0 × 2.0 

cm2) are used in impedance tube tests. However, to calculate the noise absorption of leaves, the frequency 

absorption factor of leaves is vital. Therefore, the standard noise equation can be used to identify the frequency 

absorption factor of leaves through the noise absorption coefficient of leaves.  

To identify the performance of trees as noise barriers, detecting and visualizing the tree canopy are crucial 

because the tree canopy consists of leaves. The surface area of leaves is vital for noise absorption. When 

considering individual trees, the surface area of the leaves and the depth of the trees are vital to noise absorption. 

A combination of terrestrial laser scanning (TLS) for side scanning and Mini-Unmanned Aerial Vehicles (UAV) 

for nadir scanning provides a high spatial resolution survey for tree canopy detection. TLS is better for detecting 

the point clouds of a tree. UAVs (using first-and last-return signals) are better for detecting the depth of a tree. 

For that identification of the surface area of leaves is vital. The voxel-based, convex hull by slices and 3D convex 

hull methods are usually used to visualize a tree canopy in 3D. Recently, voxel grid modelling has been used for 

individual tree modelling, and the voxel size is important for 3D modelling. However, gaps in the tree canopy 

structure are not visualised in this voxel-based method (Ross et al., 2022). Decreasing the voxel size enables 

accuracy of visualization. To avoid voxel complications, the convex hull- slice method is one solution (Dong et 

al., 2021). In this method, all the point clouds are formulated in the direction of the Z-axis according to a certain 

interval. In processing, the interval is taken as one to five times the point density. Due to the inaccurate volume 

estimation of the convex hull using the slices method, the minimal vertex visualization (3D convex hull) method 

is the solution required to eliminate this inaccurate volume estimation. Not only the convex hull method, the 

concave hull method can also be used for tree canopy estimation. However, a convex hull provides an 

overestimated volume, while a concave hull provides an extract-fitting surface to the point clouds while 

managing accuracy and reliability. As a development of tree canopy visualization, a modern approach has been 

developed to find the surface area of leaves using horizontal voxel-based layers. Here, the size of the voxel is the 

average surface area of a leaf. Furthermore, this approach describes the surface area of the leaves instead of the 

total canopy area. However, simple geometric object modelling for tree canopies alone can be used to visualize 

tree canopies. Geometric object modelling provides inaccurate calculation of the surface area of leaves.  However, 

it is mentioned in this review paper, the 3D Tree Visualization Developments provide higher accuracy to identify 

the surface area of leaves. The laser scanning proceedings of this method is prime to detect leaves of a canopy.  

4. Conclusion  

Recently, road traffic noise pollution has become a major social issue in both developed and developing 

cities. Although developed cities have addressed these problems robustly, developing countries are not focusing 

on noise pollution issues due to the economic pressure they are facing. The leaves of trees act as natural sound 

barriers, absorbing a significant amount of noise. Tree belts, tree groupings, and lone trees are some of the tree 

structures that can be used to muffle traffic noise. Tree belts are more effective to noise reduction than group of 

trees and isolated trees. Establishing green belts as barriers is more cost-effective. Moreover, a combination of 

grass areas and shrubs among trees inside tree belts improves the noise absorption ability of green belts and is 

vital for noise reduction. When detecting the noise absorption performance of green belts, the individual tree 

properties are crucial and the tree canopy in particular impacts noise reduction. 

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In addition to these benefits, the leaves on the trees are very effective in blocking out road noise. It is 

crucial to understand how well plants absorb noise for this reason. Well-manicured vegetation belts are a useful 

tool for reducing noise pollution from moving vehicles. Consequently, it is critical to identify and suggest 

appropriate pants for vegetation belts. Important variables influencing noise absorption are tree depths, leaf 

surface areas (per unit volume), and leaf noise absorption coefficients. Furthermore, the ability of a leaf to absorb 

noise is influenced by its size, thickness, age, moisture content, dryness, and greenness. But for effective noise 

reduction, the noise absorption coefficient of leaves must be calculated precisely. Moreover, these circumstances 

modify the noise absorption coefficient of leaves. Determine each leaf's noise absorption coefficient (if possible) 

rather than relying on only one figure. It is feasible to precisely distinguish which trees absorb traffic noise, and 

consequently, which trees are the greatest at doing so. Apart from their surface area, the noise absorption 

coefficient is a crucial element that influences noise reduction. The noise-absorption coefficient of leaves is 

significantly influenced by the frequency of sounds. The noise-absorption coefficient of leaves under various 

frequencies in an impedance tube should be examined, and an average value should be taken, according to the 

noise absorption equation. Alternatively, instead of choosing an average value, it has been recommended that 

studying the actual noise absorption coefficient of the leaves for each tree may improve the accuracy of the noise 

absorption. 

The visualization of 3D trees has become a viable method for precisely measuring leaf surface areas and 

tree depths in recent years. Tree modelling programs utilizing terrestrial laser scanning (TLS) have been shown 

to be successful in gathering comprehensive data on tree attributes, including tree depths, canopy areas, and 

canopy volumes. Furthermore, 3D visualization is a crucial tool for figuring out leaf surface areas because it can 

improve the accuracy of the canopies' details. This work used the TLS method to detect 3D trees; however, to 

accurately detect all of the information, it is more effective to combine the TLS and ALS approaches for 3D tree 

detection. 

It is still difficult to estimate the true surface areas accurately. Creating surfaces from point clouds can be 

accomplished in part by embedding LiDAR point clouds into a surface fitting technique (such a convex one). It 

has been demonstrated that this approach is more accurate than other surface fitting techniques such as voxel or 

the triangular irregular network (TIN). However, more investigation is required to increase the accuracy of the 

leaf surface area computation. However, the 3D convex hull approach overestimates 3D tree canopies due to this 

surface fit with the outside points of point clouds. This removes a little point cloud gap from this. The 3D concave 

hull method can be advised as a precautionary measure. It is uncommon to estimate the volume of a tree canopy 

using the idea of object modelling. A canopy points cloud extract fitting surface is not represented by object 

modelling. However, it takes some geometric shapes from the canopies of trees. Consequently, the estimation of 

the volume of tree canopies using object modelling is an imprecise technique. Techniques to generate a surface 

for the tree canopy are voxel-based, convex, and concave hull. However, the entire leaf surface area within the 

tree canopy cannot be clearly identified using these methods. Therefore, the development of tree visualization is 

essential. The leaf area density (LAD) of a tree canopy can be found using mathematical techniques that have 

been developed in several studies.  

As future suggestions; planting trees along the roads is vital to reduce noise pollution. To identify the 

suitable height and width of the tree belts, the 3D visualization of tree is vital. Due to the individual performance 

of a tree in a tree bel being vital, the noise absorption coefficient of leaves should be taken into account. This 

means that the type of tree is a considerable factor in the absorption of noise from road traffic. To identify the 

properties of the trees such surface area of the leaves and depth of the trees, the 3D tree detection and 3D tree 

visualization is vital accurately. 

5. Acknowledgments 

This work was supported by the Ministry of Higher Education through the Fundamental Research Grant 

Scheme (FRGS/1/2022/WAB07/UTM/02/3). 

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6. References 

Adhika, D. R., Prasetiyo, I., Noeriman, A., Hidayah, N., & Widayani, S. (2023). Sound Absorption Characteristics of 
Pineapple Leaf/Epoxy Composite. Archives of Acoustics, 45(2), 233–240. [Crossref] 

Ali, M., Alabdulkarem, A., Nuhait, A., Al-Salem, K., Iannace, G., Almuzaiqer, R., Al-turki, A., Al-Ajlan, F., Al-Mosabi, Y., & 
Al-Sulaimi, A. (2020). Thermal and acoustic characteristics of novel thermal insulating materials made of Eucalyptus 
Globulus leaves and wheat straw fibers. Journal of Building Engineering, 32, 101452. [Crossref] 

Almansi, K. Y., Ujang, U., Azri, S., & Wickramathilaka, N. (2024). Traffic noise prediction model using GIS and ensemble 
machine learning: a case study at Universiti Teknologi Malaysia (UTM) Campus. Environmental Science and Pollution 
Research, 1–22. [Crossref] 

Béland, M., Baldocchi, D. D., Widlowski, J.-L., Fournier, R. A., & Verstraete, M. M. (2014). On seeing the wood from the 
leaves and the role of voxel size in determining leaf area distribution of forests with terrestrial LiDAR. Agricultural 
and Forest Meteorology, 184, 82–97. [Crossref[ 

Bohatkiewicz, J. (2016). Noise Control Plans in Cities – Selected Issues and Necessary Changes in Approach to Measures 
and Methods of Protection. Transportation Research Procedia, 14, 2744–2753. [Crossref] 

Can, A., Leclercq, L., & Lelong, J. (2008). Dynamic estimation of urban traffic noise: Influence of traffic and noise source 
representations. Applied Acoustics, 69(10), 858–867. [Crossref] 

Chakraborty, M., Khot, L. R., Sankaran, S., & Jacoby, P. W. (2019). Evaluation of mobile 3D light detection and ranging 
based canopy mapping system for tree fruit crops. Computers and Electronics in Agriculture, 158, 284–293. [Crossref] 

Colaço, A. F., Trevisan, R. G., Molin, J. P., Rosell-Polo, J. R., & Escolà, A. (2017). Orange tree canopy volume estimation 
by manual and LiDAR-based methods. Advances in Animal Biosciences, 8(2), 477–480. [Crossref] 

Disney, M. I., Kalogirou, V., Lewis, P., Prieto-Blanco, A., Hancock, S., & Pfeifer, M. (2010). Simulating the impact of discrete-
return lidar system and survey characteristics over young conifer and broadleaf forests. Remote Sensing of Environment, 
114(7), 1546–1560. [Crossref] 

Dobson, M., & Ryan, J. (2000). Trees and shrubs for noise control. Arboricultural Advisory and Information Service. 
Dong, X., Kim, W.-Y., & Lee, K.-H. (2021). Drone-Based Three-Dimensional Photogrammetry and Concave Hull by Slices 

Algorithm for Apple Tree Volume Mapping. Journal of Biosystems Engineering, 46(4), 474–484. [Crossref] 
Donoghue, D. N. M., Watt, P. J., Cox, N. J., & Wilson, J. (2007). Remote sensing of species mixtures in conifer plantations 

using LiDAR height and intensity data. Remote Sensing of Environment, 110(4), 509–522. [Crossref] 
Dubey, R., Bharadwaj, S., Sharma, V. B., Bhatt, A., & Biswas, S. (2022). Smartphone-based Traffic Noise Mapping System. 

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B4-2, 613–620. 
[Crossref] 

Dunbar, M. D., Moskal, L. M., & Jakubauskas, M. E. (2004). 3D Visualization for the Analysis of Forest Cover Change. 
Geocarto International, 19(2), 103–112. [Crossref] 

Fang, C.-F., & Ling, D.-L. (2003). Investigation of the noise reduction provided by tree belts. Landscape and Urban Planning, 
63(4), 187–195. [Crossref] 

Fernández-Sarría, A., Velázquez-Martí, B., Sajdak, M., Martínez, L., & Estornell, J. (2013). Residual biomass calculation 
from individual tree architecture using terrestrial laser scanner and ground-level measurements. Computers and 
Electronics in Agriculture, 93, 90–97. [Crossref] 

Gharibi, S., & Shayesteh, K. (2024). Evaluation of flow, supply, and demand for noise reduction in urban area, Hamadan in 
Iran. PLOS ONE, 19(6), e0303581. [Crossref] 

Gilani, T. A., & Mir, M. S. (2021). Modelling road traffic Noise under heterogeneous traffic conditions using the graph-
theoretic approach. Environmental Science and Pollution Research, 28(27), 36651–36668. [Crossref] 

Goodwin, N. R., Coops, N. C., & Culvenor, D. S. (2006). Assessment of forest structure with airborne LiDAR and the effects 
of platform altitude. Remote Sensing of Environment, 103(2), 140–152. [Crossref] 

Gu, C., Zhao, C., Zou, W., Yang, S., Dou, H., & Zhai, C. (2022). Innovative Leaf Area Detection Models for Orchard Tree 
Thick Canopy Based on LiDAR Point Cloud Data. Agriculture, 12(8), 1241. [Crossref] 

Guarnaccia, C., Quartieri, J., & others. (2012). Analysis of road traffic noise propagation. International Journal of Mathematical 
Models and Methods in Applied Sciences, 6(8), 926–933. 

Halim, H., Yusob, M. F. M., Abdullah, R., Nor, M. J. M., Rahman, N. A., Sukor, N. S. A., & Haron, Z. (2018). Noise barrier 
as an option to reduce road traffic noise from highways in Klang valley, Malaysia. AIP Conference Proceedings, 2030(1), 
020276. [Crossref] 

Hancock, S., Anderson, K., Disney, M., & Gaston, K. J. (2017). Measurement of fine-spatial-resolution 3D vegetation 
structure with airborne waveform lidar: Calibration and validation with voxelised terrestrial lidar. Remote Sensing of 
Environment, 188, 37–50. [Crossref] 

Hao, Z., Lin, L., Post, C. J., Mikhailova, E. A., Li, M., Chen, Y., Yu, K., & Liu, J. (2021). Automated tree-crown and height 
detection in a young forest plantation using mask region-based convolutional neural network (Mask R-CNN). ISPRS 
Journal of Photogrammetry and Remote Sensing, 178, 112–123. [Crossref] 

https://doi.org/10.14710/geoplanning.11.2.149-164
https://doi.org/10.24425/aoa.2020.133144
https://doi.org/10.1016/j.jobe.2020.101452
https://doi.org/10.1007/s11356-024-35243-0
https://doi.org/10.1016/j.agrformet.2013.09.005
https://doi.org/10.1016/j.trpro.2016.05.458
https://doi.org/10.1016/j.apacoust.2007.05.014
https://doi.org/10.1016/j.compag.2019.02.012
https://doi.org/10.1017/S2040470017001133
https://doi.org/10.1016/j.rse.2010.02.009
https://doi.org/10.1007/s42853-021-00120-y
https://doi.org/10.1016/j.rse.2007.02.032
https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-613-2022
https://doi.org/10.1080/10106040408542310
https://doi.org/10.1016/S0169-2046(02)00190-1
https://doi.org/10.1016/j.compag.2013.01.012
https://doi.org/10.1371/journal.pone.0303581
https://doi.org/10.1007/s11356-021-13328-4
https://doi.org/10.1016/j.rse.2006.03.003
https://doi.org/10.3390/agriculture12081241
https://doi.org/10.1063/1.5066917
https://doi.org/10.1016/j.rse.2016.10.041
https://doi.org/10.1016/j.isprsjprs.2021.06.003


Wickramathilaka et al. / Geoplanning: Journal of Geomatics and Planning, Vol 11, No 2, 2024, 149-164 
DOI: 10.14710/geoplanning.11.2.149-164 

 

162 

Hillman, S., Wallace, L., Reinke, K., & Jones, S. (2021). A comparison between TLS and UAS LiDAR to represent eucalypt 
crown fuel characteristics. ISPRS Journal of Photogrammetry and Remote Sensing, 181, 295–307. [Crossref[ 

Hosoi, F., & Omasa, K. (2006). Voxel-Based 3-D Modeling of Individual Trees for Estimating Leaf Area Density Using 
High-Resolution Portable Scanning Lidar. IEEE Transactions on Geoscience and Remote Sensing, 44(12), 3610–3618. 
[Crossref] 

Huang, Y., Wang, L., Hou, Y., Zhang, W., & Zhang, Y. (2018). A prototype IOT based wireless sensor network for traffic 
information monitoring. International Journal of Pavement Research and Technology, 11(2), 146–152. [Crossref] 

Huddart, L. (1990). The use of vegetation for traffic noise screening. 
Islam, Z., Abdullah, F., & Khanom, M. (2021). Evaluation of Traffic Accessibility Condition and Noise Pollution in Dhaka 

City of Bangladesh. American Journal of Traffic and Transportation Engineering, 6(2), 43. [Crossref] 
Itakura, K., & Hosoi, F. (2018). Automatic individual tree detection and canopy segmentation from three-dimensional point 

cloud images obtained from ground-based lidar. Journal of Agricultural Meteorology, 74(3), 109–113. [Crossref] 
Jamrah, A., Al-Omari, A., & Sharabi, R. (2006). Evaluation of Traffic Noise Pollution in Amman, Jordan. Environmental 

Monitoring and Assessment, 120(1–3), 499–525. [Crossref] 
Jang, E.-S. (2023). Sound Absorbing Properties of Selected Green Material—A Review. Forests, 14(7), 1366. [Crossref] 
Jichen, C., Xiu, W., Jian, S., Songlin, W., Shuo, Y., & Chunjiang, Z. (2017). Development of real-time laser-scanning system 

to detect tree canopy characteristics for variable-rate pesticide application. International Journal of Agricultural and 
Biological Engineering, 10(6), 155–163. [Crossref] 

Joshi, A., Deshmukh, V., Joshi, N., & Rane, P. (2013). Studies on foliar sound absorption capacities of some urban trees by 
impedance tube method. Pollution Research, 32(3), 113–117. 

Jung, S. Y., Yeom, D.-H., Kong, R.-K., Shin, G. G., Lee, K. S., & Byeon, H. S. (2020). Sound Absorption Property of the 
Leaves of Two Evergreen Broad-Leaved Tree Species, Dendropanax morbiferus and Fatsia japonica. Journal of the 
Korean Wood Science and Technology, 48(5), 631–640. [Crossref] 

Kalansuriya, C. M., Pannila, A. S., & Sonnadara, D. U. J. (2009). Effect of roadside vegetation on the reduction of traffic 
noise levels. Proceedings of the Technical Sessions, 25(2009), 1–6. 

Karbalaei, S. S., Karimi, E., Naji, H. R., Ghasempoori, S. M., Hosseini, S. M., & Abdollahi, M. (2015). Investigation of the 
Traffic Noise Attenuation Provided by Roadside Green Belts. Fluctuation and Noise Letters, 14(04), 1550036. 
[Crossref] 

Kargar, A. R., MacKenzie, R., Asner, G. P., & van Aardt, J. (2019). A density-based approach for leaf area index assessment 
in a complex forest environment using a terrestrial laser scanner. Remote Sensing, 11(15), 1791. 

Karlinasari, L., Hermawan, D., Maddu, A., Martianto, B., Lucky, I. K., Nugroho, N., & Hadi, Y. S. (2012). Acoustical 
properties of particleboards made from Betung bamboo (Dendrocalamus asper) as building construction material. 
BioResources, 7(4). 

Kempf, C., Tian, J., Kurz, F., D’Angelo, P., Schneider, T., & Reinartz, P. (2021). Oblique view individual tree crown 
delineation. International Journal of Applied Earth Observation and Geoinformation, 99, 102314. [Crossref] 

Kowalska-Koczwara, A., Pachla, F., Tatara, T., & Nering, K. (2021). Green Areas in the City as an Element of Noise 
Protection. IOP Conference Series: Materials Science and Engineering, 1203(3), 032025. [Crossref] 

Kragh, J. (1981). Road traffic noise attenuation by belts of trees. Journal of Sound and Vibration, 74(2), 235–241. [Crossref] 
Kurakula, V. K., & Kuffer, M. (2008). 3D noise modeling for urban environmental planning and management. na. 
Lecigne, B., Delagrange, S., & Messier, C. (2018). Exploring trees in three dimensions: VoxR, a novel voxel-based R package 

dedicated to analysing the complex arrangement of tree crowns. Annals of Botany, 121(4), 589–601. [Crossref] 
Li, H., & Xie, H. (2021). Noise exposure of the residential areas close to urban expressways in a high-rise mountainous city. 

Environment and Planning B: Urban Analytics and City Science, 48(6), 1414–1429. [Crossref] 
Li, L., Li, D., Zhu, H., & Li, Y. (2016). A dual growing method for the automatic extraction of individual trees from mobile 

laser scanning data. ISPRS Journal of Photogrammetry and Remote Sensing, 120, 37–52. [Crossref] 
Li, M., & Nan, L. (2021). Feature-preserving 3D mesh simplification for urban buildings. ISPRS Journal of Photogrammetry 

and Remote Sensing, 173, 135–150. [Crossref] 
Li, S., Dai, L., Wang, H., Wang, Y., He, Z., & Lin, S. (2017). Estimating Leaf Area Density of Individual Trees Using the 

Point Cloud Segmentation of Terrestrial LiDAR Data and a Voxel-Based Model. Remote Sensing, 9(11), 1202. 
[Crossref] 

Lim, K. S., & Treitz, P. M. (2004). Estimation of above ground forest biomass from airborne discrete return laser scanner 
data using canopy-based quantile estimators. Scandinavian Journal of Forest Research, 19(6), 558–570. [Crossref] 

Lovell, J. L., Jupp, D. L. B., Newnham, G. J., Coops, N. C., & Culvenor, D. S. (2005). Simulation study for finding optimal 
lidar acquisition parameters for forest height retrieval. Forest Ecology and Management, 214(1–3), 398–412. [Crossref] 

Margaritis, E., Kang, J., Filipan, K., & Botteldooren, D. (2018). The influence of vegetation and surrounding traffic noise 
parameters on the sound environment of urban parks. Applied Geography, 94, 199–212. [Crossref] 

Maleki, K., Hosseini, S. M., & Nasiri, P. (2010). The effect of pure and mixed plantations of Robinia Pseudoacasia and Pinus 
Eldarica on traffic noise decrease. International Journal of Environmental Sciences, 1(2), 213–224. 

 

https://doi.org/10.14710/geoplanning.11.2.149-164
https://doi.org/10.1016/j.isprsjprs.2021.09.008
https://doi.org/10.1109/TGRS.2006.881743
https://doi.org/10.1016/j.ijprt.2017.07.005
https://doi.org/10.11648/j.ajtte.20210602.12
https://doi.org/10.2480/agrmet.D-18-00012
https://doi.org/10.1007/s10661-005-9077-5
https://doi.org/10.3390/f14071366
https://doi.org/10.25165/j.ijabe.20171006.3140
https://doi.org/10.5658/WOOD.2020.48.5.631
https://doi.org/10.1142/S0219477515500364
https://doi.org/10.1016/j.jag.2021.102314
https://doi.org/10.1088/1757-899X/1203/3/032025
https://doi.org/10.1016/0022-460X(81)90506-X
https://doi.org/10.1093/aob/mcx095
https://doi.org/10.1177/23998083211015249
https://doi.org/10.1016/j.isprsjprs.2016.07.009
https://doi.org/10.1016/j.isprsjprs.2021.01.006
https://doi.org/10.3390/rs9111202
https://doi.org/10.1080/02827580410019490
https://doi.org/10.1016/j.foreco.2004.07.077
https://doi.org/10.1016/j.apgeog.2018.02.017


Wickramathilaka et al. / Geoplanning: Journal of Geomatics and Planning, Vol 11, No 2, 2024, 149-164 
DOI: 10.14710/geoplanning.11.2.149-164 

 

163 

Martínez-Sala, R., Rubio, C., García-Raffi, L. M., Sánchez-Pérez, J. V., Sánchez-Pérez, E. A., & Llinares, J. (2006). Control 
of noise by trees arranged like sonic crystals. Journal of Sound and Vibration, 291(1–2), 100–106. [Crossref] 

Melville, H. I. A. S., Cauldwell, A. E., & Bothma, J. D. P. (1999). A comparison of two techniques for estimating tree canopy 
volume. South African Journal of Wildlife Research-24-Month Delayed Open Access, 29(4), 113–117. [Crossref] 

Murthy, V. K., Majumder, A. K., Khanal, S. N., & Subedi, D. P. (1970). Assessment of Traffic Noise Pollution in Banepa, A 
Semi Urban Town of Nepal. Kathmandu University Journal of Science, Engineering and Technology, 3(2), 12–20. 
[Crossref] 

Ow, L. F., & Ghosh, S. (2017). Urban cities and road traffic noise: Reduction through vegetation. Applied Acoustics, 120, 15–
20. [Crossref] 

Pamanikabud, P., & Tansatcha, M. (2009). Geoinformatic prediction of motorway noise on buildings in 3D GIS. 
Transportation Research Part D: Transport and Environment, 14(5), 367–372. [Crossref] 

Paris, C., Kelbe, D., van Aardt, J., & Bruzzone, L. (2017). A Novel Automatic Method for the Fusion of ALS and TLS LiDAR 
Data for Robust Assessment of Tree Crown Structure. IEEE Transactions on Geoscience and Remote Sensing, 55(7), 
3679–3693. [Crossref][ 

Park, H. J., Lim, S., Trinder, J. C., & Turner, R. (2010). Voxel-based volume modelling of individual trees using terrestrial 
laser scanners. Proceedings of 15th Australasian Remote Sensing & Photogrammetry Conf., Alice Springs, Australia, 1125–
1133. 

Parmehr, E. G., & Amati, M. (2021). Individual Tree Canopy Parameters Estimation Using UAV-Based Photogrammetric 
and LiDAR Point Clouds in an Urban Park. Remote Sensing, 13(11), 2062. [Crossref] 

Pathak, V., Tripathi, B. D., & Mishra, V. K. (2011). Evaluation of Anticipated Performance Index of some tree species for 
green belt development to mitigate traffic generated noise. Urban Forestry & Urban Greening, 10(1), 61–66. [Crossref] 

Peng, J., Bullen, R., & Kean, S. (2014). The effects of vegetation on road traffic noise. INTER-NOISE and NOISE-CON 
Congress and Conference Proceedings, 249(8), 600–609. 

Peng, J., Parnell, J., & Kessissoglou, N. (2021). Spatially differentiated profiles for road traffic noise pollution across a state 
road network. Applied Acoustics, 172, 107641. [Crossref] 

Romanova, A., Horoshenkov, K. V, & Hurrell, A. (2019). An application of a parametric transducer to measure acoustic 
absorption of a living green wall. Applied Acoustics, 145, 89–97. [Crossref] 

Ross, C. W., Loudermilk, E. L., Skowronski, N., Pokswinski, S., Hiers, J. K., & O’Brien, J. (2022). LiDAR voxel-size 
optimization for canopy gap estimation. Remote Sensing, 14(5), 1054. 

Safikhani, T., Abdullah, A. M., Ossen, D. R., & Baharvand, M. (2014). A review of energy characteristic of vertical greenery 
systems. Renewable and Sustainable Energy Reviews, 40, 450–462. [Crossref] 

Samara, T., & Tsitsoni, T. (2011). The effects of vegetation on reducing traffic noise from a city ring road. Noise Control 
Engineering Journal, 59(1), 68. [Crossref] 

Shimizu, K., Nishizono, T., Kitahara, F., Fukumoto, K., & Saito, H. (2022). Integrating terrestrial laser scanning and 
unmanned aerial vehicle photogrammetry to estimate individual tree attributes in managed coniferous forests in Japan. 
International Journal of Applied Earth Observation and Geoinformation, 106, 102658. [Crossref] 

Smart, R., & Robinson, M. (1991). Sunlight into wine: a handbook for winegrape canopy management. 
Soma, M., Pimont, F., & Dupuy, J.-L. (2021). Sensitivity of voxel-based estimations of leaf area density with terrestrial 

LiDAR to vegetation structure and sampling limitations: A simulation experiment. Remote Sensing of Environment, 
257, 112354. [Crossref] 

Subramani, T., Kavitha, M., & Sivaraj, K. P. (2012). Modelling of traffic noise pollution. International Journal of Engineering 
Research and Applications, 2(3), 3175–3182. 

Sultan, T., Shahid, S., Rasheed, J., & Alsubai, S. (2024). Investigation of tree characteristics for traffic noise abatement. 
Environment Protection Engineering, 50(1). [Crossref] 

Suwardhi, D., Fauzan, K. N., Harto, A. B., Soeksmantono, B., Virtriana, R., & Murtiyoso, A. (2022). 3D Modeling of 
Individual Trees from LiDAR and Photogrammetric Point Clouds by Explicit Parametric Representations for Green 
Open Space (GOS) Management. ISPRS International Journal of Geo-Information, 11(3), 174. [Crossref] 

Tang, S., Dong, P., & Buckles, B. P. (2013). Three-dimensional surface reconstruction of tree canopy from lidar point clouds 
using a region-based level set method. International Journal of Remote Sensing, 34(4), 1373–1385. [Crossref] 

Tang, S. H., & Ong, P. P. (1988). A Monte Carlo technique to determine the effectiveness of roadside trees for containing 
traffic noise. Applied Acoustics, 23(4), 263–271. [Crossref] 

Tobollik, M., Hintzsche, M., Wothge, J., Myck, T., & Plass, D. (2019). Burden of Disease Due to Traffic Noise in Germany. 
International Journal of Environmental Research and Public Health, 16(13), 2304. [Crossref] 

Tudor, E. M., Dettendorfer, A., Kain, G., Barbu, M. C., Réh, R., & Krišťák, Ľ. (2020). Sound-Absorption Coefficient of 
Bark-Based Insulation Panels. Polymers, 12(5), 1012. [Crossref] 

Van Renterghem, T. (2014). Guidelines for optimizing road traffic noise shielding by non-deep tree belts. Ecological 
Engineering, 69, 276–286. [Crossref] 

 
 

https://doi.org/10.14710/geoplanning.11.2.149-164
https://doi.org/10.1016/j.jsv.2005.05.030
https://doi.org/https:/hdl.handle.net/10520/EJC117081
https://doi.org/10.3126/kuset.v3i2.2891
https://doi.org/10.1016/j.apacoust.2017.01.007
https://doi.org/10.1016/j.trd.2009.04.001
https://doi.org/10.1109/TGRS.2017.2675963
https://doi.org/10.3390/rs13112062
https://doi.org/10.1016/j.ufug.2010.06.008
https://doi.org/10.1016/j.apacoust.2020.107641
https://doi.org/10.1016/j.apacoust.2018.09.020
https://doi.org/10.1016/j.rser.2014.07.166
https://doi.org/10.3397/1.3528970
https://doi.org/10.1016/j.jag.2021.102658
https://doi.org/10.1016/j.rse.2021.112354
https://doi.org/10.37190/epe240103
https://doi.org/10.3390/ijgi11030174
https://doi.org/10.1080/01431161.2012.720046
https://doi.org/10.1016/0003-682X(88)90040-0
https://doi.org/10.3390/ijerph16132304
https://doi.org/10.3390/polym12051012
https://doi.org/10.1016/j.ecoleng.2014.04.029


Wickramathilaka et al. / Geoplanning: Journal of Geomatics and Planning, Vol 11, No 2, 2024, 149-164 
DOI: 10.14710/geoplanning.11.2.149-164 

 

164 

Wallace, L. O., Lucieer, A., & Watson, C. S. (2012). Assessing the Feasibility of UAV-based LIDAR for High Resolution 
Forest Change Detection. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information 
Sciences, XXXIX-B7, 499–504. [Crossref] 

Watanabe, T., & Yamada, S. (1996). Sound attenuation through absorption by vegetation. Journal of the Acoustical Society of 
Japan (E), 17(4), 175–182. [Crossref] 

Wickramathilaka, N., Ujang, U., & Azri, S. (2024). Improving Traffic-noise-mitigation Strategies with LiDAR-based 3D 
Tree-canopy Analysis. Geomatics and Environmental Engineering, 18(3), 81–103. [Crossef] 

Wickramathilaka, N., Ujang, U., Azri, S., & Choon, T. L. (2022). Influence of Urban Green Spaces on Road Traffic Noise 
Levels: A Review. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 
XLVIII-4/W, 195–201. [Crossref] 

Wickramathilaka, N., Ujang, U., Azri, S., & Choon, T. L. (2023). Three-dimensional visualisation of traffic noise based on 
the Henk de-Klujijver model. Noise Mapping, 10(1), 20220170. [Crossref] 

Wulder, M. A., Bater, C. W., Coops, N. C., Hilker, T., & White, J. C. (2008). The role of LiDAR in sustainable forest 
management. The Forestry Chronicle, 84(6), 807–826. [Crossref] 

Xu, Y., Tong, X., & Stilla, U. (2021). Voxel-based representation of 3D point clouds: Methods, applications, and its potential 
use in the construction industry. Automation in Construction, 126, 103675. [Crossref] 

Yan, Z., Liu, R., Cheng, L., Zhou, X., Ruan, X., & Xiao, Y. (2019). A Concave Hull Methodology for Calculating the Crown 
Volume of Individual Trees Based on Vehicle-Borne LiDAR Data. Remote Sensing, 11(6), 623. [Crossref] 

Yang, W., He, J., He, C., & Cai, M. (2020). Evaluation of urban traffic noise pollution based on noise maps. Transportation 
Research Part D: Transport and Environment, 87, 102516. [Crossref] 

Yofianti, D., & Usman, K. (2021). Relationship of plant types to noise pollution absorption level to improve the quality of 
the road environment. IOP Conference Series: Earth and Environmental Science, 926(1), 012074. [Crossref] 

Zhang, W., He, Z., & Li, X. (2022). Voxel-based urban vegetation volume analysis with LiDAR point cloud. Proceedings of 
the Fábos Conference on Landscape and Greenway Planning, 7(1), 67. 

Zhong, L., Cheng, L., Xu, H., Wu, Y., Chen, Y., & Li, M. (2016). Segmentation of Individual Trees From TLS and MLS 
Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(2), 774–787. [Crossref] 

 

https://doi.org/10.14710/geoplanning.11.2.149-164
https://doi.org/10.5194/isprsarchives-XXXIX-B7-499-2012
https://doi.org/10.1250/ast.17.175
https://doi.org/10.7494/geom.2024.18.3.81
https://doi.org/10.5194/isprs-archives-XLVIII-4-W3-2022-195-2022
https://doi.org/10.1515/noise-2022-0170
https://doi.org/10.5558/tfc84807-6
https://doi.org/10.1016/j.autcon.2021.103675
https://doi.org/10.3390/rs11060623
https://doi.org/10.1016/j.trd.2020.102516
https://doi.org/10.1088/1755-1315/926/1/012074
https://doi.org/https:/doi.org/10.1109/JSTARS.2016.2565519

