DOI: https://doi.org/10.14710/geoplanning.11.2.149-164

Traffic Noise Absorption and Propagation in A Three-Dimensional Spatial Environment

Nevil Vidyamanee Wickramathilaka  -  3D GIS Research Lab, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia, Sri Lanka
*Uznir Ujang  -  3D GIS Research Lab, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia, Malaysia
Suhaibah Azri  -  3D GIS Research Lab, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia, Malaysia
Tan Liat Choon  -  3D GIS Research Lab, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia, Malaysia
Attygalage Ranjith Rupasinghe  -  General Sir John Kotelawala Defence University, Southern Campus, Edison Hill, Nugegalayaya, Sewanagala, Sri Lanka, Sri Lanka

Citation Format:
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.

Fulltext View|Download
Keywords: Green Belts, Noise Barriers, Noise Propagation, Tree Canopy Detection, Tree Canopy Three-Dimensional Visualization

Article Metrics:

Article Info
Section: Original Research
Language : EN
Recent articles
Traffic Noise Absorption and Propagation in A Three-Dimensional Spatial Environment Groundwater Nitrate Modeling in Tehran Metropolis Using Artificial Neural Network and Kriging Methods Utilizing Open Access Spatial Data for Flood Risk Mapping: A Case Study in the Upper Solo Watershed More recent articles
Most cited articles
ASSESSMENT OF MANGROVE FOREST DEGRADATION THROUGH CANOPY FRACTIONAL COVER IN KARIMUNJAWA ISLAND, CENTRAL JAVA, INDONESIA Evaluation of MODIS-Derived LST Products with Air Temperature Measurements in Cyprus GIS-BASED LANDSLIDE SUSCEPTIBILITY ASSESSMENT AND FACTOR EFFECT ANALYSIS BY CERTAINTY FACTOR IN UPSTREAM OF JENEBERANG RIVER, INDONESIA SPATIAL VARIETY AND DISTRIBUTION OF TRADITIONAL MARKETS IN SURAKARTA AS POTENTIAL FACTORS IN IMPROVING SPATIAL-BASED MANAGEMENT DETEKSI PERUBAHAN PENGGUNAAN LAHAN DENGAN CITRA LANDSAT DAN SISTEM INFORMASI GEOGRAFIS: STUDI KASUS DI WILAYAH METROPOLITAN BANDUNG, INDONESIA More cited articles

  1. 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. https://doi.org/10.24425/aoa.2020.133144">[Crossref]

  2. 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. https://doi.org/10.1016/j.jobe.2020.101452">[Crossref]

  3. 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. https://doi.org/10.1007/s11356-024-35243-0">[Crossref]

  4. 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. https://doi.org/10.1016/j.agrformet.2013.09.005">[Crossref[

  5. 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. https://doi.org/10.1016/j.trpro.2016.05.458">[Crossref]

  6. 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. https://doi.org/10.1016/j.apacoust.2007.05.014">[Crossref]

  7. 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. https://doi.org/10.1016/j.compag.2019.02.012">[Crossref]

  8. 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. https://doi.org/10.1017/S2040470017001133">[Crossref]

  9. 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. https://doi.org/10.1016/j.rse.2010.02.009">[Crossref]

  10. Dobson, M., & Ryan, J. (2000). Trees and shrubs for noise control. Arboricultural Advisory and Information Service.

  11. 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. https://doi.org/10.1007/s42853-021-00120-y">[Crossref]

  12. 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. https://doi.org/10.1016/j.rse.2007.02.032">[Crossref]

  13. 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. https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-613-2022">[Crossref]

  14. 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. https://doi.org/10.1080/10106040408542310">[Crossref]

  15. Fang, C.-F., & Ling, D.-L. (2003). Investigation of the noise reduction provided by tree belts. Landscape and Urban Planning, 63(4), 187–195. https://doi.org/10.1016/S0169-2046(02)00190-1">[Crossref]

  16. 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. https://doi.org/10.1016/j.compag.2013.01.012">[Crossref]

  17. 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. https://doi.org/10.1371/journal.pone.0303581">[Crossref]

  18. 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. https://doi.org/10.1007/s11356-021-13328-4">[Crossref]

  19. 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. https://doi.org/10.1016/j.rse.2006.03.003">[Crossref]

  20. 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. https://doi.org/10.3390/agriculture12081241">[Crossref]

  21. 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.

  22. 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.https://doi.org/10.1063/1.5066917"> [Crossref]

  23. 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. https://doi.org/10.1016/j.rse.2016.10.041">[Crossref]

  24. 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. https://doi.org/10.1016/j.isprsjprs.2021.06.003">[Crossref]

  25. 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. https://doi.org/10.1016/j.isprsjprs.2021.09.008">[Crossref[

  26. 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. https://doi.org/10.1109/TGRS.2006.881743">[Crossref]

  27. 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. https://doi.org/10.1016/j.ijprt.2017.07.005">[Crossref]

  28. Huddart, L. (1990). The use of vegetation for traffic noise screening.

  29. 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. https://doi.org/10.11648/j.ajtte.20210602.12">[Crossref]

  30. 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. https://doi.org/10.2480/agrmet.D-18-00012">[Crossref]

  31. 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. https://doi.org/10.1007/s10661-005-9077-5">[Crossref]

  32. Jang, E.-S. (2023). Sound Absorbing Properties of Selected Green Material—A Review. Forests, 14(7), 1366. https://doi.org/10.3390/f14071366">[Crossref]

  33. 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. https://doi.org/10.25165/j.ijabe.20171006.3140">[Crossref]

  34. 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.

  35. 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. https://doi.org/10.5658/WOOD.2020.48.5.631">[Crossref]

  36. 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.

  37. 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. https://doi.org/10.1142/S0219477515500364">[Crossref]

  38. 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.

  39. 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).

  40. 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. https://doi.org/10.1016/j.jag.2021.102314">[Crossref]

  41. 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. https://doi.org/10.1088/1757-899X/1203/3/032025">[Crossref]

  42. Kragh, J. (1981). Road traffic noise attenuation by belts of trees. Journal of Sound and Vibration, 74(2), 235–241. https://doi.org/10.1016/0022-460X(81)90506-X">[Crossref]

  43. Kurakula, V. K., & Kuffer, M. (2008). 3D noise modeling for urban environmental planning and management. na.

  44. 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. https://doi.org/10.1093/aob/mcx095">[Crossref]

  45. 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. https://doi.org/10.1177/23998083211015249">[Crossref]

  46. 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. https://doi.org/10.1016/j.isprsjprs.2016.07.009">[Crossref]

  47. Li, M., & Nan, L. (2021). Feature-preserving 3D mesh simplification for urban buildings. ISPRS Journal of Photogrammetry and Remote Sensing, 173, 135–150. https://doi.org/10.1016/j.isprsjprs.2021.01.006">[Crossref]

  48. 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. https://doi.org/10.3390/rs9111202">[Crossref]

  49. 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. https://doi.org/10.1080/02827580410019490">[Crossref]

  50. 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. https://doi.org/10.1016/j.foreco.2004.07.077">[Crossref]

  51. 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. https://doi.org/10.1016/j.apgeog.2018.02.017

  52. 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.

  53. 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. https://doi.org/10.1016/j.jsv.2005.05.030">[Crossref]

  54. 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. https://doi.org/https:/hdl.handle.net/10520/EJC117081">[Crossref]

  55. 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. https://doi.org/10.3126/kuset.v3i2.2891">[Crossref]

  56. Ow, L. F., & Ghosh, S. (2017). Urban cities and road traffic noise: Reduction through vegetation. Applied Acoustics, 120, 15–20. https://doi.org/10.1016/j.apacoust.2017.01.007">[Crossref]

  57. 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. https://doi.org/10.1016/j.trd.2009.04.001">[Crossref]

  58. 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. https://doi.org/10.1109/TGRS.2017.2675963">[Crossref[

  59. 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.

  60. 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. https://doi.org/10.3390/rs13112062">[Crossref]

  61. 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. https://doi.org/10.1016/j.ufug.2010.06.008">[Crossref]

  62. 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.

  63. Peng, J., Parnell, J., & Kessissoglou, N. (2021). Spatially differentiated profiles for road traffic noise pollution across a state road network. Applied Acoustics, 172, 107641. https://doi.org/10.1016/j.apacoust.2020.107641">[Crossref]

  64. 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. https://doi.org/10.1016/j.apacoust.2018.09.020">[Crossref]

  65. 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.

  66. 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. https://doi.org/10.1016/j.rser.2014.07.166">[Crossref]

  67. 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. https://doi.org/10.3397/1.3528970">[Crossref]

  68. 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, 102658https://doi.org/10.1016/j.jag.2021.102658">. [Crossref]

  69. Smart, R., & Robinson, M. (1991). Sunlight into wine: a handbook for winegrape canopy management.

  70. 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. https://doi.org/10.1016/j.rse.2021.112354">[Crossref]

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

  72. Sultan, T., Shahid, S., Rasheed, J., & Alsubai, S. (2024). Investigation of tree characteristics for traffic noise abatement. Environment Protection Engineering, 50(1). https://doi.org/10.37190/epe240103">[Crossref]

  73. 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. https://doi.org/10.3390/ijgi11030174">[Crossref]

  74. 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. https://doi.org/10.1080/01431161.2012.720046">[Crossref]

  75. 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. https://doi.org/10.1016/0003-682X(88)90040-0">[Crossref]

  76. 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. https://doi.org/10.3390/ijerph16132304">[Crossref]

  77. 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. https://doi.org/10.3390/polym12051012">[Crossref]

  78. Van Renterghem, T. (2014). Guidelines for optimizing road traffic noise shielding by non-deep tree belts. Ecological Engineering, 69, 276–286. https://doi.org/10.1016/j.ecoleng.2014.04.029">[Crossref]

  79.  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. https://doi.org/10.5194/isprsarchives-XXXIX-B7-499-2012">[Crossref]

  80. Watanabe, T., & Yamada, S. (1996). Sound attenuation through absorption by vegetation. Journal of the Acoustical Society of Japan (E), 17(4), 175–182. https://doi.org/10.1250/ast.17.175">[Crossref]

  81. 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. https://doi.org/10.7494/geom.2024.18.3.81">[Crossef]

  82. 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. https://doi.org/10.5194/isprs-archives-XLVIII-4-W3-2022-195-2022">[Crossref]

  83. 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. https://doi.org/10.1515/noise-2022-0170">[Crossref]

  84. 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. https://doi.org/10.5558/tfc84807-6">[Crossref]

  85. 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. https://doi.org/10.1016/j.autcon.2021.103675">[Crossref]

  86. 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. https://doi.org/10.3390/rs11060623">[Crossref]

  87. 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. https://doi.org/10.1016/j.trd.2020.102516">[Crossref]

  88. 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. https://doi.org/10.1088/1755-1315/926/1/012074">[Crossref]

  89. 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.

  90. 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. https://doi.org/https:/doi.org/10.1109/JSTARS.2016.2565519">[Crossref]


Last update:

No citation recorded.

Last update: 2024-12-23 04:21:02

No citation recorded.