Automatic detection of single street trees from airborne LiDAR data based on point segmentation methods

Year 2023, Volume: 8 Issue: 2, 129 - 137, 05.07.2023

Zehra Çetin , Naci Yastıklı

https://doi.org/10.26833/ijeg.1079210

Cited By: 2

Abstract

As a primary element of urban ecosystem, street trees are very essential for environmental quality and aesthetic beauty of urban landscape. Street trees play a crucial role in everyday life of city inhabitants and therefore, comprehensive and accurate inventory information for street trees is required. In this research, an automatic method is proposed to detect single street trees from airborne Light Detection and Ranging (LiDAR) point cloud instead of traditional field work or photo interpretation. Firstly, raw LiDAR point cloud data have been classified to obtain high vegetation class with a hierarchical rule-based classification method. Then, the LiDAR points in high vegetation class were segmented with mean shift and Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms to acquire single urban street trees in the Davutpasa Campus of Yildiz Technical University, Istanbul, Turkey. The accuracy assessment of the acquired street trees was also conducted using completeness and correctness analyses. The acquired results from urban study area approved the success of the proposed point-based approach for automatic detection of single street trees using LiDAR point cloud.  

Keywords

Classification, Segmentation, Mean shift, DBSCAN, Urban street trees

References

• Wu, B., Yu, B., Yue, W., Shu, S., Tan, W., Hu, C., Huang, Y., Wu, J., & Liu, H. (2013). A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data. Remote Sensing, 2013 (5), 584-611.
• Aval, J., Demuynck, J., Zenou, E., Fabre, S., Sheeren, D., Fauvel, M., Adeline, K., & Briottet, X. (2018). Detection of individual trees in urban alignment from airborne data and contextual information: A marked point process approach, ISPRS Journal of Photogrammetry and Remote Sensing, 146, 197-210.
• Husain, A. & Vaishya, R. C. (2019). Detection and thinning of street trees for calculation of morphological parameters using mobile laser scanner data, Remote Sensing Applications: Society and Environment, 13, 2019, 375-388.
• Wang, Y., Wang, J., Chang, S., Sun, L., An, L., Chen, Y., & Xu, J. (2021). Classification of Street Tree Species Using UAV Tilt Photogrammetry, Remote Sensing, 2021, 13, 216.
• Jutras, P., Prasher, S., & Mehuys, G. (2009). Prediction of street tree morphological parameters using artificial neural networks. Computers and Electronics in Agriculture, 67, 9-17.
• Huang, P., Chen, Y., Li, J., Yu, Y., Wang, C., & Nie, H. (2015). Extraction of street trees from mobile laser scanning point clouds based on subdivided dimensional features. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 557-560, Milan, Italy.
• Zhang, J., Sohn, G., & Brédif, M. (2013). Single Tree Detection from Airborne Laser Scanning Data Using A Marked Point Process Based Method, ISPRS Annals of the Photogrammetry, Remote Sensing Spatial Information Sciences, II-3/W1, 41–46.
• Ke, Y. & Quackenbush, L.J., (2011). A review of methods for automatic individual tree-crown detection and delineation from passive remote sensing. International Journal of Remote Sensing. 32 (17), 4725–4747.
• Leckie, D.G., Gougeon, F.A., Tinis, S., Nelson, T., Burnett, C.N., & Paradine, D., (2005). Automated tree recognition in old growth conifer stands with high resolutiondigital imagery. Remote Sensing Environment, 94, 311–326.
• Vega, C., Hamrouni, A., El Mokhtari, S., Morel, J., Bock, J., Renaud, J. P., Bouvier, M., & Durrieu, S. (2014). Ptrees: A Point-Based Approach to Forest Tree Extraction from Lidar Data. International Journal of Applied Earth Observation and Geoinformation, 33: 98–108.
• Mongus, D., & Žalik, B. (2015). An Efficient Approach to 3D Single Tree-Crown Delineation in Lidar Data. ISPRS Journal of Photogrammetry and Remote Sensing, 108: 219–233.
• Li, W., Guo, Q., Jakubowski, M. K., & Kelly, M. (2012). A New Method for Segmenting Individual Trees from the Lidar Point Cloud. Photogrammetric Engineering and Remote Sensing, 78 (1): 75–84.
• Solberg, S., Naesset, E., & Bollandsas, O.M. (2006). Single tree segmentation usingairborne laser scanner data in a structurally heterogeneous spruce forest. Photogrammetric Engineering and Remote Sensing, 72 (12), 1369–1378.
• Kwak, D.-H., Lee, W.-K., Lee, J.H., Biging, G.S., & Gong, P. (2007). Detection of individualtrees and estimation of tree height using LiDAR data. Journal of Forestry Research, 12, 425–434.
• Korpela, I., Tuomola, T., & Välimäki, E. (2007). Mapping forest plots: an efficientmethod combining photogrammetry and field triangulation. Silva Fennica, 41 (3),457–469.
• Falkowski, M.J., Smith, A.M.S., Hudak, A.T., Gessler, P.E., Vierling, L.A., & Crookston, N. L. (2006). Automated estimation of individual conifer tree height and crown diam-eter via two-dimensional spatial wavelet analysis of lidar data. Canadian Journal of Remote Sensing, 32 (2), 153.
• Jing, L., Hu, B., Li, J., & Noland, T. (2012). Automated delineation of individual tree crowns from LiDAR data by multi-scale analysis and segmentation. Photogrammetric engineering and remote sensing 78, 1275-1284.
• Popescu, S.C., Wynne, R.H., & Nelson, R.F. (2002). Estimating plot-level tree heights withlidar: local filtering with a canopy-height based variable window size. Computers and Electronics in Agriculture, 37, 71–95.
• Yastikli, N. & Cetin, Z. (2021). Classification of raw LiDAR point cloud using point-based methods with spatial features for 3D building reconstruction. Arabian Journal of Geosciences, 14, 146.
• Yastikli, N. & Cetin, Z. (2016). Classification of LiDAR data with point based classification methods. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 41(B3):441–445.
• Doğruluk, M., Aydın, C.C. & Yanalak, M. (2018). Kırsal Alanlarda SYM Üretiminde Filtreleme Yöntemlerinin Performans Analizi: Hava LiDAR Uygulaması; İstanbul Örneği. Geomatik, 3 (3), 242-253.
• Kuçak, R. A., Erol, S. & Alkan, R. M. (2023). iPad Pro LiDAR sensörünün profesyonel bir yersel lazer tarayıcı ile karşılaştırmalı performans analizi. Geomatik, 8 (1), 35-41.
• Karasaka, L. & Beg, A. A. R. (2021). Yersel Lazer Tarama Yöntemi ile Farklı Geometrik Yapıdaki Özelliklerin Modellenmesi. Geomatik, 6 (1), 54-60.
• Chehata, N. & Bretar, F. (2008). Terrain modeling from lidar data: hierarchical K-means filtering and Markovian regularization. 15th IEEE International Conference on Image Processing, 1900-1903, San Diego, CA.
• Mallet, C., Bretar, F., Roux, M., Soergel, U., & Heipke C (2011). Relevance assessment of full-waveform Lidar data for urban area classification. ISPRS Journal of Photogrammetry and Remote Sensing, 66(6):71–84.
• Kim, H. B. & Sohn, G. (2013). Point-based classification of power line corridor scene using random forests. Photogrammetric Engineering and Remote Sensing, 79: 821–833.
• Canaz Sevgen, S. (2019). Airborne lidar data classification in complex urban area using random forest: a case study of Bergama, Turkey. International Journal of Engineering and Geosciences, 4 (1), 45-51.
• Che, E., Olsen, M. J., Parrish, C. E., & Jung, J., 2019. Pavement marking retro-reflectivity estimation and evaluation using mobile lidar data. Photogrammetric Engineering and Remote Sensing (submitted for publication), 85(8):573-583.
• Zolanvari, S. M. I., Laefer, D. F., & Natanzi, A. S. (2018). Three-dimensional building façade segmentation and opening area detection from point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 143.
• Fukunaga, K. & Hostetler, L. D. (1975). The estimation of the gradient of a density function, with applications in pattern recognition, IEEE Transactions on Information Theory, 21, pp.32-40.
• Wen, Z. Q. & Cai, Z. X. (2006). Mean shift algorithm and its application in tracking of objects. In: Proceedings of 5th International Conference on Machine Learning and Cybernetics, Dalian 2006, 4024- 4028.
• Anand, S., Mittal, S., Tuzel, O., & Meer, P., (2014). Semi-supervised kernel mean shift clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 36, 1201–1215 (2014).
• Friedman, L., Netanyahu, N.S., & Shoshany, M. (2013) Mean shift based clustering of remotely sensed data with agricultural and land-cover applications. International Journal of Remote Sensing, 34:17, 6037-6053.
• Meng, F., Liu, H., Liang, Y., Liu, W., & Pei, J. (2017). A Bidirectional Adaptive Bandwidth Mean Shift Strategy for Clustering. ICIP, 2017.
• Ester, M., Kriegel, H. H. P., Sander, J. J., & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Second International Conference on Knowledge Discovery and Data Mining, 2, 226–231.
• Boonchoo, T., Ao, X., Liu, Y., Zhao, W., Zhuang, F., & He, Q. (2019). Grid-based DBSCAN: Indexing and inference. Pattern Recognition, 90.
• Elbarawy, Y. M., Mohamed, R. F., & Ghali, N. I. (2014) Improving social network community detection using DBSCAN algorithm. IEEE World Symposium on Computer Applications & Research (WSCAR), 1-6, Sousse, Tunisia.
• Khatoon, M., Banu, W. A., (2019). An efficient method to detect communities in social networks using DBSCAN algorithm, Social Network Analysis and Mining, (2019) 9: 9.
• Nasiboglu, R., Tezel, B. T., & Nasibov, E. (2019). Learning the stress function pattern of ordered weighted average aggregation using DBSCAN clustering. International Journal of Intelligent Systems, 2019, 34(3):477-492.
• Gallego, C. E. V., Gómez Comendador, V. F., Saez Nieto, F. J., & Martinez, M. G. (2018). Discussion On Density-Based Clustering Methods Applied for Automated Identification of Airspace Flows. IEEE/AIAA 37th Digital Avionics Systems Conference (DASC), 2018, 1-10, London, UK.
• Ferrara, R., Virdis, S., Ventura, A., Ghisu, T., Duce, P., & Pellizzaro, G. (2018). An automated approach for wood-leaf separation from terrestrial LIDAR point clouds using the density based clustering algorithm DBSCAN. Agricultural and Forest Meteorology. 262.
• Ozdemir, S., Akbulut, Z., Karslı, F. & Acar, H. (2021). Automatic extraction of trees by using multiple return properties of the lidar point cloud. International Journal of Engineering and Geosciences, 6 (1), 20-26.
• Rottensteiner, F., Trinder, J., Clode, S., & Kubik, K. (2007). Building detection by fusion of airborne laser scanner data and multi spectral images: performance evaluation and sensitivity analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 135-149, 2007.
• Uzar, M. & Yastikli, N. (2013). Automatic building extraction using LiDAR and aerial photographs, Boletim de Ciências Geodésicas ,19, 153–171.
• Akbulut, Z., Ozdemir, S., Acar, H., Dihkan, M. & Karslı, F. (2018). Automatic extraction of building boundaries from high resolution images with active contour segmentation. International Journal of Engineering and Geosciences, 3 (1), 36-42.
• Li, W., Guo, Q., Jakubowski, M., & Kelly, M. (2012). A New Method for Segmenting Individual Trees from the Lidar Point Cloud. Photogrammetric Engineering and Remote Sensing. 78. 75-84.
• Yastikli, N. & Cetin, Z. (2020). Detection of Individual Trees in Urban Areas Using the Point Cloud Produced by Dense Image Matching Algorithms, FIG Working Week 2020, Amsterdam, Holland.