Abstract
As a remote sensing technique, unmanned aerial vehicles (UAVs) have great potential in several fields, such as monitoring vegetation in an urban area at low altitudes at a reasonable cost. In this study,we assessed the potential of individual tree detection by the structure of the motion algorithm (SfM) based on UAV images and derived point cloud. Urban broadleaved forests (Fateh garden) were photographed in the spring of 2018 with different structures, a mixed uneven-aged dense stand (MUDS), a mixed uneven-aged sparse stand (MUSS), and a pure even-aged dense stand (PDEs). The results of using the local maxima algorithm for the different structures showed a detection accuracy rate of 0.90, 0.54, and 0.32 for PDES, MUDS, and MUSS, respectively. Based on the results, the accuracy of tree detection is affected by the height of the trees (individuals with a height of fewer than 5 meters were not detected), and the species (Poplar trees were detected better than other species), as well as the searching window size. The fixed tree window size of 3×3 was the best window size, and the fixed smoothing window size was variable for each site. Using mean and Gaussian filters did not noticeably affect the results. In general, our study showed that the canopy height model (CHM) from UAV can detect trees with very high accuracy in urban forests with homogenous even-aged structures, while in uneven-aged stands, the accuracy of tree detection is medium to low.
Similar content being viewed by others
References
Amoatey, P., & Sulaiman, H. (2019). Quantifying carbon storage potential of urban plantations and landscapes in Muscat, Oman. Environment, Development, and Sustainability. https://doi.org/10.1007/s10668-019-00556-5
Asner, G. P., & Heidebrecht, K. B. (2002). Spectral unmixing of vegetation, soil and dry carbon cover in arid regions: Comparing multispectral and hyperspectral observations. International Journal of Remote Sensing, 23(19), 3939–3958. https://doi.org/10.1080/01431160110115960
Brovkina, O., Cienciala, E., Surový, P., & Janata, P. (2018). Unmanned aerial vehicles (UAV) for assessment of qualitative classification of Norway spruce in temperate forest stands. Geo-Spatial Information Science, 21(1), 12–20. https://doi.org/10.1080/10095020.2017.1416994
Cochrane, M. A. (2000). Using vegetation reflectance variability for species-level classification of hyperspectral data. International Journal of Remote Sensing, 21(10), 2075–2087. https://doi.org/10.1080/01431160050021303
Dawson, R. A., Petropoulos, G. P., Toulios, L., & Srivastava, P. K. (2019). Mapping and monitoring of the land use/cover changes in the wider area of Itanos, Crete, using very high-resolution EO imagery with specific interest in archaeological sites. Environment Development and Sustainability, 22(4), 3433–3460. https://doi.org/10.1007/s10668-019-00353-0
Duncanson, L. I., Cook, B. D., Hurtt, G. C., & Dubayah, R. O. (2014). An efficient, multi-layered crown delineation algorithm for mapping individual tree structure across multiple ecosystems. Remote Sensing of Environment, 154, 378–386. https://doi.org/10.1016/J.RSE.2013.07.044
Escobedo, F. J., & Nowak, D. J. (2009). Spatial heterogeneity and air pollution removal by an urban forest. Landscape and Urban Planning, 90(3–4), 102–110. https://doi.org/10.1016/J.LANDURBPLAN.2008.10.021
Fankhauser, K. E., Strigul, N. S., & Gatziolis, D. (2018). Augmentation of traditional forest inventory and airborne laser scanning with unmanned aerial systems and photogrammetry for forest monitoring. Remote Sensing, 10(10), 1–17. https://doi.org/10.3390/rs10101562
Feng, Q., Liu, J., & Gong, J. (2015). UAV remote sensing for urban vegetation mapping using random forest and texture analysis. Remote Sensing, 7(1), 1074–1094. https://doi.org/10.3390/rs70101074
Frey, J., Kovach, K., Stemmler, S., & Koch, B. (2018). UAV photogrammetry of forests as a vulnerable process. A sensitivity analysis for a structure from motion RGB-image pipeline. Remote Sensing. https://doi.org/10.3390/rs10060912
Goldbergs, G., Maier, S., Levick, S., Edwards, A., Goldbergs, G., Maier, S. W., Levick, S. R., & Edwards, A. (2018). Efficiency of individual tree detection approaches based on light-weight and low-cost UAS imagery in australian savannas. Remote Sensing, 10(2), 161. https://doi.org/10.3390/rs10020161
Guerra-Hernández, J., Cosenza, D. N., Rodriguez, L. C. E., Silva, M., Tomé, M., Díaz-Varela, R. A., & González-Ferreiro, E. (2018). Comparison of ALS- and UAV(SfM)-derived high-density point clouds for individual tree detection in Eucalyptus plantations. International Journal of Remote Sensing, 39(15–16), 5211–5235. https://doi.org/10.1080/01431161.2018.1486519
Harikumar, A., Bovolo, F., & Bruzzone, L. (2019). A local projection-based approach to individual tree detection and 3-D crown delineation in multistoried coniferous forests using high-density airborne LiDAR data. IEEE Transactions on Geoscience and Remote Sensing, 57(2), 1168–1182. https://doi.org/10.1109/TGRS.2018.2865014
Huang, H., Li, X., & Chen, C. (2018). Individual tree crown detection and delineation from very-high-resolution UAV images based on bias field and marker-controlled watershed segmentation algorithms. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(7), 2253–2262. https://doi.org/10.1109/JSTARS.2018.2830410
Jeroue, L. M. (2014). Predicting Urban Tree attributes for major species in urbanized areas of the Western Pacific States. Master of Science Diss., Oregon State University.
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. https://doi.org/10.1080/01431161.2010.494184.
Koch, B., Heyder, U., & Weinacker, H. (2006). Detection of individual tree crowns in airborne lidar data. Photogrammetric Engineering & Remote Sensing, 72(4), 357–363. https://doi.org/10.14358/PERS.72.4.357
Larsen, M., Eriksson, M., Descombes, X., Perrin, G., Brandtberg, T., & Gougeon, F. A. (2011). Comparison of six individual tree crown detection algorithms evaluated under varying forest conditions. International Journal of Remote Sensing, 32(20), 5827–5852. https://doi.org/10.1080/01431161.2010.507790
Majumdar, S., Deng, J., Zhang, Y., & Pierskalla, C. (2011). Using contingent valuation to estimate the willingness of tourists to pay for urban forests: A study in Savannah, Georgia. Urban Forestry & Urban Greening, 10(4), 275–280. https://doi.org/10.1016/J.UFUG.2011.07.006
Maltamo, M., & Yu, X. (2004). Adaptive methods for individual tree detection on airborne laser. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 36(8), 187–191.
Miraki, M., Sohrabi, H., Fatehi, P., & Kneubuehler, M. (2021). Individual tree crown delineation from high-resolution UAV images in broadleaf forest. Ecological Informatics, 61, 101207. https://doi.org/10.1016/j.ecoinf.2020.101207
Mohan, M., Silva, C. A., Klauberg, C., Jat, P., Catts, G., Cardil, A., Hudak, A. T., & Dia, M. (2017). Individual tree detection from unmanned aerial vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest. Forests, 8(9), 1–17. https://doi.org/10.3390/f8090340
Nevalainen, O., Honkavaara, E., Tuominen, S., Viljanen, N., Hakala, T., Yu, X., Hyyppä, J., Saari, H., Pölönen, I., Imai, N., & Tommaselli, A. (2017). Individual tree detection and classification with UAV-based photogrammetric point clouds and hyperspectral imaging. Remote Sensing, 9(3), 185. https://doi.org/10.3390/rs9030185
Plowright, A. (2018). ForestTools Analyzing remotely sensed forest data. R package version 0.2.0. In https://cran.r-project.org package=ForestTools
Panagiotidis, D., Abdollahnejad, A., Surový, P., & Chiteculo, V. (2016). Determining tree height and crown diameter from high-resolution UAV imagery. International Journal of Remote Sensing, 38(8–10), 2392–2410. https://doi.org/10.1080/01431161.2016.1264028
Popescu, S. C., Wynne, R. H., & Nelson, R. F. (2003). Estimating plot-level tree heights with lidar: local filtering with a canopy-height based variable window size. Computers and Electronics in Agriculture, 37(1–3), 71–95. https://doi.org/10.1016/S0168-1699(02)00121-7
Puliti, S., Solberg, S., Granhus, A., Puliti, S., Solberg, S., & Granhus, A. (2019). Use of UAV photogrammetric data for estimation of biophysical properties in forest stands under regeneration. Remote Sensing, 11(3), 233. https://doi.org/10.3390/rs11030233
Shifaw, E., Sha, J., Li, X., Jiali, S., & Bao, Z. (2020). Remote sensing and GIS-based analysis of urban dynamics and modelling of its drivers, the case of Pingtan, China. Environment Development and Sustainability, 22(3), 2159–2186. https://doi.org/10.1007/s10668-018-0283-z
Shin, P., Sankey, T., Moore, M., Thode, A., Shin, P., Sankey, T., Moore, M. M., & Thode, A. E. (2018). Evaluating unmanned aerial vehicle images for estimating forest canopy fuels in a ponderosa pine stand. Remote Sensing, 10(8), 1266. https://doi.org/10.3390/rs10081266
Surovy, P., Almeida Ribeiro, N., & Panagiotidis, D. (2018). Estimation of positions and heights from UAV-sensed imagery in tree plantations in agrosilvopastoral systems. International Journal of Remote Sensing, 39(14), 4786–4800. https://doi.org/10.1080/01431161.2018.1434329
Tanhuanpaa, T., Saarinen, N., Kankare, V., Nurminen, K., Vastaranta, M., Honkavaara, E., Karjalainen, M., Yu, X., Holopainen, M., Hyyppä, J., Tanhuanpää, T., Saarinen, N., Kankare, V., Nurminen, K., Vastaranta, M., Honkavaara, E., Karjalainen, M., Yu, X., Holopainen, M., & Hyyppä, J. (2016). Evaluating the performance of high-altitude aerial image-based digital surface models in detecting individual tree crowns in mature boreal forests. Forests, 7(12), 143. https://doi.org/10.3390/f7070143
Vauhkonen, J., Ene, L., Gupta, S., Heinzel, J., Holmgren, J., Pitkanen, J., Solberg, S., Wang, Y., Weinacker, H., Hauglin, K. M., Lien, V., Packalen, P., Gobakken, T., Koch, B., Naesset, E., Tokola, T., & Maltamo, M. (2012). Comparative testing of single-tree detection algorithms under different types of forest. Forestry, 85(1), 27–40. https://doi.org/10.1093/forestry/cpr051
Vauhkonen, J., Seppänen, A., Packalén, P., & Tokola, T. (2012). Remote sensing of environment improving species-speci fi c plot volume estimates based on airborne laser scanning and image data using alpha shape metrics and balanced fi eld data. Remote Sensing of Environment, 124, 534–541. https://doi.org/10.1016/j.rse.2012.06.002
Wang, L., Gong, P., & Biging, G. (2004). Individual tree-crown delineation and treetop detection in high-spatial-resolution aerial imagery. Photogrammetric Engineering & Remote Sensing, 70(3), 351–357. https://doi.org/10.14358/PERS.70.3.351
Wulder, M., Niemann, K. O., & Goodenough, D. G. (2000). Local maximum filtering for the extraction of tree locations and basal area from high spatial resolution imagery. Remote Sensing of Environment, 73(1), 103–114. https://doi.org/10.1016/S0034-4257(00)00101-2
Yilmaz, V., & Gungor, O. (2019). Estimating crown diameters in urban forests with unmanned aerial system-based photogrammetric point clouds. International Journal of Remote Sensing, 40(2), 468–505. https://doi.org/10.1080/01431161.2018.1562255
Yin, D., & Wang, L. (2016). How to assess the accuracy of the individual tree-based forest inventory derived from remotely sensed data: A review. International Journal of Remote Sensing, 37(19), 4521–4553. https://doi.org/10.1080/01431161.2016.1214302
Zhang, C., Zhou, Y., Qiu, F., Zhang, C., Zhou, Y., & Qiu, F. (2015). Individual tree segmentation from LiDAR point clouds for urban forest inventory. Remote Sensing, 7(6), 7892–7913. https://doi.org/10.3390/rs70607892
Zhen, Z., Quackenbush, L. J., & Zhang, L. (2016). Trends in automatic individual tree crown detection and delineation-evolution of LiDAR data. Remote Sensing, 8(4), 333. https://doi.org/10.3390/rs8040333
Acknowledgements
We would like to thank Ardalan Daryaei, Fatemeh Bahmaei, and Shirin Jaafari for their valuable help in the field inventory.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Azizi, Z., Miraki, M. Individual urban trees detection based on point clouds derived from UAV-RGB imagery and local maxima algorithm, a case study of Fateh Garden, Iran. Environ Dev Sustain 26, 2331–2344 (2024). https://doi.org/10.1007/s10668-022-02820-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10668-022-02820-7