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A new method for detecting individual trees in aerial LiDAR point clouds using absolute height maxima

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Abstract

Data acquired from aerial laser scanner systems are increasingly used for detecting individual trees in operational inventories. In conventional analyses, tree detection is often performed on raster models that use local height maxima filters; an option that is likely to accumulate important errors. In order to reduce errors and improve the detection of individual trees, a new method is proposed that uses an Absolute Height Maxima (AHM) filter applied on the original point clouds obtained from Aerial Laser Scanning (ALS). ALS point clouds at a density of 2 to 4 points per square meter were acquired over forest stands in Hyrcanian forests. In the new method, false trees and commission errors were automatically found and excluded. To evaluate the efficiency of this new method, 121 sample trees in the field were located, with a DGPS and a mapping camera. The height and crown radius of the sample trees were also measured. The field-surveyed variables were compared to the closest detected tree, with an overall detection accuracy of 75.2%. The initial results of this analysis allowed us to hypothesize that a higher detection of tree may be expected with larger densities.

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References

  • Ahokas, E., Hyyppä, J., Kaartinen, H., Kukko, A., Kaasalainen, S., Krooks, A. (2010). The effect of biomass and scanning angle on laser beam transmittance. In: Wagner W., Székely, B. (eds.): ISPRS TC VII symposium—100 years ISPRS, Vienna, Austria, IAPRS, Vol. XXXVIII, Part 7.

  • Anonymous. (2008). Forest management plan of Shast-Kolateh forest. Published by Forests.

  • Brandtberg, T., Warner, T., Landenberger, R., & McGraw, J. (2003). Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America. Remote Sensing of Environment, 85, 290–303.

    Article  Google Scholar 

  • Chasmer, L., Hopkinson, C., & Treitz, P. (2006). Investigating laser pulse penetration of a conifer canopy through the integration of airborne and terrestrial lidar. Canadian Journal of Remote Sensing, 32(2), 116–125.

    Article  Google Scholar 

  • Chen, Q. (2007). Airborne LiDAR data processing and information extraction. Photogrammetric Engineering and Remote Sensing, 73, 109–112.

    Google Scholar 

  • Duan, G., Zhao, D., Zeng, Y., Zhao, Y., Wu, B., & Zhu, J. (2015). Assessing and correcting topographic effects on forest canopy height retrieval using airborne LiDAR data. Sensors, 15, 12133–12155.

    Article  Google Scholar 

  • Guo, Q., Wi, L., H, Y., & Alvarez, O. (2010). Effects of topographic variability and lidar sampling density on several DEM interpolation methods. Photogrammetric Engineering & Remote Sensing, 76(6), 701–712.

    Article  Google Scholar 

  • Gupta, S., Weinacker, H., Sterenczak, K., & Koch, B. (2013). Single tree delineation using airborne LiDAR data. European Scientific Journal, 9(32), 1857–7881.

    Google Scholar 

  • Heurich, M., Persson, Å., Holmgren, J., & Kennel, E. (2004). Detecting and measuring individual trees with laser scanning in mixed mountain forest of central Europe using an algorithm developed for Swedish boreal forest conditions. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 307–312.

  • Holmgren, J., & Persson, Å. (2004). Identifying species of individual trees using airborne laser scanner. Remote Sensing of Environment, 90(4), 415–423.

    Article  Google Scholar 

  • Holopainen, M., Haapanen, R., Karjalainen, M., Vastaranta, M., Hyyppä, J., Yu, X., Tuominen, S., & Hyyppä, H. (2010). Comparing accuracy of airborne laser scanning and TerrSAR-Xradar images in the estimation of plot-level forest variables. Remote Sensing, 2(2), 432–445.

    Article  Google Scholar 

  • Hyyppä, J., & Inkinen, M. (1999). Detecting and estimating attributes for single trees using laser scanner. Photogrammetric Journal of Finland, 16(2), 27–42.

    Google Scholar 

  • Hyyppä, J., Hyyppä, H., Leckie, D., Gougeon, F., Yu, X., & Maltamo, M. (2008). Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests. International Journal of Remote Sensing, 29, 1339–1366.

    Article  Google Scholar 

  • Kaartinen, H., & Hyyppä, J. (2008). Tree extraction. Final report of EuroSDR/ISPRS project, commission II, EuroSDR Official Publication, 53.

  • Kaartinen, H., Hyyppä, J., Yu, X., Vastaranta, M., Hyyppä, H., Kukko, A., Holopainen, M., Heipke, C., Hirschmugl, M., Morsdorf, F., Næsset, E., Pitkänen, J., Popescu, S., Solberg, S., Wolf, B. M., & Wu, J. C. (2012). An international comparison of individual tree detection and extraction using airborne laser scanning. Remote Sensing, 4, 950–974.

    Article  Google Scholar 

  • Koch, B., Heyder, U., & Weinacker, H. (2006). Detection of individual tree crowns in airborne LIDAR data. Photogrammetric Engineering and Remote Sensing, 72(4), 357–363.

    Article  Google Scholar 

  • Korpela, I., Dahlin, B., Schäfer, H., Bruun, E., Haapaniemi, F., Honkasalo, J., et al. (2007). Single-tree forest inventory using Lidar and aerial images for 3D treetop positioning, species recognition, height and crown width estimation. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 36, 227–233.

    Google Scholar 

  • Kwak, D., Lee, W., Lee, J. G., & Gong, P. (2017). Detection of individual trees and estimation of tree height using LiDAR data. Journal of Forest Research, 12(6), 425–434.

    Article  Google Scholar 

  • Lee, H., Slatton, K. C., Roth, B. E., & Cropper Jr., W. P. (2010). Adaptive clustering of airborne LiDAR data to segment individual tree crowns in managed pine forests. International Journal of Remote Sensing, 31(1), 117–139.

    Article  Google Scholar 

  • Lefsky, M. A., Choen, W. B., Parker, G. G., & Harding, D. J. (2002). Lidar remote sensing for ecosystem studies. BioScience, 52(1), 19–30.

    Article  Google Scholar 

  • Li, W., Guo, Q., Jakubowski, K. M., & Kelly, M. (2012). A new method for segmenting individual trees from the LiDAR point cloud. Photogrammetric Engineering & Remote Sensing, 78(1), 75–84.

    Article  Google Scholar 

  • Magnusson, M., Fransson, J. E. S., & Holmgren, J. (2007). Effects on estimation accuracy of forest variables using different pulse density of laser data. Forest Science, 53, 619–626.

    Google Scholar 

  • Maltamo, M., Mustonen, K., Hyyppä, J., Pitkänen, J., & Yu, X. (2004). The accuracy of estimating individual tree variables with airborne laser scanning in boreal nature reserve. Canadian Journal of Forest Research, 34, 1791–1801.

    Article  Google Scholar 

  • Monnet, J.M., Mermin, E., Chanussot, J., & Berger, F.(2010). Treetop detection using local maxima filtering: a parameter sensitivity analysis. 10th International conference on LiDAR applications for assessing forest ecosystems (Silvilaser), Freiburg, Germany.

  • Moradi, A., Satari, M., & Momeni, M. (2016). Individual tree of urban forest extraction from very high density LiDAR data. The international archives of the photogrammetry, remote sensing and spatial information sciences, 23 ISPRS Congress, Prague, Czech Republic.

  • Ørka, H. O., Bollandsås, O. M., Hansen, E. H., Næsset, E., & Gobakken, T. (2018). Effects of terrain slope and aspect on the error of ALS-based predictions of forest attributes. Forestry, 91, 225–237. https://doi.org/10.1093/forestry/cpx058.

    Article  Google Scholar 

  • Persson, Å., Holmgren, J., & Söderman, U. (2002). Detecting and measuring individual trees using an airborne laser scanner. Photogrammetric Engineering and Remote Sensing, 68, 925–932.

    Google Scholar 

  • Popescu, S. C., & Wynne, R. H. (2004). Seeing the trees in the forest using lidar and multispectral data fusion with local filtering and variable window size for estimating tree height. Photogrammetric Engineering & Remote Sensing, 70(5), 589–604.

    Article  Google Scholar 

  • Popescu, S. C., Wynne, R. H., & Nelson, R. H. (2003). Measuring individual tree crown diameter with LiDAR and assessing its influence on estimating forest volume and biomass. Canadian Journal of Remote Sensing, 29, 564–577.

    Article  Google Scholar 

  • Reitberger, J., Schnörr, C. L., Krzystek, P., & Stilla, U. (2009). 3D segmentation of single trees exploiting full waveform LiDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 64, 561–574.

    Article  Google Scholar 

  • Solberg, S., Næsset, E., & Bollandsas, O. M. (2006). Single tree segmentation using airborne laser scanner data in a structurally heterogeneous spruce forest. Photogrammetric Engineering and Remote Sensing, 72, 1369–1378.

    Article  Google Scholar 

  • Takahashi, T., Awaya, Y., Hirata, Y., Furuya, N., Sakai, T., & Sakai, A. (2008). Effect of flight altitude on LiDAR-derived tree heights in mountainous forests with poor laser penetration rates. Photogrammetric Journal of Finland, 21(1), 86–96.

    Google Scholar 

  • Tittmann, P., Shafii, S., Hartsough, S., & Hamann, B. (2011). Tree detection, and measurement from LIDAR point cloud using RANSAC. Proceedings of SilviLaser, 11th International Conference on LIDAR Applications for Assessing Forest Ecosystems, 1–13.

  • Vauhkonen, J., Tokola, T., Maltamo, M., & Packalen, P. (2008). Effects of pulse density on predicting characteristics of individual trees of Scandinavian commercial species using alpha shape metrics based on airborne laser scanning data. Canadian Journal of Remote Sensing, 34, S441–S459.

    Article  Google Scholar 

  • Vauhkonen, J., Ene, L., Gupta, S., Heinzel, J., Holmgren, J., Pitkänen, J., et al. (2012). Comparative testing of single-tree detection algorithms under different types of forest. Forestry, 85, 27–40. https://doi.org/10.1093/forestry/cpr051.

    Article  Google Scholar 

  • Wallace, L., Lucieer, A., & Watson, C. S. (2014). Evaluating tree detection and segmentation routines on very high resolution UAV LiDAR data. IEEE Transactions on Geoscience and Remote Sensing, 52, 7619–7628.

    Article  Google Scholar 

  • Yu, X., Hyyppä, J., Vastaranta, M., Holopainen, M., & Viitala, R. (2011). Predicting individual tree attributes from airborne laser point clouds based on the random forests technique. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 28–37.

    Article  Google Scholar 

  • Yu, X., Litkey, P., Hyyppä, J., Holopainen, M., & Vastaranta, M. (2014). Assessment of low density full-waveform airborne laser scanning for individual tree detection and tree species classification. Forests, 5, 1011–1031.

    Article  Google Scholar 

  • Yu, X., Hyyppä, J., Litkey, P., Kaartinen, H., Vastaranta, M., & Holopainen, M. (2017). Single-sensor solution to tree species classification using multispectral airborne laser scanning. Remote Sensing,www.mdpi.com/journal/remotesensing, 9.

  • Zhang, C., Zhou, Y., & Qiu, F. (2015). Individual tree segmentation from LiDAR point clouds for urban forest inventory. Remote Sense, 7, 7892–7913.

    Article  Google Scholar 

  • Zhao, K. C., Suarez, J., Garcia, M., Hu, T., Wang, C., & Londo, A. (2018). Utility of multitemporal lidar for forest and carbon monitoring: tree growth, biomass dynamics and carbon flux. Remote Sensing of Environment, 204(2018), 883–897. https://doi.org/10.1016/j.rse.2017.09.007.

    Article  Google Scholar 

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Acknowledgments

We would like to thank Dr. Juan C. Suárez and Dr. Mohammadreaz Babaee for their constructive comments and suggestions that greatly improve the manuscript. Special thanks go to Mohammad Reza and Manouchehr Yousefi for acquisition and processing of LiDAR data.

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Authors and Affiliations

  1. Forests and Rangelands Research Department, Mazandaran Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), PO Box 48175-556, Sari, Iran

    Ramzanali Khorrami, Zahra Naeimi & Masoud Tabari

  2. Department of Physics, University of Tehran, Tehran, Iran

    Ramzanali Khorrami, Zahra Naeimi & Masoud Tabari

  3. Department of Forestry, Tarbiat Modares University, Nour, Iran

    Ramzanali Khorrami, Zahra Naeimi & Masoud Tabari

  4. School of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran

    Ramzanali Khorrami, Zahra Naeimi & Masoud Tabari

  5. Department of Applied Mathematics, Tarbiat Modares University, Nour, Iran

    Mohammad Reza Eslahchi

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  1. Ramzanali Khorrami
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  2. Zahra Naeimi
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  3. Masoud Tabari
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  4. Mohammad Reza Eslahchi
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Correspondence to Ramzanali Khorrami.

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Khorrami, R., Naeimi, Z., Tabari, M. et al. A new method for detecting individual trees in aerial LiDAR point clouds using absolute height maxima. Environ Monit Assess 190, 708 (2018). https://doi.org/10.1007/s10661-018-7082-8

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  • DOI: https://doi.org/10.1007/s10661-018-7082-8

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