Evaluation of Tree Detection and Segmentation Algorithms in Peat Swamp Forest Based on LiDAR Point Clouds Data
Abstract
Application of LiDAR for tree detection and tree canopy segmentation has been widely used in conifer plantation forest in temperate countries with high accuracy, however its application on tropical natural forest especially peat swamp forest hardly found. The objective of this study was evaluated algorithms of individual tree detection and canopy segmentation used LiDAR data in peat swamp forest. The algorithms included (a) Local Maxima (LM) with various variable window size combined with growing region, (b) LM with various variable window size combined with Voronoi Tessellation, (c) LM with various fixed window size combined with growing region, (d) LM with various fixed window size combined with Voronoi Tessellation, and (e) Tree Relative Distance algorithm. The results show that algorithm with the best accuracy was the Tree Relative Distance algorithm with the highest overall F-score of 0.63. The tree relative distance algorithm also provides the highest accuracy in determining three tree parameters which are position, height and diameter of tree canopy with a RMSE value 1.08 m, 6.45 m and 1.19 m, respectively.
References
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Goldbergs, G., Levick, S. R., Lawes, M., & Edwards, A. (2018). Hierarchical integration of individual tree and area-based approaches for savanna biomass uncertainty estimation from airborne LiDAR. Remote Sensing and Environment, 205, 141–150. https://doi.org/10.1016/j.rse.2017.11.010
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Jakubowski, M. K., Li, W., Guo, Q., & Kelly, M. (2013). Delineating individual trees from LiDAR data: a comparison of vector-and raster-based segmentation approaches. Remote Sensing, 5, 4163–4186. https://doi.org/10.3390/rs5094163
Jeronimo, S. M., Kane, V. R., Churchill, D. J., McGaughey, R. J., & Franklin, J. F. (2018). Applying LiDAR individual tree detection to management of structurally diverse forest landscapes. Journal of Forestry, 116, 336–346.
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Khosravipour, A., Skidmore, A. K., Isenburg, M., Wang, T., & Hussin, Y. A. (2014). Generating pit-free canopy height models from airborne lidar. Photogrammetric Engineering Remote Sensing, 80, 863–872. https://doi.org/10.14358/PERS.80.9.863
Kim, E., Woo-Kyun, L., Yoon, M., Lee, J.-Y., Lee, E. J., & Moon, J. (2016). Detecting individual tree position and height using Airborne LiDAR data in Chollipo Arboretum, South Korea. TAO: Terrestrial, Atmospheric Oceanic Sciences, 27, 593–604. https://doi.org/10.3319/TAO.2016.03.29.01(ISRS)
Koch, B., Heyder, U., & Weinacker, H. (2006). Detection of individual tree crowns in airborne LiDAR data. Photogrammetric Engineering Remote Sensing, 72, 357–363. https://doi.org/10.14358/PERS.72.4.357
Li, W., Guo, Q., Jakubowski, M. K., & Kelly, M. (2012). A new method for segmenting individual trees from the LiDAR point cloud. Photogrammetric Engineering Remote Sensing, 78, 75–84. https://doi.org/10.14358/PERS.78.1.75
Lim, K., Treitz, P., Wulder, M., St-Onge, B., & Flood, M. (2003). LiDAR remote sensing of forest structure. Progress in Physical Geography: Earth and Environment, 27, 88–106. https://doi.org/10.1191/0309133303pp360ra
Lu, X., Guo, Q., Li, W., & Flanagan, J. (2014). A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data. ISPRS Journal of Photogrammetry and Remote Sensing, 94, 1–12. https://doi.org/10.1016/j.isprsjprs.2014.03.014
Ma, H., Song, J., Wang, J., Xiao, Z., & Fu, Z. (2014). Improvement of spatially continuous forest LAI retrieval by integration of discrete airborne LiDAR and remote sensing multi-angle optical data. Agricultural and Forest Meteorology, 189190, 60–70. https://doi.org/10.1016/j.agrformet.2014.01.009
Mielcarek, M., Stereńczak, K., & Khosravipour, A. (2018). Testing and evaluating different LiDAR-derived canopy height model generation methods for tree height estimation. International Journal of Applied Earth Observation Geoinformation, 71, 132–143. https://doi.org/10.1016/j.jag.2018.05.002
Morsdorf, F., Meier, E., Kötz, B., Itten, K. I., Dobbertin, M., & Allgöwer, B. (2004). LiDAR-based geometric reconstruction of boreal type forest stands at single tree level for forest and wildland fire management. Remote Sensing of Environment, 92, 353–362. https://doi.org/10.1016/j.rse.2004.05.013
Popescu, S. C., & Wynne, R. H. (2004). Seeing the trees in the forest. Photogrammetric Engineering & Remote Sensing, 70, 589–604. https://doi.org/10.14358/PERS.70.5.589
Popescu, S. C., Wynne, R. H., & Nelson, R. F. (2002). Estimating plot-level tree heights with lidar: Local filtering with a canopy-height based variable window size. Computers and Electronics in Agriculture, 37, 71–95. https://doi.org/10.1016/S0168-1699(02)00121-7
Popescu, S. C., Wynne, R. H., & Nelson, R. F. (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. https://doi.org/10.5589/m03-027
Roussel, J.-R., Auty, D., Boissieu, F. D., & Meador, A. S. (2019). LidR: Airborne LiDAR data manipulation and visualization for forestry applications.
Silva, C. A., Hudak, A. T., Vierling, L. A., Loudermilk, E. L., O’Brien, J. J., Hiers, J. K., …, & Khosravipour, A. (2016). Imputation of individual longleaf pine (Pinus palustris Mill.) tree attributes from field and LiDAR data. Canadian Journal of Remote Sensing, 42, 554–573. https://doi.org/10.1080/07038992.2016.1196582
Vega, C., Hamrouni, A., El Mokhtari, S., Morel, J., Bock, J., Renaud, J. P., …, & 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. https://doi.org/10.1016/j.jag.2014.05.001
Wallace, L., Lucieer, A., Watson, C. S. J. I. T. o. G., & Sensing, R. (2014). Evaluating tree detection and segmentation routines on very high resolution UAV LiDAR data. IEEE Transactions on Geoscience and Remote Sensing, 52, 7619–7628. https://doi.org/10.1109/TGRS.2014.2315649
White, J. C., Coops, N. C., Wulder, M. A., Vastaranta, M., Hilker, T., & Tompalski, P. (2016). Remote sensing technologies for enhancing forest inventories: a review. Canadian Journal of Remote Sensing, 42, 619–641. https://doi.org/10.1080/07038992.2016.1207484
Yao, W., Krull, J., Krzystek, P., & Heurich, M. (2014). Sensitivity analysis of 3D individual tree detection from LiDAR point clouds of temperate forests. Forests, 5, 1122–1142. https://doi.org/10.3390/f5061122
Zawawi, A. A., Shiba, M., & Jemali, N. J. N. (2015). Accuracy of LiDAR-based tree height estimation and crown recognition in a subtropical evergreen broad-leaved forest in Okinawa, Japan. Forest systems, 24, 1–11. https://doi.org/10.5424/fs/2015241-05476
Zhao, D., Pang, Y., Li, Z., & Sun, G. (2013). Filling invalid values in a LiDAR-derived canopy height model with morphological crown control. International Journal of Remote Sensing, 34, 4636–4654. https://doi.org/10.1080/01431161.2013.779398
Zhen, Z., Quackenbush, L. J., & Zhang, L. (2016). Trends in automatic individual tree crown detection and delineation-Evolution of LiDAR data. Remote Sensing, 8, 1–26. https://doi.org/10.3390/rs8040333
Ben-Arie, J. R., Hay, G. J., Powers, R. P., Castilla, G., & St-Onge, B. (2009). Development of a pit filling algorithm for LiDAR canopy height models. Computers Geosciences, 35, 1940–1949. https://doi.org/10.1016/j.cageo.2009.02.003
Chen, Q., Baldocchi, D., Gong, P., & Kelly, M. (2006). Isolating individual trees in a savanna woodland using small footprint lidar data. Photogrammetric Engineering Remote Sensing, 72, 923–932. https://doi.org/10.14358/PERS.72.8.923
Dalponte, M., & Coomes, D. A. (2016). Tree‐centric mapping of forest carbon density from airborne laser scanning and hyperspectral data. Methods in Ecology, 7, 1236–1245. https://doi.org/10.1111/2041-210X.12575
Dalponte, M., Reyes, F., Kandare, K., & Gianelle, D. (2015). Delineation of individual tree crowns from ALS and hyperspectral data: A comparison among four methods. European Journal of Remote Sensing, 48, 365–382. https://doi.org/10.5721/EuJRS20154821
Ene, L., Næsset, E., & Gobakken, T. (2012). Single tree detection in heterogeneous boreal forests using airborne laser scanning and area-based stem number estimates. International Journal of Remote Sensing, 33, 5171–5193. https://doi.org/10.1080/01431161.2012.657363
Eysn, L., Hollaus, M., Lindberg, E., Berger, F., Monnet, J.-M., Dalponte, M., Kobal, M., Pellegrini, M., Lingua, E., & Mongus, D. (2015). A benchmark of LiDAR-based single tree detection methods using heterogeneous forest data from the alpine space. Forests, 6, 1721–1747.
Ferraz, A., Saatchi, S., Mallet, C., & Meyer, V. (2016). LiDAR detection of individual tree size in tropical forests. Remote Sensing of Environment, 183, 318–333. https://doi.org/10.1016/j.rse.2016.05.028
Goldbergs, G., Levick, S. R., Lawes, M., & Edwards, A. (2018). Hierarchical integration of individual tree and area-based approaches for savanna biomass uncertainty estimation from airborne LiDAR. Remote Sensing and Environment, 205, 141–150. https://doi.org/10.1016/j.rse.2017.11.010
Goutte C., & Gaussier E. (2005). A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation. In: Losada D.E., Fernández-Luna J.M. (eds) Advances in Information Retrieval (pp. 345–359). ECIR 2005. Lecture Notes in Computer Science, vol 3408. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31865-1_25
Hamraz, H., Contreras, M. A., & Zhang, J. (2017). Vertical stratification of forest canopy for segmentation of understory trees within small-footprint airborne LiDAR point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 385–392. https://doi.org/10.1016/j.isprsjprs.2017.07.001
Hyyppa, J., Kelle, O., Lehikoinen, M., & Inkinen, M. (2001). A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners. IEEE Transactions on Geoscience Remote Sensing, 39, 969–975. https://doi.org/10.1109/36.921414
Jakubowski, M. K., Li, W., Guo, Q., & Kelly, M. (2013). Delineating individual trees from LiDAR data: a comparison of vector-and raster-based segmentation approaches. Remote Sensing, 5, 4163–4186. https://doi.org/10.3390/rs5094163
Jeronimo, S. M., Kane, V. R., Churchill, D. J., McGaughey, R. J., & Franklin, J. F. (2018). Applying LiDAR individual tree detection to management of structurally diverse forest landscapes. Journal of Forestry, 116, 336–346.
Kaartinen, H., Hyyppä, J., Yu, X., Vastaranta, M., Hyyppä, H., Kukko, A., …, & Wu, J-C. (2012). An international comparison of individual tree detection and extraction using airborne laser scanning. Remote Sensing, 4(4), 950–974. https://doi.org/10.3390/rs4040950
Khosravipour, A., Skidmore, A. K., Isenburg, M., Wang, T., & Hussin, Y. A. (2014). Generating pit-free canopy height models from airborne lidar. Photogrammetric Engineering Remote Sensing, 80, 863–872. https://doi.org/10.14358/PERS.80.9.863
Kim, E., Woo-Kyun, L., Yoon, M., Lee, J.-Y., Lee, E. J., & Moon, J. (2016). Detecting individual tree position and height using Airborne LiDAR data in Chollipo Arboretum, South Korea. TAO: Terrestrial, Atmospheric Oceanic Sciences, 27, 593–604. https://doi.org/10.3319/TAO.2016.03.29.01(ISRS)
Koch, B., Heyder, U., & Weinacker, H. (2006). Detection of individual tree crowns in airborne LiDAR data. Photogrammetric Engineering Remote Sensing, 72, 357–363. https://doi.org/10.14358/PERS.72.4.357
Li, W., Guo, Q., Jakubowski, M. K., & Kelly, M. (2012). A new method for segmenting individual trees from the LiDAR point cloud. Photogrammetric Engineering Remote Sensing, 78, 75–84. https://doi.org/10.14358/PERS.78.1.75
Lim, K., Treitz, P., Wulder, M., St-Onge, B., & Flood, M. (2003). LiDAR remote sensing of forest structure. Progress in Physical Geography: Earth and Environment, 27, 88–106. https://doi.org/10.1191/0309133303pp360ra
Lu, X., Guo, Q., Li, W., & Flanagan, J. (2014). A bottom-up approach to segment individual deciduous trees using leaf-off lidar point cloud data. ISPRS Journal of Photogrammetry and Remote Sensing, 94, 1–12. https://doi.org/10.1016/j.isprsjprs.2014.03.014
Ma, H., Song, J., Wang, J., Xiao, Z., & Fu, Z. (2014). Improvement of spatially continuous forest LAI retrieval by integration of discrete airborne LiDAR and remote sensing multi-angle optical data. Agricultural and Forest Meteorology, 189190, 60–70. https://doi.org/10.1016/j.agrformet.2014.01.009
Mielcarek, M., Stereńczak, K., & Khosravipour, A. (2018). Testing and evaluating different LiDAR-derived canopy height model generation methods for tree height estimation. International Journal of Applied Earth Observation Geoinformation, 71, 132–143. https://doi.org/10.1016/j.jag.2018.05.002
Morsdorf, F., Meier, E., Kötz, B., Itten, K. I., Dobbertin, M., & Allgöwer, B. (2004). LiDAR-based geometric reconstruction of boreal type forest stands at single tree level for forest and wildland fire management. Remote Sensing of Environment, 92, 353–362. https://doi.org/10.1016/j.rse.2004.05.013
Popescu, S. C., & Wynne, R. H. (2004). Seeing the trees in the forest. Photogrammetric Engineering & Remote Sensing, 70, 589–604. https://doi.org/10.14358/PERS.70.5.589
Popescu, S. C., Wynne, R. H., & Nelson, R. F. (2002). Estimating plot-level tree heights with lidar: Local filtering with a canopy-height based variable window size. Computers and Electronics in Agriculture, 37, 71–95. https://doi.org/10.1016/S0168-1699(02)00121-7
Popescu, S. C., Wynne, R. H., & Nelson, R. F. (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. https://doi.org/10.5589/m03-027
Roussel, J.-R., Auty, D., Boissieu, F. D., & Meador, A. S. (2019). LidR: Airborne LiDAR data manipulation and visualization for forestry applications.
Silva, C. A., Hudak, A. T., Vierling, L. A., Loudermilk, E. L., O’Brien, J. J., Hiers, J. K., …, & Khosravipour, A. (2016). Imputation of individual longleaf pine (Pinus palustris Mill.) tree attributes from field and LiDAR data. Canadian Journal of Remote Sensing, 42, 554–573. https://doi.org/10.1080/07038992.2016.1196582
Vega, C., Hamrouni, A., El Mokhtari, S., Morel, J., Bock, J., Renaud, J. P., …, & 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. https://doi.org/10.1016/j.jag.2014.05.001
Wallace, L., Lucieer, A., Watson, C. S. J. I. T. o. G., & Sensing, R. (2014). Evaluating tree detection and segmentation routines on very high resolution UAV LiDAR data. IEEE Transactions on Geoscience and Remote Sensing, 52, 7619–7628. https://doi.org/10.1109/TGRS.2014.2315649
White, J. C., Coops, N. C., Wulder, M. A., Vastaranta, M., Hilker, T., & Tompalski, P. (2016). Remote sensing technologies for enhancing forest inventories: a review. Canadian Journal of Remote Sensing, 42, 619–641. https://doi.org/10.1080/07038992.2016.1207484
Yao, W., Krull, J., Krzystek, P., & Heurich, M. (2014). Sensitivity analysis of 3D individual tree detection from LiDAR point clouds of temperate forests. Forests, 5, 1122–1142. https://doi.org/10.3390/f5061122
Zawawi, A. A., Shiba, M., & Jemali, N. J. N. (2015). Accuracy of LiDAR-based tree height estimation and crown recognition in a subtropical evergreen broad-leaved forest in Okinawa, Japan. Forest systems, 24, 1–11. https://doi.org/10.5424/fs/2015241-05476
Zhao, D., Pang, Y., Li, Z., & Sun, G. (2013). Filling invalid values in a LiDAR-derived canopy height model with morphological crown control. International Journal of Remote Sensing, 34, 4636–4654. https://doi.org/10.1080/01431161.2013.779398
Zhen, Z., Quackenbush, L. J., & Zhang, L. (2016). Trends in automatic individual tree crown detection and delineation-Evolution of LiDAR data. Remote Sensing, 8, 1–26. https://doi.org/10.3390/rs8040333
Authors
Irlan, SalehM. B., PrasetyoL. B., & SetiawanY. (2020). Evaluation of Tree Detection and Segmentation Algorithms in Peat Swamp Forest Based on LiDAR Point Clouds Data. Jurnal Manajemen Hutan Tropika, 26(2), 123. https://doi.org/10.7226/jtfm.26.2.123
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