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NTIS 바로가기한국산림과학회지 = Journal of korean society of forest science, v.112 no.2, 2023년, pp.195 - 208
이용규 (강원대학교 산림경영학과) , 이상진 (강원대학교 산림경영학과) , 이정수 (강원대학교 산림경영학과)
This study aimed to use three-dimensional point cloud data (PCD) obtained from Terrestrial Laser Scanning (TLS) and Mobile Laser Scanning (MLS) to evaluate a deep learning-based species classification model for two tree species: Pinus koraiensis and Larix kaempferi. Sixteen models were constructed b...
Axelsson, A., Lindberg, E. and Olsson, H. 2018. Exploring multispectral ALS data for tree species classification. Remote Sensing 10(2): 183.
Ballanti, L., Blesius, L., Hines, E. and Kruse, B. 2016. Tree species classification using hyperspectral imagery: a comparison of two classifiers. Remote Sensing 8(6): 445.
Briechle, S., Krzystek, P. and Vosselman, G. 2020. Classification of tree species and standing dead trees by fusing UAV-based lidar data and multispectral imagery in the 3D deep neural network PointNet++. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2: 203-210.
Chen, J., Chen, Y. and Liu, Z. 2021. Classification of typical tree species in laser point cloud based on deep learning. Remote Sensing 13(23): 4750.
Chen, S., Liu, H., Feng, Z., Shen, C. and Chen, P. 2019. Applicability of personal laser scanning in forestry inventory. PLoS One 14(2): e0211392.
Chen, Y., Liu, G., Xu, Y., Pan, P. and Xing, Y. 2021. PointNet++ network architecture with individual point level and global features on centroid for ALS point cloud classification. Remote Sensing 13(3): 472.
Del Perugia, B., Giannetti, F., Chirici, G. and Travaglini, D. 2019. Influence of scan density on the estimation of single-tree attributes by hand-held mobile laser scanning. Forests 10(3): 277.
Guan, H., Yu, Y., Ji, Z., Li, J. and Zhang, Q. 2015. Deep learning-based tree classification using mobile LiDAR data. Remote Sensing Letters 6(11): 864-873.
Hartley, R.J., Jayathunga, S., Massam, P.D., de Silva, D., Estarija, H.J., Davidson, S.J., Wuraola, A. and Pearse, G.D. 2022. Assessing the potential of backpack-mounted mobile laser scanning systems for tree phenotyping. Remote Sensing 14(14): 3344.
Jones, T.G., Coops, N.C. and Sharma, T. 2010. Assessing the utility of airborne hyperspectral and LiDAR data for species distribution mapping in the coastal Pacific Northwest, Canada. Remote Sensing of Environment 114(12): 2841-2852.
Ko, B.J., Park, S.I., Park, H.J. and Lee, S.H. 2022. Measurement of tree height and diameter using terrestrial laser scanner in coniferous forests. Journal of Environmental Science International 31(6): 479-490.
Ko, C.U., Lee, J.W., Kim, D. and Kang, J.T. 2022. The application of terrestrial light detection and ranging to forest resource inventories for timber yield and carbon sink estimation. Forests 13(12): 2087.
Korea Forest Service and Korea Forestry Promotion Institute. 2021. The 8th national forest inventory and forest health monitoring. -Field manual-. Seoul: Korea Forestry Promotion Institute. pp. 7-8.
Landis, J.R. and Koch, G.G. 1977. The measurement of observer agreement for categorical data. biometrics. pp. 159-174.
Liang, X. et al. 2018. International benchmarking of terrestrial laser scanning approaches for forest inventories. ISPRS Journal of Photogrammetry and Remote Sensing 144: 137-179.
Lim, J.B., Kim, K.M., and Kim, M.K. 2019. The development of major tree species classification model using different satellite images and machine learning in Gwangneung area. Korean Journal of Remote Sensing 35(6): 1037-1052
Liu, B., Chen, S., Huang, H. and Tian, X. 2022. Tree Species Classification of backpack laser scanning data using the PointNet++ point cloud deep learning method. Remote Sensing 14(15): 3809.
Liu, B., Huang, H., Su, Y., Chen, S., Li, Z., Chen, E. and Tian, X. 2022. Tree species classification using ground-based LiDAR data by various point cloud deep learning methods. Remote Sensing 14(22): 5733.
Liu, B., Huang, H., Tian, X. and Ren, M. 2022. Individual tree species classification using the pointwise mlp-based point cloud deep learning method. Environmental Sciences Proceedings 22(1): 19.
Liu, M., Han, Z., Chen, Y., Liu, Z. and Han, Y. 2021. Tree species classification of LiDAR data based on 3D deep learning. Measurement 177: 109301.
Loudermilk, E.L., Pokswinski, S., Hawley, C.M., Maxwell, A., Gallagher, M.R., Skowronski, N.S., Hudak, A.T., Hoffman, C. and Hiers, J.K. 2023. Terrestrial laser scan metrics predict surface vegetation biomass and consumption in a frequently burned southeastern US ecosystem. bioRxiv, 2023-01.
Pu, R. and Landry, S. 2012. A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species. Remote Sensing of Environment 124: 516-533.
Qi, C.R., Su, H., Mo, K. and Guibas, L.J. 2017. Pointnet: deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 652-660.
Qi, C.R., Yi, L., Su, H. and Guibas, L.J. 2017. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in Neural Information Processing Systems 30: 5105-5114.
Ramadhani, F., Pullanagari, R., Kereszturi, G. and Procter, J. 2020. Mapping of rice growth phases and bare land using Landsat-8 OLI with machine learning. International Journal of Remote Sensing 41(21): 8428-8452.
Sakharova, E.K., Nurlyeva, D.D., Fedorova, A.A., Yakubov, A.R. and Kanev, A.I. 2022. Issues of tree species classification from LiDAR data using deep learning model. In advances in neural computation, machine learning, and cognitive research V: Selected papers from the XXIII international conference on neuroinformatics, Moscow, Russia. Springer International Publishing. pp. 319-324.
Shin, Y. H., Son, K. W. and Lee, D. C. 2022. Semantic segmentation and building extraction from airborne LiDAR data with multiple return using PointNet++. Applied Sciences 12(4): 1975.
Soydaner, D. 2020. A comparison of optimization algorithms for deep learning. International Journal of Pattern Recognition and Artificial Intelligence 34(13): 2052013.
Su, H., Maji, S., Kalogerakis, E. and Learned-Miller, E. 2015. Multi-view convolutional neural networks for 3d shape recognition. In Proceedings of the IEEE international conference on computer vision. pp. 945-953.
Tao, S. et al. 2015. Segmenting tree crowns from terrestrial and mobile LiDAR data by exploring ecological theories. ISPRS Journal of Photogrammetry and Remote Sensing 110: 66-76.
Wessel, M., Brandmeier, M. and Tiede, D. 2018. Evaluation of different machine learning algorithms for scalable classification of tree types and tree species based on Sentinel-2 data. Remote Sensing 10(9): 1419.
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X. and Xiao, J. 2015. 3d shapenets: A deep representation for volumetric shapes. In Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1912-1920.
Xi, Z., Hopkinson, C., Rood, S.B. and Peddle, D.R. 2020. See the forest and the trees: effective machine and deep learning algorithms for wood filtering and tree species classification from terrestrial laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing 168: 1-16.
Yan, S., Jing, L. and Wang, H. 2021. A new individual tree species recognition method based on a convolutional neural network and high-spatial resolution remote sensing imagery. Remote Sensing 13(3): 479.
Zeybek, M. and Sanlioglu, I. 2019. Point cloud filtering on UAV based point cloud. Measurement 133: 99-111.
Zhang, F., Tian, X., Zhang, H., and Jiang, M. 2022. Estimation of Aboveground carbon density of forests using deep learning and multisource remote sensing. Remote Sensing 14(13): 3022.
Zhao, X., Guo, Q., Su, Y. and Xue, B. 2016. Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas. ISPRS Journal of Photogrammetry and Remote Sensing 117: 79-91.
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