Journal of Northwest Forestry University, Volume. 40, Issue 4, 106(2025)

Optimized Tree Species Classification in Subtropical Mountain Bamboo Forests Using UAV-LiDAR and GF-2 Multispectral Imagery

TAN Yan1,2, GUO Xiaoyu2,3、*, and YANG Kele1,2
Author Affiliations
  • 1College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, Fujian, China
  • 2Fujian Provincial Key Laboratory of Resources and Environment Monitoring & Sustainable Management and Utilization, Sanming University, Sanming 365004, Fujian, China
  • 3University Key Laboratory of Bamboo Resources Development & Utilization in Fujian Province, Sanming University, Sanming 365004, Fujian, China
  • show less
    References(11)

    [6] [6] FAVA F, COLOMBO R. Remote sensing-based assessment of the 2005—2011 bamboo reproductive event in the Arakan Mountain range and its relation with wildfires[J]. Remote Sensing, 2017, 9(1): 85.

    [7] [7] ZHAO Y Y, FENG D L, JAYARAMAN D, et al. Bamboo mapping of Ethiopia, Kenya and Uganda for the year 2016 using multi-temporal Landsat imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2018, 66: 116-125.

    [12] [12] CHEN Y, LIN M X, LIN T, et al. Spatial heterogeneity of vegetation phenology caused by urbanization in China based on remote sensing[J]. Ecological Indicators, 2023, 153: 110448.

    [13] [13] ZHANG C, XIA K, FENG H L, et al. Tree species classification using deep learning and RGB optical images obtained by an unmanned aerial vehicle[J]. Journal of Forestry Research, 2021, 32(5): 1879-1888.

    [17] [17] WU J R, MAN Q X, YANG X M, et al. Fine classification of urban tree species based on UAV-based RGB imagery and LiDAR data[J]. Forests, 2024, 15(2): 390.

    [18] [18] NORTON C L, HARTFIELD K, COLLINS C D H, et al. Multi-temporal LiDAR and hyperspectral data fusion for classification of semi-arid woody cover species[J]. Remote Sensing, 2022, 14(12): 2896.

    [25] [25] HE Y S, HUANG X, ZHOU D, et al. Identification of larch caterpillar infestation severity based on unmanned aerial vehicle multispectral and LiDAR features[J]. Forests, 2024, 15: 191.

    [27] [27] LIU Y N, GONG W S, HU X Y, et al. Forest type identification with random forest using Sentinel-1A, Sentinel-2A, multi-temporal landsat-8 and DEM data[J]. Remote Sensing, 2018, 10(6): 946.

    [31] [31] CORTES C, VAPNIK V. Support-vector networks[J]. Machine Learning, 1995, 20(3): 273-297.

    [34] [34] DECHESNE C, MALLET C, LE BRIS A, et al. Semantic segmentation of forest stands of pure species combining airborne lidar data and very high resolution multispectral imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 126: 129-145.

    [35] [35] LIU C, XIONG T W, GONG P, et al. Improving large-scale moso bamboo mapping based on dense Landsat time series and auxiliary data: A case study in Fujian Province, China[J]. Remote Sensing Letters, 2018, 9(1): 1-10.

    Tools

    Get Citation

    Copy Citation Text

    TAN Yan, GUO Xiaoyu, YANG Kele. Optimized Tree Species Classification in Subtropical Mountain Bamboo Forests Using UAV-LiDAR and GF-2 Multispectral Imagery[J]. Journal of Northwest Forestry University, 2025, 40(4): 106

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Received: May. 16, 2024

    Accepted: Sep. 12, 2025

    Published Online: Sep. 12, 2025

    The Author Email: GUO Xiaoyu (fjgxy2009@126.com)

    DOI:10.3969/j.issn.1001-7461.2025.04.12

    Topics