[1]

Lu XT, Deng YB, Song XF, Yan XF, Yu KY, et al. 2023. Effect of spatial resolution of unmanned aerial vehicle (UAV) visible light remote sensing image on recognition accuracy of Phyllostachys edulis stand density. Journal of Northeast Forestry University 51:79−83,96 (in Chinese)

doi: 10.13759/j.cnki.dlxb.2023.03.014
[2]

Liu Z, Ju H, Ma Q, Sun C, Lv Y, et al. 2024. Rice yield estimation using multi-temporal remote sensing data and machine learning: a case study of Jiangsu, China. Agriculture 14:638

doi: 10.3390/agriculture14040638
[3]

Wołk K, Niklewski J, Tatara MS, Kopczyński M, Żero O. 2025. Forestry segmentation using depth information: a method for cost saving, preservation, and accuracy. Forests 16:431

doi: 10.3390/f16030431
[4]

Newton PF. 1997. Stand density management diagrams: review of their development and utility in stand-level management planning. Forest Ecology and Management 98:251−265

doi: 10.1016/S0378-1127(97)00086-8
[5]

Zeide B. 2001. Analysis of a concept: stand density. Journal of Sustainable Forestry 14:51−62

doi: 10.1300/J091v14n04_05
[6]

Du HQ, Zhou GM, Ge HL, Zhao XW, Cui LL. 2008. Bamboo information extraction based on landsat TM data. Journal of Northeast Forestry University 36:35−38 (in Chinese)

doi: 10.13759/j.cnki.dlxb.2008.03.027
[7]

Chen X, Yu K, Yu S, Hu Z, Tan H, et al. 2023. Study on single-tree segmentation of Chinese fir plantations using coupled local maximum and height-weighted improved K-means algorithm. Forests 14:2130

doi: 10.3390/f14112130
[8]

Zhu BD, Luo HB, Jin J, Yue CR. 2022. Optimization of UAV-LiDAR individual tree segmentation method for high canopy density plantations. Scientia Silvae Sinicae 58:48−59 (in Chinese)

doi: 10.11707/j.1001-7488.20220905
[9]

Yang ZP, Cao BF, Xie QY, Deng YB, Liu J, et al. 2021. Extracting Phyllostachys edulis stand density using UAV images. Journal of Northeast Forestry University 49:66−71 (in Chinese)

doi: 10.13759/j.cnki.dlxb.2021.07.012
[10]

Lv L, Zhao Y, Li X, Yu J, Song M, et al. 2024. UAV-based intelligent detection of individual trees in Moso bamboo forests with complex canopy structure. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 17:11915−11930

doi: 10.1109/JSTARS.2024.3422390
[11]

Jarron LR, Coops NC, MacKenzie WH, Tompalski P, Dykstra P. 2020. Detection of sub-canopy forest structure using airborne LiDAR. Remote Sensing of Environment 244:111770

doi: 10.1016/j.rse.2020.111770
[12]

Hui Z, Cheng P, Yang B, Zhou G. 2022. Multi-level self-adaptive individual tree detection for coniferous forest using airborne LiDAR. International Journal of Applied Earth Observation and Geoinformation 114:103028

doi: 10.1016/j.jag.2022.103028
[13]

Calders K, Adams J, Armston J, Bartholomeus H, Bauwens S, et al. 2020. Terrestrial laser scanning in forest ecology: expanding the horizon. Remote Sensing of Environment 251:112102

doi: 10.1016/j.rse.2020.112102
[14]

Wu B, Yu B, Wu Q, Huang Y, Chen Z, et al. 2016. Individual tree crown delineation using localized contour tree method and airborne LiDAR data in coniferous forests. International Journal of Applied Earth Observation and Geoinformation 52:82−94

doi: 10.1016/j.jag.2016.06.003
[15]

Liu X, Li G, Li L, Lu D. 2026. Modeling Moso bamboo tree density and aboveground biomass using multi-site UAV-LiDAR data. Remote Sensing 18:115

doi: 10.3390/rs18010115
[16]

Li JN, Li MZ, Quan Y, Wang B, Mo ZK. 2022. Tree species classification using UAV-LiDAR with hyperspectral data. Journal of Northeast Forestry University 50:63−69,116 (in Chinese)

doi: 10.13759/j.cnki.dlxb.2022.06.002
[17]

Xue D, Cheng Y, Shi X, Qin X, Wen P. 2020. Point clouds classification algorithm based on cloth filtering algorithm and improved random forest. Laser & Optoelectronics Progress 57:184−192 (in Chinese)

doi: 10.3788/LOP57.221017
[18]

Liu QW, Li ZY, Chen EX, Pang Y, Wu HG. 2008. Extracting height and crown of individual tree using airborne LiDAR data. Journal of Beijing Forestry University 30:83−89 (in Chinese)

doi: 10.13332/j.1000-1522.2008.06.007
[19]

Hologa R, Scheffczyk K, Dreiser C, Gärtner S. 2021. Tree species classification in a temperate mixed mountain forest landscape using random forest and multiple datasets. Remote Sensing 13:4657

doi: 10.3390/rs13224657
[20]

Li W, Guo Q, Jakubowski MK, Kelly M. 2012. A new method for segmenting individual trees from the lidar point cloud. Photogrammetric Engineering & Remote Sensing 78:75−84

doi: 10.14358/PERS.78.1.75
[21]

Shen W, Li J, Chen YH, Deng L, Peng GX. 2008. Algorithms study of building boundary extraction and normalization based on LiDAR data. Journal of Remote Sensing 12:692−698 (in Chinese)

doi: 10.11834/jrs.20080590
[22]

Wang X, Wang J, Lian Z, Yang N. 2023. Semi-supervised tree species classification for multi-source remote sensing images based on a graph convolutional neural network. Forests 14:1211

doi: 10.3390/f14061211
[23]

Bouvier M, Durrieu S, Fournier RA, Renaud JP. 2015. Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data. Remote Sensing of Environment 156:322−334

doi: 10.1016/j.rse.2014.10.004
[24]

Wang D, Wan B, Liu J, Su Y, Guo Q, et al. 2020. Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots, UAV-LiDAR data and Sentinel-2 imagery. International Journal of Applied Earth Observation and Geoinformation 85:101986

doi: 10.1016/j.jag.2019.101986
[25]

Chen XJ, Feng LY, Zhang YC, Liu QW, Yang ZH, et al. 2022. Inversion of aboveground biomass in the core area of Chongli Winter Olympics based on airborne LiDAR. Scientia Silvae Sinicae 58:35−46 (in Chinese)
[26]

Liu X, Ma Q, Wu X, Hu T, Liu Z, et al. 2022. A novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds. Remote Sensing of Environment 282:113280

doi: 10.1016/j.rse.2022.113280
[27]

Haralick RM, Shanmugam K, Dinstein I. 1973. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics SMC-3:610−621

doi: 10.1109/TSMC.1973.4309314
[28]

Jia CC, Wang CJ, Yang T, Fan BH, He FG. 2019. A 3D point cloud filtering algorithm based on surface variation factor classification. Procedia Computer Science 154:54−61

doi: 10.1016/j.procs.2019.06.010
[29]

Cao RZ, Liu HR, Lin WS. 2024. Semantic segmentation of individual tree point clouds based on multi-scale geometric features. Journal of Northwest Forestry University 39:28−35 (in Chinese)

doi: 10.3969/j.issn.1001-7461.2024.02.04
[30]

Ning Y, Ong MEH, Chakraborty B, Goldstein BA, Ting DSW, et al. 2022. Shapley variable importance cloud for interpretable machine learning. Patterns 3:100452

doi: 10.1016/j.patter.2022.100452
[31]

Congalton RG. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment 37:35−46

doi: 10.1016/0034-4257(91)90048-B
[32]

Yu S, Chen X, Huang X, Chen Y, Hu Z, et al. 2023. Research on the estimation of Chinese fir stand volume based on UAV-LiDAR technology. Forests 14:1252

doi: 10.3390/f14061252
[33]

Lisiewicz M, Kamińska A, Stereńczak K. 2022. Recognition of specified errors of Individual Tree Detection methods based on Canopy Height Model. Remote Sensing Applications: Society and Environment 25:100690

doi: 10.1016/j.rsase.2021.100690
[34]

Dersch S, Schöttl A, Krzystek P, Heurich M. 2023. Towards complete tree crown delineation by instance segmentation with Mask R–CNN and DETR using UAV-based multispectral imagery and lidar data. ISPRS Open Journal of Photogrammetry and Remote Sensing 8:100037

doi: 10.1016/j.ophoto.2023.100037
[35]

Xie YH, Jing XH, Sun Z, Ding ZD, Li R, et al. 2022. Tree crown extraction of UAV remote sensing high canopy density stand based on instance segmentation. Forest Research 35:14−21 (in Chinese)

doi: 10.13275/j.cnki.lykxyj.2022.005.002
[36]

Zhang YW, Zhang C, Wang J, Li HY, Bai MX, et al. 2021. Individual tree crown width extraction and DBH estimation model based on UAV remote sensing. Forest Resources Management 3:67−75 (in Chinese)

doi: 10.13466/j.cnki.lyzygl.2021.03.011
[37]

Hong XC, Liu J, Wang GY. 2022. Soundscape in urban forests. Forests 13:2056

doi: 10.3390/f13122056