欢迎访问林草资源研究

Forest and Grassland Resources Research >

2019 , Vol. 0 >Issue 4: 107 - 116

DOI: https://doi.org/10.13466/j.cnki.lyzygl.2019.04.016

Tree Vertex and Height Extraction Based on UAV Imagery and Analysis on its Influencing Factors

  • Jiangjun LIU ,
  • Haili GAO ,
  • Luming FANG ,
  • Xinyu ZHENG ,
  • Guangyu JIANG
Expand
  • 1. School of Information Engineering,Zhejiang A&F University,Hangzhou,Zhejiang 311300,China
    2. Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology, Hangzhou, Zhejiang 311300, China
    3. Hangzhou Public Welfare Forests and State-owned Forest Farm Mamagement Bureau, Hangzhou, Zhejiang 311300, China
    4. Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment,Hangzhou, Zhejiang 311300, China

Received date: 2019-06-27

  Revised date: 2019-07-19

  Online published: 2020-10-20

Fold

Abstract

In this paper,the feasibility of using local maximum algorithm to extract tree vertices and tree height based on the canopy height model(CHM) generated by UAV imagery was discussed.In addition,the effect of resolution and window size on tree vertex extraction was also discussed.In this study,a dense conifer-broadleaf forest is used as sample plot.The SfM(Structure from Motion) algorithm was combined with the UAV images to reconstruct the research area in three dimensions,and a series of three-dimensional data such as point cloud,digital surface model(DSM),digital elevation model(DEM) and CHM were generated.Then,for different resolutions of CHM,different smoothing window size and moving window size combinations were used to extract the tree vertices and the accuracies of results were evaluated.When the CHM resolution is 0.4m,the size of smoothing window is 3×3 pixels,and the size of moving window is 3×3 pixels,the extraction accuracy of tree vertex is the highest,and the F-Measure is 77.08%.The extracted tree heights corresponding to the 37 tree vertices extracted based on the combination was compared with the tree heights measured in the field,and the R 2,RMSE,rRMSE is 0.9669,1.4114m,10.69% respectively.The results showed that UAV imagery can be used to extract tree vertices and tree heights of complex forests.Extracting the tree vertices based on the local maximum algorithm needs to determine the resolution of the CHM,the smoothing window size and the moving window size according to the actual situation to obtain the best extraction result.

Key words： unmanned aerial vehicle; tree vertex; tree height; resolution; window size

Cite this article

Jiangjun LIU , Haili GAO , Luming FANG , Xinyu ZHENG , Guangyu JIANG . Tree Vertex and Height Extraction Based on UAV Imagery and Analysis on its Influencing Factors[J]. Forest and Grassland Resources Research, 2019 , 0(4) : 107 -116 . DOI: 10.13466/j.cnki.lyzygl.2019.04.016

References

[1] Hyypp? J, Hyypp? H, Leckie D , et al. Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests[J]. International Journal of Remote Sensing, 2008,29(5):1339-1366.
[2] Wang C . Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests[J]. Forest Ecology and Management, 2006,222(1-3):9-16.
[3] 侯红亚, 王立海, 徐华东 , 等. 目测法估测树高的误差分析[J]. 森林工程, 2012,28(2):6-8.
[4] 冯仲科, 隋宏大, 邓向瑞 , 等. 三角高程法树高测量与精度分析[J]. 北京林业大学学报, 2007,29(S2):31-35.
[5] 谢鸿宇, 温志庆, 钟世锦 , 等. 无棱镜全站仪测量树高及树冠的方法研究[J]. 中南林业科技大学学报, 2011,31(11):53-58.
[6] Popescu S C, Wynne R H, Nelson R F . Estimating plot-level tree heights with lidar:local filtering with a canopy-height based variable window size[J]. Computers and Electronics in Agriculture, 2002,37(1):71-95.
[7] Dandois J P, Ellis E C . High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision[J]. Remote Sensing of Environment, 2013,136:259-276.
[8] 李德仁, 李明 . 无人机遥感系统的研究进展与应用前景[J]. 武汉大学学报:信息科学版, 2014,39(5):505-513.
[9] 李响, 甄贞, 赵颖慧 . 基于局域最大值法单木位置探测的适宜模型研究[J]. 北京林业大学学报, 2015,37(3):27-33.
[10] 耿林, 李明泽, 范文义 , 等. 基于机载LiDAR的单木结构参数及林分有效冠的提取[J]. 林业科学, 2018,54(7):62-72
[11] Birdal A C, Avdan U, Türk T . Estimating tree heights with images from an unmanned aerial vehicle[J]. Geomatics,Natural Hazards and Risk, 2017,8(2):1144-1156.
[12] Wulder M, Niemann O, Goodenough D . Local maximum filtering for the extraction of tree location and basal area from high spatial resolution imagery[J]. Remote Sensing of Environment, 2000,73(1):103-114.
[13] Wang Le, Gong Peng, Biging G S . Individual tree-crown delineation and treetop detection in high-spatial-resolution aerial imagery[J]. Photogrammetric Engineering & Remote Sensing, 2004,70(3):351-358.
[14] Khosravipour A, Skidmore A K, Isenburg M , et al. Generating pit-free canopy height models from airborne Lidar[J]. Photogrammetric Engineering & Remote Sensing, 2014,80(9):863-872.
[15] Silva C A, Hudak A T, Vierling L A , et al. Imputation of individual longleaf pine(Pinus palustris Mill.) tree attributes from field and LiDAR data[J]. Canadian Journal of Remote Sensing, 2016,42(5):554-573.
[16] Snavely N, Seitz S M, Szeliski R . Modeling the world from internet photo collections[J]. International Journal of Computer Vision, 2008,80(2):189-210.
[17] Li Wenkai, Guo Qinghua, Jakubowski M K , et al. A new method for segmenting individual trees from the Lidar point cloud[J]. Photogrammetric Engineering & Remote Sensing, 2012,78(1):75-84.
[18] Vega C, Hamrouni A, El Mokhtari S , et al. PTrees:A point-based approach to forest tree extraction from lidar data[J]. International Journal of Applied Earth Observation and Geoinformation, 2014,33:98-108.
[19] L?hivaara T, Sepp?nen A, Kaipio J P , et al. Bayesian approach to tree detection based on airborne laser scanning data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014,52(5):2690-2699.
[20] Maltamo M, Mustonen K, Hyypp? J , et al. accuracy of estimating individual tree variables with airborne laser scanning in a boreal nature reserve[J]. Revue Canadienne De Recherche Forestière, 2004,34(9):1791-1801.
[21] Midhun M, Carlos S, Carine K , et al. Individual tree detection from unmanned aerial vehicle(UAV) derived canopy height model in an open canopy mixed conifer forest[J]. Forests, 2017,8(9):340.
[22] Jonathan L, Marc P D, Stéphanie B , et al. A photogrammetric workflow for the creation of a forest canopy height model from small unmanned aerial system imagery[J]. Forests, 2013,4(4):922-944.
[23] Panagiotidis D, Abdollahnejad A, Surovy P , et al. Determining tree height and crown diameter from high-resolution UAV imagery[J]. International Journal of Remote Sensing, 2017,38(8-10):2392-2410.
Options
Outlines

/

〈 〉