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倾斜摄影测量技术提取落叶松人工林地形信息

曾健 张晓丽 周雪梅 尹田

曾健, 张晓丽, 周雪梅, 尹田. 倾斜摄影测量技术提取落叶松人工林地形信息[J]. 北京林业大学学报, 2019, 41(8): 1-12. doi: 10.13332/j.1000-1522.20190126
引用本文: 曾健, 张晓丽, 周雪梅, 尹田. 倾斜摄影测量技术提取落叶松人工林地形信息[J]. 北京林业大学学报, 2019, 41(8): 1-12. doi: 10.13332/j.1000-1522.20190126
Zeng Jian, Zhang Xiaoli, Zhou Xuemei, Yin Tian. Extraction of topographic information of larch plantation by oblique photogrammetry[J]. Journal of Beijing Forestry University, 2019, 41(8): 1-12. doi: 10.13332/j.1000-1522.20190126
Citation: Zeng Jian, Zhang Xiaoli, Zhou Xuemei, Yin Tian. Extraction of topographic information of larch plantation by oblique photogrammetry[J]. Journal of Beijing Forestry University, 2019, 41(8): 1-12. doi: 10.13332/j.1000-1522.20190126

倾斜摄影测量技术提取落叶松人工林地形信息

doi: 10.13332/j.1000-1522.20190126
基金项目: 国家重点研发计划项目(2017YFD0600902)
详细信息
    作者简介:

    曾健。主要研究方向:资源监测与信息化管理。Email:347374718@qq.com 地址:100083 北京市海淀区清华东路35号北京林业大学林学院

    责任作者:

    张晓丽,教授,博士生导师。主要研究方向:定量遥感。Email:zhang-xl@263.net  地址:同上

  • 中图分类号: S771.5;TP701

Extraction of topographic information of larch plantation by oblique photogrammetry

  • 摘要: 目的林下地形是提取单木树高、冠幅等森林参数的必备条件,但由于林区地形起伏较大,传统的测量手段难以获取大范围高精度的森林地区数字地形模型(DTM)。近年来,倾斜摄影测量克服了传统测量技术的缺点,成为获取三维地理信息的新型手段。本文使用无人机倾斜摄影测量技术提取落叶松林下地形,并评测精度与适用性,为后续基于倾斜摄影测量技术提取单木参数的研究提供参考。方法选择内蒙古旺业甸林场内山区典型落叶松幼龄林、中龄林和成熟林林分在落叶季进行无人机飞行,采用Context Capture软件对获取的落叶季倾斜像片进行三维重建,生成林区点云。使用布料模拟滤波、加权线性最小二乘、渐进不规则三角网加密法、渐进形态学滤波算法从点云中提取地面点,并采用3种插值方法插值地面点生成测区完整地形。使用激光雷达DTM作为验证数据评价精度。结果不同算法的地形提取精度与郁闭度相关。在幼龄林区域和中龄林区域,布料模拟滤波提取地面点的精度最高,决定系数(R2)均达到0.999,均方根误差(RMSE)分别为1.61 m和0.47 m;在成熟林区域,渐进三角网滤波效果最好,R2为0.999,RMSE为0.39 m。在不同郁闭度林分选择最优滤波算法基础上,比较不同插值方法生成的数字地形模型(DTM)精度,结果表明:在幼龄林和中龄林,布料模拟滤波点云后经不规则三角网(TIN)插值得到的DTM精度最高,RMSE分别为1.58 m和0.44 m;成熟林分渐进不规则三角网加密滤波后地面点经克里金(Kriging)法插值得到的DTM精度最高,RMSE为0.31 m。结论实验证明,倾斜摄影测量技术可用于落叶松林分地形提取。

     

  • 图  1  研究区位置

    Figure  1.  Location of the study area

    图  2  倾斜摄影测量点云

    a. 幼龄林点云Point clouds of young forest; b. 中龄林点云Point clouds of middle-aged forest; c. 成熟林点云Point clouds of mature forest

    Figure  2.  Oblique photogrammetric point clouds

    图  3  激光雷达得到的数字地形模型

    a. 幼龄林点云DTM of young forest; b. 中龄林点云DTM of middle-aged forest; c. 成熟林点云DTM of mature forest

    Figure  3.  Digital terrain model generated from LiDAR

    图  4  研究技术流程图

    Figure  4.  Research technology flowchart

    图  5  不同滤波算法在不同林分倾斜摄影点云中滤波结果

    a. 幼龄林布料模拟滤波结果; b. 中龄林布料模拟滤波结果; c. 成熟林布料模拟滤波结果; d. 幼龄林加权最小二乘滤波结果; e. 中龄林加权最小二乘滤波结果; f. 成熟林加权最小二乘滤波结果; g. 幼龄林渐进不规则三角网加密滤波结果; h. 中龄林渐进不规则三角网加密滤波结果; i. 成熟林渐进不规则三角网加密滤波结果; j. 幼龄林渐进形态学滤波结果; k. 中龄林渐进形态学滤波结果; l. 成熟林渐进形态学滤波结果
    a. CSF filtering results of young forest; b. CSF filtering results of middle-aged forest; c. CSF filtering results of mature forest; d. WLS filtering results of young forest; e. WLS filtering results of middle-aged forest; f. WLS filtering results of mature forest; g. PTIN filtering results of young forest; h. PTIN filtering results of middle-aged forest; i. PTIN filtering results of mature forest; j. PMF filtering results of young forest; k. PMF filtering results of mature forest; l. PMF filtering results of mature forest

    Figure  5.  Filtering results of different filtering algorithms in oblique photographic point cloud

    图  6  不同滤波方法滤波精度

    a. 幼龄林布料模拟滤波精度; b. 中龄林布料模拟滤波精度; c. 成熟林布料模拟滤波精度; d. 幼龄林加权最小二乘滤波精度; e. 中龄林加权最小二乘滤波精度; f. 成熟林加权最小二乘滤波精度; g. 幼龄林渐进不规则三角网加密滤波精度; h. 中龄林渐进不规则三角网加密滤波精度; i. 成熟林渐进不规则三角网加密滤波精度; j. 幼龄林渐进形态学滤波精度; k. 中龄林渐进形态学滤波精度; l. 成熟林渐进形态学滤波精度
    a. CSF filtering accuracy of young forest;b. CSF filtering accuracy of middle-aged forest; c. CSF filtering accuracy of mature forest; d. WLS filtering accuracy of young forest; e. WLS filtering accuracy of middle-aged forest; f. WLS filtering accuracy of mature forest; g. PTIN filtering accuracy of young forest; h. PTIN filtering accuracy of middle-aged forest; i. PTIN filtering accuracy of mature forest; j. PMF filtering accuracy of young forest; k. PMF filtering accuracy of mature forest; l. PMF filtering accuracy of mature forest

    Figure  6.  Accuracy of filtering methods using different methods in different forest areas

    图  7  倾斜摄影地面点插值后的地形

    a. 幼龄林点云DTM of young forest; b. 中龄林点云DTM of middle-aged forest; c. 成熟林点云DTM of mature forest

    Figure  7.  Optimal DTMs of different forests after interpolation

    表  1  无人机相机及飞行参数

    Table  1.   Unmanned aerial vehicle camera and flight parameters

    传感器及飞行参数
    Sensors and flight parameters
    参数值
    Parameter values
    尺寸 Size35.9 mm × 24 mm
    航向重叠 Course overlap0.8
    旁向重叠 Side overlap0.7
    水平速度 Horizontal velocity/(m·s− 14 ~ 8
    飞行高度 Flight altitude/m200
    地面分辨率 Ground resolution/cm3
    曝光间隔 Exposure interval/s< 4.5
    焦距 Focal length/mm35
    倾斜角度 Oblique angle45°
    单个相机像素 Single camera pixel4 200 × 104
    下载: 导出CSV

    表  2  无人机激光雷达及飞行参数

    Table  2.   Unmanned aerial vehicle LiDAR and flight parameters

    传感器及飞行参数
    Sensors and flight parameters
    参数值
    Parameter values
    激光有效扫描角 Laser effective scanning angle/(°)1 550
    光束发散角 Beam divergence angle mrad0.5
    光斑直径 Spot diameter/cm20
    扫描速度 Scanning speed/(r·s− 1)360
    脉冲发射频率 Pulse emission frequency/Hz112
    幅宽 Camera width/m1 040
    飞行高度 Flight alitutude/m300
    飞行速度 Flight speed/(m·s− 1)4.8
    下载: 导出CSV

    表  3  倾斜摄影点云误差改正值

    Table  3.   Elevation error correction number of point cloud by oblique photogrammetry

    林分类型 Forest type幼龄林 Young forest中龄林 Middle-aged forest成熟林 Mature forest
    高程系统误差 Elevation system error/m1.1781.7315.250
    下载: 导出CSV

    表  4  加权最小线性二乘法参数表

    Table  4.   Parameters in weighted linear least squares prediction filtering algorithm

    林分类型 Forest type  参数g Parameter g参数w Parameter w格网大小 Grid size/m迭代次数 Iterative time
    幼龄林 Young forest0.10.348
    中龄林 Middle-aged forest0.10.368
    成熟林 Mature forest0 0.4810
    下载: 导出CSV

    表  5  成熟林区域不同滤波算法组合插值方法地形精度

    Table  5.   Topographic accuracy of combination interpolation method with different filtering algorithms in mature forest region m

    项目 ItemRMSE
    不规则三角网插值
    Triangulated irregular network (TIN)
    反距离权重插值
    Inverse distance weighted (IDW)
    克里金插值
    kriging (KRG)
    布料模拟滤波
    Cloth simulation filter (CSF)
    0.370.420.42
    加权线性最小二乘
    Weighted linear least squares (WLS)
    0.350.440.43
    渐进不规则三角网
    Progressive triangulated irregular network (PTIN)
    0.320.320.31
    渐进形态学滤波
    Progressive morphological filter (PMF)
    0.340.360.36
    下载: 导出CSV
  • [1] Mcelhinny C, Gibbons P, Brack C, et al. Forest and woodland stand structural complexity: its definition and measurement[J]. Forest Ecology and Management, 2005, 218(1−3): 1−24. doi: 10.1016/j.foreco.2005.08.034
    [2] Keith H, Mackey B G, Lindenmayer D B. Re-evaluation of forest biomass carbon stocks and lessons from the world’s most carbon-dense forests[J]. Proceedings of the National Academy of Sciences of the United States of America, 2009, 106(28): 11635−11640. doi: 10.1073/pnas.0901970106
    [3] 冯静静, 张晓丽, 刘会玲. 基于灰度梯度图像分割的单木树冠提取研究[J]. 北京林业大学学报, 2017, 39(3):16−23.

    Feng J J, Zhang X L, Liu H L. Single tree crown extraction based on gray gradient image segmentation[J]. Journal of Beijing Forestry University, 2017, 39(3): 16−23.
    [4] 李欢, 李明泽, 范文义, 等. 基于机载激光雷达的林隙结构参数提取[J]. 林业科学, 2018, 54(10):98−107. doi: 10.11707/j.1001-7488.20181012

    Li H, Li M Z, Fan W Y, et al. Canopy gap structure parameters extraction based on light detection and ranging (LiDAR)[J]. Scientia Silvae Sinicae, 2018, 54(10): 98−107. doi: 10.11707/j.1001-7488.20181012
    [5] Barnes C, Balzter H, Barrett K, et al. Individual tree crown delineation from airborne laser scanning for diseased larch forest stands[J]. Remote Sensing, 2017, 9(3): 231−251. doi: 10.3390/rs9030231
    [6] Ota T, Ogawa M, Shimizu K, et al. Aboveground biomass estimation using structure from motion approach with aerial photographs in a seasonal tropical forest[J]. Forests, 2015, 6(11): 3882−3898.
    [7] 李增元, 刘清旺, 庞勇. 激光雷达森林参数反演研究进展[J]. 遥感学报, 2016, 20(5):1138−1150.

    Li Z Y, Liu Q W , Pang Y. Review on forest parameters inversion using LiDAR[J]. Journal of Remote Sensing, 2016, 20(5): 1138−1150.
    [8] 刘清旺, 李世明, 李增元, 等. 无人机激光雷达与摄影测量林业应用研究进展[J]. 林业科学, 2017, 53(7):134−148. doi: 10.11707/j.1001-7488.20170714

    Liu Q W, Li S M, Li Z Y, et al. Review on the applications of UAV-Based LiDAR and photogrammetry in forestry[J]. Scientia Silvae Sinicae, 2017, 53(7): 134−148. doi: 10.11707/j.1001-7488.20170714
    [9] 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. doi: 10.1016/j.rse.2013.04.005
    [10] 徐思奇, 黄先锋, 张帆, 等. 倾斜摄影测量技术在大比例尺地形图测绘中的应用[J]. 测绘通报, 2018(2):111−115.

    Xu S Q, Huang X F, Zhang F, et al. Oblique photogrammetric technique applied in surveying and mapping large-scale topographic map[J]. Bulletin of Surveying and Mapping, 2018(2): 111−115.
    [11] 周晓敏, 孟晓林, 张雪萍, 等. 倾斜摄影测量的城市真三维模型构建方法[J]. 测绘科学, 2016, 41(9):159−163.

    Zhou X M, Meng X L, Zhang X P, et al. A method for urban real 3D model building based on oblique photogrammetry[J]. Science of Surveying and Mapping, 2016, 41(9): 159−163.
    [12] 杨国东, 王民水. 倾斜摄影测量技术应用及展望[J]. 测绘与空间地理信息, 2016, 39(1):13−15, 18. doi: 10.3969/j.issn.1672-5867.2016.01.004

    Yang G D, Wang M S. The tilt photographic measuration technique and expectation[J]. Geomatics & Spatial Information Technology, 2016, 39(1): 13−15, 18. doi: 10.3969/j.issn.1672-5867.2016.01.004
    [13] 魏占玉, Ramon A, 何宏林, 等. 基于SfM方法的高密度点云数据生成及精度分析[J]. 地震地质, 2015, 37(2):636−648. doi: 10.3969/j.issn.0253-4967.2015.02.024

    Wei Z Y, Ramon A, Heng H L, et al. Accuracy analysis of terrain point cloud acquired by “structure from motion” using aerial photos[J]. Seismology and Geology, 2015, 37(2): 636−648. doi: 10.3969/j.issn.0253-4967.2015.02.024
    [14] Lin J, Wang M, Ma M, et al. Aboveground tree biomass estimation of sparse subalpine coniferous forest with UAV oblique photography[J]. Remote Sensing, 2018, 10(11): 1849−1868. doi: 10.3390/rs10111849
    [15] 陈崇成, 李旭, 黄洪宇. 基于无人机影像匹配点云的苗圃单木冠层三维分割[J]. 农业机械学报, 2018, 49(2):149−155, 206. doi: 10.6041/j.issn.1000-1298.2018.02.020

    Chen C C, Li X, Huang H Y. 3D Segmentation of individual tree canopy in forest nursery based on drone image-matching point cloud[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(2): 149−155, 206. doi: 10.6041/j.issn.1000-1298.2018.02.020
    [16] Wallace L, Lucieer A, Malenovsky Z, et al. Assessment of forest structure using two UAV techniques: a comparison of airborne laser scanning and structure from motion (SfM) point clouds[J]. Forests, 2016, 7(3): 62−78.
    [17] Sithole G, Vosselman G. Experimental comparison of filter algorithms for bare-earth extraction from airborne laser scanning point clouds[J]. Isprs Journal of Photogrammetry and Remote Sensing, 2004, 59(1−2): 85−101. doi: 10.1016/j.isprsjprs.2004.05.004
    [18] Silva C A, Klauberg C, Hentz M K, et al. Comparing the performance of ground filtering algorithms for terrain modeling in a forest environment using airborne LiDAR data[J/OL]. Floresta e Ambiente, 2018, 25(2): e20160150 [2018−08−12]. http://dx.doi.org/10.1590/2179-8087.015016.
    [19] 汪垚, 张志玉, 倪文俭, 等. 基于机载LiDAR数据的林下地形提取算法比较与组合分析[J]. 北京林业大学学报, 2017, 39(12):25−35.

    Wang Y, Zhang Z Y, Ni W J, et al. Comparison of filter algorithms and combination analysis for DEM extracting based on airborne laser scanning point clouds[J]. Journal of Beijing Forestry University, 2017, 39(12): 25−35.
    [20] 胡永杰, 程朋根, 陈晓勇, 等. 机载激光雷达点云滤波算法分析与比较[J]. 测绘科学技术学报, 2015, 32(1):72−77. doi: 10.3969/j.issn.1673-6338.2015.01.015

    Hu Y J, Cheng P G, Chen X Y, et al. The analysis and comparison of airborne LiDAR point cloud filter algorithms[J]. Journal of Geomatics Science and Technology, 2015, 32(1): 72−77. doi: 10.3969/j.issn.1673-6338.2015.01.015
    [21] White J C, Tompalski P, Coops N C, et al. Comparison of airborne laser scanning and digital stereo imagery for characterizing forest canopy gaps in coastal temperate rainforests[J]. Remote Sensing of Environment, 2018, 208: 1−14. doi: 10.1016/j.rse.2018.02.002
    [22] Zhang W, Qi J, Wan P, et al. An easy-to-use airborne LiDAR data filtering method based on cloth simulation[J]. Remote Sensing, 2016, 8(6): 501−523. doi: 10.3390/rs8060501
    [23] Axelsson P. DEM generation from laser scanner data using adaptive TIN models[J]. International Archives of Photogrammetry and Remote Sensing, 2000, 33(B4/1, PART4): 111−118.
    [24] Vosselman G. Slope based filtering of laser altimetry data[J]. International Archives of Photogrammetry and Remote Sensing, 2000, 33(B3/2, PART3): 935−942.
    [25] Zhang K Q, Chen S Q. A progressive morphological filter for removing nonground measurements from airborne LIDAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 4(41): 872−882.
    [26] 段祝庚, 肖化顺, 袁伟湘. 基于离散点云数据的森林冠层高度模型插值方法[J]. 林业科学, 2016, 52(9):86−94.

    Duan Z G, Xiao H S, Yuan W X. Comparison of interpolation methods of forest canopy height model using discrete point cloud data[J]. Scientia Silvae Sinicae, 2016, 52(9): 86−94.
    [27] 王彬, 孙虎, 徐倩, 等. 基于无人3D摄影技术的雪松(Cedrus deodara)群落高度测定[J]. 生态学报, 2018, 38(10):3524−3533.

    Wang B, Sun H, Xu Q, et al. Height measurement of a cedar (Cedrus deodara) community based on unmanned aerial vehicles (UAV) 3D photogrammetry technology[J]. Acta Ecologica Sinica, 2018, 38(10): 3524−3533.
    [28] Ni W, Sun G, Pang Y, et al. Mapping three-dimensional structures of forest canopy using UAV stereo imagery: evaluating impacts of forward overlaps and image resolutions with LiDAR data as reference[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(10): 3578−3589. doi: 10.1109/JSTARS.2018.2867945
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出版历程
  • 收稿日期:  2019-03-07
  • 修回日期:  2019-04-20
  • 网络出版日期:  2019-06-19
  • 刊出日期:  2019-08-01

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