针对现有机载激光雷达（light detection and ranging，lidar）数据单木分割算法在密集林区中探测精度较低的问题，结合林木冠层空间结构分层的特点，提出一种从机载点云数据直接分离单木的方法。首先，对原始点云数据进行去噪、滤波、高程归一化；然后基于冠层高度模型（canopy height model，chm）计算局部最大值以确定冠层表面的明显树顶，以此作为单木位置的先验知识，继而采用归一化割（normalized cut，Ncut）方法实现冠层的初始分割；最后，以全局最大值代替局部最大值，并将冠层形状、冠层最小点数作为约束条件，再次利用Ncut方法完成对漏检单木的探测，进而实现单木的精确探测。实验结果表明，针对密集林区的单木分割，本方法有效地减少了漏识单木，整体精度达90%以上，将有助于单木三维结构定量描述及参数反演。
The accuracy of individual tree segmentation using airborne LiDAR (light detection and ranging) data is generally low for dense forests. A new method, which considers the vertical stratification of forest canopy,is proposed in this study to detect individual tree with high accuracy using airborne LiDAR data. First, several data preprocessing steps are conducted, including noise removal, point cloud filtering, and elevation normalization. Secondly, the initial canopy segmentation is achieved by the normalized cut (Ncut) segmentation with a prior knowledge of individual tree position derived from the local maximum of the canopy height model (CHM). Finally, the Ncut method is used to reduce the leakage rate of individual tree detection by setting the global maximum of CHM as the individual tree position and considering the shape and the minimum point number of the canopy. The results show that the proposed method effectively improves the accuracy of tree detection,which contributes to the quantitative description and parameter inversion in individual tree scale.
[1] 李亮, 王成, 李世华, 等. 基于机载LiDAR数据的建筑屋顶点云提取方法[J]. 中国科学院大学学报, 2016, 33(4):537-541.
[2] 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.
[3] 邢艳秋, 尤号田, 霍达, 等. 小光斑激光雷达数据估测森林树高研究进展[J]. 世界林业研究, 2014, 27(2):29-34.
[4] 李旺, 牛铮, 王成, 等. 机载LiDAR数据估算样地和单木尺度森林地上生物量[J]. 遥感学报, 2015, 19(4):669-679.
[5] 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.
[6] 甄贞, 李响, 修思玉, 等. 基于标记控制区域生长法的单木树冠提取[J]. 东北林业大学学报, 2016, 44(10):22-29.
[7] Chen Q, Baldocchi D, Gong P, et al. Isolating individual trees in a Savanna Woodland using small footprint LiDAR data[J]. Photogrammetric Engineering & Remote Sensing, 2006, 72(8):923-932.
[8] Zhen Z, Quackenbush L, Zhang L. Trends in automatic individual tree crown detection and delineation:evolution of LiDAR data[J]. Remote Sensing, 2016, 8(4):333.
[9] Zhang C, Zhou Y, Qiu F. Individual tree segmentation from LiDAR point clouds for urban forest inventory[J]. Remote Sensing, 2015, 7(6):7892-7913.
[10] Li W, Guo Q, 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.
[11] Kandare K, Dalponte M, Gianelle D, et al. A new procedure for identifying single trees in understory layer using discrete LiDAR data[C]//Geoscience and Remote Sensing Symposium. IEEE, 2014:1357-1360.
[12] Wang Y, Weinacker H, Koch B. A LiDAR point cloud based procedure for vertical canopy structure analysis and 3d single tree modelling in forest[J]. Sensors, 2008, 8(6):3938-3951.
[13] 朱俊锋, 胡翔云, 张祖勋, 等. 多尺度点云噪声检测的密度分析法[J]. 测绘学报, 2015, 44(3):282-291.
[14] 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.
[15] Lee H, Slatton K C, Roth B E, et al. Adaptive clustering of airborne LiDAR data to segment individual tree crowns in managed pine forests[J]. International Journal of Remote Sensing, 2010, 31(1):117-139.
[16] Benarie J R, Hay G J, Powers R P, et al. Development of a pit filling algorithm for LiDAR canopy height models.[J]. Computers & Geosciences, 2009, 35(9):1940-1949.
[17] Shi J, Malik J. Normalised cuts and image segmentation[J]. IEEE Transactions Pattern Analysis & Machine Intelligence, 2000, 22(8):888-905.
[18] Reitberger J, Schnörr C, Krzystek P, et al. 3D segmentation of single trees exploiting full waveform LiDAR data[J]. ISPRS Journal of Photogrammetry & Remote Sensing, 2009, 64(6):561-574.
[19] Yao W, Krzystek P, Heurich M. Enhanced detection of 3D individual trees in forested areas using airborne full-waveform LiDAR data by combining normalized cuts with spatial density clustering[J]. Biopolymers, 2013:349-354.
[20] Guo Q, Kelly M, Gong P, et al. An object-based classification approach in mapping tree mortality using high spatial resolution imagery[J]. Mapping Sciences & Remote Sensing, 2007, 44(1):24-47.
