[1]赵静,李静,柳钦火. 森林垂直结构参数遥感反演综述[J]. 遥感学报,2013,17(4):697-716.
[2]庄永健,冯仲科,李亚藏,等. 激光雷达技术测树方法原理与应用[J]. 林业资源管理,2016(6):116-119.
[3]郭庆华,苏艳军,胡天宇. 激光雷达森林生态应用:理论、方法及实例[M]. 北京:高等教育出版社,2018.
[4]Hosoi F,Ueno S,Mizuki S,et al. Estimation of leaf area density profiles of Ginkgo trees by the ground and high position measurements using a portable scanning lidar[J]. Eco-Engineering,2014,26(2):45-49.
[5]Béland M,Baldocchi D D,Widlowski J L,et al. On seeing the wood from the leaves and the role of voxel size in determining leaf area distribution of forests with terrestrial LiDAR[J]. Agricultural and Forest Meteorology,2014,184:82-97.
[6]Wu D,Phinn S,Johansen K,et al. Estimating changes in leaf area,leaf area density,and vertical leaf area profile for mango,avocado,and Macadamia tree crowns using terrestrial laser scanning[J]. Remote Sensing,2018,10(11):1750.
[7]赵方博,王佳,高赫,等. 地面激光雷达的单木真实叶面积指数提取[J]. 测绘科学,2019,44(4):81-86,109.
[8]雷蕾,邱春霞,杨浩,等. 基于背包LiDAR的苹果树叶面积密度反演研究[J]. 信息技术与信息化,2019(10):202-205.
[9]Lovell J L,Jupp D L B,Culvenor D S,et al. Using airborne and ground-based ranging lidar to measure canopy structure in Australian forests[J]. Canadian Journal of Remote Sensing,2003,29(5):607-622.
[10]Hosoi F,Nakai Y,Omasa K. Estimation and error analysis of woody canopy leaf area density profiles using 3-D airborne and ground-based scanning lidar remote-sensing techniques[J]. IEEE Transactions on Geoscience & Remote Sensing,2010,48(5):2215-2223.
[11]Dutta D,Wang K,Lee E,et al. Characterizing vegetation canopy structure using airborne remote sensing data[J]. IEEE Transactions on Geoscience & Remote Sensing,2017,55(2):1160-1178.
[12]戴雷宇. 基于地基与机载LiDAR数据的阔叶林LAD反演[D]. 成都:电子科技大学,2019:58-65.
[13]马秦靖,闫秀婧,刘李姣,等. 基于随机森林算法的林区环境因子异常检测[J]. 林业科技通讯,2021(6):49-52.
[14]孙圆,林秀云,熊金鑫,等. 基于地面激光强度校正数据的单木枝叶分离[J]. 中国激光,2021,48(1):56-66.
[15]巴比尔江·迪力夏提,玉米提·哈力克,艾萨迪拉·玉苏甫,等. 应用地基激光雷达数据估算塔里木河下游胡杨叶面积指数[J]. 东北林业大学学报,2020,48(11):46-50.
[16]Jiapaer G,Yi Q X,Yao F,et al. Comparison of non-destructive LAI determination methods and optimization of sampling schemes in an open Populus euphratica ecosystem[J]. Urban Forestry & Urban Greening,2017,26:114-123.
[17]刘盈,吕开云. 基于四参数-ICP的点云配准研究[J]. 江西测绘,2018(2):61-64.
[18]Kaneko S,Kondo T,Miyamoto A. Robust matching of 3D contours using iterative closest point algorithm improved by M-estimation[J]. Pattern Recognition,2003,36(9):2041-2047.
[19]Bergstrm P. Reliable updates of the transformation in the iterative closest point algorithm[J]. Computational Optimization and Applications,2016,63(2):543-557.
[20]Li S H,Dai L Y,Wang H S,et al. Estimating leaf area density of individual trees using the point cloud segmentation of terrestrial LiDAR data and a voxel-based model[J]. Remote Sensing,2017,9(11):1202.
[21]王丽英,王圣,李玉. 强度体元基元下的机载LiDAR 3D滤波[J]. 地球信息科学学报,2019,21(12):1945-1954.
[22]Luo S Z,Wang C,Pan F F,et al. Estimation of wetland vegetation height and leaf area index using airborne laser scanning data[J]. Ecological Indicators,2015,48:550-559.
[23]Zheng G,Moskal L M. Computational-geometry-based retrieval of effective leaf area index using terrestrial laser scanning[J]. Transactions on Geoscience and Remote Sensing,2012,50(10):3958-3969.
[24]Lei L,Qiu C X,Li Z H,et al. Effect of leaf occlusion on leaf area index inversion of maize using UAV-LiDAR data[J]. Remote Sensing,2019,11(9):1067.
[25]Luo S Z,Chen J M,Wang C,et al. Comparative performances of airborne LiDAR height and intensity data for leaf area index estimation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2018,11(1):300-310.
[25]骆社周,王成,张贵宾,等. 机载激光雷达森林叶面积指数反演研究[J]. 地球物理学报,2013,56(5):1467-1475.
[26]刘远,周买春. 遥感反演植被叶面积指数的不确定性来源综述[J]. 江苏农业科学,2017,45(12):12-19.
[27]田罗,屈永华,Lauri K,等. 考虑目标光谱差异的机载离散激光雷达叶面积指数反演[J]. 遥感学报,2020,24(12):1450-1463.
[28]Cifuentes R,van der Zande D,Farifteh J,et al. Effects of voxel size and sampling setup on the estimation of forest canopy gap fraction from terrestrial laser scanning data[J]. Agricultural and Forest Meteorology,2014,194:230-240.
[1]陈洪,赵庆展,李沛婷.无校正点的机载LiDAR农作物点云数据精度评价[J].江苏农业科学,2018,46(12):179.
Chen Hong,et al.Accuracy evaluation of airborne LiDAR crop cloud data without correction points[J].Jiangsu Agricultural Sciences,2018,46(24):179.