| 机载LiDAR点云密度和插值方法对DEM及地表粗糙度精度影响分析 |
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| 引用本文： | 贝祎轩,陈传法,王鑫,孙延宁,何青鑫,李坤禹.机载LiDAR点云密度和插值方法对DEM及地表粗糙度精度影响分析[J].地球信息科学,2023,25(2):265-276. |
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| 作者姓名： | 贝祎轩 陈传法 王鑫 孙延宁 何青鑫 李坤禹 |
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| 作者单位： | 1.山东科技大学测绘与空间信息学院，青岛 2665902.山东省水利科学研究院，济南 250101 |
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| 基金项目： | 国家自然科学基金(42271438);山东省自然科学基金项目(ZR2020YQ26);山东省自然科学基金项目(ZR2019MD007);山东省高等学校青创科技支持计划(2019KJH007) |
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| 摘 要： | 机载LiDAR点云是获取高质量数字高程模型(Digital Elevation Model, DEM)的主要数据源,而地表粗糙度作为DEM的主要派生产品,在地学研究中发挥了重要作用,但点云密度和插值方法对DEM及地表粗糙度精度影响程度并没有明确结论。为此,本文利用不同地形条件下的林区机载LiDAR点云为实验对象,将原始点云随机缩减为不同的采样密度,利用5种常用插值方法(克里金(Ordinary Kriging, OK),径向基函数(Radial Basis Function, RBF),不规则三角网(Triangulated Irregular Network, TIN),自然邻域(Natural Neighbor, NN)和反距离加权(Inverse Distance Weighting, IDW))构建各个测区不同采样密度条件下的DEM,并通过空间特征和统计特征两方面对DEM及其地表粗糙度精度分析。结果表明:(1) DEM插值算法的精度随点云密度缩减而降低,且数据量缩减至原始数据量的30%后,不同算法精度区别较为明显,其中,RBF和OK精度最优,IDW精度最低;(2) DEM误差与...
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| 关 键 词： | 数字高程模型 空间插值 机载激光雷达 精度分析 地表粗糙度 点云密度 点云抽稀 |
| 收稿时间： | 2022-07-07 |
Effects of Airborne LiDAR Point Cloud Density and Interpolation Methods on the Accuracy of DEM and Surface Roughness |
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| BEI Yixuan,CHEN Chuanfa,WANG Xin,SUN Yanning,HE Qingxin,LI Kunyu.Effects of Airborne LiDAR Point Cloud Density and Interpolation Methods on the Accuracy of DEM and Surface Roughness[J].Geo-information Science,2023,25(2):265-276. |
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| Authors: | BEI Yixuan CHEN Chuanfa WANG Xin SUN Yanning HE Qingxin LI Kunyu |
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| Institution: | 1. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China2. Water Resources research Institute of shandong province, Jinan 250101, China |
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| Abstract: | Airborne LiDAR point clouds are the main data source for obtaining high-quality Digital Elevation Model (DEM), and surface roughness, as the main derivative of DEM, plays an important role in geoscience research. However, there is no clear conclusion about the influence of the airborne LiDAR point cloud data density and interpolation methods on the accuracy of DEMs and surface roughness. Thus, this paper evaluates the performance of five classical interpolation methods including Ordinary Kriging (OK), Radial Basis Function (RBF), Triangulated Irregular Network (TIN), Natural Neighbor (NN), and Inverse Distance Weighting (IDW) for quantifying surface roughness using different LiDAR data density (90%, 70%, 50%, 30%, and 10% of the original data) in three study sites with different terrain characteristics. The results show that: (1) the accuracy of each DEM interpolation algorithm decreases with the decrease of point cloud density, and when the data amount is reduced to 30% of the original data amount, the accuracy of different algorithms is obviously different. Among them, RBF and OK have the highest accuracy, while IDW has the lowest accuracy; (2) the DEM error is positively correlated with surface roughness. With the decrease of data density, the correlation coefficients between DEM error and roughness obtained by OK, RBF, and IDW methods all decrease, and the correlation coefficients between DEM error and roughness obtained by TIN and NN decrease first and then increase at density of 30%; (3) The surface roughness error extracted from DEM based on all interpolation methods increases with the decrease of data density, and the accuracy of IDW derived roughness is the highest when the data density is 90% and 70%. When the data density is reduced by 50%, RBF can capture terrain changes more accurately. |
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| Keywords: | digital elevation model spatial interpolation airborne LiDAR accuracy analysis terrain roughness data density data simplification |
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