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

doi:10.12082/dqxxkx.2023.220486

地球信息科学学报 ›› 2023, Vol. 25 ›› Issue (2): 265-276.doi: 10.12082/dqxxkx.2023.220486

• 地球信息科学理论与方法 • 上一篇下一篇

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

贝祎轩¹(), 陈传法¹^,^*(), 王鑫¹, 孙延宁², 何青鑫¹, 李坤禹¹

1.山东科技大学测绘与空间信息学院，青岛 266590
2.山东省水利科学研究院，济南 250101

收稿日期:2022-07-07 修回日期:2022-08-24 出版日期:2023-02-25 发布日期:2023-04-19
通讯作者: *陈传法（1982— ），男，山东淄博人，教授，主要从事数字地形建模、激光雷达点云数据处理等研究。 E-mail: chencf@Ireis.ac.cn
作者简介:贝祎轩（1997— ），女，山东淄博人，硕士生，主要从事数字地形建模研究。E-mail: TGK_Conch@163.com
基金资助:
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)

Effects of Airborne LiDAR Point Cloud Density and Interpolation Methods on the Accuracy of DEM and Surface Roughness

BEI Yixuan¹(), CHEN Chuanfa¹^,^*(), WANG Xin¹, SUN Yanning², HE Qingxin¹, LI Kunyu¹

1. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
2. Water Resources research Institute of shandong province, Jinan 250101, China

Received:2022-07-07 Revised:2022-08-24 Online:2023-02-25 Published:2023-04-19
Contact: CHEN Chuanfa
Supported by:
National Natural Science Foundation of China(42271438);Shandong Provincial Natural Science Foundation, China(ZR2020YQ26);Shandong Provincial Natural Science Foundation, China(ZR2019MD007);A Project of Shandong Province Higher Educational Youth Innovation Science and Technology Program(2019KJH007)

摘要/Abstract

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误差与地表粗糙度存在正相关，随数据密度降低，OK、RBF、IDW所得粗糙度与DEM误差的相关系数均降低，与TIN和NN的相关系数先降低后在30%处升高；③ 从插值生成的DEM中提取地表粗糙度，其误差随数据密度缩减而增大，其中IDW所得粗糙度的精度在密度为90%和70%时最高，而数据密度缩减至50%后，RBF能够更准确地捕捉到地形变化。

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

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.

Key words: digital elevation model, spatial interpolation, airborne LiDAR, accuracy analysis, terrain roughness, data density, data simplification

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]. 地球信息科学学报, 2023, 25(2): 265-276.DOI:10.12082/dqxxkx.2023.220486

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]. Journal of Geo-information Science, 2023, 25(2): 265-276.DOI:10.12082/dqxxkx.2023.220486

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试验区域	地形特征	点云密度/（pts/m²）	平均坡度/°	平均高程/m	高程范围/m
Samp1	覆盖高大植被的斜坡	1.31	29.85	340.0	239.6~451.4
Samp2	地形复杂的峰鞘沟谷	0.59	30.16	102.2	15.3~185.9
Samp3	陡坡	2.57	30.65	376.5	253.6~489.6

试验区域	位置	采集时间	扫描设备	飞行高度/m	扫描角度/°	重叠度/%
Samp1	加利福尼亚州	2017年10月	Optech Titan	650~1250	±20	>50
Samp2	奥克兰	2016—2018年	Optech Galaxy	1975	±34	30
Samp3	俄勒冈州	2015年6月	Optech Gemini	900	±15	50

试验区域	平均点间距	DEM分辨率
Samp1	0.88	0.45
Samp2	1.30	0.65
Samp3	0.62	0.30

试验区域	点云数量/个
试验区域	90%	70%	50%	30%	10%
Samp1	294 512	206 158	147 256	88 354	29 451
Samp2	132 078	92 455	66 039	39 623	13 208
Samp3	579 075	405 353	289 538	173 723	57 908

试验区域	数据密度/%	RMSE
试验区域	数据密度/%	OK	RBF	TIN	NN	IDW
Samp1	90	0.168	0.167	0.168	0.172	0.154
	70	0.169	0.167	0.169	0.178	0.158
	50	0.169	0.169	0.173	0.189	0.169
	30	0.178	0.175	0.198	0.208	0.188
	10	0.197	0.190	0.258	0.258	0.221
Samp2	90	0.243	0.241	0.245	0.247	0.231
	70	0.245	0.242	0.246	0.248	0.237
	50	0.250	0.247	0.250	0.255	0.251
	30	0.258	0.254	0.269	0.311	0.278
	10	0.280	0.271	0.664	0.554	0.323
Samp3	90	0.133	0.133	0.136	0.143	0.130
	70	0.134	0.134	0.134	0.144	0.133
	50	0.136	0.136	0.138	0.136	0.137
	30	0.141	0.139	0.149	0.150	0.148
	10	0.152	0.148	0.202	0.211	0.167

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

Effects of Airborne LiDAR Point Cloud Density and Interpolation Methods on the Accuracy of DEM and Surface Roughness

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