Modelling vertical error in LiDAR-derived digital elevation models
Modelling vertical error in LiDAR-derived digital elevation models
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DOI：
10.1016/j.isprsjprs.2009.09.003
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发表时间：
2010-01-01
影响因子：
12.7
通讯作者：
Perez, Jose L.
中科院分区：
文献类型：
--
作者：
Aguilar, Fernando J.;Mills, Jon P.;Perez, Jose L.
我们开发了一种混合理论-经验模型，用于对激光雷达衍生的非开放地形数字高程模型 (DEM) 中的误差进行建模。理论部分旨在对样本数据误差 (SDE) 的传播进行建模，即开放地形中地面采样点的光探测和测距 (LiDAR) 数据捕获向插值点的传播。用于填充间隙的插值方法可能会产生不可忽略的误差，称为网格误差。在这种情况下，使用反距离加权（IDW）方法并在五个最近邻居的局部支持下执行插值，尽管也可以使用其他插值方法。经验部分指的是所谓的“信息损失”。这是纯粹由于仅从离散数量的点对连续地形表面进行建模而产生的误差加上插值过程产生的误差。 SDE 必须预先根据位于开阔地形中的适当数量的检查点计算，并假设 LiDAR 点密度足够高以忽略网格误差。为了进行模型校准，通过立体摄影测量方法获取了西班牙东南部阿尔梅里亚省周围不同地区的 29 个研究地点的数据，大小为 200 x 20)。所开发的方法已针对两个不同的激光雷达数据集进行了验证。使用的第一个数据集是在英国布里斯托尔地区进行的地形测量局 (OS) 激光雷达调查。第二个数据集是位于西班牙阿尔梅里亚省南部加多尔山脉的地区。在校准阶段，地形坡度和采样密度都被纳入经验部分，导致预测数据和观测数据之间非常一致（R(2) = 0.9856；p < 0.001）。在验证中，布里斯托尔观察到与不同激光雷达点密度相对应的垂直误差，为预测误差提供了相当好的拟合。在加多尔山脉数据集的更崎岖的形态中取得了更好的结果。本文提出的研究结果可用作在非开放地形中与 LiDAR 测量相关的项目（例如涉及林业应用的项目）中选择适当操作参数（本质上是点密度，以优化测量成本）的指南。 (C) 2009 年国际摄影测量与遥感协会 (ISPRS)。由 Elsevier B.V. 出版。保留所有权利。
A hybrid theoretical-empirical model has been developed for modelling the error in LiDAR-derived digital elevation models (DEMs) of non-open terrain. The theoretical component seeks to model the propagation of the sample data error (SDE), i.e. the error from light detection and ranging (LiDAR) data capture of ground sampled points in open terrain, towards interpolated points. The interpolation methods used for infilling gaps may produce a non-negligible error that is referred to as gridding error. in this case, interpolation is performed using an inverse distance weighting (IDW) method with the local support of the five closest neighbours, although it would be possible to utilize other interpolation methods. The empirical component refers to what is known as "information loss". This is the error purely due to modelling the continuous terrain surface from only a discrete number of points plus the error arising from the interpolation process. The SDE must be previously calculated from a suitable number of check points located in open terrain and assumes that the LiDAR point density was sufficiently high to neglect the gridding error. For model calibration, data for 29 study sites, 200 x 20) in in size, belonging to different areas around Almeria province, south-east Spain, were acquired by means of stereo photogrammetric methods. The developed methodology was validated against two different LiDAR datasets. The first dataset used was an Ordnance Survey (OS) LiDAR survey carried out over a region of Bristol in the UK. The second dataset was an area located at Gador mountain range, south of Almeria province, Spain. Both terrain slope and sampling density were incorporated in the empirical component through the calibration phase, resulting in a very good agreement between predicted and observed data (R(2) = 0.9856; p < 0.001). In validation, Bristol observed vertical errors, corresponding to different LiDAR point densities, offered a reasonably good fit to the predicted errors. Even better results were achieved in the more rugged morphology of the Gador mountain range dataset. The findings presented in this article could be used as a guide for the selection of appropriate operational parameters (essentially point density in order to optimize survey cost), in projects related to LiDAR survey in non-open terrain, for instance those projects dealing with forestry applications. (C) 2009 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.