Effects of Topographic Variability and Lidar Sampling Density on Several DEM Interpolation Methods
Effects of Topographic Variability and Lidar Sampling Density on Several DEM Interpolation Methods
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DOI：
10.14358/pers.76.6.701
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发表时间：
2010-06-01
影响因子：
1.3
通讯作者：
Alvarez, Otto
中科院分区：
文献类型：
--
作者：
Guo, Qinghua;Li, Wenkai;Alvarez, Otto
本研究的目的是量化地形变异性（测量高程变异系数，CV）和激光雷达（光探测和测距）采样密度的DEM（数字高程模型）的精度从几种插值方法在不同的空间分辨率的影响。插值方法包括自然邻域（NN）、反距离加权（IDW）、不规则三角网（TIN）、样条、普通克里金法（OK）和泛克里金法（UK）。本研究的独特之处在于，三个影响因素（CV，采样密度和空间分辨率）的综合影响的综合评价激光雷达派生DEM精度进行了使用不同的插值方法。结果表明，简单的插值方法，如IDW，NN和TIN，是更有效地从激光雷达数据生成DEM，但克里金法为基础的方法，如OK和UK，更可靠，如果精度是最重要的考虑因素。此外，空间分辨率在从激光雷达数据生成DEM时也起着重要作用。我们的研究结果可以用来指导选择合适的激光雷达插值方法生成DEM的分辨率，采样密度和地形变化。
This study aims to quantify the effects of topographic variability (measured by coefficient variation of elevation, CV) and lidar (Light Detection and Ranging) sampling density on the DEM (Digital Elevation Model) accuracy derived from several interpolation methods at different spatial resolutions. Interpolation methods include natural neighbor (NN), inverse distance weighted (IDW), triangulated irregular network (TIN), spline, ordinary kriging (OK), and universal kriging (UK). This study is unique in that a comprehensive evaluation of the combined effects of three influencing factors (CV, sampling density, and spatial resolution) on lidar-derived DEM accuracy is carried out using different interpolation methods. Results indicate that simple interpolation methods, such as IDW, NN, and TIN, are more efficient at generating DEMs from lidar data, but kriging-based methods, such as OK and UK, are more reliable if accuracy is the most important consideration. Moreover, spatial resolution also plays an important role when generating DEMs from lidar data. Our results could be used to guide the choice of appropriate lidar interpolation methods for DEM generation given the resolution, sampling density, and topographic variability.