Identifying LiDAR sample uncertainty on terrain features from DEM simulation

Hone-Jay Chu, Ruey An Chen, Yi-Hsing Tseng, Cheng Kai Wang

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Light detection and ranging (LiDAR) is an effective technology to detect highly dense-point elevation data from terrain surfaces. The density of LiDAR data points significantly affects the level of detail of a high-resolution digital elevation model (DEM). In this study, the conditioned Latin hypercube sampling (cLHS) and simple random sampling (SRS) methods select sufficient LiDAR samples, and sequential Gaussian simulation (SGS) generates multiple DEM realizations that are a set of simulated DEM maps subject to a specified mean, variance, and spatial structure of measured data. Based on DEM realizations, the uncertainty of a spatial feature with a specified elevation is determined. The results suggest that LiDAR sampling patterns, including the size and configuration, affect the spatial distribution of the feature uncertainty, especially when the sample size is small. The accuracy of a DEM is dependent on the choice of sampling techniques for low sampling density data. Unlike random sampling, the cLHS method replicates the distribution of spatial elevation patterns in small sample sizes. Hence, the integrated method can assess the uncertainty of spatial features efficiently in geomorphic monitoring and management.

Original languageEnglish
Pages (from-to)325-333
Number of pages9
JournalGeomorphology
Volume204
DOIs
Publication statusPublished - 2014 Jan 1

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digital elevation model
sampling
simulation
detection
spatial distribution
monitoring
method

All Science Journal Classification (ASJC) codes

  • Earth-Surface Processes

Cite this

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title = "Identifying LiDAR sample uncertainty on terrain features from DEM simulation",
abstract = "Light detection and ranging (LiDAR) is an effective technology to detect highly dense-point elevation data from terrain surfaces. The density of LiDAR data points significantly affects the level of detail of a high-resolution digital elevation model (DEM). In this study, the conditioned Latin hypercube sampling (cLHS) and simple random sampling (SRS) methods select sufficient LiDAR samples, and sequential Gaussian simulation (SGS) generates multiple DEM realizations that are a set of simulated DEM maps subject to a specified mean, variance, and spatial structure of measured data. Based on DEM realizations, the uncertainty of a spatial feature with a specified elevation is determined. The results suggest that LiDAR sampling patterns, including the size and configuration, affect the spatial distribution of the feature uncertainty, especially when the sample size is small. The accuracy of a DEM is dependent on the choice of sampling techniques for low sampling density data. Unlike random sampling, the cLHS method replicates the distribution of spatial elevation patterns in small sample sizes. Hence, the integrated method can assess the uncertainty of spatial features efficiently in geomorphic monitoring and management.",
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Identifying LiDAR sample uncertainty on terrain features from DEM simulation. / Chu, Hone-Jay; Chen, Ruey An; Tseng, Yi-Hsing; Wang, Cheng Kai.

In: Geomorphology, Vol. 204, 01.01.2014, p. 325-333.

Research output: Contribution to journalArticle

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AU - Chen, Ruey An

AU - Tseng, Yi-Hsing

AU - Wang, Cheng Kai

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AB - Light detection and ranging (LiDAR) is an effective technology to detect highly dense-point elevation data from terrain surfaces. The density of LiDAR data points significantly affects the level of detail of a high-resolution digital elevation model (DEM). In this study, the conditioned Latin hypercube sampling (cLHS) and simple random sampling (SRS) methods select sufficient LiDAR samples, and sequential Gaussian simulation (SGS) generates multiple DEM realizations that are a set of simulated DEM maps subject to a specified mean, variance, and spatial structure of measured data. Based on DEM realizations, the uncertainty of a spatial feature with a specified elevation is determined. The results suggest that LiDAR sampling patterns, including the size and configuration, affect the spatial distribution of the feature uncertainty, especially when the sample size is small. The accuracy of a DEM is dependent on the choice of sampling techniques for low sampling density data. Unlike random sampling, the cLHS method replicates the distribution of spatial elevation patterns in small sample sizes. Hence, the integrated method can assess the uncertainty of spatial features efficiently in geomorphic monitoring and management.

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