LIDAR density and linear interpolator effects on elevation estimates

Anderson, Eric S.; Thompson, Jim; Austin, Robert

doi:10.1080/01431160500181671

Cited by 97 publications

(70 citation statements)

References 23 publications

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“…Digital Elevation Model (DEM) plays an important role in a wide range of applications including terrain modeling, landscape modeling, and hydrological modeling (Liu and Zhang, 2008) which makes the quality of the DEM to be crucial for different spatial modeling techniques (Anderson et al, 2005, Habib et al 2005. Different factors including the density and distribution of the source data, the interpolation algorithm and the grid resolution affect the accuracy of the DEMs (Watt et al 2013).…”

Section: Introductionmentioning

confidence: 99%

EVALUATING THE EFFECTS OF REDUCTIONS IN LiDAR DATA ON THE VISUAL AND STATISTICAL CHARACTERISTICS OF THE CREATED DIGITAL ELEVATION MODELS

Asal

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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With continuous developments in LiDAR technologies high point cloud densities have been attainable but accompanied by challenges for processing big volumes of data. Reductions in high point cloud densities are expected to lower data acquisition and data processing costs; however this could affect the characteristics of the generated Digital Elevation Models (DEMs). This research aimed to evaluate the effects of reductions in airborne LiDAR point cloud data densities on the visual and statistical characteristics of the generated DEMs. DEMs have been created from a dataset which constitutes last returns of raw LiDAR data that was acquired at bare lands for Gilmer County, USA between March and April 2004, where qualitative and quantitative testing analyses have been performed. Visual analysis has shown that the DEM can withstand a considerable degree of quality with reduced densities down to 0.128pts/m 2 (47% of the data remaining), however degradations in the DEM visual characteristics appeared in coarser tones and rougher textures have occurred with more reductions. Additionally, the statistical analysis has indicated that the standard deviations of the DEM elevations have decreased by only 22% of the total decrease with data density reductions down to 0.101pts/m 2 (37% of the data remaining) while greater rate of decreasing in the standard deviations has occurred with more reductions referring to greater rate of surface smoothing and elevation approximating. Furthermore, the accuracy analysis testing has given that the DEM accuracy has degraded by only 4.83% of the total degradations with data density reductions down to 0.128pts/m 2 , however great deteriorations in the DEM accuracy have occurred with more data reductions. Finally, it is recommended that LiDAR data can withstand point density reductions down to 0.128pts/m 2 (about 50% of the data) without big deteriorations in the visual and statistical characteristics of the generated DEMs.

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Section: Introductionmentioning

confidence: 99%

EVALUATING THE EFFECTS OF REDUCTIONS IN LiDAR DATA ON THE VISUAL AND STATISTICAL CHARACTERISTICS OF THE CREATED DIGITAL ELEVATION MODELS

Asal

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…LiDAR data with high density point clouds provide more detailed information for ground features; however, the massive information sometimes create problems for data storage and feature extraction, which is similar to the problem with high-resolution photogrammetry [73,102,103]. Hence, some LiDAR applications require data reduction to reduce data size while keeping comparable accuracy [102,103].…”

Section: Density and Data Reductionmentioning

confidence: 99%

“…Examples of popular interpolation methods include Inverse Distance Weighting [73], AMLE [74], Kriging [73,74], and hybrid methods that combine linear and non-linear interpolation [75]. Studies prove that complicated interpolation methods may not generate better results than simple ones [73].…”

Section: Generate Demmentioning

confidence: 99%

“…The authors found these evaluations useful to determine the minimum point density given selected DEM resolution. Another experiment [73] examined the influence of point density to linear interpolation methods such as inverse distance and kriging interpolation. The results proved that it was possible to reduce point density but maintain a satisfactory DEM estimation, and complicated interpolation methods do not necessary produce superior results.…”

Section: Density and Data Reductionmentioning

confidence: 99%

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A multi-directional ground filtering algorithm for airborne LIDAR

Meng

Wang

Silván-Cárdenas

et al. 2009

ISPRS Journal of Photogrammetry and Remote Sensing

181

121

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This paper reviews LiDAR ground filtering algorithms used in the process of creating Digital Elevation Models. We discuss critical issues for the development and application of LiDAR ground filtering algorithms, including filtering procedures for different feature types, and criteria for study site selection, accuracy assessment, and algorithm classification. This review highlights three feature types for which current ground filtering algorithms are suboptimal, and which can be improved upon in future studies: surfaces with rough terrain or discontinuous slope, dense forest areas that laser beams cannot penetrate, and regions with low vegetation that is often ignored by ground filters.

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“…In recent years investigations have focused on the influence of environmental conditions (e.g., slope, elevation, cover type) and sensor characteristics (e.g., flight height, point density, and scan angles) on the accuracy of LiDAR-derived DTMs [2,[12][13][14][15][16][17][18][19], ultimately demonstrating the reliability of LiDAR-derived DTMs across a range of terrain and cover types with varying acquisition parameters. Other studies investigated the impact that different point interpolators have on the accuracy of LiDAR-derived DTMs [20,21].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Two Open Source LiDAR Surface Classification Algorithms

et al. 2011

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With the progression of LiDAR (Light Detection and Ranging) towards a mainstream resource management tool, it has become necessary to understand how best to process and analyze the data. While most ground surface identification algorithms remain proprietary and have high purchase costs; a few are openly available, free to use, and are supported by published results. Two of the latter are the multiscale curvature classification and the Boise Center Aerospace Laboratory LiDAR (BCAL) algorithms. This study investigated the accuracy of these two algorithms (and a combination of the two) to create a digital terrain model from a raw LiDAR point cloud in a semi-arid landscape. Accuracy OPEN ACCESSRemote Sens. 2011, 3 639 of each algorithm was assessed via comparison with >7,000 high precision survey points stratified across six different cover types. The overall performance of both algorithms differed by only 2%; however, within specific cover types significant differences were observed in accuracy. The results highlight the accuracy of both algorithms across a variety of vegetation types, and ultimately suggest specific scenarios where one approach may outperform the other. Each algorithm produced similar results except in the ceanothus and conifer cover types where BCAL produced lower errors.

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LIDAR density and linear interpolator effects on elevation estimates

Cited by 97 publications

References 23 publications

EVALUATING THE EFFECTS OF REDUCTIONS IN LiDAR DATA ON THE VISUAL AND STATISTICAL CHARACTERISTICS OF THE CREATED DIGITAL ELEVATION MODELS

EVALUATING THE EFFECTS OF REDUCTIONS IN LiDAR DATA ON THE VISUAL AND STATISTICAL CHARACTERISTICS OF THE CREATED DIGITAL ELEVATION MODELS

A multi-directional ground filtering algorithm for airborne LIDAR

A Comparison of Two Open Source LiDAR Surface Classification Algorithms

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