Investigating performance of Airborne LiDAR data filtering algorithms for DTM generation

Polat, Nizar; Uysal, Murat

doi:10.1016/j.measurement.2014.12.017

Cited by 60 publications

(28 citation statements)

References 10 publications

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“…Besides the conventional methods (field surveying, photogrammetry or cartographic digitisation) for creating DTMs, new technologies such as satellite remote sensing and airborne laser scanning revolutionise the construction of high‐quality DTMs in a cost‐effective manner. Due to advancements in reliability and spatial resolution over the past decades, airborne Light Detection and Ranging (LiDAR) is becoming the privileged data acquisition technique for high‐resolution DTMs over large areas owing to providing 3D non‐uniformly spaced dense point information very effectively (Liu ; Polat and Uysal ). LiDAR has become a well‐established resource used to enhance spatial knowledge of the topography in order to construct DTMs while preserving terrain structure (topographic orderliness) (Liu et al .…”

Section: Introductionmentioning

confidence: 99%

Comparison of data reduction algorithms for LiDAR‐derived digital terrain model generalisation

Yılmaz

Uysal

2016

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A digital terrain model (DTM) is a three-dimensional representation of the terrain relief created from discrete points related to each other through their elevations. New technologies such as satellite remote sensing, airborne laser scanning and radar interferometry are efficient methods for constructing high-quality DTMs in a cost-effective manner. The accuracy of a DTM is influenced by a number of factors, including the accuracy, density and spatial distribution of elevation points, the terrain surface characteristics, etc. In this paper, direct comparisons of absolute and relative vertical accuracies are made between data reduction algorithms for the generalisation of DTM extracted from airborne Light Detection and Ranging (LiDAR) data. The absolute vertical accuracies are presented in terms of the mean error (ME), the mean absolute error (MAE) and the root mean square error (RMSE) and the relative vertical accuracies are characterised as per cent slope over Mount St Helens in southwest Washington State. The results show that LiDAR datasets can be reduced to 50 per cent density level by a uniform data reduction algorithm using triangulation with a linear interpolation method for the generalisation of DTM while still maintaining the quality of the original data.

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Section: Introductionmentioning

confidence: 99%

Comparison of data reduction algorithms for LiDAR‐derived digital terrain model generalisation

Yılmaz

Uysal

2016

Area

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“…The production of bare-earth DEM from the LiDAR point cloud involves two main steps: ground filtering and processing of filtered ground points in an interpolation routine (Aryal et al, 2017). In the separating the ground and non-ground points, TIN, slope, interpolation, segmentation, morphological or interpretation -based approaches are widely used (Dragos and Karstenb, 2008;Polat and Uysal, 2015;Dong and Chen, 2017).…”

Section: Dem Generationmentioning

confidence: 99%

Comparison of ASTER, contour lines and LiDAR based DEMs in terms of topographic differences in forested area

Yurtseven¹

2019

Eurasian Journal of Forest Science

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DEMs (Digital Elevation Model) generated with different remote sensing techniques and technologies. DEMs are used to determine the changes of vegetation in forests depending on topographical factors. The accuracy of DEMs has a major impact on the planning and management of forests. In this study, the accuracy of two different DEM data sources, which are frequently used in the modeling of topographic changes in large field studies in forestry, was compared with the LiDAR-based DEM dataset on a forest site. In this context, three different DEM source were used. One of them was the 10 m interval contour lines of 1:25,000 scale aerial photogrammetry based standard topographical maps which are produced by National General Directorate of Mapping. Topomap contour lines are transformed to grid based DEMs by using TIN and ANUDEM based approaches at 2.5, 5, 10 and 30 m resolutions. The other data was ASTER GDEM (1 arc-second ASTER GDEM Version 2, approximately 30 m resolution). The final and reference data is the LiDAR based, 0.25 m resolution DEM. In total, 33 DEM datasets are compared with the LiDAR-based DEM dataset. For these data sets, five difference metrics were calculated: pixel based difference models, the areal and volumetric difference of surface models, the areal difference of slope classes and the areal difference of aspect classes. According to the results of the analysis, the resolution, according to the topographic characteristics of the area and selected interpolation approaches has an effect on DEM modeling and DEM-derived metrics. In addition, the forest structure has a major impact on the accuracy of ASTER GDEM data.

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“…Some of these methods have compared by Sithole and Vosselman [31]. Considering previous study [26], in this study, Adaptive TIN Model, developed by Axelsson [7], is used to filter raw LiDAR data in order to generate DEM. It is also implemented in the commercial LiDAR data processing software, TerraScan [42].…”

Section: Data Filteringmentioning

confidence: 99%

An investigation of DEM generation process based on LiDAR data filtering, decimation, and interpolation methods for an urban area

2015

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Investigating performance of Airborne LiDAR data filtering algorithms for DTM generation

Cited by 60 publications

References 10 publications

Comparison of data reduction algorithms for LiDAR‐derived digital terrain model generalisation

Comparison of data reduction algorithms for LiDAR‐derived digital terrain model generalisation

Comparison of ASTER, contour lines and LiDAR based DEMs in terms of topographic differences in forested area

An investigation of DEM generation process based on LiDAR data filtering, decimation, and interpolation methods for an urban area

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