Assessment of LiDAR ground filtering algorithms for determining ground surface of non-natural terrain overgrown with forest and steppe vegetation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F20%3A82145" target="_blank" >RIV/60460709:41330/20:82145 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21110/20:00334691
Result on the web
<a href="https://www.sciencedirect.com/science/article/abs/pii/S0263224119309133" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0263224119309133</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.measurement.2019.107047" target="_blank" >10.1016/j.measurement.2019.107047</a>

Result language
angličtina
Original language name
Assessment of LiDAR ground filtering algorithms for determining ground surface of non-natural terrain overgrown with forest and steppe vegetation
Original language description
Ground filtering is an inevitable step of processing the Light detection and ranging-acquired point clouds. Our objective was to evaluate the performance of six filtering algorithms. The point clouds filtering and vertical accuracy were evaluated qualitatively, quantitatively and by comparison with a GNSS survey. All tested algorithms achieved good results but their performance was affected by the terrain slope and vegetation cover. Algorithms performed better in forests than in steppes with a high density of low vegetation. The performance of all algorithms decreased with slopes over 15 degrees. Our results show that some algorithms tended to cause Type I error while others tended more to the Type II error. Furthermore, for some algorithms this tendency depended on the vegetation and terrain character. The Progressive Triangulated Irregular Network algorithm provided overall well-balanced results in all environments. We propose that software developers should provide users with recommendations of op
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
21100 - Other engineering and technologies

Project
<a href="/en/project/GJ17-17156Y" target="_blank" >GJ17-17156Y: Fusion of LiDAR and UAV borne multispectral data to assess physiographic diversity of post-mining sites</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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