Improvements to airborne laser scanning data filtering in sandstone landscapes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F22%3A10447314" target="_blank" >RIV/00216208:11310/22:10447314 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=kN0c.a0qvj" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=kN0c.a0qvj</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.geomorph.2022.108377" target="_blank" >10.1016/j.geomorph.2022.108377</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improvements to airborne laser scanning data filtering in sandstone landscapes

Popis výsledku v původním jazyce

Sandstone rock outcrops form a unique part of the landscape of Central Europe. Due to climate conditions, they are mostly covered by dense vegetation. With some limitations, laser scanning technology allows the ground to be captured even under the tree canopy, making it a powerful tool for mapping vegetated and inaccessible terrain. A challenge in deriving a digital terrain model from an acquired point cloud lies in filtering, i.e., discrimination between ground and non-ground points. Conventional filtering methods applied on complex high-energy terrain with formations resembling man-made objects, e.g., rock walls, do not provide satisfactory results with respect to the accuracy of point assignment to ground and non-ground classes and consequently terrain modelling. However, the quality of digital terrain models is critical for geomorphometric applications and recognition of spatial patterns. This study proposes three filtering methods adapted to various morphological conditions of sandstone landscape regions i.e., spatially conditioned filtering, object-oriented classification, and filtering with additional terrain data. Spatially conditioned filtering is based on a well-known method of triangulated irregular network densification, but it adjusts selected parameters in an iterative way. This has been proven to be successful in filtering sandstone rocks by the application of distinct sets of parameters to areas with and without rock formations. Object-oriented classification distinguishes between rock pillars and trees in rock cities based on features describing the distribution of points inside these objects. The method achieved an overall accuracy of 85 % with respect to manually filtered data and outperformed the conventional methods. Filtering with additional terrain data requires already filtered and co-registered references for spatial querying. Evaluation by GNSS measurements showed that the digital terrain model derived using this method achieved a higher accuracy than that derived by conventional methods.

Název v anglickém jazyce

Improvements to airborne laser scanning data filtering in sandstone landscapes

Popis výsledku anglicky

Sandstone rock outcrops form a unique part of the landscape of Central Europe. Due to climate conditions, they are mostly covered by dense vegetation. With some limitations, laser scanning technology allows the ground to be captured even under the tree canopy, making it a powerful tool for mapping vegetated and inaccessible terrain. A challenge in deriving a digital terrain model from an acquired point cloud lies in filtering, i.e., discrimination between ground and non-ground points. Conventional filtering methods applied on complex high-energy terrain with formations resembling man-made objects, e.g., rock walls, do not provide satisfactory results with respect to the accuracy of point assignment to ground and non-ground classes and consequently terrain modelling. However, the quality of digital terrain models is critical for geomorphometric applications and recognition of spatial patterns. This study proposes three filtering methods adapted to various morphological conditions of sandstone landscape regions i.e., spatially conditioned filtering, object-oriented classification, and filtering with additional terrain data. Spatially conditioned filtering is based on a well-known method of triangulated irregular network densification, but it adjusts selected parameters in an iterative way. This has been proven to be successful in filtering sandstone rocks by the application of distinct sets of parameters to areas with and without rock formations. Object-oriented classification distinguishes between rock pillars and trees in rock cities based on features describing the distribution of points inside these objects. The method achieved an overall accuracy of 85 % with respect to manually filtered data and outperformed the conventional methods. Filtering with additional terrain data requires already filtered and co-registered references for spatial querying. Evaluation by GNSS measurements showed that the digital terrain model derived using this method achieved a higher accuracy than that derived by conventional methods.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10508 - Physical geography

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Geomorphology

ISSN

0169-555X

e-ISSN

—

Svazek periodika

414

Číslo periodika v rámci svazku

1 October 2022

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

19

Strana od-do

1-19

Kód UT WoS článku

000864865200002

EID výsledku v databázi Scopus

2-s2.0-85135109985