Use of Contactless Spatial Data Collection Methods for Snow Cover Monitoring: Case Studies from Czech Mountains
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13520%2F25%3A43899813" target="_blank" >RIV/44555601:13520/25:43899813 - isvavai.cz</a>
Result on the web
<a href="https://ojs.cvut.cz/ojs/index.php/cej/issue/view/915" target="_blank" >https://ojs.cvut.cz/ojs/index.php/cej/issue/view/915</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.14311/CEJ.2025.02.0014" target="_blank" >10.14311/CEJ.2025.02.0014</a>
Alternative languages
Result language
angličtina
Original language name
Use of Contactless Spatial Data Collection Methods for Snow Cover Monitoring: Case Studies from Czech Mountains
Original language description
Accurate documentation of snow cover is critical for hydrological modeling, climate adaptation planning, and risk assessment in mountainous regions. This study presents a comprehensive methodology for snow cover monitoring using UAV-based LiDAR scanning, tailored to the specific environmental and technical constraints of Central European mountain ranges. Field campaigns were conducted across several Czech border mountain locations (Ore Mountains, Giant Mountains, Beskids Mountains), utilizing DJI Matrice 300 RTK equipped with Zenmuse L1 or L2 LiDAR sensors. Due to limitations in deploying traditional ground control points (GCPs) in remote and protected areas, the methodology emphasizes reliance on GNSS RTK corrections and minimal GCP use. The influence of two GNSS reference networks (CZEPOS and TopNet) was evaluated through photogrammetric analysis, revealing systematic elevation biases and spatial autocorrelation, with TopNet yielding slightly better results. Various point cloud post-processing workflows were tested, including smoothing and noise filtering in DJI Terra, TerraSolid, and CloudCompare. The best visual and statistical results were obtained using a combined approach supplemented by a single foldable GCP. Ground point classification methods were assessed in both snow-free and snow-covered conditions. The most reliable method for snow-free filtering was the Spatix-based algorithm in TerraSolid, while snowcovered scenes required custom multi-criteria filtering in CloudCompare. Validation was performed using over 4,500 RTK GNSS ground points and manual snow probe measurements. The methodology proved robust despite uncertainties from vegetation interference and manual measurement limits. This study delivers practical guidelines for operational snow cover documentation under constrained field conditions, and proposes improvements for future automation and validation.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10509 - Meteorology and atmospheric sciences
Result continuities
Project
<a href="/en/project/SS05010157" target="_blank" >SS05010157: Analysis of the spatiotemporal dynamics of snow cover for the purposes of prediction and prevention of hydrological extremes and dimensioning of adaptation measures within land cosolidation process</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Stavební obzor
ISSN
1210-4027
e-ISSN
1805-2576
Volume of the periodical
34
Issue of the periodical within the volume
2
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
18
Pages from-to
196-213
UT code for WoS article
001542393300004
EID of the result in the Scopus database
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