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  4. Uav-Based LIDAR High-Resolution Snow Depth Mapping in the Swiss Alps: Comparing Flat and Steep Forests
 
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Uav-Based LIDAR High-Resolution Snow Depth Mapping in the Swiss Alps: Comparing Flat and Steep Forests

Auteur(s)
Koutantou, Kalliopi
Mazzotti, Giulia
Brunner, Philip 
Centre d'hydrogéologie et de géothermie 
Maison d'édition
: ISPRS Congress
Date de parution
2021-11-10
Mots-clés
  • UAV

  • Lidar

  • forests

  • snow depth mapping

  • 3D registration

  • C2C

  • DoD

Résumé
Snow depth mapping in Alpine forests is of high importance for hydrogeology, ecology, tourism, and natural hazards prevention.
Different remote sensing approaches have been employed for the precise mapping of snow depth within forests. However, optical
sensors cannot provide below-canopy information. While Airborne Laser Scanning (ALS) systems have been used successfully in this
context and allow obtaining data below canopies, the costs of acquisitions are very high, not allowing frequent data acquisitions. UAV-based Lidar technology potentially can provide the critical below-canopy information at lower cost and allows for frequent acquisitions.
First attempts to employ a UAV-based Lidar system in forests have proven promising, but they are limited to flat forests and to grid-level snow depth calculations. In this study, we present UAV-based Lidar data of both flat and steep forests. Different Lidar processing
workflows are analyzed and compared, and snow depth algorithms are used both at the point and the grid level. Whereas the UAV-Lidar system proved capable of mapping snow in both environments, the steep forests' data processing comes with greater challenges,
especially for the 3D registration, ground classification, and point-to-point snow depth calculations.
Notes
, 2021
Nom de l'événement
XXIV Congress 2021 from the International Society for Photogrammetry and Remote Sensing (ISPRS)
Lieu
Nice
URI
https://libra.unine.ch/handle/123456789/29638
DOI
10.5194/isprs-archives-XLIII-B3-2021-477-2021
Autre version
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-477-2021
Type de publication
Resource Types::text::conference output::conference proceedings::conference paper
Dossier(s) à télécharger
 main article: 2021-11-10_110_9062.pdf (1.9 MB)
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