Snow depth mapping by airplane photogrammetry (2017 - ongoing)

The available datasets are snow depth maps with a spatial resolution of 0.5 m derived from images of the survey camera Vexcel Ultracam mounted on a piloted airplane. Image acquisition was carried out during the approximately peak of winter (time when the thickest snowpack is expected) in spring. The snow depth maps are calculated by the subtraction of a summer-DTM from the processed winter- DSM of the corresponding date. The summer-DTM used was derived from a point cloud of an airborne laser scanner from 2020.

Due to the occurrence of inaccuracies of the calculated snow depth values caused by the photogrammetric method, we applied different masks to significantly increase the reliability of the snow depth maps. We masked out settled areas, high-frequented streets and technical constructions, pixels with high vegetation (height > 0.5 m) , outliers and unrealistic snow depth values. In addition, we modified the snow depth values of snow-free pixels to 0. The information on buildings and infrastructure comes from the exactly classified ALS point cloud and the TLM dataset from Swisstopo (https://www.swisstopo.admin.ch/de/geodata/landscape/tlm3d.html#links). High vegetation is also derived from the classification and the calculated object height from the point cloud. Outliers and unrealistic snow depth values are defined as negative snow depth values and snow depths exceeding 10 m. The classification of each pixel of the corresponding orthophoto into snow-covered or snow-free is based on the application of a threshold of the NDSI or manually determined ratios of the RGB values.

An extensive accuracy assessment proves the high accuracy of the snow depth maps with a root mean square error of 0.25 m for the year 2017 and 0.15 m for the following snow depth maps.

The work is published in:

Funding Information:

This work was supported by:
  • Swiss National Science Foundation (Grant/Award: Grant N° 200021_172800).)
  • Swiss National Science Foundation (Grant/Award: Grant N*200021_207519)

Related Datasets

  • Marty, Mauro; Bühler, Yves; Ginzler, Christian (2019). Snow Depth Mapping. EnviDat. doi:10.16904/envidat.62.

Related Publications

Bührle, L. J., Marty, M., Eberhard, L. A., Stoffel, A., Hafner, E. D., and Bühler, Y.: Spatially continuous snow depth mapping by aeroplane photogrammetry for annual peak of winter from 2017 to 2021 in open areas, The Cryosphere, 17, 3383-3408, 10.5194/tc-17-3383-2023, 2023.

Citation:

Bührle, Leon; Ruttner-Jansen, Pia; Marty, Mauro; Bühler, Yves (2022). Snow depth mapping by airplane photogrammetry (2017 - ongoing). EnviDat. doi:10.16904/envidat.418.

DataCite ISO 19139 GCMD DIF README.txt BibTex RIS

Data and Resources

Metadata

Field Values
DOI 10.16904/envidat.418
Publication State Published
Authors
  • Email: leon.buehrlefoo(at)slf.ch Given Name: Leon Family Name: Bührle Affiliation: SLF
  • Email: pia.ruttnerfoo(at)slf.ch Given Name: Pia Family Name: Ruttner-Jansen Affiliation: SLF
  • Email: mauro.martyfoo(at)wsl.ch ORCID: 0000-0002-0943-2454 Given Name: Mauro Family Name: Marty Affiliation: WSL DataCRediT: Supervision
  • Email: buehlerfoo(at)slf.ch ORCID: 0000-0002-0815-2717 Given Name: Yves Family Name: Bühler Affiliation: WSL Institute for Snow and Avalanche Research SLF DataCRediT: Supervision
Contact Person Given Name: Yves Family Name: Bühler Email: buehlerfoo(at)slf.ch Affiliation: WSL Institute for Snow and Avalanche Research SLF ORCID: 0000-0002-0815-2717
Subtitles
Publication Publisher: EnviDat Year: 2022
Dates
  • Type: Created Date: 2017-03-16 End Date: 2017-03-16
  • Type: Created Date: 2018-04-11 End Date: 2018-04-11
  • Type: Created Date: 2019-03-16 End Date: 2019-03-16
  • Type: Created Date: 2020-04-06 End Date: 2020-04-06
  • Type: Created Date: 2021-04-16 End Date: 2021-04-16
Version 1.0
Type dataset
General Type Dataset
Language English
Location Switzerland
Content License ODbL with Database Contents License (DbCL)    [Open Data]
Last Updated April 15, 2024, 11:14 (UTC)
Created March 31, 2022, 14:42 (UTC)