Published 2024 | Version 1.0.0
Dataset Open

Dataset for "Assmann et al. High variation in the surface extent of freshwater ponds creates dynamic Arctic tundra landscapes in the lowlands of Eastern Siberia"

  • 1. University of Zurich
  • 2. ROR icon Swiss Federal Institute for Forest, Snow and Landscape Research
  • 3. ROR icon Institut National de la Recherche Scientifique
  • 4. Institute for Biological Problems of the Cryolithozone Siberian Branch Russian Academy of Sciences

Description

Overview

Time-series of true colour drone imagery (RGB) and associated digital surface models (DSM) for three tundra landscapes. These data are required to replicate the analysis in "Assmann et al. High variation in the surface extent of freshwater ponds creates dynamic Arctic tundra landscapes in the lowlands of Eastern Siberia" (link to preprint).

Location:                                   Kytalyk National Park, Russian Federation
Time-span:                                2014-2021 (near-annual)
What:                                         drone derived RGB raster imagery and digital surface models (.tif)
Ground-sampling distance:    12 cm
Projection:                                UTM55N (EPSG: 32655)

The data are intended to be integrated into the GitHub repository (https://github.com/jakobjassmann/pond_project, also archived on Zenodo: https://doi.org/10.5281/zenodo.17318379). Instructions below.

For further detail on data generation and flight information see manuscript and supplementary materials, as well as information provided on the GitHub code repository. 

Site names

Please note that the abbreviated site names used here and in the code repository differ from those used in the manuscript. 

dataset / code manuscript
cbh "high"
tlb "med"
rdg "low"

Integration with code repository

To integrate the data with the code repository:

  1. Clone the code repository to your local machine. (https://github.com/jakobjassmann/pond_project, also archived on Zenodo: https://doi.org/10.5281/zenodo.17318379)
  2. Download the compressed data in this repository. 
  3. Extract archive contents as follows:
    • cbh.7z    ->     data/drone_data/cbh/
    • tlb.7z     ->     data/drone_data/tlb/
    • rdg.7z    ->     data/drone_data/rdg/

Citation

When uisng this data in the context of scientific work, we kindly ask to cite the accompanying manuscript (below) in addtion to the dataset citation provided by Zenodo. At the time of writing the final manuscript is in press, when referencing the dataset before publication, please cite the following preprint:

Assmann, J.J., Akandil, C., Plekhanova, E., Le Moigne, A., Karsanaev, S.V., Maximov, T.C., Schaepman-Strub, G. (2025). Freshwater ponds create highly dynamic Arctic tundra landscapes. Preprint on Research Square. https://doi.org/10.21203/rs.3.rs-5581925/v1

Acknowledgements

We would like to thank all drone-pilots and field team members who contributed to the data collection during the field seasons 2014 – 2021, including Inge Grünberg (nee Juszak), Maitane Iturrate-Garcia and Vitalii Zemlianskii. This study was supported by the Swiss National Science Foundation (grant no. 178753) and the University Research Priority Program on Global Change and Biodiversity of the University of Zurich.

Files

Files (2.2 GB)

Name Size Download all
md5:999715cbfc5270d0e889c78b0dfb8c02
812.1 MB Download
md5:24a72ce5355abd7833d4bbdf401dcda2
563.1 MB Download
md5:307cef0192a0bdba9087e6f4f6351ce3
810.4 MB Download

Additional details

Funding

Swiss National Science Foundation
178753
University of Zurich
University Research Priority Program on Global Change and Biodiversity