Dataset Open Access

Before-, during-, and after-flood UAV-generated digital elevation models, orthomosaics, and GIS datasets of the distal part of Zackenberg river, northeast Greenland (August 2017)

Tomczyk, Aleksandra Magdalena; Ewertowski, Marek Wojciech

This dataset contains a time series of very high-resolution dataset derived from UAV-generated images. Data covers the distal part of Zackenberg River, northeast Greenland. Images were captured using an unmanned aerial vehicle (UAV) immediately before (5th August 2017), during (6th August 2017), and after (8th August 2017) the glacier lake outburst flood.

UAV-generated images were processed using the structure-from-motion approach in Agisoft Metashape software. Resultant products include digital elevation models, orthomosaics, and hillshade models. The second group of files ( contains vector files resultant from geomorphological mapping.  

Potential applications of the presented dataset include:

  1.  assessment and quantification of landscape changes as an immediate result of glacier lake outburst flood;
  2. long-term monitoring of high-Arctic river valley development (in conjunction with other datasets);
  3. establishing a baseline for quantification of geomorphological impacts of future glacier lake outburst floods;
  4. assessment of geohazards related to bank erosion and debris flow development (hazards for research station infrastructure – station buildings and bridge);
  5. monitoring of permafrost degradation; and
  6. modelling flood impacts on river ecosystem, transport capacity, and channel stability.

This dataset contains photogrammetric products derived from the UAV-generated images (orthomosaics, digital elevation models, hillshade models) and GIS products. Unprocessed images, which were the basis for structure-from-motion processing, can be accessed at: (Tomczyk and Ewertowski, 2021)

Tomczyk A.M., Ewertowski M.W., 2021: Before-, during-, and after-flood UAV-generated images of the distal part of Zackenberg river, northeast Greenland (August 2017),

The research leading to these results has received funding from the European Union's Horizon 2020 project INTERACT, under grant agreement No. 730938, project number: 119 [ArcticFan].
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