Preprints
https://doi.org/10.5194/egusphere-2023-1399
https://doi.org/10.5194/egusphere-2023-1399
12 Sep 2023
 | 12 Sep 2023

Arctic shoreline displacement with open satellite imagery and data fusion: A pilot study 1984–2022

Tua Nylén, Mikel Calle, and Carlos Gonzales-Inca

Abstract. The Arctic coast is facing rapid changes due to thawing permafrost and melting glaciers and sea ice. Communities all around the Arctic urgently need local-scale information on coastal change. This study aimed at developing a scalable and transferable procedure for mapping shoreline displacement in Arctic conditions by using an archive of satellite images. Our approach utilizes cloud computing in Google Earth Engine to process a large number of open satellite images for large areas and a long period of time (here 39 years). The procedure was iteratively developed in two contrasting study areas in Arctic Norway. It applies data fusion (including sensor fusion, algorithm fusion, and decision fusion) to improve classification accuracy and processing efficiency. For one 2 500 km2 area of interest, the procedure utilizes c. 600 satellite images to create coastal land cover and shoreline time series in less than one hour. Data fusion reduces problems related to the low availability and quality of satellite data in the Arctic before 2013 and reduces the impacts of noise and short-term changes. However, low data availability tends to create local gaps in the time series. Validation in the Tanafjorden and north-western Svalbard coasts indicates an overall classification accuracy of more than 99 % (against an independent sample of 2000 coastal points) and a median shoreline error distance of less than 15 m (against manually digitized shoreline) in 2019–2022. We exemplify how the method produces new information for identifying coastal change hotspots and examining long-term trends and local scale processes. We give examples of glacier retreat, spit migration, and delta development. This procedure is scalable and transferable to any coastal area demonstrating potential for producing the first circumpolar dataset of shoreline displacement.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Tua Nylén, Mikel Calle, and Carlos Gonzales-Inca

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1399', Ionut Cristi Nicu, 25 Sep 2023
    • AC1: 'Reply on RC1', Tua Nylén, 23 Oct 2023
  • RC2: 'Comment on egusphere-2023-1399', Anonymous Referee #2, 13 Feb 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1399', Ionut Cristi Nicu, 25 Sep 2023
    • AC1: 'Reply on RC1', Tua Nylén, 23 Oct 2023
  • RC2: 'Comment on egusphere-2023-1399', Anonymous Referee #2, 13 Feb 2024
Tua Nylén, Mikel Calle, and Carlos Gonzales-Inca

Data sets

Arctic shoreline displacement and validation data for two pilot study areas Tua Nylén, Mikel Calle-Navarro, Carlos Gonzales-Inca https://doi.org/10.5281/zenodo.7993787

Tua Nylén, Mikel Calle, and Carlos Gonzales-Inca

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Short summary
Communities all around the Arctic urgently need information on how their coast is changing in response to climate change. We developed an automized method for mapping Arctic shoreline displacement from open satellite images. We show how coastal change hotspots, glacier retreat, spit migration and delta development can be identified from such data. Being highly efficient and accurate, our method has potential for calculating the first 40-year time series of shoreline displacement in the Arctic.