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A Circum-Arctic Monitoring Framework for Quantifying Annual Erosion Rates of Permafrost Coasts

Philipp, Marius and Dietz, Andreas and Ullmann, Tobias and Kuenzer, Claudia (2023) A Circum-Arctic Monitoring Framework for Quantifying Annual Erosion Rates of Permafrost Coasts. Remote Sensing, 15 (3), pp. 1-28. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs15030818. ISSN 2072-4292.

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Official URL: https://www.mdpi.com/2072-4292/15/3/818

Abstract

This study demonstrates a circum-Arctic monitoring framework for quantifying annual change of permafrost-affected coasts at a spatial resolution of 10 m. Frequent cloud coverage and challenging lighting conditions, including polar night, limit the usability of optical data in Arctic regions. For this reason, Synthetic Aperture RADAR (SAR) data in the form of annual median and standard deviation (sd) Sentinel-1 (S1) backscatter images covering the months June–September for the years 2017–2021 were computed. Annual composites for the year 2020 were hereby utilized as input for the generation of a high-quality coastline product via a Deep Learning (DL) workflow, covering 161,600 km of the Arctic coastline. The previously computed annual S1 composites for the years 2017 and 2021 were employed as input data for the Change Vector Analysis (CVA)-based coastal change investigation. The generated DL coastline product served hereby as a reference. Maximum erosion rates of up to 67 m per year could be observed based on 400 m coastline segments. Overall highest average annual erosion can be reported for the United States (Alaska) with 0.75 m per year, followed by Russia with 0.62 m per year. Out of all seas covered in this study, the Beaufort Sea featured the overall strongest average annual coastal erosion of 1.12 m. Several quality layers are provided for both the DL coastline product and the CVA-based coastal change analysis to assess the applicability and accuracy of the output products. The predicted coastal change rates show good agreement with findings published in previous literature. The proposed methods and data may act as a valuable tool for future analysis of permafrost loss and carbon emissions in Arctic coastal environments.

Item URL in elib:https://elib.dlr.de/194996/
Document Type:Article
Title:A Circum-Arctic Monitoring Framework for Quantifying Annual Erosion Rates of Permafrost Coasts
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Philipp, MariusUniversität WürzburgUNSPECIFIEDUNSPECIFIED
Dietz, AndreasUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Ullmann, TobiasUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Kuenzer, ClaudiaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:31 January 2023
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:15
DOI:10.3390/rs15030818
Page Range:pp. 1-28
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:permafrost; coastal erosion; circum-Arctic; deep learning; change vector analysis; Google Earth Engine; synthetic aperture RADAR
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
Deposited By: Dietz, Andreas
Deposited On:19 Jun 2023 09:27
Last Modified:19 Jun 2023 09:27

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