IceLines - Sentinel-1 - Antarctica

IceLines (Ice Shelf and Glacier Front Time Series) is an automated calving front monitoring service providing monthly ice shelf front time series of major Antarctic ice shelves. The provided time series allows to discover the dynamics of ice shelf front changes and calving events. The front positions are automatically derived from Sentinel-1 data based on a deep neuronal network called HED-U-Net. The time series covers the timespan 2014 to today (partly limited due to Sentinel-1 data availability). Incorrectly extracted fronts are truncated which might lead to gaps in the time series especially between December to March due to strong surface melt. Annual averages are calculated based on the extracted monthly fronts (excluding the summer months) and provide more robust results due to temporal aggregation

Citation proposal
Celia Baumhoer (German Aerospace Center (DLR)). IceLines - Sentinel-1 - Antarctica. https://gdk.gdi-de.org/geonetwork/srv/api/records/7ffa8888-fe28-4f12-b3a1-d08eae6f265e

Simple

Date ( Creation ): 2022-06-01T00:00:00

Identifier: https://geoservice.dlr.de/catalogue/srv/metadata/7ffa8888-fe28-4f12-b3a1-d08eae6f265e

Presentation form: Digital map

Other citation details

Purpose: The main objective of IceLines is to provide dense time series of calving front change to improve our knowledge on Antarctic glacier and ice shelf front dynamics.

Status: ongoing

Author

German Aerospace Center (DLR) - Celia Baumhoer

Maintenance and update frequency: As needed

Keywords

opendata
IceLines
Antarctica
Landcover
Sentinel-1
time-series
yearly
monthly
quarterly
daily
inspireidentifiziert

GEMET - INSPIRE themes, version 1.0 ( Theme )

Land cover

Keywords ( Place )

regional

Use limitation: Nutzungseinschränkungen: Das DLR ist nicht haftbar für Schäden, die sich aus der Nutzung ergeben. / Use Limitations: DLR not liable for damage resulting from use.

Access constraints: Other restrictions

Use constraints: Other restrictions

Other constraints: Nutzungsbedingungen: Lizenz, https://creativecommons.org/licenses/by/4.0 / terms of use: https://creativecommons.org/licenses/by/4.0/

Other constraints

{"id": "cc-by/4.0",

"name": "Creative Commons Namensnennung – 4.0 International (CC BY 4.0)",

"url": "http://dcat-ap.de/def/licenses/cc-by/4.0",

"quelle": "Copyright DLR (year of production)"}

Spatial representation type: Vector

Denominator: 80000

Metadata language: English

Character set: UTF8

Topic category

Environment

Topology level: Topology 1D

Geometric object type: Curve

Distribution format

GeoPackage ()

OnLine resource: ICELINES_S1_P1Y
WMS Access: Icelines Yearly

OnLine resource: ICELINES_S1_P1M
WMS Access: Icelines Monthly

OnLine resource: ICELINES_SHELFNAMES
WMS Access: Icelines Shelfnames

OnLine resource: TDM_POLARDEM_ANT_COASTLINE
WMS Access: TanDEM-X PolarDEM High Resolution Coastlines of Antarctica 2013 – 2014

OnLine resource: TDM_POLARDEM90_ANT_HSC
WMS Access: TanDEM-X PolarDEM 90m Hillshade Combined of Antarctica (HSC)

OnLine resource: https://geoservice.dlr.de/eoc/land/wms?SERVICE=WMS%26REQUEST=GetCapabilities

OnLine resource: EOC Geoservice map context
EOC Geoservice map context

OnLine resource: EOC Download Service
EOC Download Service

OnLine resource: IceLines - EOC News
IceLines - EOC News

Hierarchy level: Series

Domain consistency

Measure identification: INSPIRE / Conformity_001

Conformance result

Date ( Publication ): 2010-12-08

Explanation: See the referenced specification

Pass: true

Statement

Created from Sentinel-1 Copernicus data provided by the Alaska Satellite Facility (ASF) https://vertex.daac.asf.alaska.edu/ and the European Space Agency (ESA) https://scihub.copernicus.eu/.

Processing steps include the pre-processing of Sentinel-1 dual and single polarized GRD scenes with the ESA SNAP toolbox. The fronts are extracted with a deep learning approach described in:

Heidler, K., Mou, L., Baumhoer, C., Dietz, A., and Zhu, X. X.: HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline, IEEE Trans. Geosci. Remote Sensing, 1–14, https://doi.org/10.1109/TGRS.2021.3064606, 2021.

Baumhoer, C. A., Dietz, A. J., Kneisel, C., and Kuenzer, C.: Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning, Remote Sensing, 11, 2529, https://doi.org/10.3390/rs11212529, 2019.

Description

IceLines Processing:

Processing:

Calving front locations are derived from single- and dual-polarized Sentinel-1 EW data which were pre-processed with the ESA SNAP Toolbox 8.0. Front extraction is performed with the deep learning architecture “HED-UNet” and several post-processing steps including elevation thresholding, morphological filtering and vectorization. On average, the true mean distance error for fronts extracted from dual-pol imagery is 210±11 m (5.2 pixel) with 95% confidence and 354±17 m (8.8 pixel) for single-polarized imagery depending on ice shelf front morphology, occurrence of surface melt and calving pattern.

Quality Assurance:

The daily product of IceLines has been validated against 94 manually delineated fronts. For each ice shelf, two manually delineated front positions were created for random dates outside the training data period. On average, the true mean distance error for fronts extracted from dual-pol imagery is 210±11 m (5.2 pixel) with 95% confidence and 354±17 m (8.8 pixel) for single-polarized imagery. Frontal movement along centerlines can be accessed with an accuracy of 85±75 m (2.1 pixel) with 95% confidence.