Towards Circumpolar Mapping of Arctic Settlements and Infrastructure Based on Sentinel-1 and Sentinel-2

Infrastructure expands rapidly in the Arctic due to industrial development. At the same time, climate change impacts are pronounced in the Arctic. Ground temperatures are, for example, increasing as well as coastal erosion. A consistent account of the current human footprint is needed in order to ev...

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Published in:Remote Sensing
Main Authors: Annett Bartsch, Georg Pointner, Thomas Ingeman-Nielsen, Wenjun Lu
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2020
Subjects:
SAR
Online Access:https://doi.org/10.3390/rs12152368
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spelling ftmdpi:oai:mdpi.com:/2072-4292/12/15/2368/ 2023-08-20T04:03:32+02:00 Towards Circumpolar Mapping of Arctic Settlements and Infrastructure Based on Sentinel-1 and Sentinel-2 Annett Bartsch Georg Pointner Thomas Ingeman-Nielsen Wenjun Lu agris 2020-07-23 application/pdf https://doi.org/10.3390/rs12152368 EN eng Multidisciplinary Digital Publishing Institute Urban Remote Sensing https://dx.doi.org/10.3390/rs12152368 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 12; Issue 15; Pages: 2368 arctic settlements infrastructure SAR multi-spectral machine learning Text 2020 ftmdpi https://doi.org/10.3390/rs12152368 2023-07-31T23:49:06Z Infrastructure expands rapidly in the Arctic due to industrial development. At the same time, climate change impacts are pronounced in the Arctic. Ground temperatures are, for example, increasing as well as coastal erosion. A consistent account of the current human footprint is needed in order to evaluate the impact on the environments as well as risk for infrastructure. Identification of roads and settlements with satellite data is challenging due to the size of single features and low density of clusters. Spatial resolution and spectral characteristics of satellite data are the main issues regarding their separation. The Copernicus Sentinel-1 and -2 missions recently provided good spatial coverage and at the same time comparably high pixel spacing starting with 10 m for modes available across the entire Arctic. The purpose of this study was to assess the capabilities of both, Sentinel-1 C-band Synthetic Aperture Radar (SAR) and the Sentinel-2 multispectral information for Arctic focused mapping. Settings differ across the Arctic (historic settlements versus industrial, locations on bedrock versus tundra landscapes) and reference data are scarce and inconsistent. The type of features and data scarcity demand specific classification approaches. The machine learning approaches Gradient Boosting Machines (GBM) and deep learning (DL)-based semantic segmentation have been tested. Records for the Alaskan North Slope, Western Greenland, and Svalbard in addition to high-resolution satellite data have been used for validation and calibration. Deep learning is superior to GBM with respect to users accuracy. GBM therefore requires comprehensive postprocessing. SAR provides added value in case of GBM. VV is of benefit for road identification and HH for detection of buildings. Unfortunately, the Sentinel-1 acquisition strategy is varying across the Arctic. The majority is covered in VV+VH only. DL is of benefit for road and building detection but misses large proportions of other human-impacted areas, such as gravel pads ... Text Arctic Climate change Greenland Svalbard Tundra MDPI Open Access Publishing Arctic Svalbard Greenland The Sentinel ENVELOPE(73.317,73.317,-52.983,-52.983) Remote Sensing 12 15 2368
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic arctic
settlements
infrastructure
SAR
multi-spectral
machine learning
spellingShingle arctic
settlements
infrastructure
SAR
multi-spectral
machine learning
Annett Bartsch
Georg Pointner
Thomas Ingeman-Nielsen
Wenjun Lu
Towards Circumpolar Mapping of Arctic Settlements and Infrastructure Based on Sentinel-1 and Sentinel-2
topic_facet arctic
settlements
infrastructure
SAR
multi-spectral
machine learning
description Infrastructure expands rapidly in the Arctic due to industrial development. At the same time, climate change impacts are pronounced in the Arctic. Ground temperatures are, for example, increasing as well as coastal erosion. A consistent account of the current human footprint is needed in order to evaluate the impact on the environments as well as risk for infrastructure. Identification of roads and settlements with satellite data is challenging due to the size of single features and low density of clusters. Spatial resolution and spectral characteristics of satellite data are the main issues regarding their separation. The Copernicus Sentinel-1 and -2 missions recently provided good spatial coverage and at the same time comparably high pixel spacing starting with 10 m for modes available across the entire Arctic. The purpose of this study was to assess the capabilities of both, Sentinel-1 C-band Synthetic Aperture Radar (SAR) and the Sentinel-2 multispectral information for Arctic focused mapping. Settings differ across the Arctic (historic settlements versus industrial, locations on bedrock versus tundra landscapes) and reference data are scarce and inconsistent. The type of features and data scarcity demand specific classification approaches. The machine learning approaches Gradient Boosting Machines (GBM) and deep learning (DL)-based semantic segmentation have been tested. Records for the Alaskan North Slope, Western Greenland, and Svalbard in addition to high-resolution satellite data have been used for validation and calibration. Deep learning is superior to GBM with respect to users accuracy. GBM therefore requires comprehensive postprocessing. SAR provides added value in case of GBM. VV is of benefit for road identification and HH for detection of buildings. Unfortunately, the Sentinel-1 acquisition strategy is varying across the Arctic. The majority is covered in VV+VH only. DL is of benefit for road and building detection but misses large proportions of other human-impacted areas, such as gravel pads ...
format Text
author Annett Bartsch
Georg Pointner
Thomas Ingeman-Nielsen
Wenjun Lu
author_facet Annett Bartsch
Georg Pointner
Thomas Ingeman-Nielsen
Wenjun Lu
author_sort Annett Bartsch
title Towards Circumpolar Mapping of Arctic Settlements and Infrastructure Based on Sentinel-1 and Sentinel-2
title_short Towards Circumpolar Mapping of Arctic Settlements and Infrastructure Based on Sentinel-1 and Sentinel-2
title_full Towards Circumpolar Mapping of Arctic Settlements and Infrastructure Based on Sentinel-1 and Sentinel-2
title_fullStr Towards Circumpolar Mapping of Arctic Settlements and Infrastructure Based on Sentinel-1 and Sentinel-2
title_full_unstemmed Towards Circumpolar Mapping of Arctic Settlements and Infrastructure Based on Sentinel-1 and Sentinel-2
title_sort towards circumpolar mapping of arctic settlements and infrastructure based on sentinel-1 and sentinel-2
publisher Multidisciplinary Digital Publishing Institute
publishDate 2020
url https://doi.org/10.3390/rs12152368
op_coverage agris
long_lat ENVELOPE(73.317,73.317,-52.983,-52.983)
geographic Arctic
Svalbard
Greenland
The Sentinel
geographic_facet Arctic
Svalbard
Greenland
The Sentinel
genre Arctic
Climate change
Greenland
Svalbard
Tundra
genre_facet Arctic
Climate change
Greenland
Svalbard
Tundra
op_source Remote Sensing; Volume 12; Issue 15; Pages: 2368
op_relation Urban Remote Sensing
https://dx.doi.org/10.3390/rs12152368
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/rs12152368
container_title Remote Sensing
container_volume 12
container_issue 15
container_start_page 2368
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