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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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 |
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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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1774713926175424512 |