Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine
Climate warming is occurring at an unprecedented rate in the Arctic due to regional amplification, potentially accelerating land cover change. Measuring and monitoring land cover change utilizing optical remote sensing in the Arctic has been challenging due to persistent cloud and snow cover issues...
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Online Access: | https://doi.org/10.3390/rs10081226 |
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ftmdpi:oai:mdpi.com:/2072-4292/10/8/1226/ 2023-08-20T04:04:06+02:00 Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine Kelsey E. Nyland Grant E. Gunn Nikolay I. Shiklomanov Ryan N. Engstrom Dmitry A. Streletskiy agris 2018-08-04 application/pdf https://doi.org/10.3390/rs10081226 EN eng Multidisciplinary Digital Publishing Institute Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs10081226 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 10; Issue 8; Pages: 1226 Landsat dense stacking Google Earth Engine climate change land cover change permafrost change Siberia Text 2018 ftmdpi https://doi.org/10.3390/rs10081226 2023-07-31T21:39:43Z Climate warming is occurring at an unprecedented rate in the Arctic due to regional amplification, potentially accelerating land cover change. Measuring and monitoring land cover change utilizing optical remote sensing in the Arctic has been challenging due to persistent cloud and snow cover issues and the spectrally similar land cover types. Google Earth Engine (GEE) represents a powerful tool to efficiently investigate these changes using a large repository of available optical imagery. This work examines land cover change in the Lower Yenisei River region of arctic central Siberia and exemplifies the application of GEE using the random forest classification algorithm for Landsat dense stacks spanning the 32-year period from 1985 to 2017, referencing 1641 images in total. The semiautomated methodology presented here classifies the study area on a per-pixel basis utilizing the complete Landsat record available for the region by only drawing from minimally cloud- and snow-affected pixels. Climatic changes observed within the study area’s natural environments show a statistically significant steady greening (~21,000 km2 transition from tundra to taiga) and a slight decrease (~700 km2) in the abundance of large lakes, indicative of substantial permafrost degradation. The results of this work provide an effective semiautomated classification strategy for remote sensing in permafrost regions and map products that can be applied to future regional environmental modeling of the Lower Yenisei River region. Text Arctic Climate change permafrost taiga Tundra Siberia MDPI Open Access Publishing Arctic Yenisei River ENVELOPE(84.738,84.738,69.718,69.718) Remote Sensing 10 8 1226 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
Landsat dense stacking Google Earth Engine climate change land cover change permafrost change Siberia |
spellingShingle |
Landsat dense stacking Google Earth Engine climate change land cover change permafrost change Siberia Kelsey E. Nyland Grant E. Gunn Nikolay I. Shiklomanov Ryan N. Engstrom Dmitry A. Streletskiy Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine |
topic_facet |
Landsat dense stacking Google Earth Engine climate change land cover change permafrost change Siberia |
description |
Climate warming is occurring at an unprecedented rate in the Arctic due to regional amplification, potentially accelerating land cover change. Measuring and monitoring land cover change utilizing optical remote sensing in the Arctic has been challenging due to persistent cloud and snow cover issues and the spectrally similar land cover types. Google Earth Engine (GEE) represents a powerful tool to efficiently investigate these changes using a large repository of available optical imagery. This work examines land cover change in the Lower Yenisei River region of arctic central Siberia and exemplifies the application of GEE using the random forest classification algorithm for Landsat dense stacks spanning the 32-year period from 1985 to 2017, referencing 1641 images in total. The semiautomated methodology presented here classifies the study area on a per-pixel basis utilizing the complete Landsat record available for the region by only drawing from minimally cloud- and snow-affected pixels. Climatic changes observed within the study area’s natural environments show a statistically significant steady greening (~21,000 km2 transition from tundra to taiga) and a slight decrease (~700 km2) in the abundance of large lakes, indicative of substantial permafrost degradation. The results of this work provide an effective semiautomated classification strategy for remote sensing in permafrost regions and map products that can be applied to future regional environmental modeling of the Lower Yenisei River region. |
format |
Text |
author |
Kelsey E. Nyland Grant E. Gunn Nikolay I. Shiklomanov Ryan N. Engstrom Dmitry A. Streletskiy |
author_facet |
Kelsey E. Nyland Grant E. Gunn Nikolay I. Shiklomanov Ryan N. Engstrom Dmitry A. Streletskiy |
author_sort |
Kelsey E. Nyland |
title |
Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine |
title_short |
Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine |
title_full |
Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine |
title_fullStr |
Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine |
title_full_unstemmed |
Land Cover Change in the Lower Yenisei River Using Dense Stacking of Landsat Imagery in Google Earth Engine |
title_sort |
land cover change in the lower yenisei river using dense stacking of landsat imagery in google earth engine |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2018 |
url |
https://doi.org/10.3390/rs10081226 |
op_coverage |
agris |
long_lat |
ENVELOPE(84.738,84.738,69.718,69.718) |
geographic |
Arctic Yenisei River |
geographic_facet |
Arctic Yenisei River |
genre |
Arctic Climate change permafrost taiga Tundra Siberia |
genre_facet |
Arctic Climate change permafrost taiga Tundra Siberia |
op_source |
Remote Sensing; Volume 10; Issue 8; Pages: 1226 |
op_relation |
Remote Sensing in Geology, Geomorphology and Hydrology https://dx.doi.org/10.3390/rs10081226 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs10081226 |
container_title |
Remote Sensing |
container_volume |
10 |
container_issue |
8 |
container_start_page |
1226 |
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1774714519702994944 |