Towards High-Resolution Land-Cover Classification of Greenland: A Case Study Covering Kobbefjord, Disko and Zackenberg
Mapping of the Arctic region is increasingly important in light of global warming as land cover maps can provide the foundation for upscaling of ecosystem properties and processes. To this end, satellite images provide an invaluable source of Earth observations to monitor land cover in areas that ar...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/18/3559 |
| _version_ | 1797517326143193088 |
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| author | Daniel Alexander Rudd Mojtaba Karami Rasmus Fensholt |
| author_facet | Daniel Alexander Rudd Mojtaba Karami Rasmus Fensholt |
| author_sort | Daniel Alexander Rudd |
| collection | DOAJ |
| description | Mapping of the Arctic region is increasingly important in light of global warming as land cover maps can provide the foundation for upscaling of ecosystem properties and processes. To this end, satellite images provide an invaluable source of Earth observations to monitor land cover in areas that are otherwise difficult to access. With the continuous development of new satellites, it is important to optimize the existing maps for further monitoring of Arctic ecosystems. This study presents a scalable classification framework, producing novel 10 m resolution land cover maps for Kobbefjord, Disko, and Zackenberg in Greenland. Based on Sentinel-2, a digital elevation model, and Google Earth Engine (GEE), this framework classifies the areas into nine classes. A vegetation land cover classification for 2019 is achieved through a multi-temporal analysis based on 41 layers comprising phenology, spectral indices, and topographical features. Reference data (1164 field observations) were used to train a random forest classifier, achieving a cross-validation accuracy of 91.8%. The red-edge bands of Sentinel-2 data proved to be particularly well suited for mapping the fen vegetation class. The study presents land cover mapping in the three study areas with an unprecedented spatial resolution and can be extended via GEE for further ecological monitoring in Greenland. |
| first_indexed | 2024-03-10T07:15:06Z |
| format | Article |
| id | doaj.art-18262f6914cb46aab07ddaa59ca71c90 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T07:15:06Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-18262f6914cb46aab07ddaa59ca71c902023-11-22T15:04:51ZengMDPI AGRemote Sensing2072-42922021-09-011318355910.3390/rs13183559Towards High-Resolution Land-Cover Classification of Greenland: A Case Study Covering Kobbefjord, Disko and ZackenbergDaniel Alexander Rudd0Mojtaba Karami1Rasmus Fensholt2Department of Geosciences and Natural Resource Management, University of Copenhagen, 1350 Copenhagen, DenmarkBASF Digital Farming GmbH, Im Zollhafen 24, 50678 Cologne, GermanyDepartment of Geosciences and Natural Resource Management, University of Copenhagen, 1350 Copenhagen, DenmarkMapping of the Arctic region is increasingly important in light of global warming as land cover maps can provide the foundation for upscaling of ecosystem properties and processes. To this end, satellite images provide an invaluable source of Earth observations to monitor land cover in areas that are otherwise difficult to access. With the continuous development of new satellites, it is important to optimize the existing maps for further monitoring of Arctic ecosystems. This study presents a scalable classification framework, producing novel 10 m resolution land cover maps for Kobbefjord, Disko, and Zackenberg in Greenland. Based on Sentinel-2, a digital elevation model, and Google Earth Engine (GEE), this framework classifies the areas into nine classes. A vegetation land cover classification for 2019 is achieved through a multi-temporal analysis based on 41 layers comprising phenology, spectral indices, and topographical features. Reference data (1164 field observations) were used to train a random forest classifier, achieving a cross-validation accuracy of 91.8%. The red-edge bands of Sentinel-2 data proved to be particularly well suited for mapping the fen vegetation class. The study presents land cover mapping in the three study areas with an unprecedented spatial resolution and can be extended via GEE for further ecological monitoring in Greenland.https://www.mdpi.com/2072-4292/13/18/3559Sentinel-2google earth enginevegetation phenologyrandom forestred-edge |
| spellingShingle | Daniel Alexander Rudd Mojtaba Karami Rasmus Fensholt Towards High-Resolution Land-Cover Classification of Greenland: A Case Study Covering Kobbefjord, Disko and Zackenberg Remote Sensing Sentinel-2 google earth engine vegetation phenology random forest red-edge |
| title | Towards High-Resolution Land-Cover Classification of Greenland: A Case Study Covering Kobbefjord, Disko and Zackenberg |
| title_full | Towards High-Resolution Land-Cover Classification of Greenland: A Case Study Covering Kobbefjord, Disko and Zackenberg |
| title_fullStr | Towards High-Resolution Land-Cover Classification of Greenland: A Case Study Covering Kobbefjord, Disko and Zackenberg |
| title_full_unstemmed | Towards High-Resolution Land-Cover Classification of Greenland: A Case Study Covering Kobbefjord, Disko and Zackenberg |
| title_short | Towards High-Resolution Land-Cover Classification of Greenland: A Case Study Covering Kobbefjord, Disko and Zackenberg |
| title_sort | towards high resolution land cover classification of greenland a case study covering kobbefjord disko and zackenberg |
| topic | Sentinel-2 google earth engine vegetation phenology random forest red-edge |
| url | https://www.mdpi.com/2072-4292/13/18/3559 |
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