A fully automatic framework for sub-pixel mapping of thermokarst lakes using Sentinel-2 images
Mapping and monitoring thermokarst lakes are crucial to understanding the impact of climate change on permafrost regions and quantifying permafrost-related carbon emissions. Several automatic methods based on remote sensing images have been developed for thermokarst lake mapping. However, mixed pixe...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Science of Remote Sensing |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666017223000366 |
| _version_ | 1827593591123345408 |
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| author | Yuanyuan Qin Chengyuan Zhang Ping Lu |
| author_facet | Yuanyuan Qin Chengyuan Zhang Ping Lu |
| author_sort | Yuanyuan Qin |
| collection | DOAJ |
| description | Mapping and monitoring thermokarst lakes are crucial to understanding the impact of climate change on permafrost regions and quantifying permafrost-related carbon emissions. Several automatic methods based on remote sensing images have been developed for thermokarst lake mapping. However, mixed pixels containing both land and water characteristics in the lakeshore zones pose a significant challenge to the accuracy of these methods. Furthermore, few approaches were able to fully automate the identification of thermokarst lakes without the manual training sample selection or parameter tuning. In this study, we present a fully automatic framework for thermokarst lake mapping using moderate-resolution Sentinel-2 images. The proposed method combines multidimensional hierarchical clustering and sub-pixel mapping (SPM) based on the radial basis function (RBF) interpolation and Markov random field (MRF) (referred to as RBF-then-MRF SPM), so as to achieve thermokarst lake mapping at a spatial resolution of 3.3 m. We apply the proposed method to two representative thermokarst lake distribution regions in the Northern Hemisphere and achieve a mean Kappa coefficient of 0.89 and 0.99, and a mean Quality of 89.86% and 96.60% on the central Tibetan Plateau and the northern Seward Peninsula, respectively. The results demonstrate that the proposed method significantly improves the accuracy of mixed pixel extraction, and the automatic thermokarst lake mapping is applicable to diverse permafrost regions. |
| first_indexed | 2024-03-09T02:13:55Z |
| format | Article |
| id | doaj.art-999991a99c3a4218bb6a792b6b25ae40 |
| institution | Directory Open Access Journal |
| issn | 2666-0172 |
| language | English |
| last_indexed | 2024-03-09T02:13:55Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Science of Remote Sensing |
| spelling | doaj.art-999991a99c3a4218bb6a792b6b25ae402023-12-07T05:30:21ZengElsevierScience of Remote Sensing2666-01722023-12-018100111A fully automatic framework for sub-pixel mapping of thermokarst lakes using Sentinel-2 imagesYuanyuan Qin0Chengyuan Zhang1Ping Lu2College of Surveying and Geo-Informatics, Tongji University, Siping Road 1239, Shanghai, ChinaCollege of Surveying and Geo-Informatics, Tongji University, Siping Road 1239, Shanghai, ChinaCorresponding author.; College of Surveying and Geo-Informatics, Tongji University, Siping Road 1239, Shanghai, ChinaMapping and monitoring thermokarst lakes are crucial to understanding the impact of climate change on permafrost regions and quantifying permafrost-related carbon emissions. Several automatic methods based on remote sensing images have been developed for thermokarst lake mapping. However, mixed pixels containing both land and water characteristics in the lakeshore zones pose a significant challenge to the accuracy of these methods. Furthermore, few approaches were able to fully automate the identification of thermokarst lakes without the manual training sample selection or parameter tuning. In this study, we present a fully automatic framework for thermokarst lake mapping using moderate-resolution Sentinel-2 images. The proposed method combines multidimensional hierarchical clustering and sub-pixel mapping (SPM) based on the radial basis function (RBF) interpolation and Markov random field (MRF) (referred to as RBF-then-MRF SPM), so as to achieve thermokarst lake mapping at a spatial resolution of 3.3 m. We apply the proposed method to two representative thermokarst lake distribution regions in the Northern Hemisphere and achieve a mean Kappa coefficient of 0.89 and 0.99, and a mean Quality of 89.86% and 96.60% on the central Tibetan Plateau and the northern Seward Peninsula, respectively. The results demonstrate that the proposed method significantly improves the accuracy of mixed pixel extraction, and the automatic thermokarst lake mapping is applicable to diverse permafrost regions.http://www.sciencedirect.com/science/article/pii/S2666017223000366Thermokarst lakesPermafrostSentinel-2Multidimensional hierarchical clusteringSub-pixel mapping (SPM) |
| spellingShingle | Yuanyuan Qin Chengyuan Zhang Ping Lu A fully automatic framework for sub-pixel mapping of thermokarst lakes using Sentinel-2 images Science of Remote Sensing Thermokarst lakes Permafrost Sentinel-2 Multidimensional hierarchical clustering Sub-pixel mapping (SPM) |
| title | A fully automatic framework for sub-pixel mapping of thermokarst lakes using Sentinel-2 images |
| title_full | A fully automatic framework for sub-pixel mapping of thermokarst lakes using Sentinel-2 images |
| title_fullStr | A fully automatic framework for sub-pixel mapping of thermokarst lakes using Sentinel-2 images |
| title_full_unstemmed | A fully automatic framework for sub-pixel mapping of thermokarst lakes using Sentinel-2 images |
| title_short | A fully automatic framework for sub-pixel mapping of thermokarst lakes using Sentinel-2 images |
| title_sort | fully automatic framework for sub pixel mapping of thermokarst lakes using sentinel 2 images |
| topic | Thermokarst lakes Permafrost Sentinel-2 Multidimensional hierarchical clustering Sub-pixel mapping (SPM) |
| url | http://www.sciencedirect.com/science/article/pii/S2666017223000366 |
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