Antarctic iceberg freeboard retrieval via deep learning from optical satellite imagery
Antarctic icebergs are critical components of the Antarctic ice sheet-ice shelf-ocean system and play a key role in understanding the impacts of climate change, particularly in estimating iceberg volume through the freeboard measurements. This study presents an innovative deep learning-based approac...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-08-01
|
| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2548005 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849224403530809344 |
|---|---|
| author | Zitong Zhou Outong Li Songtao Ai Xinde Chu Shoukat Ali Shah |
| author_facet | Zitong Zhou Outong Li Songtao Ai Xinde Chu Shoukat Ali Shah |
| author_sort | Zitong Zhou |
| collection | DOAJ |
| description | Antarctic icebergs are critical components of the Antarctic ice sheet-ice shelf-ocean system and play a key role in understanding the impacts of climate change, particularly in estimating iceberg volume through the freeboard measurements. This study presents an innovative deep learning-based approach (U-Net) for automatically measuring iceberg freeboard from optical remote sensing data in the Antarctic coastal region, following data acquisition. Landsat 8 imagery from September 2022 was used to test the method, successfully extracting 85,083 icebergs, the maximum freeboard reaching 95.16 m at (66.44°S, 91.28°E). Results revealed significant regional disparities in iceberg distribution, with the West Antarctic region exhibiting a higher iceberg density than the East Antarctica region. Iceberg coverage was notably sparse around large ice shelves. Freeboard analysis categorized icebergs into five height levels, with small to medium-sized icebergs (less than 25 m) comprising 80.07% of the total. A decreasing trend in freeboard was observed with increasing distance from the coastline. The study also identified the Bellingshausen-Amundsen Sea region as having the highest iceberg concentration, with consistent density patterns across height categories in regions between 90°W–160°W and 15°W–30°W. This research provides valuable data for understanding iceberg formation and its potential impacts on oceanic and climatic systems. |
| format | Article |
| id | doaj-art-e5ccb3ae41fc4b688a5e507cbd3d99cf |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-e5ccb3ae41fc4b688a5e507cbd3d99cf2025-08-25T11:24:59ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2548005Antarctic iceberg freeboard retrieval via deep learning from optical satellite imageryZitong Zhou0Outong Li1Songtao Ai2Xinde Chu3Shoukat Ali Shah4Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan, People’s Republic of ChinaChinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan, People’s Republic of ChinaChinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan, People’s Republic of ChinaChinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan, People’s Republic of ChinaChinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan, People’s Republic of ChinaAntarctic icebergs are critical components of the Antarctic ice sheet-ice shelf-ocean system and play a key role in understanding the impacts of climate change, particularly in estimating iceberg volume through the freeboard measurements. This study presents an innovative deep learning-based approach (U-Net) for automatically measuring iceberg freeboard from optical remote sensing data in the Antarctic coastal region, following data acquisition. Landsat 8 imagery from September 2022 was used to test the method, successfully extracting 85,083 icebergs, the maximum freeboard reaching 95.16 m at (66.44°S, 91.28°E). Results revealed significant regional disparities in iceberg distribution, with the West Antarctic region exhibiting a higher iceberg density than the East Antarctica region. Iceberg coverage was notably sparse around large ice shelves. Freeboard analysis categorized icebergs into five height levels, with small to medium-sized icebergs (less than 25 m) comprising 80.07% of the total. A decreasing trend in freeboard was observed with increasing distance from the coastline. The study also identified the Bellingshausen-Amundsen Sea region as having the highest iceberg concentration, with consistent density patterns across height categories in regions between 90°W–160°W and 15°W–30°W. This research provides valuable data for understanding iceberg formation and its potential impacts on oceanic and climatic systems.https://www.tandfonline.com/doi/10.1080/17538947.2025.2548005Antarcticicebergsdeep learningU-Net networkiceberg freeboard |
| spellingShingle | Zitong Zhou Outong Li Songtao Ai Xinde Chu Shoukat Ali Shah Antarctic iceberg freeboard retrieval via deep learning from optical satellite imagery International Journal of Digital Earth Antarctic icebergs deep learning U-Net network iceberg freeboard |
| title | Antarctic iceberg freeboard retrieval via deep learning from optical satellite imagery |
| title_full | Antarctic iceberg freeboard retrieval via deep learning from optical satellite imagery |
| title_fullStr | Antarctic iceberg freeboard retrieval via deep learning from optical satellite imagery |
| title_full_unstemmed | Antarctic iceberg freeboard retrieval via deep learning from optical satellite imagery |
| title_short | Antarctic iceberg freeboard retrieval via deep learning from optical satellite imagery |
| title_sort | antarctic iceberg freeboard retrieval via deep learning from optical satellite imagery |
| topic | Antarctic icebergs deep learning U-Net network iceberg freeboard |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2548005 |
| work_keys_str_mv | AT zitongzhou antarcticicebergfreeboardretrievalviadeeplearningfromopticalsatelliteimagery AT outongli antarcticicebergfreeboardretrievalviadeeplearningfromopticalsatelliteimagery AT songtaoai antarcticicebergfreeboardretrievalviadeeplearningfromopticalsatelliteimagery AT xindechu antarcticicebergfreeboardretrievalviadeeplearningfromopticalsatelliteimagery AT shoukatalishah antarcticicebergfreeboardretrievalviadeeplearningfromopticalsatelliteimagery |