Abstract
Commonly recognized as an important symbol of the tropics and global warming, the glacier loss on Mt. Kilimanjaro has received worldwide attention for decades. In this paper, we propose a high-resolution glacier-cover (GC) record of Mt. Kilimanjaro over the period from 1984 to 2020, using a novel deep learning-based semantic segmentation method and Google Earth images, as well as digital elevation model (DEM) and ERA5-Land (ERA5) for snowline and temperature variations analysis. Our method achieves an accuracy of 94.37%, which proves the model's capability to record the GC areas precisely. The results show that (1) the GC area dramatically decreases from 19.2 km2 to 3.6 km2 during 37 years, which decreases about 4% and 2% per year from 1984 to 2000 and from 2000 to 2020 respectively, (2) the snowline altitude rises from 4,651 m to 5,088 m by about 437 m, and (3) the average 5,000 m air temperature on Mt. Kilimanjaro increases from -2.1 °C to -1.1 °C by about 1 °C. This study indicates that there will be no GC within a few decades if the current loss continues.
| Original language | English |
|---|---|
| Title of host publication | IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium |
| Subtitle of host publication | Proceedings |
| Publisher | IEEE |
| Pages | 3943-3946 |
| ISBN (Electronic) | 9781665427920, 978-1-6654-2791-3 |
| ISBN (Print) | 978-1-6654-2793-7 |
| DOIs | |
| Publication status | Published - 2022 |
| Event | 2022 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2022) - Kuala Lumpur Convention Centre & Virtual, Kuala Lumpur, Malaysia Duration: 17 Jul 2022 → 22 Jul 2022 https://igarss2022.org/# |
Publication series
| Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
|---|---|
| ISSN (Print) | 2153-6996 |
| ISSN (Electronic) | 2153-7003 |
Conference
| Conference | 2022 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2022) |
|---|---|
| Place | Malaysia |
| City | Kuala Lumpur |
| Period | 17/07/22 → 22/07/22 |
| Internet address |
Research Keywords
- climate change
- deep learning
- glacier loss
- high-resolution images
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