Glaciers adjust their sizes as a response to changing climatic conditions which make them a good indicator of climate change. Remote-sensing based glacier monitoring provides a robust way to inventory the health of glaciers and are estimated as a measure of changes in their area, length, volume and mass balance over a period. This research uses remote sensing methods to map glacier extents from satellite images and explores the efficacy of three machine learning algorithms for accurate glacier classification. The results indicated that the Columbia icefield lost 42 km2 of its area cover between 1985 and 2018. It was observed that smaller glaciers lost more of their area at a faster pace than larger ones. Change analysis showed the Columbia glacier experienced the highest area loss (-5.62 km2) and retreat (-3.37 km) while the Athabasca glacier recorded the highest mass ice lose (-2.54 m w.e.) over the study period.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-3422 |
| Date | 01 May 2020 |
| Creators | Intsiful, Adjoa Dwamena |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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