Glacier Melting Risk: A predictive model of glacial melting by correlating timeseries geoglacial data with fractal- analysis of remote-sensed images

Overview

Jesuit High School Portland, OR Glacier recession and thermal expansion account for 75% of observed sea level rise. The importance of studying glacial melting is amplified by number of unmonitored glaciers in the world and complicated by the feasibility of exhaustive field monitoring. Remote sensing techniques such as multiyear satellite imaging provide a valuable dataset for such comprehensive surveillance. Current techniques using remote sensed data involve volume-area scaling analysis that rely on active field measurements. This study demonstrates the potential utility of fractal analysis of multi-year glacier landsat images to serve as a predictive indicator of glacial melt. Specifically, it investigated how annual changes in the observed surface geometry of glaciers correlate with the 1) mass balance of the glacier, 2) observed temperature around the glacier. The results show that the fractal dimension changes in the landsat images are predictive of the glaciers’ mass balance (p=0.0008), and mean temperatures (p<0.0005). This study suggests that it is possible to rely on readily available remote sensing techniques to rapidly and continuously monitor the world’s glaciers and identify critical glacial regions undergoing melting.

Competition history

• JSHS 2020 Category not listed

Resources

• National JSHS abstract book ↗

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