Abstract:
In recent decades, global temperature rises have significantly influenced glacier dynamics
[1][2], underscoring the vital need for accurately delineating glacier boundaries
to comprehend these shifts and document regional patterns. Despite this urgency,
conventional methods struggle to map debris-covered glaciers (DCGs) due
to their intricate nature. Climate change exacerbates glacier mass loss and intensifies
glacier-related risks, necessitating ongoing monitoring and thorough analysis
of climate-glacier interactions. Our research assesses the effectiveness of a convolutional
neural network (CNN) in glacier mapping, utilizing Landsat satellite images,
digital elevation models (DEMs), and DEM-derived land-surface parameters.
Specifically, we seek to enhance the GlacierNet methodology by employing a CNN
segmentation model to precisely identify regional DCG ablation zones. By training
the models with satellite data from USGS and snow labeling from QGIS, and testing
them on glaciers in the Karakoram region, we achieve improved estimations of
the ablation zone, yielding high intersection over union scores. This study advances
glacier mapping techniques, offering critical insights into climate change impacts on
glacier dynamics in the Karakoram region. Furthermore, it marks a significant stride
towards automating comprehensive glacier mapping, with potential applications in
accurate glacier modeling and mass-balance analysis.
