Document Type
Article
Publication Date
8-2022
Publication Source
International Journal of Applied Earth Observation and Geoinformation
Abstract
In recent decades, climate change has significantly affected glacier dynamics, resulting in mass loss and an increased risk of glacier-related hazards including supraglacial and proglacial lake development, as well as catastrophic outburst flooding. Rapidly changing conditions dictate the need for continuous and detailed ob-servations and analysis of climate-glacier dynamics. Thematic and quantitative information regarding glacier geometry is fundamental for understanding climate forcing and the sensitivity of glaciers to climate change, however, accurately mapping debris-cover glaciers (DCGs) is notoriously difficult based upon the use of spectral information and conventional machine-learning techniques. The objective of this research is to improve upon an earlier proposed deep-learning-based approach, GlacierNet, which was developed to exploit a convolutional neural-network segmentation model to accurately outline regional DCG ablation zones. Specifically, we devel-oped an enhanced GlacierNet2 architecture that incorporates multiple models, automatic post-processing, and basin-level hydrological flow techniques to improve the mapping of DCGs such that it includes both the ablation and accumulation zones. Experimental evaluations demonstrate that GlacierNet2 improves the estimation of the ablation zone and allows a high level of intersection over union (IOU: 0.8839) score, which is higher than the GlacierNet (IOU: 0.8599). The proposed architecture provides complete glacier (both accumulation and ablation zone) outlines at regional scales, with an overall IOU score of 0.8619. This is a crucial first step in automating complete glacier mapping that can be used for accurate glacier modeling or mass-balance analysis.
ISBN/ISSN
1569-8432
Document Version
Published Version
Publisher
Elsevier
Volume
112
Peer Reviewed
yes
Sponsoring Agency
Integrative Science and Engineering Center, College of Arts and Science ; School of Engineering at the University of Dayton ; NASA High Mountain Asia ; NASA Interdisciplinary Research in Earth Science ; University of Dayton’s Mann Endowed Chair in the Natural Sciences ; Japan Aerospace Exploration Agency
eCommons Citation
Xie, Zhiyuan; Haritashya, Umesh K.; Asari, Vijayan K.; Bishop, Michael P.; Kargel, Jeffrey S.; and Aspiras, Theus, "GlacierNet2: A Hybrid Multi-Model Learning Architecture for Alpine Glacier Mapping" (2022). Electrical and Computer Engineering Faculty Publications. 480.
https://ecommons.udayton.edu/ece_fac_pub/480
Included in
Computer Engineering Commons, Electrical and Electronics Commons, Electromagnetics and Photonics Commons, Optics Commons, Other Electrical and Computer Engineering Commons, Systems and Communications Commons
Comments
This open-access article is provided for download in compliance with the publisher’s policy on self-archiving. To view the version of record, use the DOI: https://doi.org/10.1016/j.jag.2022.102921