Abstract
Global Land Ice Measurements from Space (GLIMS) is an international initiative to map the world’s glaciers and to build a geospatial database of glacier vector outlines that is usable via the World Wide Web. The GLIMS initiative includes glaciologists at 82 institutions, organized into 27 Regional Centers (RCs), who analyze satellite imagery to map glaciers in their regions of expertise. The results are collected at the U.S. National Snow and Ice Data Center (NSIDC) and ingested into the GLIMS Glacier Database. A concern for users of the database is data quality. The process of classifying multispectral satellite data to extract vector outlines of glaciers has been automated to some degree, but there remain stages requiring human interpretation. To quantify the repeatability and precision of data provided by different RCs, we designed a method of comparative image analysis whereby analysts at the RCs and NSIDC could derive glacier outlines from the same set of images, chosen to contain a variety of glacier types. We carried out four such experiments. The results were compiled, compared, and analyzed to quantify inter-RC analysis consistency. These comparisons have improved RC ability to produce consistent data, and in addition show that in the lower reaches of a glacier, precision of glacier outlines is typically 3 to 4 pixels. Variability in the accumulation area and over parts of the glacier that are debris covered tends to be higher. The ingest process includes quality control steps that must be passed before data are accepted into the database. These steps ensure that ingested data are well georeferenced and internally consistent. The GLACE experiments and ingest time quality control steps have led to improved quality and consistency of GLIMS data. This chapter presents the GLACE experiments and the quality control steps incorporated in the data ingest process. More recent similar studies are referenced.
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References
Abrams, M., Hook, S., Ramachandran, B. (2002) Aster User Handbook, Version 2. NASA Jet Propulsion Laboratory, Pasadena, CA.
Albert, T.H. (2002) Evaluation of remote sensing techniques for ice-area classification applied to the tropical Quelccaya Ice Cap, Peru. Polar Geography, 26(3), 210–226.
Alt, H., Behrends, B., and Blömer, J. (1995) Approximate matching of polygonal shapes. Annals of Mathematics and Artificial Intelligence, 13, 251–265.
Bishop, M.P., Bonk, R., Kamp, U., and Shroder, J. (2001) Terrain analysis and data modeling for alpine glacier mapping. Polar Geography, 25(3), 182–201.
Hall, D.K., Riggs, G., and Salomonson, V. (1995) Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data. Remote Sensing of Environment, 54(2), 127–140.
Paul, F. (2007) The New Swiss Glacier Inventory 2000: Application of Remote Sensing and GIS (Schriftenreihe Physische Geographie, Glaziologie und Geomorphodynamik No. 52). Universität Zu¨ rich, 210 pp.
Paul, F., Kääb, A., Maisch, M., Kellenberger, T., Haeberli, W. (2002) The new remote-sensing-derived Swiss glacier inventory, I: Methods. Annals of Glaciology, 34, 355–361.
Paul, F., Huggel, C., and Kääb, A. (2004) Combining satellite multispectral image data and a digital elevation model for mapping debris-covered glaciers. Remote Sensing of Environment, 89, 510–518.
Paul, F., and Kääb, A. (2005) Perspectives on the production of a glacier inventory from multispectral satellite data in the Canadian Arctic: Cumberland Peninsula, Baffin Island. Annals of Glaciology, 42, 59–66.
Paul, F., Barry, R., Cogley, J., Frey, H., Haeberli, W., Ohmura, A., Ommanney, C., Raup, B., Rivera, A., and Zemp, M. (2009) Recommendations for the compilation of glacier inventory data from digital sources. Annals of Glaciology, 50(53), 119–126.
Paul, F., Barrand, N., Berthier, E., Bolch, T., Casey, K., Frey, H., Joshi, S., Konovalov, V., Bris, R.L., Moelg, N. et al. (2012) On the accuracy of glacier outlines derived from remote sensing data. Annals of Glaciology, 54(63), 171–182.
Racoviteanu, A.E., Paul, F., Raup, B., Khalsa, S.J.S., and Armstrong, R. (2009) Challenges and recommendations in mapping of glacier parameters from space: Results of the 2008 Global Land and Ice Measurements from Space (GLIMS) workshop, Boulder, Colorado, USA. Annals of Glaciology, 53, 53–69.
Raup, B., and Khalsa, S.J.S. (2007) GLIMS Analysis Tutorial. National Snow and Ice Data Center, Boulder, CO. Available at http://glims.org/MapsAndDocs/guides.html
Raup, B., Khalsa, S., Armstrong, R., Cawkwell, F., Georges, C., Hamilton, G., Sneed, W., Jr., and Wheate, R. (2004) Comparative image analysis to ensure data quality in the global land ice measurements from space (GLIMS) glacier database. EOS Trans. Am. Geophys. Union, 85(47), Supplement, abstract H23D-1151.
Raup, B., Kääb, A., Kargel, J.S., Bishop, M.P., Hamilton, G., Lee, E., Paul, F., Rau, F., Soltesz, D., Khalsa, S.J.S. et al. (2007a) Remote sensing and GIS technology in the Global Land Ice Measurements from Space (GLIMS) project. Computers and Geosciences, 33, 104–125, doi: 10.1016/j.cageo.2006.05.015.
Raup, B., Racoviteanu, A., Khalsa, S., Helm, C., Armstrong, R., and Arnaud, Y. (2007b) The GLIMS geospatial glacier database: A new tool for studying glacier change. Global and Planetary Change, 56, 101–110, doi: 10.1016/j.gloplacha.2006.07.018.
Sannel, A.B.K., and Brown, I.A. (2010) High-resolution remote sensing identification of thermokarst lake dynamics in a subarctic peat plateau complex. Canadian Journal of Remote Sensing, 36(Suppl. 1), S26–S40.
Sneed, W.A. (2007) Satellite remote sensing of Arctic glacier–climate interactions. Master’s thesis, University of Maine.
Acknowledgments
The GLIMS initiative at the NSIDC was begun with the support of NASA awards NNG04GF51A and NNG04GM09G. We would like to thank the late Mark Dyurgerov, Paul Geissler, Christian Georges, Chris Helm, Ella Lee, and Claudia Riedl for their involvement in the GLACE experiments. ASTER data courtesy of NASA/GSFC/METI/Japan Space Systems, the U.S./Japan ASTER Science Team, and the GLIMS project.
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Raup, B.H. et al. (2014). Quality in the GLIMS Glacier Database. In: Kargel, J., Leonard, G., Bishop, M., Kääb, A., Raup, B. (eds) Global Land Ice Measurements from Space. Springer Praxis Books(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79818-7_7
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DOI: https://doi.org/10.1007/978-3-540-79818-7_7
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