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Improving Semi-automated Glacier Mapping with a Multi-method Approach: Applications in Central Asia : Volume 9, Issue 5 (04/09/2015)

By Smith, T.

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Book Id: WPLBN0004023480
Format Type: PDF Article :
File Size: Pages 13
Reproduction Date: 2015

Title: Improving Semi-automated Glacier Mapping with a Multi-method Approach: Applications in Central Asia : Volume 9, Issue 5 (04/09/2015)  
Author: Smith, T.
Volume: Vol. 9, Issue 5
Language: English
Subject: Science, Cryosphere
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Cannon, F., Bookhagen, B., & Smith, T. (2015). Improving Semi-automated Glacier Mapping with a Multi-method Approach: Applications in Central Asia : Volume 9, Issue 5 (04/09/2015). Retrieved from

Description: Institute for Earth and Environmental Sciences, Universität Potsdam, Potsdam, Germany. Studies of glaciers generally require precise glacier outlines. Where these are not available, extensive manual digitization in a geographic information system (GIS) must be performed, as current algorithms struggle to delineate glacier areas with debris cover or other irregular spectral profiles. Although several approaches have improved upon spectral band ratio delineation of glacier areas, none have entered wide use due to complexity or computational intensity.

In this study, we present and apply a glacier mapping algorithm in Central Asia which delineates both clean glacier ice and debris-covered glacier tongues. The algorithm is built around the unique velocity and topographic characteristics of glaciers and further leverages spectral and spatial relationship data. We found that the algorithm misclassifies between 2 and 10 % of glacier areas, as compared to a ~ 750 glacier control data set, and can reliably classify a given Landsat scene in 3–5 min.

The algorithm does not completely solve the difficulties inherent in classifying glacier areas from remotely sensed imagery but does represent a significant improvement over purely spectral-based classification schemes, such as the band ratio of Landsat 7 bands three and five or the normalized difference snow index. The main caveats of the algorithm are (1) classification errors at an individual glacier level, (2) reliance on manual intervention to separate connected glacier areas, and (3) dependence on fidelity of the input Landsat data.

Improving semi-automated glacier mapping with a multi-method approach: applications in central Asia

Armstrong, R., Raup, B., Khalsa, S., Barry, R., Kargel, J., Helm, C., and Kieffer, H.: GLIMS glacier database, National Snow and Ice Data Center, Boulder, Colorado, USA, 2005.; Arendt, A., Bolch, T., Cogley, J. G., Gardner, A., Hagen, J.-O., Hock ,R., Kaser, G., Pfeffer, W. T., Moholdt, G., Paul, F., Radic, V., Andreassen, L., Bajracharya, S., Beedle, M., Berthier, E., Bhambri, R., Bliss, A., Brown, I., Burgess, E., Burgess, D., Cawkwell, F., Chinn, T., Copland, L., Davies, B., de Angelis, H., Dolgova, E., Filbert, K., Forester, R., Fountain, A., Frey, H., Giffen, B., Glasser, N., Gurney, S., Hagg, W., Hall, D., Haritashya, U. K., Hartmann, G., Helm, C., Herreid, S., Howat, I., Kapustin, G., Khromova, T., Kienholz, C., Koenig, M., Kohler, J., Kriegel, D., Kutuzov, S., Lavrentiev, I., LeBris, R., Lund, J., Manley, W., Mayer, C., Miles, E., Li, X., Menounos, B., Mercer, A., Moelg, N., Mool, P., Nosenko, G., Negrete, A., Nuth, C., Pettersson, R., Racoviteanu, A., Ranzi, R., Rastner, P., Rau, F., Rich, J., Rott, H., Schneider, C., Seliverstov, Y., Sharp, M., Sigur?sson, O., Stokes, C., Wheate, R., Winsvold, S., Wolken, G., Wyatt, F., and Zheltyhina, N.: Randolph Glacier Inventory [v2.0]: A Dataset of Global Glacier Outlines. Global Land Ice Measurements from Space, Boulder Colorado, USA, Digital Media, 2012.; Bhambri, R., Bolch, T., and Chaujar, R.: Mapping of debris-covered glaciers in the Garhwal Himalayas using ASTER DEMs and thermal data, Int. J. Remote Sens., 32, 8095–8119, 2011.; Bolch, T., Buchroithner, M. F., Kunert, A., and Kamp, U.: Automated delineation of debris-covered glaciers based on ASTER data, in: Geoinformation in Europe, Proc. of 27th EARSel Symposium, 4–7 June 2007, Bozen, Italy, 403–410, 2007.; Dozier, J.: Spectral signature of alpine snow cover from the Landsat Thematic Mapper, Remote Sens. Environ., 28, 9–22, 1989.; Bolch, T., Peters, J., Yegorov, A., Pradhan, B., Buchroithner, M., and Blagoveshchensky, V.: Identification of potentially dangerous glacial lakes in the northern Tien Shan, Nat. Hazards, 59, 1691–1714, 2011.; Cannon, F., Carvalho, L., Jones, C., and Bookhagen, B.: Multi-annual variations in winter westerly disturbance activity affecting the Himalaya, Clim. Dynam., 1–15, 2014.; Guo, W., Xu, J., Liu, S., Shangguan, D., Yao, X., Wei, J., Bao, W., Yu, P., Liu, Q., and Jiang, Z.: The second Chinese glacier inventory: data, methods and results, J. Glaciol., 226, 957–969, doi:10.3189/2015JoG14J209, 2015.; Hall, D., Ormsby, J., Bindschadler, R., and Siddalingaiah, H.: Characterization of snow and ice reflectance zones on glaciers using Landsat Thematic Mapper data, Ann. Glaciol, 9, 1–5, 1987.; Hansen, M. C. and Loveland, T. R.: A review of large area monitoring of land cover change using Landsat data, Remote Sens. Environ., 122, 66–74, 2012.; Hanshaw, M. N. and Bookhagen, B.: Glacial areas, lake areas, and snow lines from 1975 to 2012: status of the Cordillera Vilcanota, including the Quelccaya Ice Cap, northern central Andes, Peru, The Cryosphere, 8, 359–376, doi:10.5194/tc-8-359-2014, 2014.; Heid, T. and Kääb, A.: Evaluation of existing image matching methods for deriving glacier surface displacements globally from optical satellite imagery, Remote Sens. Environ., 118, 339–355, 2012.; Jarvis, A., Reuter, H. I., Nelson, A., and Guevara, E.: Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90 m Database available at: (last access: 1 July 2015), 2008.; Kääb, A.: Monitoring high-mountain terrain deformation from repeated air-and spaceborne optical data: examples using digital aerial imagery and ASTER data, ISPRS J. Photogram. Remote Sens., 57, 39–52, 2002.; Lehner, B., Verdin, K., and Jarvis, A.: New global hydrography derived from spaceborne elevation data, EOS, Trans. Am. Ge


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