Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-22T15:28:49.640Z Has data issue: false hasContentIssue false
  • English
  • Français

A hierarchical knowledge-based classification for glacier terrain mapping: a case study from Kolahoi Glacier, Kashmir Himalaya

Published online by Cambridge University Press:  03 March 2016

Aparna Shukla  and
Iram Ali
Aparna Shukla*
Affiliation:
Wadia Institute of Himalayan Geology (WIHG), Dehradun, India
Iram Ali
Affiliation:
Department of Earth Sciences, University of Kashmir, Srinagar, India
*
Correspondence: Aparna Shukla <[email protected]>
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

A glacierized terrain comprises different land covers, and their mapping using satellite data is challenged by their spectral similarity. We propose a hierarchical knowledge-based classification (HKBC) approach for differentiation of glacier terrain classes and mapping of glacier boundaries, using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery and Global Digital Elevation Model (GDEM). The methodology was tested over Kolahoi Glacier, Kashmir Himalaya. For the sequential extraction of various glacier terrain classes, several input layers were generated from the primary datasets by applying image-processing techniques. Noticeable differences in temperature and spectral response between supraglacial debris and periglacial debris facilitated the development of a thermal glacier mask and normalized-difference debris index, which together with slope enabled their differentiation. These and the other layers were then used in several discrete tests in HKBC, to map various glacier terrain classes. An ASTER visible near-infrared image and 42 field points were used to validate results. The proposed approach satisfactorily classified all the glacier terrain classes with an overall accuracy of 89%. The Z-test reveals that results obtained from HKBC are significantly (at 95% confidence level) better than those from a maximum likelihood classifier (MLC). Glacier boundaries obtained from HKBC were found to be plausibly better than those obtained from MLC and visual interpretation.

Keywords

debris-covered glaciersglacier delineationglacier mappingsupraglacial debris
Type
Paper
Information
Annals of Glaciology , Volume 57 , Issue 71 , March 2016 , pp. 1 - 10
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Author(s) 2016

References

Ahmad, N and Hashimi, NH (1974) Glacial history of Kolahoi Glacier, India. Int. J. Glaciol., 13(68), 279–283 CrossRefGoogle Scholar
Bayr, KJ, Hall, DK and Kovalick, WM (1994) Observation on glaciers in the Eastern Austria Alps using satellite data. Int. J. Remote Sens., 15, 1733–1742 CrossRefGoogle Scholar
Bhambri, R, Bolch, T and Chaujar, RK (2011) Mapping of debris-covered glaciers in the Garhwal Himalayas using ASTER DEMs and thermal data. Int. J. Remote Sens., 32, 8095–8119 Google Scholar
Bhambri, R, Bolch, T and Chaujar, RK (2012) Frontal recession of Gangotri Glacier, Garhwal Himalayas, from 1965 to 2006, measured through high resolution remote sensing data. Current sci., 102(3), 489–494 Google Scholar
Bhardwaj, A, Joshi, PK, Snehmani, SL, Singh, MK, Singh, S and Kumar, R (2015) Applicability of Landsat 8 data for characterizing glacier facies and supraglacial debris. Int. J. Appl. Earth Obs. Ceoinf., 38, 51–64 Google Scholar
Bolch, T and Kamp, U (2006) Glacier mapping in high mountains using DEMs, Landsat and ASTER data. Grazer Schr. Ceogr. Raumforsch., 41, 13–24 Google Scholar
Bolch, T, Buchroithner, MF Kunert, A and Kamp, U (2007) Automated delineation of debris-covered glaciers based on ASTER data. In Gomarasca, MA ed. Geolnformation in Europe. Proceedings of the 27th EARSeL Symposium, 4-6 June 2007, Bolzano, Italy. Millpress, Rotterdam, 403–410 Google Scholar
Bolch, T, Buchroithner, M, Pieczonka, T and Kunert, A (2008) Planimetric and volumetric glacier changes in the Khumbu Himal, Nepal, since 1 962 using Corona, Landsat TM and ASTER data. J. Glacioi, 54(187), 592–600 Google Scholar
Bolch, T, Menounos, B and Wheate, R (2010) Landsat-based glacier inventory of western Canada, 1985–2005. Remote Sens. Environ., 114, 127–137 (doi: 10.101 6/j.rse.2009.08.015)Google Scholar
Burns, P and Nolin, A (2014) Using atmospherically-corrected Landsat imagery to measure glacier area change in the Cordillera Blanca, Peru from 1987 to 2010. Remote Sens. Environ., 140, 165–178 (doi: 10.101 6/j.rse.2013.08.026)Google Scholar
Casey, KA, Kääb, A and Benn, Di (2012) Geochemical characterization of supraglacial debris via in situ and optical remote sensing methods: a case study in Khumbu Himalaya, Nepal. Cryosphere, 6, 85–100 (doi: 10.5194/tc-6-85-2012)Google Scholar
Dozier, J (1989) Spectral signature of alpine snow-cover from the Landsat Thematic Mapper. Remote Sens. Environ., 28, 9–22 CrossRefGoogle Scholar
Foody, CM (2002) Status of land cover classification accuracy assessment. Remote Sens. Environ., 80, 185–201 Google Scholar
Jansson, P, Hock, R and Schneider, T (2003) The concept of glacier storage: a review. J. Hydrol., 282(1-4), 116–129 (doi: 10.1016/S0022-1694(03)00258-0)Google Scholar
Kääb, A (2002) Monitoring high-mountain terrain deformation from air- and spaceborne optical data: examples using digital aerial imagery and ASTER data. ISPRS J. Photogramm. Remote Sens., 57(1-2), 39–52 (doi: 10.101 6/S0924-2716(02)00114-4)CrossRefGoogle Scholar
Kanth, TA, Shah, AA and Hassan, ZU (2011) Ceomorphologic character and receding trend of Kolahoi Glacier in Kashmir Himalaya. Recent. Res. Sci. Technol., 3(9), 68–73 Google Scholar
Karimi, N, Farokhnia, A, Karimi, L, Eftekhari, M and Ghalkhani, H (2012) Combining optical and thermal remote sensing data for mapping debris-covered glaciers (Alamkouh Glaciers, Iran). Cold Reg. Sci. Technol., 71, 73–83 (doi: 10.101 6/j.coldregions.2011.10.004)CrossRefGoogle Scholar
Kaul, MN (1990) Glacial and fluvial geomorphology of Western Himalayas. Concept Publishing Company, New Delhi Google Scholar
Keshri, AK, Shukla, A and Gupta, RP (2009) ASTER ratio indices for supraglacial terrain mapping. Int. J. Remote Sens., 30(2), 519–524 (doi: 10.1080/01431160802385459)CrossRefGoogle Scholar
Khan, A, Naz, SB and Bowling, LC (2015) Separating snow, clean and debris covered ice in Upper Indus Basin, Hindukush-Kara-koram, using Landsat images between 1998 and 2002. J. Hydrol., 521, 46–64 (doi: 10.1016/j.jhydrol.2014.11.048)Google Scholar
McFeeters, SK (1996) The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int. J. Remote Sens., 17(7), 1425–1432 (doi: 10.1080/01431169608948714)Google Scholar
Neve, EF (1910) Mt. Kolahoi and its Northern Glacier. Alp. J., 25, 39–42 Google Scholar
Odell, NE (1963) The Kolahoi northern glacier, Kashmir. J. Glacioi., 4(35), 633–635 Google Scholar
Paul, F and Mölg, (2014) Hasty retreat of glaciers in northern Patagonia from 1985 to 2011. J. Glaciol, 60(224), 1033–1043 (doi: 10.3189/2014JoG14J104)CrossRefGoogle Scholar
Paul, F, Huggel, C and Kääb, A (2004) Mapping of debris-covered glaciers using multispectral and DEM classification techniques. Remote Sens. Environ., 89, 510–518 (doi: 10.1016/j.rse.3003.11.007)Google Scholar
Paul, F and 19 others (2013) On the accuracy of glacier outlines derived from remote-sensing data. Ann. Glaciol., 54(63), 171–182 (doi: 10.31 89/201 3AoC63A296)CrossRefGoogle Scholar
Racoviteanu, A and Williams, MW (2012) Decision tree and texture analysis for mapping debris-covered glaciers in the Kangchen-junga area, Eastern Himalaya. Remote Sens., 4, 3078–3109 (doi: 10.3390/rs4103078)Google Scholar
Racoviteanu, AE, Manley, WF, Arnaud, Y and Williams, M (2007). Evaluating digital elevation models for glaciologic applications: an example from Nevado Coropuna, Peruvian Andes. Global Planet. Change, 59, 110–125 (doi: 10.1016/j.gloplacha.2006.11.036)Google Scholar
Ranzi, R, Crossi, G, lacovelli, L and Taschner, S (2004) Use of multispectral ASTER images for mapping debris-covered glaciers within the GLIMS project. In IGARSS 2004, International Ceoscience and Remote Sensing Symposium, 20-24 September 2004, Anchorage, Alaska, USA. Proceedings, Vol. 2. Institute of Electrical and Electronics Engineers, Piscataway, NJ, 1144–1147 Google Scholar
Rastner, P, Bolch, T and Notarnicola, C (2014) A comparison of pixel-and object-based glacier classification with optical satellite images. IEEE J. Selected Topics Appl. Earth. Obs. Remote Sens., 7(3), 853–862 (doi: 10.1109/JSTARS.201 3.2274668)Google Scholar
Reid, TD and Brock, BW (2014) Assessing ice-cliff backwasting and its contribution to total ablation of debris-covered Miage glacier, Mont Blanc massif, Italy. J. Glaciol., 60(219), 3–13 (doi: 10.3189/2014JoG13J045)Google Scholar
Richards, JA and Jia, XP (1999) Remote sensing digital image analysis. Springer-Verlag, Berlin CrossRefGoogle Scholar
Scherler, D, Bookhagen, B and Strecker, MR (2011) Spatially variable response of Himalayan glaciers to climate change affected by debris cover. Lett. Nature Geosci., 4, 156–159 (doi: 10.1038/ngeo1068)Google Scholar
Shukla, A, Gupta, RP and Arora, MK (2009) Estimation of debris cover and its temporal variation using satellite sensor data: a case study in Chenab Basin, Himalaya. J. Glaciol., 55(191), 444–452 CrossRefGoogle Scholar
Shukla, A, Arora, MK and Gupta, RP (2010a) Synergistic approach for mapping debris-covered glaciers using optical-thermal remote sensing data with inputs from geomorphometric parameters. Remote Sens. Environ., 114, 1378–1387 (doi: 10.1016/j.rse.2010.01.015)Google Scholar
Shukla, A, Gupta, RP and Arora, MK (2010b) Delineation of debris-covered glacier boundaries using optical and thermal remote sensing data. Remote Sens. Lett, 1(1), 11–17 (doi: 10.1080/01431160903159316)Google Scholar
Taschner, S and Ranzi, R (2002) Comparing opportunities of Landsat-TM and ASTER data for monitoring a debris covered glacier in the Italian Alps within the GLIMS project. In IGARSS 2002, International Ceoscience and Remote Sensing Symposium, 24-28 June 2002, Toronto, Canada. Proceedings, Vol. 2. Institute of Electrical and Electronics Engineers, Piscataway, NJ, 1044–1046 Google Scholar
Tiwari, RK, Arora, MK and Gupta, RP (in press) Comparison of maximum likelihood and knowledge-based classifications of debris cover of glaciers using ASTER optical-thermal imagery. Remote Sens. Environ, (doi: 10.1016/j.rse.2014.10.026)Google Scholar
Watanachaturaporn, P, Arora, MK and Varshney, PK (2008) Multi-source classification using support vector machines: an empirical comparison with decision tree and neural network classifiers. Photogramm Eng. Remote Sens., 74(2), 239–246 Google Scholar
Yin, D, Cao, X, Chen, X, Shao, Y and Chen, J (2013) Comparison of automatic thresholding methods for snow-cover mapping using Landsat TM imagery. Int. J. Remote Sens., 34(19), 6529–6538 (doi: 10.1080/01431161.2013.803631)Google Scholar