High-resolution multispectral mapping facies on glacier surface in the Arctic using WorldView-3 data

Vol.10,No.1(2020)

Abstract

Glaciers are important and sensitive part of our environment which can be used as indicator of global warming and climate change. Glacier facies represent distinct regions of a glacier surface characterized by near surface structure and density that develop as a function of spatial variations in surface melt and accumulation. The facies mapping aids in delineating different zones of the glacier, which are useful in computing glacier mass balance and modeling. In this study we tested traditional and advanced classification techniques on the Edithbreen glacier situated in Ny-lesund, Svalbard, using WorldView-3 and Landsat 8 OLI. The comparison of the accuracy was conducted using error matrices. Six measures of accuracy were derived from the error matrices and were compared with each other to find the method delivering the most adequate output for facies mapping. The pixel-based approach applied to Landsat-8 data yielded higher accuracies (>80%) when compared to that. The object-oriented classification revealed a much better accuracy and high kappa coefficient for both low and high-resolution datasets. The study clearly indicates that the object-oriented classification provides better results for glacier facies classification when high spatial resolution is used, but for lower spatial resolution, pixel-based methods are adequate.


Keywords:
Glacier facies, Landsat-8 OLI, WorldView-3, pixel-based classification, object-based classification
References

Ahlmam, H. W. (1933): Scientific results of the Swedish-Norwegian Arctic Expedition in the summer of 1931: Part VIII. Glaciology. Geografiska Annaler, 15(4): 261-295. https://doi.org/10.2307/519467

Bhardwaj, A., Joshi, P. K., Snehmani, S. L., Singh, M. K., Singh, S. and Kumar, R. (2015): Applicability of Landsat 8 data for characterizing glacier facies and supraglacial debris. International Journal of Applied Earth Observation and Geoinformation, 38: 51-64. https://doi.org/10.1016/j.jag.2014.12.011

Benn, D., Evans, D. J. A. (2014): Glaciers and glaciation. Routledge.second Edition, 789 p. https://doi.org/10.4324/9780203785010

Benson, C.S. (1961): Stratigraphic studies in the snow and firn of the Greenland ice sheet. Folia Geographica Danica, 9: 13-37.

Benson, C. S., Motyka, R. J. (1979): Glacier–volcano interactions on Mt. Wrangell, Alaska. University of Alaska, Fairbanks. Geophysical Institute, Annual Report, pp. 1-25.

Box, J. E,, Colgan, W. T., Christensen, T. R., Schmidt, N. M., Lund, M., Parmentier, F-J. W., Brown, R., Bhatt, U. S., Euskirchen, E. S., Romanovsky, V. E., Walsh, J. E., Overland, J. E., Wang, M., Corell, R. W., Meier, W. N., Wouters, B., Mernild, S., Mrd, J., Pawlak, J. and Olsen, M. S. (2019): Key indicators of Arctic climate change: 1971–2017. Environmental Research Letters, 14: 1-18. https://doi.org/10.1088/1748-9326/aafc1b

Gore, A., Mani, S., Hari, H. R., Shekhar, C. and Ganju, A. (2017): Glacier surface characteristics derivation and monitoring using Hyperspectral datasets: a case study of Gepang Gath glacier, Western Himalaya. Geocarto International, 6049: 1-20. https://doi.org/10.1080/ 10106049.2017.1357766.

Guo, Q., Kelly, M., Gong, P. and Liu, D. (2007): An object-based classification approach in mapping tree mortality using high spatial resolution imagery. GIScience & Remote Sensing, 44: 24-47. https://doi.org/10.2747/1548-1603.44.1.24

Hagen, J. O., Liestl, O., Roland, E. and Jrgensen, T. (1993): Glacier atlas of Svalbard and Jan Mayen. Editor: Annernor Brekke. Nor. Polarinst. Medd. 129. Norwegian Polar Institute, Oslo. 32 p.

Jawak, S. D., Wankhede, S.F. and Luis, A. J. (2017): Prospective of high resolution worldview-2 satellite data for geospatial surface facies mapping of an alpine glacier. Proceedings of the Asian Conference on Remote Sensing 2017. https://www.dropbox.com/sh/z0pws7ul29n7v1d/ AACaV9rFNcF01xNGnWwwgmF_a/177.pdf?dl=0.

Jawak, S.D., Wankhede, S. F. and Luis, A. J. (2018): Comparison of pixel and object-based classification techniques for glacier facies extraction. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-5, pp. 543-548. https://doi.org/10.5194/isprs-archives-XLII-5-543-2018

Jawak, S.D., Wankhede, S. F. and Luis, A. J. (2019): Explorative study on mapping surface facies of selected glaciers from Chandra Basin, Himalaya using WorldView-2 data. Remote Sensing, 11: 1207. https://doi.org/10.3390/rs11101207

Jensen, J. R. (2015): Introductory digital image processing: A remote sensing perspective. Prentice Hall Press. Brigham Young University. 656 p.

Kääb, A., Bolch, T., Casey, K., Heid, T., Kargel, J.S., Leonard, G.J, Paul, F. andRaup, B.H. (2014): Glacier mapping and monitoring based on spectral data. In: Kargel, Jeffrey S; Leonard, Gregory J; Bishop, Michael P; Kääb, Andreas; Raup, Bruce H. (Eds.): Global Land Ice Measurements from Space. Heidelberg: Springer, pp. 75-104. https://doi.org/10.1007/978-3-540-79818-7_4

Keshri, A., Shukla, A. and Gupta, R. (2009): ASTER ratio indices for supraglacial terrain mapping. Internation Journal of Remote Sensing, 30(2): 519-524. https://doi.org/10.1080/01431160802385459

Lillesand, T., Kieffer, R. (2000): Remote Sensing and Image Interpretation. Fourth Edition, John Wiley & Sons, New York. 736 p.

Li, M.; Zang, S., Zhang, B., Li, S. and Wu, C. (2014): A review of remote sensing image classification techniques: The role of spatio-contextual Information. European Journal of Remote Sensing, 47: 389-411. https://doi.org/10.5721/EuJRS20144723

Lu, D., Weng, Q. (2007): A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5): 823-870. https://doi.org/10.1080/01431160600746456

Lu, T., Li, S. and Fu, W. (2014): Fusion based seamless mosaic for Remote Sensing images. Sensing and Imaging, 15: 101. https://doi.org/10.1007/s11220-014-0101-0

Müller, F. (1962): Zonation in the accumulation area of the glaciers of Axel Heiberg Island, N.W.T., Canada. Journal of Glaciology, 4: 302-311. https://doi.org/10.1017/S0022143000027623

Nijhawan, R., Garg, P. andThakur, P. (2016): A comparison of classification techniques for glacier change detection using multispectral images. Perspectives in Science, 8: 377-380. https://doi.org/10.1016/j.pisc.2016.04.080

Paterson, W.S.B. (1981):The physics of glaciers. Second Edition. New York, Pergamon Press. 480 p. https://doi.org/10.1016/C2009-0-14802-X

Paul, F., Huggel, C., Kääb, A., Kellenberger, T. andMaisch, M. (2002): Comparison of TM-derived Glacier areas with higher resolution data sets. Proceedings of EARSeL-LISSIG-Workshop Observing our Cryosphere from Space, Bern, pp. 15-21.

Paul, F., Winsvold, S.H., Kääb, A. and Nagler, T. (2016): Glacier remote sensing using Sentinel-2. Part II: Mapping glacier extents and surface facies, and comparison to Landsat-8. Remote Sensing, 8(7): 575. https://doi.org/10.3390/rs8070575

Pfeffer, W. T., Arendt, A. A., Bliss, A., Bolch, T., Cogley, J. G., Gardner, A. S., Hagen, J.-O., Hock, R., Kaser, G., Kienholz, C., Miles, E. S., Moholdt, G., Mölg, N., Paul, F., Radić, V., Rastner, P., Raup, B.H., Rich, J. and Sharp, M. J. (2014): The Randolph Glacier Inventory: a globally complete inventory of glaciers. Journal of Glaciology, 60: 537-552. https://doi.org/10.3189/2014JoG13J176

Rastner, P., Bolch, T., Notarnicola, C. and Paul, F. (2014): A comparison of pixel- and object-based glacier classification with optical satellite images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7: 984-993. https://doi.org/10.1109/JSTARS.2013.2274668

Robson, B. A., Nuth, C., Dahl, S. O., Hölbling, D., Strozzi, T. and Nielsen, P. R. (2015): Automated classification of debris-covered glaciers combining optical, SAR and topographic data in an object-based environment. Remote Sensing Environment, 170: 372-387. https://doi.org/10.1016/j.rse.2015.10.001

Sand, K., Hagen, J. O., Repp, K. and Berntsen, E. (1991): Climate-related research in Svalbard (CONF-9006128-Vol2). Severin, B.A.B. (Ed.). United States. 404 p.

Schowengerdt, R. A. (2007): Remote Sensing: Models and Methods for Image Processing, Third Edition, Academic Press, San Diego, CA (ISBN 0-12-628981-6). 560 p.

Sowmya, D. R., Shenoy, P. D. and Venugopal, K. R. (2017): Remote sensing satellite image processing techniques for image classification: A comprehensive survey. International Journal of Computer Applications, 161(11): 24-37. https://doi.org/10.5120/ijca2017913306

Tyrrell, G.W. (1922): The glaciers of Spitsbergen: The Transactions of the Geological Society of Glascow, v. XVII, pt. 1, pp. 1-49. https://doi.org/10.1144/transglas.17.1.1

Weih, R.C. Jr., Riggan, N.D. Jr. (2010): Object-based classification vs. pixel-based classification: comparative importance of multi-resolution imagery. Proceedings of GEOBIA 2010: Geographic Object-Based Image Analysis, 38 (2010), 6 p.

Williams, R. S., Hall, D. K. and Benson, C. S. (1991): Analysis of glacier facies using satellite techniques. Journal of Glaciology, 37: 120-128. https://doi.org/10.1017/S0022143000042878

Yamanouchi, T., rbk, J.B. (1995): Comparative study of the surface radiation budget at N- Alesund, Svalbardand Syowa station, Antarctica, 1987. Proceedings of the NIPR Symposium on Polar Meteorology and Glaciology, 9: 118-132.

Web sources / Other sources

[1] ENVI User’s Guide (2009)

[2]Flaash (2009): Atmospheric correction module quac and flaash user’s guide version 4.7 ITT Visual Information Solution Inc.

[3] Exelis Quac and Flaash (2009): Atmospheric Correction Module: QUAC and Flaash User.

[4]eCognition Developer, T. (2014): 9.0 User Guide. Trimble Ger. GmbH Munich, Germany.

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