Abstract
The snow cover is greatly diverse in distribution due to landscape, slope, duration, wind, etc. However, the snow build-up and spatial patterns play an important role in the hydrological cycle. These characteristics can be determined through a number of weather station which widely represents the entire glacier and hilly region of Leh-Ladakh to support understanding of the spatial and temporal distribution of snow cover. Remotely sensed data overcome these natural and other anthropogenic limitations that hinder data collection. Snow and ice cover has a distinct spectral reflectance from the land surfaces, therefore, shortwave infrared (SWIR-1) bands were used to discriminate these. Snow was extracted by applying Normalized Difference Snow Index and Normalized Difference Snow Thermal Index. In this study, snow and ice of different classes like fresh snow, dirty snow, and blue ice from the optical images were interpreted and Landsat 8 OLI and Sentinel-2 images were used to extract both spatial and temporal aspects. Temporal changes of snow and ice in the year of 2015–2017 shows a decline in snow cover area. The accuracy assessment of supervised classification using maximum likelihood and support vector machine accuracy with the Sentinel-2 optical image was compared and it was 94.40%. The landsat-8 image depicted 80.88% accuracy of snow and ice.
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References
Abrams M, Bianchi R, Pieri D (1996) Revised mapping of lava flows on Mount Etna, Sicily. Photogramm Eng Remote Sens 62(12):1353–1359
Ahmad A, Quegan S (2012) Analysis of maximum likelihood classification on multispectral data. Appl Math Sci 6(129):6425–6436
Banko G (1998) A review of assessing the accuracy of classifications of remotely sensed data and of methods including remote sensing data in forest inventory, working papers 98081. International Institute for Applied Systems Analysis. http://www.iiasa.ac.at/Publications/Documents/IR-98-081.pdf. Accessed 15 May 2018
Bauer ME, Burk TE, Ek AR, Coppin PR, Lime SD, Walsh TA, Walters DK, Befort W, Heinzen DF (1994) Satellite inventory of Minnesota forest resources. Photogram Eng Remote Sens 60:287–298
Bishop RF, Strayer MR, Irvine JM (1975) Bound-state pairing singularities in the 3 He Galitskii-Feynman T-matrix: temperature dependence. J Low Temp Phys 20(5–6):573–584
Bowers TL, Rowan LC (1996) Remote mineralogic and lithologic mapping of the Ice River Alkaline Complex, British Columbia, Canada using AVIRIS data. Photogram Eng Remote Sens 62:1379–1385
Bronge LB, Bronge C (1999) Ice and snow-type classification in the Vestfold Hills, East Antarctica, using Landsat-TM data and ground radiometer measurements. Int J Remote Sens 20(2):225–240
Campbell JB (1996) Introduction to remote sensing. Virginia Polytechnic Institute and State University, Blacksburg
Chahine MT (1992) The hydrological cycle and its influence on climate. Nature 359(6394):373
Chrisman NR (1991) The error component in spatial data. Geogr Inf Syst 1:165–174
Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Measur 20(1):37–46
Congalton RG, Oderwald RG, Mead RA (1983) Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. Photogram Eng Remote Sens 49(12):1671–1678
Dietz AJ, Kuenzer C, Gessner U, Dech S (2012) Remote sensing of snow: a review of available methods. Int J Remote Sens 33(13):4094–4134
Dozier J (1989) Spectral signature of alpine snow cover from the Landsat Thematic Mapper. Remote Sens Environ 28:9–22
Foody GM (1992) On the compensation for chance agreement in image classification accuracy assessment. Photogram Eng Remote Sens 58:1459–1460
Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sens Environ 80(1):185–201
Haefner H, Seidel K, Ehrler H (1997) Applications of snow cover mapping in high mountain regions. Phys Chem Earth 22(3–4):275–278
Hall DK, Riggs GA, Salomonson VV (1995) Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data. Remote Sens Environ 54(2):127–140
Haq MA, Jain K, Menon KPR (2012) Development of new thermal ratio index for snow/ice identification. Int J Soft Comput Eng 1(6):2231–2307
Hodge HN (2013) Ancient futures: learning from Ladakh. Random House, New York
Huang C, Davis LS, Townshend JRG (2002) An assessment of support vector machines for land cover classification. Int J Remote Sens 23(4):725–749
India Meteorological Department (2015) Climatological table. http://www.imd.gov.in/pages/city_weather_show.php. Accessed 13 March 2018
Kuching S (2007) The performance of maximum likelihood, spectral angle mapper, neural network and decision tree classifiers in hyperspectral image analysis. J Comput Sci 3(6):419–423
Loram C (1996) Leh & Trekking in Ladakh. Trail Blazer Publication, Portland
Maurer EP, Rhoads JD, Dubayah RO, Lettenmaier DP (2003) Evaluation of the snow-covered area data product from MODIS. Hydrol Process 17(1):59–71
Murtaza KO, Romshoo SA (2014) Determining the suitability and accuracy of various statistical algorithms for satellite data classification. Int J Geomat Geosci 4(4):585
Najafzadeh R, Abrishamchi A, Tajrishi M (2004) Runoff simulation with snowmelt runoff modeling using RS and GIS. Case study: Zayand-e-Rood basin
Negi HS, Kokhanovsky A (2011) Retrieval of snow albedo and grain size using reflectance measurements in Himalayan basin. Cryosphere 5:203–217
Negi HS, Kulkarni AV, Semwal BS (2009) Estimation of snow cover distribution in Beas basin, Indian Himalaya using satellite data and ground measurements. J Earth Syst Sci 118(5):525
Negi HS, Singh SK, Kulkarni AV, Semwal BS (2010) Field-based spectral reflectance measurements of seasonal snow cover in the Indian Himalaya. Int J Remote Sens 31:2393–2417
Olsson J, Arheimer B, Borris M, Donnelly C, Foster K, Nikulin G, Yang W (2016) Hydrological climate change impact assessment at small and large scales: key messages from recent progress in Sweden. Climate 4(3):39
Paul F, Kääb A, Maisch M, Kellenberger T, Haeberli W (2002) The new remote-sensing-derived Swiss glacier inventory: I. Methods. Annals Glaciol 34:355–361
Racoviteanu AE, Arnaud Y, Williams MW, Ordonez J (2008) Decadal changes in glacier parameters in the Cordillera Blanca, Peru, derived from remote sensing. J Glaciol 54(186):499–510
Rango A (1993) II. Snow hydrology processes and remote sensing. Hydrol Processes 7(2):121–138
Rees WG (2005) Remote sensing of snow and ice. CRC Press, Baca Raton
Rezaei Y (2004) Investigation of Chale Khersan glacier using remote sensing data and GIS. Unpublished M.S. Thesis, Khaje Nasire Toosi, Iran
Richards JA, Jia X (2008) Using suitable neighbors to augment the training set in hyperspectral maximum likelihood classification. IEEE Geosci Remote Sens Lett 5(4):774–777
Rosenthal W, Dozier J (1996) Automated mapping of montane snow cover at subpixel resolution from the Landsat Thematic Mapper. Water Resour Res 32(1):115–130
Roshani N, Zouj MV, Rezaei Y, Nikfar M (2008) Snow mapping of Alamchal glacier using remote sensing data. Int Arch Photogram Remote Sens Spat Inf Sci 37(2):805–808
Rott H, Künzi KF (1983) Remote sensing of snow cover with passive and active microwave sensors. Hydrol Appl Remote Sens Data Trans 145:361–369
Rouse JW, Haas RH, Schell JA, Deering DW (1973) Monitoring vegetation systems in the greant plains with ERTS. In: Proceedings of the earth resources technology satellite symposium NASA SP-351, vol 1, pp 309–317. NASA, Washington, DC
Salisbury JW, D’Aria DM, Wald A (1994) Measurements of thermal infrared spectral reflectance of frost, snow, and ice. J Geophys Res Solid Earth 99(B12):24235–24240
Tekeli AE (2005) Operational hydrological forecasting of snowmelt runoff by remote sensing and geographic information systems integration. Middle East Technical University, Dissertation Google Scholar
Vapnik VN, Chervonenkis AY (2015) On the uniform convergence of relative frequencies of events to their probabilities. In: Vovk V, Papadopoulos H, Gammerman A (eds) Measures of complexity. Springer, Cham, pp 11–30. https://doi.org/10.1007/978-3-319-21852-6_3
Verleyen E, Sabbe K, Hodgson DA, Grubisic S, Taton A, Cousin S, Vyverman W (2010) Structuring effects of climate-related environmental factors on Antarctic microbial mat communities. Aquat Microb Ecol 59:11–24
Warren SG, Brandt RE, Grenfell TC (2006) Visible and near-ultraviolet absorption spectrum of ice from transmission of solar radiation into snow. Appl Opt 45(21):5320–5334
Wiesmann A, Mätzler C (1999) Microwave emission model of layered snowpacks. Remote Sens Environ 70(3):307–316
Wilson LL, Tsang L, Hwang JN, Chen CT (1999) Mapping snow water equivalent by combining a spatially distributed snow hydrology model with passive microwave remote-sensing data. IEEE Trans Geosci Remote Sens 37(2):690–704
Acknowledgements
We wish to express our sincere thanks to the Director, ICAR-Central Arid Zone Research Institute, Jodhpur for providing facilities to conduct this research. We would also like to thank all the reviewers for the time they dedicated to ensure the quality of the manuscript.
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Gaur, M.K., Goyal, R.K., Raghuvanshi, M.S. et al. Geospatially extracting snow and ice cover distribution in the cold arid zone of India. Int J Syst Assur Eng Manag 11 (Suppl 1), 84–99 (2020). https://doi.org/10.1007/s13198-019-00883-w
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DOI: https://doi.org/10.1007/s13198-019-00883-w