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Snow cover dynamics and geohazards: a case study of Bhilangna watershed, Uttarakhand Himalaya, India

Snow cover dynamics and geohazards: a case study of Bhilangna watershed, Uttarakhand Himalaya, India This study examines snow covered area (SCA) and associated geohazards in the Bhilangna watershed using the Normalized Difference Snow Index. Two Landsat images from 1990 and 2010 were analyzed. In order to estimate the average elevation of the snowline, a digital elevation model from the Shuttle Radar Topography Mission was 2 2 used. In 1990, 124 km (9 % of the watershed) was snow covered. In 2010, 96 km (7 %) was snow covered. Therefore, during the study period (i.e., 1990–2010) SCA decreased by 28 km (2 %). Four snow types were identified and mapped: frost, fine, medium and coarse snow. In 1990, 38 km (30 % of SCA) was covered by frost 2 2 snow, 86 km (69 %) was covered by fine snow, < 1 km (<1 %) was covered by medium granular snow, and an 2 2 insignificant area was covered by coarse snow. In 2010, frost and fine snow covered 19 km (20 %) and 76 km (79 %) respectively, medium snow covered 1 km (1 %), and coarse snow was not traced. The snowline shifted from 4611 m in 1990 to 4698 m in 2010. These observations show the variability of snow cover in the Bhilangna watershed. Keywords: Shuttle Radar Topography Mission, Normalized Difference Snow Index, Snow line, Geohazard Introduction balance (Cohen, 1994; Cohen and Entekhabi, 2001; Douville Snow cover plays an important role in the climate sys- and Royer, 1996; Foster et al., 1996; Stieglitz et al. 2001; tem by changing the energy and mass transfer between Yang et al., 1999). Hence, the reliable and updated informa- the atmosphere and the surface (Khosla et al., 2011). tion on snow cover may also be used in hydrological cycle The snow is most important land cover type in the and climate modeling. Obtaining snow cover information Himalaya, which act as an important source of fresh on repetitive basis from vast snow covered areas of Hima- water for rivers (Kulkarni, 2007). The monitoring of its laya using conventional survey and mapping (manned spatial extent is an important aspect of research because snow-meteorological observatories) techniques are very dif- it provides insight as to the amount of water to be ficult due to high altitude, inaccessible and rugged moun- expected from snowmelt available for runoff and water tain terrain. In the recent years, Remote Sensing technique supply (Salomonsona and Appel 2004). The reliable has emerged as a popular viable substitute for real-time, information on spatial extent of snow cover and its year-round and large spatial coverage for monitoring and dynamics may be useful in several research and develop- process studies over vast, rugged and remote areas (Konig mental activities. This information may prove as a better et al., 2001, Hall et al. 2005). This technique has been used input in hydropower generation system, water manage- extensively for snow-cover monitoring in the Himalayan re- ment, strategic planning and many other developmental gion with the help of numerous satellite sensors (Kulkarni activities in any region. In addition, the snow cover itself and Rathore, 2003). Geographical Information System is a surface condition that affects the Earth’sradiation (GIS) along with remote sensing technology facilitate fast and efficient ways to analyze, visualize and report the sea- sonal snow-cover changes (Kaur et al. 2009). In the mid- * Correspondence: manish.ks1@gmail.com 1960s, snow was successfully mapped from space on a Department of Geography, Kalindi College, University of Delhi, Delhi 110008, India weekly basis following the launch of the Environmental Full list of author information is available at the end of the article © 2016 Kumar and Kumar. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Kumar and Kumar Geoenvironmental Disasters (2016) 3:2 Page 2 of 8 Science Service Administration (ESSA-3) satellite which snowline variation in Bhilangna watershed analyzing carried the Advanced Vidicon Camera System (AVCS) that Landsat TM data set of years 1990 and 2010 using re- operated in the spectral range of 0.5–0.75 μmwitha spatial mote sensing and GIS techniques. resolution at nadir of 3.7 km. The National Oceanographic and Atmospheric Administration (NOAA) has measured Study area snow cover on weekly basis in the Northern Hemisphere The study was carried out in the Bhilangna watershed, since 1966 using a variety of sensors, including the Scan- located in the Tehri Garhwal district of Uttarakhand, ning Radiometer (SR), Very High Resolution Radiometer India which extends between 30°19′46″Nto30°51′ (VHRR) and the Advanced Very High Resolution Radiom- 36″N latitudes and 78°28′27″E to 79°01′50″E longi- eter (AVHRR) (Matson et al., 1986; Matson, 1991). The tudes, encompasses an area of 1420.64 sq km in the current NOAA product is a daily snow-cover product Garhwal Himalaya, India. The river Bhilangna is a (Ramsay, 1998). This system provide daily, global observa- major tributary of the Bhagirathi River. The main- tions to monitor the variability in space and time in the ex- stream Bhilangna, rises at the foot of the Khatling Glacier tent of snow cover utilizing space borne sensors (Frei and (elevation 3717 m (12,195 ft)) approximately 50 km Robinson, 1998; Rango et al. 2000; Robinson et al. 1993). (31 mi) south of the ice cave at Gaumukh, tradition- The different medium resolution satellite sensors, e.g., ally considered the source of both the Bhagirathi and Landsat MSS and TM have been used for the mapping of the Ganges and flows into the Bhagirathi at Old snow cover area over drainage basins (Dozier et al 1981; Tehri, the site of the Tehri dam. The altitude of the Rango and Martinec 1982; Dozier 1984, 1989). watershed varies between 621 m and 6427 m. A loca- Initially, the mapping process on satellite data was tion map is shown in Fig. 1. largely based on the conventional techniques such as manual delineation of snow cover boundaries, segmenta- Materials and methods tion of ratio images and hard or crisp classification (Negi The methodology for determining the snow cover pat- et al., 2009). Other analysis techniques such as visual, tern and dynamics of Bhilangna watershed is described hybrid (visual and supervised classification) have also in the following steps: been used to estimate the areal extent of snow cover (Kulkarni and Rathore, 2003). The major difficulties in 1. The base map of study area Bhilangna watershed monitoring snow cover using remote sensing techniques was prepared in the ArcGIS software by using the in Himalayan region are the mountain shadow and con- survey of India toposheet scaled at 1:50,000 and fusing signature of snow and cloud in the visible and then watershed boundary was checked and near-infrared region. Some researchers have introduced corrected by superimposing the DEM data the reflectance ratio/index approaches to remove the ef- (Digital elevation model) derived from the SRTM fects of radiometric errors due to changing effects in the digital elevation dataset with 90 m spatial atmosphere and topographical changes across the scene resolution and ± 15 m vertical accuracy. A (Slater, 1980). To address this issue, normalized differ- rectificationhave beendone inLandsat 5 TM ence snow index (NDSI), along with the threshold tests images using the ArcGIS 9.3 software and the have been used successfully for snow cover mapping images have been given the base map coordinates using satellite data (Hall et al., 1995, 2002, Kulkarni and (i.e., UTM projection, and 44 N zone) for the Rathore 2003, 2006; Gupta et al., 2005; Negi et al., purpose to identify the study area in the image. 2008). The NDSI is a spectral band ratio like normalized 2. To work out with snow cover area, remotely difference vegetation index -NDVI (Tucker 1979, 1986; sensed data are extremely valuable. To study the Townshend and Tucker 1984) that takes advantage of spatial and temporal pattern of snow cover and the spectral differences of snow in short-wave infrared snow lines in the watershed, a set of two Landsat (SWIR) and visible spectral bands (green) to identify 5, Thematic Mapper TM images were procured in snow versus other features in a scene (Nolin and Liang, digital format for the years October 15, 1990 and 2000). This is an effective index for mapping snow cover October 11, 2010. Landsat 5, Thematic Mapper in rugged terrain (Hall et al., 1995). It can delineate and TM with spatial resolution of 30 meter was map the snow under mountain shadow and is not influ- stacked on ERDAS imagine software. The Landsat enced by topographic conditions (Kulkarni et al., 2006). data set provided by Global Land Cover Facility Shafer and Leaf (1979) concluded that the Landsat satel- site were radiometrically and geometrically lite imagery has sufficient quality to monitor the snow (ortho-rectified with UTM/WGS 84 projection) cover area accurately. corrected. In the light of above facts, the present study is an at- 3. To segregate the snow covered from non-snow tempt to monitor and map out the snow-cover and coveredareaNDSIwas estimatedusing Band 2 Kumar and Kumar Geoenvironmental Disasters (2016) 3:2 Page 3 of 8 Fig. 1 Study area andBand5forthe both TMimages of1990and snow covered from non-snow covered area, Hall 2010 by the following equation (Hall et al.2002): et al. (1998) suggested a NDSI threshold of >0.40 which has been used to map snow cover. After TM Band 2−TM Band 5 displaying the NDSI imagery on the screen of Arc NDSI ¼ TMBand 2 þ TM Band 5 map, the lower limit of snow cover in the watershed area was digitized for both years. By superimposing Where, TM Band 2 and TM Band 5 are the the lower limit of snow cover for both the years, the reflectance of the green and shortwave infrared area of change from snow cover area to non-snow bands, respectively. cover area was worked out. The study area, i.e., Bhilangna watershed was 4. The NDSI image of the study area has been further clipped using its shape file from NDSI image. The NDSI clipped raster data of the years 1990 and 2010 classified into four snow cover sub categories based on the threshold value suggested by Hall et al. (1998, were then reclassified into two classes i.e. snow 1995) namely coarse granular snow (i.e., >0.937 nm), cover and non-snow cover area in ArcGIS software, by using the Spatial Analyst tool. To segregate the medium granular snow (i.e., 0.848 nm to 0.937 nm), Kumar and Kumar Geoenvironmental Disasters (2016) 3:2 Page 4 of 8 Fig. 2 NDSI of 1990 and 2010 fine granular snow (i.e., 0.611 nm to 0.848 nm) and point shape file has been created in Arc-catalogue frost (i.e., 0.414 nm to 0.611 nm). This step was and keeping the snapping mode on, the digitization performed in ArcGIS software, in which the NDSI was done, over the snowline of one year i.e., 1990 images were reclassified in ‘Spatial Analyst toolbar’ and then the digitized points were masked by the entering threshold values. mask function from DEM data, so, that each point 5. The NDSI was reclassified into four categories i.e., bear some heights and then those points were coarse granular snow, medium granular snow, fine exported into the Microsoft excel sheet and the granular snow and frost were then converted into average height have been estimated. The same vector based polygon format for the estimation of process was repeated for the year 2010 snow line total snow cover area and to know about the area height estimation. falling within each category of snow cover. 6. In order to draw snow line, a shape file was created Results and discussion in ArcGIS software in ‘Arc catalogue’ and snow line Spatio-temporal dynamics of snow cover has been digitized for the year 1990 and 2010. To study the spatio-temporal pattern of snow cover in 7. To estimate snow line height, the snow line of both Bhilangna watershed, a set of two TM images were ana- the years were overlaid on the DEM data and then a lyzed using NDSI model in ERDAS Imagine software. Fig. 3 Snow cover classification and distribution of NDSI Kumar and Kumar Geoenvironmental Disasters (2016) 3:2 Page 5 of 8 Table 1 Snow cover in Bhilangna Watershed Watershed was under snow cover while in 2011 the snow cover was found 95.52 sq km (6.72 %). It is also Year Snow cover status shown in Table 1 that the snow cover area in the Bhi- Sq km (%) langna watershed got depleted considerably during the 1990 124.03 8.73 20 years of research span (i.e., 1990 to 2010). During this 2010 95.52 6.72 period, about 28.51 sq km snow cover of the Bhilangna Change (1990–2010) 28.51 2.01 watershed has been converted into non-snow cover area Source: Landsat TM at an average rate of 1.42 sq km/ year. In order to identify the sub classes of snow cover, NDSI images of the year 1990 and 2010 were analyzed using Spatial Analyst toolbar in ArcGIS software. This Initially, the DNs of the seven-band of TM images analysis was performed on the basis of threshold values were processed and converted into extra-atmospheric suggested by Hall et al. (1998, 1995). Four snow sub reflectance values (reflectance above the atmosphere), classes were mapped on the NDSI images, viz., a. Coarse using the published Landsat TM post-launch gains granular snow; b. Medium granular snow; c. Fine granu- and offsets (ENVI software version 3.2). The resultant lar snow; d. Frost. Figure 4 and Table 2 reveals that in reflectanceimageswerethenusedfor NDSI estima- the year 1990, about 37.82 sq km (30.49 %) area was tion. NDSI was calculated on a pixel-by-pixel basis covered by frost snow, 85.74 sq km (69.12 %) area under and generated gray scale images with high values fine granular snow, 0.47 sq km (0.37 %) area under (bright pixels) representing snow. The results ob- medium granular snow and 0.0018 sq km (0.0014 %) tained through this analysis are diagrammatically il- area was covered by coarse granular snow while during lustrated in Fig. 2 which depicts the distribution of the year 2010 the area under frost and fine granular NDSI variation in year 1990 and 2010 in the study cover classes were found to be 18.73 sq km (19.60 %) area. A pixel value in the resultant NDSI images var- and 75.68 sq km (79.22 %) respectively while the ies between 0.937 to −0.977 and 0.890 to −0.712 in medium granular snow cover was found to be 1.11 sq the year 1990 and 2010 respectively. On the basis of km (1.16 %). The result reveals that due to overall de- threshold value of >0.40, both NDSI images were crease in the snow cover area, the frost and fine granular classified to map out the snow cover (i.e., if NDSI > snow was also decreasing. 0.40 pixel is snow, else not snow). Figure 3 depicts spatial distribution of snow cover area in 1990 and Spatio-temporal pattern and dynamics of snowline 2010 in the study area. The calculation of area under In order to analyze snowline and their shifting, the snow cover during the years 1990 and 2010 were digitization process was performed using Arc cata- worked out which is represented in Table 1. The ob- logue and ArcMap editor in ArcGIS software. The tained result from NDSI reveals that in the year 1990 snowlines were digitized for the year 1990 and 2010 about 124.03 sq km (8.73 %) area of the Bhilangna (Fig.5). In ordertoestimatesnow lineheight, DEM Fig. 4 Snow cover sub classes Kumar and Kumar Geoenvironmental Disasters (2016) 3:2 Page 6 of 8 Table 2 Snow cover sub classes and change detection (1990–2010) Snow cover NDSI 1990 2010 Change (1990–2010) classes threshold Snow area Snow area Snow area value 2 2 2 km (%) km (%) km (Hall et al. 1995, 1998) Frost 0.414 37.82 30.49 18.73 19.60 −19.09 Fine granular 0.611 85.74 69.12 75.68 79.22 −10.06 Medium granular 0.848 0.47 0.37 1.11 1.16 −0.64 Coarse granular 0.937 0.0018 0.0014 Not Traceable - Source: Landsat TM data was used and analyzed. The results depict that mass movement lead to the most severe glacier catastro- in 1990, the average height of snowline of Bhilangna phe. Long term trends in temperature or other climatic watershed was 4611 meters above the mean sea level variables can be known by changes in seasonal snowline and in year 2000 it was shifted to 4698 meters. This elevation. shift in higher elevation is due to decrease in snow Landslide may occur due to bedrock discontinuities in cover area. Hence, the total shifting of snowline was the headscarp and may be triggered by cracking of observed about 87 meters during research span, at steeply dipping slope bounded by discontinuities (Dortch the rate of 4.35 m/year. et al., 2008). Snow cover and geohazards in high moun- tain regions are associated with each other. The area Snow cover and geohazard under investigation occupies a complex geological and The dynamics of Glacial and periglacial environment tectonic set-up. Uttarakhand Himalaya lying in the west- are strongly influenced by climate change. Retreat of ern part of Himalayan range of Indian sub-continent, snowline and degradation of permafrost can lead to snow cover plays an important role in the climate sys- horrendous experience never witnessed before. This tem by changing the energy and mass transfer between coupled with human activities intensifies potential the atmosphere and surface. Climate change induced conflict with natural geohazards. Few of the major snow melting releases more water trapped in snow and geohazards are landslide, ground subsidence, snow av- glacier. Water availability on moderate to steep sloppy alanches and fluvial scour. Hazard recognition and terrain makes the watershed more vulnerable to various analysis are the vital steps that are required for any geohazards such as avalanches, landslide, rockfall etc. as risk assessment. it acts as lubricant (Fig. 6). Combination of glacial hazards such as hazards from Frequent avalanches and rockfall in the river valley glacial clad volcanoes, glacier related flood, paraglacial result in the damming of river channel and formation Fig. 5 Snowline and its elevation Kumar and Kumar Geoenvironmental Disasters (2016) 3:2 Page 7 of 8 Fig. 6 Snow Cover and Geohazard. Source: modified after Knight, 1999 of Lake behind the big builders of rocks and debris. dates may play a vital role in environmental planning This results into Landslide Lake Outburst Floods and watershed management. (LLOFs) and many secondary LLOFs in the highly Competing interests unstable and fragile terrain of Bhilangna Watershed. The authors declare that they have no competing interests. Increased frequency of these geohazards and snow cover melting in the recent decades are the manifest- Authors' contribution MK has collected the satellite data from various sources, performed the ation of global climate change. classification and drafted the manuscript. PK has done the statistical analysis. Both the author modified and enriched the final manuscript more relevant and rational. The authors read and approved the final manuscript. Conclusion In this study, Landsat TM images of the year 1990 and Authors’ Information The corresponding author, Dr. Manish Kumar is serving as an Assistant 2010 were processed in remote sensing and GIS software Professor at Kalindi College, University of Delhi, is professionally a to monitor snow cover in Bhilangna watershed of Garh- Geographer. From January 2009 to December 2014, Dr. Kumar served as wal Himalaya. This study demonstrate the usefulness of Course In-Charge of M.Sc. Remote Sensing and GIS Programme of Kumaun University, Uttarakhand. Dr. Kumar has worked as Research Associate on remote sensing and GIS techniques in analyzing spatial UNDP Project on "'Rurbanization': Making Small Towns Hubs of Rural Prod- extent, nature and magnitude of snow cover area. This uctivity at Delhi Policy Group. Dr. Kumar has also served as Visiting Faculty at study provides beneficial insight into the extent and na- School of Planning and Architecture, New Delhi. Dr. Kumar holds a PhD from Kumaun University, Nainital, Uttarakhand. He also holds a Post Graduate Dip- ture of snow cover changes that has taken place in the loma in Remote Sensing and GIS from Indian Institute of Remote Sensing, watershed from 1990 to 2010, and lays the foundation ISRO, Dehradun, Uttarakhand. His area of research domain includes applica- for further research to be conducted. This study reveals tion of remote sensing and GIS in urban and regional planning, urban heat island, land use and land cover dynamics, climate change, snow cover dy- that during the last two decades about 28.51 sq km area namics etc. of the watershed has been converted into non-snow Dr. Pankaj Kumar,(MA, Ph.D) is working as Assistant Professor in the cover area. With the help of these data, it can be extrap- Department of Geography, Delhi School of Economics, University of Delhi. Dr. Kumar did his Graduation, Master, M.Phil and Ph.D from Delhi University. olated that the snow cover area in Bhilangna watershed His specialization is Mountain Environment, Applied Glaciology, Remote is depleting at an average rate of 1.42 km²/ year. It is evi- Sensing and Geographical Information System (GIS) and Regional and Urban dent from this study that the snow cover area is deplet- Geography. ing steadily in the Garhwal Himalaya. 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Estimation of snow cover 7 Immediate publication on acceptance distribution in Beas basin, Indian Himalaya using satellite data and ground 7 Open access: articles freely available online measurements. Journal of Earth System Science 118: 525–538. 7 High visibility within the fi eld Nolin, A., and S. Liang. 2000. Progress in bidirectional reflectance modeling and 7 Retaining the copyright to your article applications for surface particulate media: Snow and soils. Remote Sensing Reviews 14: 307–342. Ramsay, B.H. 1998. The interactive multi-sensor snow and ice mapping system. Submit your next manuscript at 7 springeropen.com Hydrological Processes 12: 1537–1546. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Geoenvironmental Disasters Springer Journals

Snow cover dynamics and geohazards: a case study of Bhilangna watershed, Uttarakhand Himalaya, India

Kumar, Manish; Kumar, Pankaj
Geoenvironmental Disasters , Volume 3 (1) – Feb 23, 2016
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Springer Journals
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Copyright © 2016 by Kumar and Kumar.
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Environment; Environment, general; Earth Sciences, general; Geography, general; Geoecology/Natural Processes; Natural Hazards; Environmental Science and Engineering
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2197-8670
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10.1186/s40677-016-0035-z
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Abstract

This study examines snow covered area (SCA) and associated geohazards in the Bhilangna watershed using the Normalized Difference Snow Index. Two Landsat images from 1990 and 2010 were analyzed. In order to estimate the average elevation of the snowline, a digital elevation model from the Shuttle Radar Topography Mission was 2 2 used. In 1990, 124 km (9 % of the watershed) was snow covered. In 2010, 96 km (7 %) was snow covered. Therefore, during the study period (i.e., 1990–2010) SCA decreased by 28 km (2 %). Four snow types were identified and mapped: frost, fine, medium and coarse snow. In 1990, 38 km (30 % of SCA) was covered by frost 2 2 snow, 86 km (69 %) was covered by fine snow, < 1 km (<1 %) was covered by medium granular snow, and an 2 2 insignificant area was covered by coarse snow. In 2010, frost and fine snow covered 19 km (20 %) and 76 km (79 %) respectively, medium snow covered 1 km (1 %), and coarse snow was not traced. The snowline shifted from 4611 m in 1990 to 4698 m in 2010. These observations show the variability of snow cover in the Bhilangna watershed. Keywords: Shuttle Radar Topography Mission, Normalized Difference Snow Index, Snow line, Geohazard Introduction balance (Cohen, 1994; Cohen and Entekhabi, 2001; Douville Snow cover plays an important role in the climate sys- and Royer, 1996; Foster et al., 1996; Stieglitz et al. 2001; tem by changing the energy and mass transfer between Yang et al., 1999). Hence, the reliable and updated informa- the atmosphere and the surface (Khosla et al., 2011). tion on snow cover may also be used in hydrological cycle The snow is most important land cover type in the and climate modeling. Obtaining snow cover information Himalaya, which act as an important source of fresh on repetitive basis from vast snow covered areas of Hima- water for rivers (Kulkarni, 2007). The monitoring of its laya using conventional survey and mapping (manned spatial extent is an important aspect of research because snow-meteorological observatories) techniques are very dif- it provides insight as to the amount of water to be ficult due to high altitude, inaccessible and rugged moun- expected from snowmelt available for runoff and water tain terrain. In the recent years, Remote Sensing technique supply (Salomonsona and Appel 2004). The reliable has emerged as a popular viable substitute for real-time, information on spatial extent of snow cover and its year-round and large spatial coverage for monitoring and dynamics may be useful in several research and develop- process studies over vast, rugged and remote areas (Konig mental activities. This information may prove as a better et al., 2001, Hall et al. 2005). This technique has been used input in hydropower generation system, water manage- extensively for snow-cover monitoring in the Himalayan re- ment, strategic planning and many other developmental gion with the help of numerous satellite sensors (Kulkarni activities in any region. In addition, the snow cover itself and Rathore, 2003). Geographical Information System is a surface condition that affects the Earth’sradiation (GIS) along with remote sensing technology facilitate fast and efficient ways to analyze, visualize and report the sea- sonal snow-cover changes (Kaur et al. 2009). In the mid- * Correspondence: manish.ks1@gmail.com 1960s, snow was successfully mapped from space on a Department of Geography, Kalindi College, University of Delhi, Delhi 110008, India weekly basis following the launch of the Environmental Full list of author information is available at the end of the article © 2016 Kumar and Kumar. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Kumar and Kumar Geoenvironmental Disasters (2016) 3:2 Page 2 of 8 Science Service Administration (ESSA-3) satellite which snowline variation in Bhilangna watershed analyzing carried the Advanced Vidicon Camera System (AVCS) that Landsat TM data set of years 1990 and 2010 using re- operated in the spectral range of 0.5–0.75 μmwitha spatial mote sensing and GIS techniques. resolution at nadir of 3.7 km. The National Oceanographic and Atmospheric Administration (NOAA) has measured Study area snow cover on weekly basis in the Northern Hemisphere The study was carried out in the Bhilangna watershed, since 1966 using a variety of sensors, including the Scan- located in the Tehri Garhwal district of Uttarakhand, ning Radiometer (SR), Very High Resolution Radiometer India which extends between 30°19′46″Nto30°51′ (VHRR) and the Advanced Very High Resolution Radiom- 36″N latitudes and 78°28′27″E to 79°01′50″E longi- eter (AVHRR) (Matson et al., 1986; Matson, 1991). The tudes, encompasses an area of 1420.64 sq km in the current NOAA product is a daily snow-cover product Garhwal Himalaya, India. The river Bhilangna is a (Ramsay, 1998). This system provide daily, global observa- major tributary of the Bhagirathi River. The main- tions to monitor the variability in space and time in the ex- stream Bhilangna, rises at the foot of the Khatling Glacier tent of snow cover utilizing space borne sensors (Frei and (elevation 3717 m (12,195 ft)) approximately 50 km Robinson, 1998; Rango et al. 2000; Robinson et al. 1993). (31 mi) south of the ice cave at Gaumukh, tradition- The different medium resolution satellite sensors, e.g., ally considered the source of both the Bhagirathi and Landsat MSS and TM have been used for the mapping of the Ganges and flows into the Bhagirathi at Old snow cover area over drainage basins (Dozier et al 1981; Tehri, the site of the Tehri dam. The altitude of the Rango and Martinec 1982; Dozier 1984, 1989). watershed varies between 621 m and 6427 m. A loca- Initially, the mapping process on satellite data was tion map is shown in Fig. 1. largely based on the conventional techniques such as manual delineation of snow cover boundaries, segmenta- Materials and methods tion of ratio images and hard or crisp classification (Negi The methodology for determining the snow cover pat- et al., 2009). Other analysis techniques such as visual, tern and dynamics of Bhilangna watershed is described hybrid (visual and supervised classification) have also in the following steps: been used to estimate the areal extent of snow cover (Kulkarni and Rathore, 2003). The major difficulties in 1. The base map of study area Bhilangna watershed monitoring snow cover using remote sensing techniques was prepared in the ArcGIS software by using the in Himalayan region are the mountain shadow and con- survey of India toposheet scaled at 1:50,000 and fusing signature of snow and cloud in the visible and then watershed boundary was checked and near-infrared region. Some researchers have introduced corrected by superimposing the DEM data the reflectance ratio/index approaches to remove the ef- (Digital elevation model) derived from the SRTM fects of radiometric errors due to changing effects in the digital elevation dataset with 90 m spatial atmosphere and topographical changes across the scene resolution and ± 15 m vertical accuracy. A (Slater, 1980). To address this issue, normalized differ- rectificationhave beendone inLandsat 5 TM ence snow index (NDSI), along with the threshold tests images using the ArcGIS 9.3 software and the have been used successfully for snow cover mapping images have been given the base map coordinates using satellite data (Hall et al., 1995, 2002, Kulkarni and (i.e., UTM projection, and 44 N zone) for the Rathore 2003, 2006; Gupta et al., 2005; Negi et al., purpose to identify the study area in the image. 2008). The NDSI is a spectral band ratio like normalized 2. To work out with snow cover area, remotely difference vegetation index -NDVI (Tucker 1979, 1986; sensed data are extremely valuable. To study the Townshend and Tucker 1984) that takes advantage of spatial and temporal pattern of snow cover and the spectral differences of snow in short-wave infrared snow lines in the watershed, a set of two Landsat (SWIR) and visible spectral bands (green) to identify 5, Thematic Mapper TM images were procured in snow versus other features in a scene (Nolin and Liang, digital format for the years October 15, 1990 and 2000). This is an effective index for mapping snow cover October 11, 2010. Landsat 5, Thematic Mapper in rugged terrain (Hall et al., 1995). It can delineate and TM with spatial resolution of 30 meter was map the snow under mountain shadow and is not influ- stacked on ERDAS imagine software. The Landsat enced by topographic conditions (Kulkarni et al., 2006). data set provided by Global Land Cover Facility Shafer and Leaf (1979) concluded that the Landsat satel- site were radiometrically and geometrically lite imagery has sufficient quality to monitor the snow (ortho-rectified with UTM/WGS 84 projection) cover area accurately. corrected. In the light of above facts, the present study is an at- 3. To segregate the snow covered from non-snow tempt to monitor and map out the snow-cover and coveredareaNDSIwas estimatedusing Band 2 Kumar and Kumar Geoenvironmental Disasters (2016) 3:2 Page 3 of 8 Fig. 1 Study area andBand5forthe both TMimages of1990and snow covered from non-snow covered area, Hall 2010 by the following equation (Hall et al.2002): et al. (1998) suggested a NDSI threshold of >0.40 which has been used to map snow cover. After TM Band 2−TM Band 5 displaying the NDSI imagery on the screen of Arc NDSI ¼ TMBand 2 þ TM Band 5 map, the lower limit of snow cover in the watershed area was digitized for both years. By superimposing Where, TM Band 2 and TM Band 5 are the the lower limit of snow cover for both the years, the reflectance of the green and shortwave infrared area of change from snow cover area to non-snow bands, respectively. cover area was worked out. The study area, i.e., Bhilangna watershed was 4. The NDSI image of the study area has been further clipped using its shape file from NDSI image. The NDSI clipped raster data of the years 1990 and 2010 classified into four snow cover sub categories based on the threshold value suggested by Hall et al. (1998, were then reclassified into two classes i.e. snow 1995) namely coarse granular snow (i.e., >0.937 nm), cover and non-snow cover area in ArcGIS software, by using the Spatial Analyst tool. To segregate the medium granular snow (i.e., 0.848 nm to 0.937 nm), Kumar and Kumar Geoenvironmental Disasters (2016) 3:2 Page 4 of 8 Fig. 2 NDSI of 1990 and 2010 fine granular snow (i.e., 0.611 nm to 0.848 nm) and point shape file has been created in Arc-catalogue frost (i.e., 0.414 nm to 0.611 nm). This step was and keeping the snapping mode on, the digitization performed in ArcGIS software, in which the NDSI was done, over the snowline of one year i.e., 1990 images were reclassified in ‘Spatial Analyst toolbar’ and then the digitized points were masked by the entering threshold values. mask function from DEM data, so, that each point 5. The NDSI was reclassified into four categories i.e., bear some heights and then those points were coarse granular snow, medium granular snow, fine exported into the Microsoft excel sheet and the granular snow and frost were then converted into average height have been estimated. The same vector based polygon format for the estimation of process was repeated for the year 2010 snow line total snow cover area and to know about the area height estimation. falling within each category of snow cover. 6. In order to draw snow line, a shape file was created Results and discussion in ArcGIS software in ‘Arc catalogue’ and snow line Spatio-temporal dynamics of snow cover has been digitized for the year 1990 and 2010. To study the spatio-temporal pattern of snow cover in 7. To estimate snow line height, the snow line of both Bhilangna watershed, a set of two TM images were ana- the years were overlaid on the DEM data and then a lyzed using NDSI model in ERDAS Imagine software. Fig. 3 Snow cover classification and distribution of NDSI Kumar and Kumar Geoenvironmental Disasters (2016) 3:2 Page 5 of 8 Table 1 Snow cover in Bhilangna Watershed Watershed was under snow cover while in 2011 the snow cover was found 95.52 sq km (6.72 %). It is also Year Snow cover status shown in Table 1 that the snow cover area in the Bhi- Sq km (%) langna watershed got depleted considerably during the 1990 124.03 8.73 20 years of research span (i.e., 1990 to 2010). During this 2010 95.52 6.72 period, about 28.51 sq km snow cover of the Bhilangna Change (1990–2010) 28.51 2.01 watershed has been converted into non-snow cover area Source: Landsat TM at an average rate of 1.42 sq km/ year. In order to identify the sub classes of snow cover, NDSI images of the year 1990 and 2010 were analyzed using Spatial Analyst toolbar in ArcGIS software. This Initially, the DNs of the seven-band of TM images analysis was performed on the basis of threshold values were processed and converted into extra-atmospheric suggested by Hall et al. (1998, 1995). Four snow sub reflectance values (reflectance above the atmosphere), classes were mapped on the NDSI images, viz., a. Coarse using the published Landsat TM post-launch gains granular snow; b. Medium granular snow; c. Fine granu- and offsets (ENVI software version 3.2). The resultant lar snow; d. Frost. Figure 4 and Table 2 reveals that in reflectanceimageswerethenusedfor NDSI estima- the year 1990, about 37.82 sq km (30.49 %) area was tion. NDSI was calculated on a pixel-by-pixel basis covered by frost snow, 85.74 sq km (69.12 %) area under and generated gray scale images with high values fine granular snow, 0.47 sq km (0.37 %) area under (bright pixels) representing snow. The results ob- medium granular snow and 0.0018 sq km (0.0014 %) tained through this analysis are diagrammatically il- area was covered by coarse granular snow while during lustrated in Fig. 2 which depicts the distribution of the year 2010 the area under frost and fine granular NDSI variation in year 1990 and 2010 in the study cover classes were found to be 18.73 sq km (19.60 %) area. A pixel value in the resultant NDSI images var- and 75.68 sq km (79.22 %) respectively while the ies between 0.937 to −0.977 and 0.890 to −0.712 in medium granular snow cover was found to be 1.11 sq the year 1990 and 2010 respectively. On the basis of km (1.16 %). The result reveals that due to overall de- threshold value of >0.40, both NDSI images were crease in the snow cover area, the frost and fine granular classified to map out the snow cover (i.e., if NDSI > snow was also decreasing. 0.40 pixel is snow, else not snow). Figure 3 depicts spatial distribution of snow cover area in 1990 and Spatio-temporal pattern and dynamics of snowline 2010 in the study area. The calculation of area under In order to analyze snowline and their shifting, the snow cover during the years 1990 and 2010 were digitization process was performed using Arc cata- worked out which is represented in Table 1. The ob- logue and ArcMap editor in ArcGIS software. The tained result from NDSI reveals that in the year 1990 snowlines were digitized for the year 1990 and 2010 about 124.03 sq km (8.73 %) area of the Bhilangna (Fig.5). In ordertoestimatesnow lineheight, DEM Fig. 4 Snow cover sub classes Kumar and Kumar Geoenvironmental Disasters (2016) 3:2 Page 6 of 8 Table 2 Snow cover sub classes and change detection (1990–2010) Snow cover NDSI 1990 2010 Change (1990–2010) classes threshold Snow area Snow area Snow area value 2 2 2 km (%) km (%) km (Hall et al. 1995, 1998) Frost 0.414 37.82 30.49 18.73 19.60 −19.09 Fine granular 0.611 85.74 69.12 75.68 79.22 −10.06 Medium granular 0.848 0.47 0.37 1.11 1.16 −0.64 Coarse granular 0.937 0.0018 0.0014 Not Traceable - Source: Landsat TM data was used and analyzed. The results depict that mass movement lead to the most severe glacier catastro- in 1990, the average height of snowline of Bhilangna phe. Long term trends in temperature or other climatic watershed was 4611 meters above the mean sea level variables can be known by changes in seasonal snowline and in year 2000 it was shifted to 4698 meters. This elevation. shift in higher elevation is due to decrease in snow Landslide may occur due to bedrock discontinuities in cover area. Hence, the total shifting of snowline was the headscarp and may be triggered by cracking of observed about 87 meters during research span, at steeply dipping slope bounded by discontinuities (Dortch the rate of 4.35 m/year. et al., 2008). Snow cover and geohazards in high moun- tain regions are associated with each other. The area Snow cover and geohazard under investigation occupies a complex geological and The dynamics of Glacial and periglacial environment tectonic set-up. Uttarakhand Himalaya lying in the west- are strongly influenced by climate change. Retreat of ern part of Himalayan range of Indian sub-continent, snowline and degradation of permafrost can lead to snow cover plays an important role in the climate sys- horrendous experience never witnessed before. This tem by changing the energy and mass transfer between coupled with human activities intensifies potential the atmosphere and surface. Climate change induced conflict with natural geohazards. Few of the major snow melting releases more water trapped in snow and geohazards are landslide, ground subsidence, snow av- glacier. Water availability on moderate to steep sloppy alanches and fluvial scour. Hazard recognition and terrain makes the watershed more vulnerable to various analysis are the vital steps that are required for any geohazards such as avalanches, landslide, rockfall etc. as risk assessment. it acts as lubricant (Fig. 6). Combination of glacial hazards such as hazards from Frequent avalanches and rockfall in the river valley glacial clad volcanoes, glacier related flood, paraglacial result in the damming of river channel and formation Fig. 5 Snowline and its elevation Kumar and Kumar Geoenvironmental Disasters (2016) 3:2 Page 7 of 8 Fig. 6 Snow Cover and Geohazard. Source: modified after Knight, 1999 of Lake behind the big builders of rocks and debris. dates may play a vital role in environmental planning This results into Landslide Lake Outburst Floods and watershed management. (LLOFs) and many secondary LLOFs in the highly Competing interests unstable and fragile terrain of Bhilangna Watershed. The authors declare that they have no competing interests. Increased frequency of these geohazards and snow cover melting in the recent decades are the manifest- Authors' contribution MK has collected the satellite data from various sources, performed the ation of global climate change. classification and drafted the manuscript. PK has done the statistical analysis. Both the author modified and enriched the final manuscript more relevant and rational. The authors read and approved the final manuscript. Conclusion In this study, Landsat TM images of the year 1990 and Authors’ Information The corresponding author, Dr. Manish Kumar is serving as an Assistant 2010 were processed in remote sensing and GIS software Professor at Kalindi College, University of Delhi, is professionally a to monitor snow cover in Bhilangna watershed of Garh- Geographer. From January 2009 to December 2014, Dr. Kumar served as wal Himalaya. This study demonstrate the usefulness of Course In-Charge of M.Sc. Remote Sensing and GIS Programme of Kumaun University, Uttarakhand. Dr. Kumar has worked as Research Associate on remote sensing and GIS techniques in analyzing spatial UNDP Project on "'Rurbanization': Making Small Towns Hubs of Rural Prod- extent, nature and magnitude of snow cover area. This uctivity at Delhi Policy Group. Dr. Kumar has also served as Visiting Faculty at study provides beneficial insight into the extent and na- School of Planning and Architecture, New Delhi. Dr. Kumar holds a PhD from Kumaun University, Nainital, Uttarakhand. He also holds a Post Graduate Dip- ture of snow cover changes that has taken place in the loma in Remote Sensing and GIS from Indian Institute of Remote Sensing, watershed from 1990 to 2010, and lays the foundation ISRO, Dehradun, Uttarakhand. His area of research domain includes applica- for further research to be conducted. This study reveals tion of remote sensing and GIS in urban and regional planning, urban heat island, land use and land cover dynamics, climate change, snow cover dy- that during the last two decades about 28.51 sq km area namics etc. of the watershed has been converted into non-snow Dr. Pankaj Kumar,(MA, Ph.D) is working as Assistant Professor in the cover area. With the help of these data, it can be extrap- Department of Geography, Delhi School of Economics, University of Delhi. Dr. Kumar did his Graduation, Master, M.Phil and Ph.D from Delhi University. olated that the snow cover area in Bhilangna watershed His specialization is Mountain Environment, Applied Glaciology, Remote is depleting at an average rate of 1.42 km²/ year. It is evi- Sensing and Geographical Information System (GIS) and Regional and Urban dent from this study that the snow cover area is deplet- Geography. ing steadily in the Garhwal Himalaya. 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