M Anul Haq, Dr. Kamal Jain
Department of Civil Engineering
Indian Institute of Technology, Roorkee, India
Dr K.P.R. Menon
Head Data Dissemination
NRSC Balanagar, Hyderabad, India
Remote sensing mapping technique is valuable for studying inaccessible glaciers and their lakes. It makes preliminary assessments on a catchment-wide scale cheaper and quicker than traditional field investigations. It has been used for delineation and mapping of glacial lakes in Himalaya (Kulkarni 1990, Randhawa 2005) using false colour composites. An automatic detection method of the lake surface using a normalised difference water index (NDWI) was attempted on Imja Glacial Lake by Bolch et al (2008). This study aims to describe changes in the area and number of glacial lake on Gangotri glacier using Landsat and ASTER imagery. ASTER offers powerful capabilities to monitor supraglacial lakes in terms of (1) surface area, growth and disappearance [spatial resolution =15 m], (2) turbidity [15 m resolution], and (3) temperature [90 m resolution](Wessel’s et. al, 2002).
During the study, supraglacial and proglacial lakes of Gangotri glacier were mapped. Gangotri glacier originates in the Chaukhamba massif (6853–7138 m a.s.l.) and flows northwest towards Gaumukh. The Gangotri glacier, one of the largest ice bodies in the Garhwal Himalayas, is located in the Uttarkashi district of the state of Uttarakhand in India (See Fig 1). Gangotri glacier between 79o4’ 46.13” E-79o16’ 9.45” E and 30o43’ 47.00” N-30o55’ 51.05” N (Haq et. al., 2011). It has varying elevation of 4082–6351 meters above sea level (ASTER and SRTM Data Analysis). Flash floods caused by bursting of glacial lakes are well known in the Himalayas (Coxon et. al., 1996). The progressive thinning of the Himalayan glaciers during the past century has resulted in the formation of new moraine dammed lakes (Mayewski et. al., 1980).
Fig. 1 Subset of Corona Air Photo of the Gangotri Glacier 1968
The ‘Corona Air Photo’ of 1968, multi-spectral satellite data of Landsat MSS for the year 1972 and 1990, and ASTER data for 2001 and 2010, were used in the study (see table 1). The Landsat data used in current investigation system was downloaded for free from the USGS Global Visualization Viewer (GLOVIS) and ASTER data was provided by ECHO under the umbrella of NASA LPDAAC. Orthorectified VNIR images of the advanced spaceborne thermal emission and reﬂection radiometer (ASTER) on Terra satellite with a spatial resolution of 15 m(2001,2010), Landsat MSS(1972), Landsat TM(1990)were used for this analysis. The Landsat data used in current investigation system was downloaded for free from the USGS Global Visualization Viewer (GLOVIS) and ASTER data was provided by ECHO under the umbrella of NASA LPDAAC. The images used in the analysis are listed in table 1.The Lake areas were identified using the Normalized Differenced Water Index (NDWI). Visual interpretation and editing were also performed to rectify errors due to ice areas and shadows on the lake.
Table-1 Details of Satellite data used in the analysis
The Corona data is only panchromatic therefore radiometric correction and georefrencing procedures were required. For Corona image, we took 35 GCPs, acquired from ASTER imagery, for Image to Image registration processing. The main focus was on the adjustment of the area around both glaciers on Corona images in respect of ASTER imagery for consistency of results, during rectification of Corona imagery. Landsat MSS and TM images were co-registered with the ASTER DEM and ASTER imagery. To align two or more images geometrically that represents the same object. Geometric relationship between the warp image and base image was obtained through 30 tie points and modeled using transformation capabilities.
Glacial lakes were automatically identified using the Normalized Differenced Water Index (NDWI, [NIR-BLUE]/[NIR+BLUE]). This method was successfully applied for the detection of water bodies (Huggel et al., 2002). The NDWI performed slightly better than the band ratio ASTER1/ASTER3 (GREEN/NIR)(Bolch et. al., 2008.In some scenes the glacial lakes were partly covered by ice (Table 1). Therefore, visual checking and editing was necessary. Manual delineation had to be applied for the Landsat MSS and TM data. The contrast of the latter was stretched in order to improve the image quality. It was, however, still difficult to identify small, partly ice-covered lakes. Due to these problems and the coarse resolution of the Landsat MSS scene, researchers considered the changes of the glacial lakes which were larger than 0.0036 km2. The results show that the identification of glacial lakes using the NDWI led to accurate results. This also confirmed earlier studies (Bolch et al., 2008). Lake Identification based on ASTER was slightly more accurate than of Landsat as the comparison with the referenced data. The automated lake identification can be problematic with turbid lakes and lakes with partial ice cover/icebergs, and in shadow areas. In these cases, an improvement based on visual interpretation had been performed with the help of DEM.
Due to determinations, based on spaceborne imagery, the overall area of the proglacial and supraglacial lakes in the study region increased from 89520 m2 in 1968 to nearly 103975 m2 in 2010. The number of supraglacial lakes on the Gangotri Glacier increased from 8 in 1968 to 22 in 2010.There was 8 lakes identified in 1968 having total surface area 89520 Sq m (0.08952 km2) based on Landsat MSS scene, however in 1990 the number of lakes identified and mapped was 15 but the total are decreased to 83661 Sq m(0.0837 Km2) based on Landsat TM scene. In 2001 the total number of lakes identified were 18 and covers an area 138600 m2 (0.1386 Km2), However in 2010, total number of lakes identified were 22 and covers an area 103975 m2 (0.1039 Km2).
Table 2. Total number and overall area of glacial lakes of Gangotri glacier in different observation years
The application of multitemporal remote sensing made possible to map small lakes formed at the higher altitudes, which would have not been possible by field investigations. In addition, remote sensing is the best way to investigate a larger number of glaciers, glacial lakes As shown in this study, changes in Himalayan glacial lakes have been observable from repeated remotely sensed images since 1968. In particular, frequent multitemporal imaging will be valuable for understanding the underlying expansion mechanisms of glacial lakes in detail. Remote sensing-based measurements of glacier characteristics can provide area-wide information of glacier activity for entire glacier tongues instead of point wise measurements. During the investigation a total of 8 lakes were mapped of the Gangotri glacier in 1968 and 22 lakes in 2010. (Landsat and ASTER Data analysis). The monitoring of all of Gangotri glacier lakes has suggested that however the number of lakes are increasing but area of all lakes are not increasing up to manageable level in last 42 years.
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