WHAT IS CLIMATE
Climate refers to time and space patterns of precipitation,
temperature, and wind.
Climate change is a significant and lasting change in the statistical
distribution of weather patterns over periods ranging from decades
to millions of years
Glacier is a persistent body of dense ice that moves constantly
under its own weight, it forms where accumulation of snow is greater
Rising temperatures due to changing climatic conditions and Global Warming
as one of the main reasons has resulted in melting of glaciers all around the
Investigations carried out by Intergovernmental Panel on Climate Change
(IPCC) have concluded that the earth’s average temperature has increased
by 0.6 ± 0.2°C in the 20th century.
This increase in temperature will continue in the 21st century and average
surface temperature of the earth can rise by 1.4 to 5.8°C by the end of the
This projected rate of warming is much larger than the observed changes
during the 20th century.
In Asia the Himalayan region has suffered the wrath of increasing annual
temperatures, and from more than a decade now scientists are having a close
eye on impact of climate change on Glacial Melt in the Himalayan Region.
Mapping of glaciers
A study - conducted from 1999 to 2004 - generated important
baseline information about glaciers based on the World Glacier
Monitoring Service (WGMS) attribute list.
It included the Hindu Kush-Himalayan countries of Bhutan, Nepal,
Pakistan, the Ganges Basin in the Tibet Autonomous Region of
China, and selected river basins in India (Tista River basin, Himachal
Pradesh, and Uttarakhand).
The study identified more than 15,003 glaciers within the surveyed
area of 33,344 sq.km.
Majority of glaciers in the region were in a general condition of
Mapping and monitoring of glaciers has become easier with the advent of more
readily available remote sensing data.
A.V.Kulkarni & J.Srinivasulu (2002) conducted field based spectral reflectance
studies - developed NDSI method based on - high reflectance of snow in visible
region & low reflectance in SWIR region.
Snow cover monitoring - radiometers developed by SAC-ISRO viz.,
S.No RADIOMETER BANDS
1 Multi-band Ground Truth
12 spectral bands ranging from visible to near-infrared
2 Near-Infrared Ground Truth
3 Ratio-Radiometer (RR) 2 spectral bands, one in the visible range, and another in
the SWIR range
This study validates the usefulness of NDSI based method for discriminating snow and
clouds on a satellite image.
LITERATURE REVIEW continued..
B.P.Rathore & Anil Kulkarni (2008) calculated Snow line & future change in areal extent
of glacier was estimated using mass balance, response time and rate of melting at
terminus and for permanent snow cover IRS 1D LISS III images were used.
Average areal extent of snow for each month was estimated, using multi-date satellite
data of WiFS and Advance Wide Field Sensor (AWiFS) of Indian Remote Sensing Satellite
To estimate seasonal snow cover, supervised classification for WiFS and NDSI method for
AWiFS was used.
Prajjwal K. Panday & Karen E Frey (2011) studied detection of the timing and duration of
snowmelt in the Hindu Kush-Himalaya using QuikSCAT, 2000–2008.
This study provides an overall perspective of regional differences in melt onset, freeze-
up, and melt duration that have important implications for glaciological and
hydrological processes across the HKH region.
They developed a dynamic threshold based melt detection algorithm calibrated with in
situ air temperature records to identify melt and freeze-up events from enhanced
resolution data from time series of QuikSCAT radar scatterometer data.
LITERATURE REVIEW continued..
Prajjwal K. Panday & Henry Bulley (2012) proposed an approach of glacial lake
classification and demonstrated utility of this approach in producing a semi-
automated and replicable technique for supraglacial characterization and
delineation of glacial lakes.
Supraglacial debris poses a major challenge in automatic delineation of glaciers
due to the spectral similarity to the surrounding debris.
Kathryn Semmens & Joan Ramage (2014) analyed brightness temperatures from
the passive Advanced Microwave Scanning Radiometer for Earth Observing
System (AMSR-E) used to characterize melt regime patterns over large
glacierized areas in Alaska and Patagonia.
CASE STUDY 1
TITILE- Monitoring of Gangotri glacier using remote sensing & ground
AUTHORS- H.S. Negi, N.K. Thakur, A. Ganju, Snehmani
JOURNAL- Journal of Earth System Science(2012)
In this study, Gangotri glacier was monitored using Indian Remote
Sensing (IRS) LISS-III sensor data in combination with field collected
snow-meteorological data for a period of seven years (2001–2008).
STUDY AREA & DATA USED
Gangotri glacier - longitude 78°59'30" and 79º17'45"E,
latitude 30º43'00" and 30º57'15"N
The total study area - approx. 417 km² (54% glaciated).
Length – apprx. 30 km.
LISS-III sensor data - IRS-1C/1D satellites - monitor snow cover
33 multitemporal cloud-free satellite imageries - Oct 2001 to May 2008
PAN imagery of IRS satellite (for the mapping of latest glacier extent)
False colour composite (FCC) of visible and near-infrared images used to map
glacier boundary, due to significant difference in reflectance from glacial and
• Green (0.52–0.59 μm),
• Red (0.62–0.68 μm),
• NIR (0.77–0.86 μm)
• SWIR (1.55–1.70 μm) wavelength regions.
• 23.5 m spatial resolution in first three bands (VNIR)
• 70.5 m spatial resolution in SWIR band
• swath of 141 km.
Study area shown by IRS-1C, LISS-III image of 9 August 2005;
Pink polygon represents Gangotri sub-basin;
Green polygon represents glacier boundary based on SoI mapsheet of
1962 and yellow points are settlements.
The snow cover analysis - satellite & ground data on seasonal basis.
(Daily snow-melt data such as temperatures (maximum and
minimum), fresh snowfall and rainfall recorded from field
observatory were analyzed for comparison/ validation of snow
cover area and snow characteristics).
The topographic analysis - A DEM of the study area was generated
at 6 m pixel resolution using contours and spot heights of 1:50,000
scale map sheets.
The images were classified - unsupervised classification
The digital database of Gangotri glacier consisting different GIS layers such
as glacier boundary, sub-basin boundary, and settlements generated using
mapsheets and satellite imageries
The seasonal snow cover area analysis shows an overall decreasing trend
An upward shifting trend of wet snow line was observed in the beginning of
melt period, i.e., in May and dominant wet snow conditions were observed
between May and October.
The change in glacier health is effectively confined to the abrupt slope
changes, such as the walls of open crevasses and other holes on the
This study has shown that the changes on glacier surface are due to
climatic and topographic (local geomorphology) factors, A deglaciation
of 6% in overall area of Gangotri glacier was observed between the years
1962 and 2006.
Estimated snow cover area using
satellite data of
(a) winter season (November–
April) and (b) summer season
Comparison of estimated dry and wet
area using satellite data of (a) winter
season (November–April) and (b)
summer season (May–October).
Changes in glacio-morphology constrained by topography. (a) LISS-III data of 10
October 2001; (b) LISS-III data of 9 October 2006; (c) change detection image
with red colour indicating snow class present in earlier image changed
into another class/decreased reflectance in later image; (d) Cartosat-1 image of
28 September 2006.
CASE STUDY 2
TITILE- Glacier Change in Mount Suphan Using Remote Sensing and
AUTHORS- Dogukan Dogu Yavalı, M. Kirami Olgen
PUBLISHER- BALWOIS 2008, Conference on Water Observation and
Information System for Decision Support
Mount Suphan was studied using Landsat images. MSS false colour
composite images and TM4/TM5 band combination used for
delineating glaciers, and applied the change detection techniques to
determine the change on glacial area.
Also, the results were compared with meteorological data & it was
concluded that there was a recession in Mount Suphan glaciers due to
global climate change.
STUDY AREA & DATA USED
Mt. Suphan is a stratovolcano located in eastern Turkey
It is the second highest volcano in Turkey
with an elevation of 4,058 m
The largest glacier on the Mount Suphan is
Hızır Glacier located on the northern slope
of the crater
Glacier is 1.5 km long, 3400 meters high
Length of the glacier is 1.2 kilometers.
The LANDSAT data available on NASA’s GLCF for Mount Suphan
Colour composite of Landsat images: a) false-colour (321=RGB) MSS image,
b) TM image (543=RGB), c) ETM image (543=RGB). Except for the image in
1989, the images are acquired during a time of minimum snow cover. Cyan
colour represents snow in the image of 1989.
Different kinds of methods for mapping or monitoring the glaciers
with remote sensing
(A) Manual delineation of the glacier outline from different colour
(B) Segmentation of ratio images
(C) Various supervised and unsupervised classification techniques.
MSS image - manual delineation - FCC
TM image (snow covered) - the glaciers have to be distinguished
from snow. Glaciers are the results of metamorphosis of snow,
(spectral properties very similar).
Dividing near infrared (0.76-0.90 μm) part of spectrum by middle
infrared (1.55-1.75 μm) part - band ratio techniques.
Result- high values over ice and snow
low values over other terrain,
Formula for TM is TM4/TM5.
Then, snow and glacier was separated from other terrain with a
After defining the glaciers, the outline of it was manually digitized and
the areas are calculated in ArcGIS 9.2.
Meteorological data obtained from Turkish State Meteorological
Service was used to determine the change in climatic conditions.
Areal change of glaciers on Mount Suphan.
The colored lines show the extent of glacier for
the dates indicated in the legend. The
background image is ETM (543=RGB) image of
the year 2000.
The total glacier area in Mount Suphan ,1977
with 1.20 km² was retreated to 1.09 km² in
1989, and 0.33 km² in 2000
Trends of two variables, temperature and precipitation, are
responsible for glacier regime and mass balance.
The assessment of annual maximum temperatures show little
decrease trend in 61 years
However, annual minimum temperatures are clearly in an increasing
trend in annual maximum
trend in annual minimum
To sum up, the qualitative analysis of satellite imagery of Mount
Suphan shows clear recession of glaciers. 3/4 of total glacier area
lost in 23 years.
The analysis of climatic factors shows this recession is coherent with
the warming trend.
Presently, the most successful strategy is based on the combination
of remote sensing, modelling with Geographical Information
Systems (GIS) or numerical models and other local field surveys to
study the glaciers and the impact of climate change on their
The use of remote sensing data and techniques and geographic
information system (GIS) data, complemented by field verification, is
an effective method for the mapping and inventorying of glaciers.
Field based spectral reflectance studies to develop NDSI Method for snow cover
monitoring- A.V. Kulkarni, J. Srinivasulu , S.S. Manjul , P. Mathur - JOURNAL OF
INDIAN SOCIETY OF REMOTE SENSING, 2002
Effect of global warming on snow ablation pattern in the Himalaya- D. T. Mourya
et al. Current Science, Vol. 83, No. 2. 2002.
Understanding future changes in snow and glacier melt runoff due to
globalwarming in Wangar Gad basin, India - B.P.Rathore & Anil Kulkarni –
CURRENT SCIENCE JOURNAL OF RESEARCH, 2008
Glacier Change in Mount Suphan Using Remote Sensing and Meteorological
Data- D. D. Yavalı & M. Kirami Olgen , BALWOIS 2008, Conference on Water
Observation and Information System for Decision Support, 2008
Detection of the timing and duration of snowmelt in the Hindu Kush-Himalaya
using QuikSCAT - Prajjwal K. Panday & Karen E Frey – IOP SCIENCE,
ENVIRONMENTAL RESEARCH LETTERS, 2011
Supraglacial lake classification in the everest region of Nepal-
Prajjwal K. Panday & Henry Bulley - SPRINGER LINK, 2012
Monitoring of Gangotri glacier using remote sensing & ground
observations- H.S. Negi, N.K. Thakur, A. Ganju, Snehmani Journal of
Earth System Science, 2012
Melt Patterns and Dynamics in Alaska and Patagonia Derived from
Passive Microwave Brightness Temperatures - Kathryn Semmens &
Joan Ramage - MDPI, REMOTE SENSING JOURNAL, 2014
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