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 A monthly house bulletin of Defence Research & Development Organisation ■ Vol. 31 No. 7 ■ July 2011ISSN : 0971-4413BULLETIN OF DEFENCE RESEARCH AND

DEVELOPMENT ORGANISATIONVol. 20 No. 2 April 2012

Troops guarding the high altitude snowbound

border areas are always in perpetual danger

of losing life due to avalanche. Forecasting

of avalanche can help in minimising such incident.

One of the mandate of DRDO is to facilitate high

operational mobility of troops in snowbound high

altitude avalanche prone areas. Snow and Avalanche

Study Establishment (SASE), a constituent laboratory

of DRDO, provides precision avalanche forecastingsupport to the Services including advice on

avalanche control measures and enhance avalanche

forecasting through systematic data collection in

snowbound areas. The task involves collection of

snow and meteorological data at every hour and at

a closely spaced grid of less than a kilometer from

dierent altitude ranges of the Himalaya.

Given the ruggedness of the terrain and

inhospitable conditions, it is not possible to collect

data at high temporal and spatial resolution. Even

automatic weather stations (AWS) do not suce

since it is not possible to install and maintain

these at some places. In such a scenario, remote

sensing is the only alternative to collect data of a

larger area. SASE has made use of remote sensing

technology since 1998 for better understanding and

monitoring of cryospheric regions of the Himalayas.

Remote sensing has augmented the conventional

measurements, which SASE has been taking at

select points since 1969. SASE has also started

collaborative research with Space Application

Centre, Ahemdabad; Centre of Studies in Resources

Engineering, Indian institute of Technology, Bombay

(IIT-B), Mumbai; IIT-K, Kanpur; and Department of

Geomatics Engineering, IIT, Roorkee.

The Establishment has a strong foundation and

extensive understanding of the remote sensing

technology and its application to snow and

avalanche-related studies. It is fully equipped with

the state-of-the-art software and hardware facilities

to make use of optical and microwave satellite data.

The Establishment has developed methodologies/

algorithms for snow and avalanche-related

applications and has taken up research in Polarregions also.

Some of the new applications and technologies

pertaining to avalanche hazard assessment, snow

cover monitoring, topographic parameters using

multispectral, hyperspectral, microwave and other

remote sensing data are being highlighted in this

special issue of Technology Focus .

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 From the Special Editor 

The mountain ranges of Himalayas form a distinct geographical divide that separates the Indian

subcontinent from Central Asia. The area is strategically very important and affects the socio-economic

development. The rugged foothills of Himalayas are carved into deep gorges and ravines by innumerable

streams. The road connectivity to these areas is rather short due to climatic conditions. The passes are

closed as they remain covered with snow during most of the year. The roads are plagued by frequent snow

avalanches, etc.

Himalaya experience severe snowfall of varying magnitudes during the winter period. The snow

accumulation depends on altitude and other geographical parameters of the region. The unstable snow

pack results in avalanches that cause danger and loss to human lives and property, and also causes a lot of

hindrance in transportation, communication and deployment of army personnel and inconvenience to civilian

population in these areas. The hazard potential of the region is a cause of concern for us at SASE especiallythe ones directly emanating from the interaction of snow, ice, terrain and weather. SASE has been providing

yeoman’s service for more than four decades in combating the hazards due to avalanches and the foremost

menace of avalanches have been controlled to a large extent due to the sustained efforts of its scientists and

the constant help rendered by the Indian Army.

Operational avalanche forecast primarily depends on the snow-met parameters. SASE’s network of

existing ground observatories, automatic weather stations and radio-based remote telemetry systems for

collection of snow and meteorological parameters provide only point information and are often very sparse

and unevenly spatially distributed, which needs to be extrapolated to cover a bigger region of interest. The

manual collection of data is extremely difcult in rugged nearly inaccessible terrains. SASE made a humble

beginning in establishing the remote sensing group in early nineties for better understanding and handling

of the avalanche problems and monitoring of snow-covered terrain of Indian Himalaya. Remote sensing

augments the conventional measurements as its repeativity is high and the area coverage is quite large.

Presently, SASE is fully geared with the latest state-of-the-art software and hardware facilities to deal with

optical and microwave satellite data, GIS-based applications and terrain visualisation. SASE also undertakes

the reception of the optical and microwave satellite imageries, analysis of received data, development of

methodologies and algorithms for snow and avalanche-related applications for the Indian Himalaya and

generation of nal products for real-time monitoring of the snow-cover and hazard analysis.

This special issue of Technology Focus  brings out the various remote sensing technologies developed

by SASE in respect of high altitude and cold region engineering, avalanche forecasting, avalanche control

measures, virtual y through models, controlled release of avalanches, hazard zonation, etc.

  (Ashwagosha Ganju)

Director 

Snow & Avalanche Study Establishment, Chandigarh

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Snow-met Parameters and Radiation Fluxes

Extraction of Atmospheric Profiles of

Temperature and Moisture

Moderate resolution imaging spectroradiometer

(MODIS) is a key instrument onboard the Earth

observing Terra and Aqua satellites. It provides high

density proles of temperature and humidity at a

resolution of about 0.1o in latitude, 0.25o in longitude,

and 20 pressure levels in the vertical. These proles

are useful for capturing the meso to micro scalesthermodynamic elds, which help in better simulation

of localised terrain-disrupted airow (related to the

temperature prole in boundary layer) and relative

humidity distribution.

SASE has developed an application for the extraction

and analysis of the atmospheric proles of temperature

and moisture from MODIS data. These proles were

compared with the radiosonde (upper air prole) data

for accuracy and are now being used in numericalweather prediction models.

TECHNOLOGY DEVELOPMENT

Application for MODIS profile extraction.

 Soft Computing-based Satellite Data Segmenta-

tion for Snow and Land Features Extraction

Classication of a multispectral satellite image is a

challenging task and has a number of applications such

as feature identication, change detection, etc. Various

soft computing methods and articial intelligence

techniques like neural network, fuzzy logic and support

vector machine are being used for classication of

the optical satellite―MODIS and advanced wide eld

sensor (AWiFS)―

for snow-covered areas, its types, andother land features.

Schematic of the work flow.

GUI of the fuzzy classification module.

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Classified map of an area.

Another application to implement these techniques

for classication of multispectral images has been

developed. This application comprises dierentmodules for dierent techniques like GUI of fuzzy

classication module and classied map using MODIS

data.

Snow depth map.

Snow water equivalent map.

Modelling for Snow Depth/Snow WaterEquivalent Estimation

Operational Algorithm for the Estimation of

 Snow Depth and Snow Water Equivalent inWestern Himalayas

Varied inputs in the form of snow parameters (e.g.

snow water equivalent) are required in scientic

models pertaining to avalanche and weather

forecasting. These parameters are usually collected

from eld observatories at dierent locations in the

snowbound regions. However, the vast snow-covered

areas in high altitudes are remote and inaccessible.

The integration of remote sensing data supplemented

with eld snow-depth measurements is an eective

way for the accurate determination of snow depth and

snow water equivalent at spatial level.

SASE and IIT Roorkee have jointly developed a spatial

interpolation model for estimation of snow depth and

snow water equivalent at each 500 m MODIS pixel

resolution. The model uses discrete point data to

create a model of the snow depth from which a value

for any location can be estimated. The proposed spatial

interpolation method is based on snow depth and

altitude above mean sea level. The dependency is later

adjusted through in situ  snow depth observations to

represent the local and regional characteristics of the

snow distribution. The model has been further rened

by snowline variations in specic areas retrieved from

MODIS sensor data. It estimates snow water equivalent

from snow depth maps generated using snow-covered

area maps retrieved at sub-pixel accuracy and snow-

covered density observations.

Snow depth estimation has also been attempted in

parts of north-west Himalayas in the GIS-framework.

A quasi physical method has been developed for

the estimation of snow depth. To generate snow

depth map from observation point, snow depth is

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calculated at each pixel of the study area with respect

to observation point using the elevation information

from digital elevation model (DEM). A weighted

inverse distance technique has been used to calculate

the depth at each pixel from the observation point. The

nal snow depth map has been generated by taking

weighted sum of the snow depths of all observation

points. The calculated snow depth values have been

validated using data from AWS.Hyperspectral Remote Sensing for SnowCharacterisation

Hyperspectral Spectroscopy 

Hyperspectral sensors capture data in contiguous

narrow bands (~10 nm spectral resolution) of the

electromagnetic spectrum and allow whole spectral

curves to be recorded with individual absorption

features. Hyperspectral data is, therefore, used for

characterisation, quantication, identication anddetection of subtle changes in snow in the snow-

covered areas. SASE has generated a library of spectral

signatures of dierent type of snow/ice and other

ambient objects by conducting eld investigations

using spectroradiometer (350-2500 nm).

Snow depth map from a hybrid model.

SASE has also carried out experiments to understand

the inuence of size of snow grain, contamination,

moisture, snow depth, slope aspect, and snow mixedobjects on spectral reectance and to determine the

sensitive/suitable wavelengths for mapping of snow

and estimation of snow characteristics using satellite

data.

Collection of spectral signature of snow using spectroradiometerin eld.

Hyperspectral Imaging

The Establishment has also explored space-borne

Hyperion sensor data for the estimation of snow-cover

characteristics in the Himalayan region. Snow grain

size was estimated using spectral angle mapper (SAM)

method. The retrieved grain sizes were compared/

validated with grain sizes retrieved from grain index

and asymptotic radiative transfer (ART) theory-based

methods. A very good overall accuracy of matching

snow-grain size classes was observed among dierent

methods.

The fractional snow-cover maps were also generated

using Hyperion data and image spectra were validated

using mix-object snow spectra collected from eld

experiments. Further, dierent level of vegetation/

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soil mixed snow-cover areas were delineated using

Hyperion data and validated using high resolution

satellite data.

Modelling for Snow Albedo Estimation

 Snow Albedo Estimation using Reflectance

 Measurements

  The Establishment has developed a modelto retrieve snow albedo from spectral reectance

measurements. The ART theory has been applied

to retrieve the plane and spherical albedo from the

reectance observations. The retrieved plane albedo

was compared with the measured spectral albedo and

a good agreement, with only ±10 per cent dierence,

was found between the two. Retrieved integrated

albedo was also found in good agreement, within ±6

per cent dierence, with ground-observed broadband

albedo.

This methodology was also implemented for the

retrieval of spectral albedo using single observation

from the satellite data, and found very useful for

operational snow-cover and glacier monitoring of the

Himalayan region using space- and air-borne sensors.

 Algorithm to Estimate Broadband Albedo of

 Snow

Algorithms to estimate narrowband to broadband

albedo (NBBA) of snow using AWiFS and MODIS

sensor images have been developed for the westernHimalayan region of India. The in situ  measurements

of spectral reectance and transmitted spectral solar

irradiance of snow surface by spectroradiometer have

been used to calibrate and validate these algorithms.

Snow grain size retrieved from Hyperion data using different methods for a part of the Himalayan region.

Spatial distribution of reectance at 1240 nm and its histogrammeasured by Hyperion over a part of middle Himalaya.

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The AWiFS and MODIS snow broadband albedo derived

using the developed algorithms have been validated

with in situ  observations at dierent eld locations. An

RMSE better than 0.03 and correlation of 0.94 and 0.88

between modelled and observed albedo values have

been obtained for AWiFS and MODIS, respectively.

Snow Grain Size Estimation Snow Grain Size Estimation from Reflectance

Data

A model has been developed to retrieve snow grain

size from reectance data using dierent models

based on ART theory and compared with dierent

snow types in the Himalayan region. This includes

single-channel, two-channel (bi-spectral), two-channel

ratio, and three-channel methods. It was found that

the grain size model using bi-spectral method, one in

visible and another in near infrared (NIR) region, works

well for the seasonal snow-cover in the Himalayas and

is in good agreement with temporal changes of grain

size as the season progresses. It can also take care of

soot eect (if present) in NIR region. This model was

also implemented on Hyperion sensor data for the

Himalayan regions. Only ve spectral bands (440, 500,

 Snow Grain Size Estimation from SAR Data

Snow grain size is estimated from SAR data in a

dry snow pack. A dry snow layer is a heterogeneous

medium composed of ice particles with dierent size

and microstructures. In dry snow, volume scattering

from the snow pack is the the principle mechanism

and density and grain size are the most important

variables. Backscattering increases as the size of snow

grain increases. The sensitivity of radar cross-sectionto grain size was assessed by changing the volumetric

correlation length of snow grains.

Net Shortwave Radiation Flux

An algorithm has been developed to estimate net

radiation ux over large snow-covered areas of north-

1050, 1240 and 1650 nm) of Hyperion data were used

for snow grain size retrieval. The grain sizes retrieved

using satellite were compared with grain sizes retrievedfrom eld spectroradiometer and also validated with

snow meteorological data collected from eld. The

model was able to retrieve the spatial variations in

snow grain size parameter in dierent parts of the

western Himalaya, which is natural given the variability

that exists in snow climatic and terrain conditions

of the Himalaya. This methodology is important for

operational snow-cover and glacier monitoring in the

Himalayan region using space- and air-borne sensors.

Broadband albedo of snow-covered area using MODIS data.

Spatial distribution of snow grain size retrieved using 1240 nm byHyperion for a part of lower Himalaya.

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west Himalayas for clear sky days using AWS and MODIS

data.

Geospatial maps of air temperature and relative

humidity have been generated using AWS data and

DEM of the study area. These geospatial maps forms

inputs for the parameterisation scheme used for

estimation of incoming shortwave radiation at spatial

level. Geospatial maps of the snow-covered albedo

were generated from MODIS sensor data. Finally,

geospatial maps of net shortwave radiation ux

were generated from the input of incoming shortwave

radiation ux and albedo maps.

Snow Density Estimation using SAR

An empirical model has been developed to estimate

snow density from SAR backscattering. In the absence

of free water and ice layers in the snowpack, the

microwave scattering and emission behaviour are

governed by the snowpack depth and its density.

The complexity in behaviour of snow density to the

total radar backscattering makes it dicult to develop

a model for estimating snow density from singlepolarisation C-band SAR data. Therefore, an empirical

model has been adapted. The total backscattering has

been considered as a third-order polynomial of snow

density.

Net shortwave radiation flux.

Incoming shortwave radiation flux.

Retrieved grain size from SAR data.

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Snow Wetness Measurement using SAR

An algorithm has been developed to estimate

snow wetness using Envisat-ASAR C-band data from

the estimated dielectric constant and snow density.

Dielectric constant was derived from back scattering

coecient through inversion equation model.

APPLICATION/ENABLING TECHNOLOGIES

Snow density map of Beas Kund.

the classication of image, thereby decreasing the

classication accuracy of the image contaminated by

mixed pixels. Sub-pixel classication overcomes this

mixed pixel problem by predicting the proportionalmembership of each pixel to each class. The output of

sub-pixel classication is not a single classied image

but a number of images known as fraction images.

Linear mixture model (LMM) sub-pixel classication

technique has been applied for retrieving snow-cover

fraction images in north-west Himalayas and an overall

decreasing snow cover trend has been observed.

 Snow-cover Mapping using SAR

Microwave remote sensing is a tool with potential to

estimate snowpack parameters. SASE is working on the

development of algorithms/models/methodologies

using active microwave data of ENVISAT advanced

synthetic aperture radar (ASAR), TerraSAR-X,

RADARSAT-1/2 and ALOS PALSAR sensor data for snow-

Snow wetness (per cent ) estimation using ASAR C-band data.

Snow-cover Mapping, Monitoring and

Snowmelt Run-o Modelling

 Snow-cover Monitoring using MODIS and AWiFSData

Snow-cover monitoring in the north-west Himalayas

and its various sub-basins have been carried out using

AWiFS and MODIS sensor data. Binary maps of snow-

covered areas generated using green band and short

wave infrared band (SWIR) of the MODIS and AWiFS

images at sub-pixel accuracy, as per-pixel classication

techniques, have limitations in classifying images

dominated by mixed pixels.

A mixed pixel displays a composite spectral response

that may be dissimilar to the spectral response of

each of its component land cover classes. Therefore,

pixel may not be allocated to any of its component

land cover classes. Hence, error is likely to occur in

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North-west Himalayas and its different basins.

covered area mapping and monitoring and estimation

of snowpack parameters. ENVISAT-ASAR C-band data

has been used for estimation of snow-cover area using

Snow and non snow-cover mapping using SAR data.

multi-date image threshold technique for the Manali

(HP) region.

 Snow Melt Run-off Modelling

A well-established ‘Degree Day’ approach has

been used for the estimation of snow melt and river

stream ow of a basin. However, the important inputs

used in snow melt run-o modelling, which includes

seasonal snow-covered areas, glacier extent, etc.,

were derived using remote sensing techniques and

hydro-meteorological data obtained from ground

observatories. For estimation of snow-covered areas,

MODIS sensor images were used. The model output is

in good agreement with the in situ  discharge data.

Glaciers and Polar Region Study

 Sea-Ice Study using Passive Microwave Satellite

Data

Sea-ice cover acts as a barrier between ocean and

atmosphere and thus aects the energy, gases and

momentum transferred between them. Conversion

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Comparison of actual and calculated stream flow in Jhelum basin (2004-09).

of open sea water into the frozen ice reduces the

amount of absorbed solar radiation and is one of

the main component responsible for global climatic

perturbations.

The sea-ice around Antarctica is generally seasonal,

i.e., it melts in summer and again freezes in winters.

Due to cold and hostile climate and uctuating

sea-ice conditions, in situ   collection of data is very

dicult. Some automatic data collection methods

have been used worldwide but still monitoring of

dierent cryosphere features (shelf ice, sea ice and

seasonal snow-cover), which cover millions of sq km,

is not possible in the true manner. Passive microwave

remote sensing satellites are promising tools for global

monitoring of the cryosphere with high temporal

repetition rate and also due to their working capabilityduring day and night.

 Sea-Ice Concentration and Sea-Ice Areal Extent

Special sensor microwave imager (SSM/I) data has

been used for the estimation of sea-ice areal extent

(SIAE). SSM/I scans the earth at the frequencies of 19.3,

37 and 85.5 GHz in vertical and horizontal polarisations

and at 22.2 GHz in the vertical polarisation only. The

Sea-ice concentration (October 2011).

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ascending mode data at 25 km resolution has been

used. In processing, rst brightness temperature values

are estimated for dierent frequency and polarisation.Daily, weekly and monthly brightness and temperature

maps have been generated and temporal variation has

been studied. Polarisation ratio (PR) and gradient ratio

(GR) have been calculated using three SSM/I channels,

i.e., 19 GHz (V), 19 GHz (H) and 37 GHz (V). These PR and

GR values were further used as inputs for estimation of

rst year sea-ice concentration (CF) and multiyear sea-

ice concentration (CM). Estimated CF and CM values

were used for estimation of total sea-ice concentration

(CT). To estimate the SIAE, concentration values weremultiplied with the area of pixel, i.e., 25*25 km2.

  The MODIS sensor data onboard Aqua satellites

have also been used for the estimation of SIAE. The

validation of SSM/I retrieved SIAE has been done using

the MODIS imageries (1 km spatial resolution) and

linear regression equation generated for corrected

SIAE values using daily available SSM/I satellite data.Glacier Monitoring using Geomatics Techniques

SASE has recently started to monitor a few Indian

Himalayan glaciers using ground, air- and space-borne

geomatics techniques. The main objective is to monitor

Variation of sea-ice areal extent around Antarctica (1988-2011).

Field photograph collected at the end of ablation season of aglacier near Patsio (HP) showing the terminus and snow lineposition (top) and Landsat-7, ETM+ imagery of the same glacier(bottom).

the response of climate change on the Himalayan

glaciers and its associated hazards. SAR interferometry

technique has been used for producing high resolutionDEM and changes on the glacier surface. The

measurement of interferometer correlation provides

the information of changes during the time scale of the

satellite receptivity and size scale on the order of a SAR

wavelength.

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 Antarctic Ice Sheet Study

Models and methodologies have been developed

using MODIS sensor data for spatial and temporalvariability study of snow/ice albedo, radiative uxes

and other parameters in the Dronning Maudland, east

Antarctica.

Ground Penetrating Radar Survey

 Airborne Ground Penetrating Radar Survey for

 Snow and Glacier Applications

  A ground-based radio echo sounding equipment

is very dicult to operate for snow and glacier studies

in the Indian Himalayas because of the rugged terrain

and topography. Also, a large number of buried/hidden

crevasses can be hazardous during the eld survey.

However, helicopter-mounted air-borne ground

penetrating radar (GPR) has proved to be very useful

for quick data collection from remote and inaccessible

snowbound areas.

The potential of an airborne GPR was explored for

the estimation of snow depth over rugged Himalayanterrain. The 350 MHz antenna was mounted on a

helicopter for the estimation of snow depth over a

glaciated and non-glaciated area of the north-west

Himalaya. The snow depths estimated from GPR signal

were found in good agreement with the snow depths

MODIS image of Dronning Maudland, Antarctica. Inset: Spatial variation of snow/ice albedo near Maitri region.

measured on ground. A GPR survey was conducted at

dierent locations of the north-west Himalayas. The

calibration and validation of estimated snow depthwere carried out using dierent snowpack properties

measured at an experimental site. A detailed survey

was carried out along Samudratapu glacier for 2009-

2010 and a snow accumulation map was generated

using GPR survey. Changes in the snow accumulation

duirng 2009-2010 were recorded for Samudratapu

glacier. Subsequently, a change detection analysisof snow accumulation

was also conducted

over Samudratapu

glacier during 2009-

2010. For the estimation

of changes in snow

accumulation, the snow

depth information

from common GPS

coordinates of survey

data from both theyears were selected.

Thus a limited area was

obtained using numbers

of common points to

nd the changes in snowAirborne GPR assembly.

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accumulation in one year. It was observed that snow

depth, for a part of Samudratapu glacier, was lower in

2010 in a fairly larger area of the glacier than in 2009.This was conrmed from the cumulative snowfall data of

2009 and 2010 recorded at meteorological observatory

at Patseo. This proved that the retrieved data using

GPR can identify change in snow accumulation pattern

in a glacier.

Changes in the snow accumulation between 2009-2010 for a partof Samudratapu glacier.

Snow accumulation over Samudratapu glacier estimated fromairborne GPR (March 2010).

Ground Penetrating Radar Experiments for

 Snow Depth and Snow Layer Interface

Estimation

Snow depth and snowpack stratigraphy along

with its temporal and spatial evolution are important

inputs for operational avalanche forecasting and

snow-water equivalent assessment for hydrological

applications. GPR with antenna frequency of

1000 MHz was used for snowpack characterisation in

Pir Panjal and greater Himalayan range. Snow depth

was estimated at certain locations in both the ranges

and further validated with the ground measurements.

The estimated snow depth using GPR at Solang (PirPanjal) was having a good correlation coecient

(0.86) with the manually observed values. Snow fork

was also used for the calibration and validation of GPR

data, which gives dielectric constant, volumetric water

content and density of the snowpack.

Experiments were also conducted at Patseo for

snowpack layer identication. By analysing the

proles, the prominent snow layers present within

the snowpack at Patseo were detected. Manual

stratigraphy was also performed along with the GPR

proles and it was found that layer positions in the

radargram correspond fairly well with the startigraphic

layer positions. Real and complex dielectric constant of

snow, which are important parameters for acquisition

of GPR proles were also measured using snow fork.

Topography and Avalanche Hazard

Assessment

Generation of Digital Elevation Models from

Cartosat-I Satellite Imagery 

  The Cartosat-1 satellite has a pair of panchromatic

cameras having an along-track stereoscopic capability

using its near-nadir viewing and forward viewing

telescopes to acquire stereo image data with a base-

to-height ratio of about 0.63. The spatial resolution is

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Snow depth estimation using 1000 MHz GPR antenna. Snow pack charectrisation using snow.

Snow layer interface identification and validation.

2.5 m in the horizontal plane. Each camera has a pixel

array of size 12000 giving a swath of about 27 km.

Presently, Cartosat-1 is the only global stereo capable

satellite for carrying out scientic studies.

The methodology adopted to produce the CartosatDEM involves stereo-strip triangulation of stereo pairs

using high precise ground control points, interactive

cloud-masking and automatic dense conjugate pair

generation. The automatically generated DEM was

further evaluated for quality and editing to remove

anomalies. Two images of same area were taken from

dierent angles using Cartosat-1. Stereo correlation

was applied to extract the matching point in two

stereo images. Sensor geometric model was used

to calculate the elevations. Rational polynomial

coecients were supplied with imagery product anddene the relationship between normalised pixel

and normalised ground coordinates. The rational

polynomial coecients and DGPS points were used by

Photogrammetric software to transform the ground-

to-image geometric correction.

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their ability to predict the avalanche hazard and avoid

the dangers they pose. There are three main causative

variables that inuence the occurrence of avalanches:the terrain characteristics, snowpack conditions, and

prevailing meteorological conditions. GIS provides

the possibility to address these complex, spatially and

temporally distributed and multi-parameter dependent

problems. As GIS framework provides capabilities of

data integration/management, geospatial/temporal

analysis and presenting the results in the map/report

forms. The framework was used for the dynamic

avalanche hazard zonation to generate more accurate

and dynamic avalanche hazard maps.

The methodology combines expert knowledge,

computational routines and statistical analysis to

identify the areas aected by avalanche threats. A high

resolution DEM was used for extracting the desired

DEM with a resolution of 10 m derived from Cartosat-1.

GIS-based Dynamic Avalanche Hazard

 Zonation

The avalanche researchers need to understand the

spatial and temporal patterns of avalanches to improve

Avalanche hazard zones based on (a) terrain parameters, (b) snow-met parameters and (c) nal hazard map obtainedby combining (a) and (b).

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terrain parameters. The model has three distinct sub-

modules. The rst sub-module denes the avalanche

hazard zones on the criteria related to slope, curvatureand land cover. Weights and ratings to these causative

factors and their cumulative eects were assigned

on the basis of experience and knowledge of eld

experts. The second sub-module uses inverse distance

weighted method to generate the meteorological

parameters (air temperature, precipitation and wind)

maps from the eld observatories and AWSs located at

Manali, Dhundi, and Patsio. Weights and ratings to the

meteorological parameters were assigned on the basis

of regression analysis of past avalanche occurrenceand meteorological data. In the third sub-module, the

meteorological-parameter maps were superimposed

on the terrain-based avalanche hazard maps to

generate the dynamic avalanche hazard maps.

Maps of the meteorological parameters, generated

from the observatory and AWS data, were used for

generation of the meteorological parameters-based

hazard maps. Finally, weighted meteorological

parameter maps were combined with the avalanchehazard map (terrain parameters based) using overlay

method to generate the nal avalanche hazard maps

of the study area.

GIS-based Identication and Mapping ofAvalanche Hazard Areas

 Avalanche Hazard Data Cards

Avalanche hazard data cards have been prepared

using remote sensing and GIS techniques for

identication and mapping of avalanche proneareas. The data cards have information about DEM,

slope, and ground cover maps validated with ground

reconnaissance. These data cards include salient

features of respective areas, past major avalanche

accidents, route charts with the details of avalanche

hazards. The cards are in the folded form for its easy

handling and reference during move. A large number

Avalanche hazard data card.

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of hard copies of each avalanche hazard data card have

been issued to the users.

Digital Avalanche Atlas

SASE has successfully developed a GIS-based digital

avalanche atlas, covering the vast avalanche prone

areas of various parts of the Indian Himalayas. Besides

having all the information available in the conventional

atlases, the digital avalanche atlas is having updatedinformation with wider selection criteria for each site.

DEM, slope aspect, track prole, digital terrain model

(DTM), etc., are some of the GIS-based characteristics

Digital avalanche atlas (top) and its different modules (bottom).

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for each site that can be accessed by selecting the

desired option on the graphic user interface (GUI). This

facilitates the user to view the potential avalancheprone areas as well as specic avalanche sites

interactively. Some other useful features are: general

information about avalanches, dierent control

measures, rescue operations, past history of avalanches,

and accidents. It also provides wide selection criteria

for accessing information for specic avalanche sites

for eective decision making. The digital avalanche

atlas is a speedy and user-friendly tool for military

Commanders, BRO personnel and other authorised civil

authorities for identifyingavalanche prone regions,

area familiarisation,

educating the local people

and provides necessary

information to mitigate

the avalanche hazard.

High Resolution Digital

Terrain Mapping and

 Avalanche Hazard

 Zonation

One of the

primary products of

photogrammetric data is

very accurate and precise

DEM. To exploit the

potential of this state-of-

the-art technology, SASE

had conducted a large

format digital camera-

based aerial survey, covering Manali and its nearbyareas. An aircraft, equipped with global positioning

system (GPS) and inertial measurement unit (IMU),

was used for photogrammetric surveys (altitude range

~23,000-26,000 ft). Large number of digital imagery

were collected using large format digital mapping

Photogrammetricsurvey area.

Contours generated fromphotogrammetry technique.

camera system (focal length = 100.5 mm; pixel size = 6

µm) with forward and side overlaps of 80 per cent and

60 per cent, respectively. For camera calibration andaerial triangulation, 23 ground control points (GCPs)

were established using dual frequency DGPS (PDOP

< 3). Overall accuracy of DTM achieved was between

7-20 cm GSD. Ortho-rectication of images was carried

out and the ortho-rectied mosaic was created at 0.20

m pixel size. DTMs were further analysed to locate

the probable avalanche release zones that was not

possible earlier at coarse resolution data. The detained

high resolution cartographic maps can help in the

preparation of detailedcivil engineering designs,

e.g., bridges, tunnels,

roads, buildings, etc., on

mountain slopes.

The detailed maps also

help in understanding

geomorphic details

of mountain terrains

including the past

glaciations. Thus, conti-

nuous and wide-area

coverage with digital

camera is very cost-

eective technique since

individual measure-

ment points can strongly

deviate due to dierent

error sources, such as

steep slope, x, y shift,

image correlation, scan position, etc. The study hasamply shown that aerial photogrammetric data

can help in qualitative and quantitative mapping

and monitoring of snow cover, glacier movement,

snow accumulation pattern, and onset of avalanche

occurrence, etc., with passage of time.

Rockwell CommanderC-690 aircraft

Captured image.

Digital aerialphotograph

of Dhundiavalanche.

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Technology Focus focuses on the technological developments in the Organisation, covering the products, processes and technologies.

Editorial Committee

Coordinator 

Dr AL Moorthy, Director, DESIDOC, Metcalfe House, DelhiMembers

Cmde PK Mishra, Director of Naval Research & Development

DRDO Bhavan, New Delhi

Shri Sudhir K Mishra, Director of Missiles, DRDO Bhavan, New Delhi

Dr K Muraleedharan, Director of Materials, DRDO Bhavan, New Delhi

Dr Rajeev Varshney, SO to SA to RM, DRDO Bhavan, New Delhi

Virtual Reality and Virtual GIS Lab

A virtual reality laboratory has been set-up as part ofSASE’s strategy in avalanche safety and rescue training

mission. The set-up is used for familiarisation of

troops about harsh rugged snowbound mountainous

regions of norht-west Himalaya in the virtual format.

Pre-knowledge of the terrain features, avalanche

locations, formation zone width and run-out zone

length increases chances of survival in an avalancheprone area. The hardware set for virtual reality lab

consists of CRT projector for stereoscopic projection,

at white board screen for 3-D stereo display and 3-D

active glasses for immersive feel. Three-dimensional

terrain rendering has been done by SASE using high

resolution satellite data.Virtual reality lab at Chandigarh.

Virtual reality visualisation using high resolution data.