RETRIEVAL OF FOREST BACKSCATTER FROM SAR DATA TO IMPROVE SNOW COVER AREA ESTIMATION IN THE CONIFEROUS FOREST OF NORTH WESTERN HIMALAYA CATCHMENT

RETRIEVAL OF FOREST
BACKSCATTER FROM SAR DATA
TO IMPROVE SNOW COVER
AREA ESTIMATION IN THE
CONIFEROUS FOREST OF
NORTH WESTERN HIMALAYA
CATCHMENT
SURYA GANGULY
May, 2012

SUPERVISORS:
Mr. Praveen Thakur, IIRS
Dr. Sarnam Singh, IIRS
Mr. Gerrit Huurneman. ITC

RETRIEVAL OF FOREST
BACKSCATTER FROM SAR DATA
TO IMPROVE SNOW COVER
AREA ESTIMATION IN THE
CONIFEROUS FOREST OF
NORTH WESTERN HIMALAYA
CATCHMENT
SURYA GANGULY
Enschede, The Netherlands, May, 2012
Thesis submitted to the Faculty of Geo-Information Science and Earth
Observation of the University of Twente in partial fulfilment of the
requirements for the degree of Master of Science in Geo-information Science
and Earth Observation.
Specialization: Geoinformatics

SUPERVISORS:
Mr. Praveen Thakur, IIRS
Dr. Sarnam Singh, IIRS
Mr. Gerrit Huurneman, ITC
THESIS ASSESSMENT BOARD:
Dr. Alfred Stein (Chair).
Mr. Snehmani, Scientist-'E', Research and Development Centre (RDC),
Snow and Avalanche Study Establishment (SASE), Defence Research
Development Organisation (DRDO), Chandigarh. (External Examiner).

DISCLAIMER
This document describes work undertaken as part of a programme of study at the Faculty of Geo-Information Science and
Earth Observation of the University of Twente. All views and opinions expressed therein remain the sole responsibility of the
author, and do not necessarily represent those of the Faculty.

Dedicated to my loving baba and mAA

ABSTRACT
Himalayan snow cover area(SCA) is an essential parameter for environmental, meteorological,
hydrological and climatologically applications. Due to the hostile climate condition and remoteness of
Himalaya, it is very difficult to estimate the SCA. Conventional methods have limitations in measuring
SCA in this region especially during extreme weather condition. Optical remote sensing have given good
results of SCA in cloud free conditions. But haze, fog and cloud in the snow melting season hinders the
capability of optical SCA estimation. Passive microwave remote sensing have given good results in low
forested areas. In the dense forest, passive microwave does not provides accurate SCA estimation. The
Himalayan glacier and snow lines are very poorly surveyed and continuous monitoring is needed.
Comprehensive measurement of SCA has been made in major forested area around the world, but there
remains a significant gap in Himalayan snow cover research. Microwave remote sensing with its all
weather and cloud penetration ability has already proven good result in estimating SCA in forest and
mountainous areas. Considering the Himalayas and coniferous forest characteristics - SCA determining
methodology using SAR data have been done for this region in this study. The microwave scattering
models - Water Cloud model and Semi-empirical model have been used to estimate the radar
backscattering contribution of forest and snow below the forest from the total backscatter using L-band
and C-band SAR data. Once the modelling has been done by using the forest in-situ measurements, the
forest backscatter contribution have been subtracted to get the backscatter contribution from wet snow
covered forest floor. Single reference ratio technique have been used on the forest floor backscatter to
determine wet SCA. The forest backscatter Water Cloud Model in L-band have shown a promising result
with low RMSE and high coefficient of determination. After the forest minimisation with this model, the
SCA estimation (33 Km2) have shown good co-relation (94%) with MODIS SCA estimation (35 Km2).
Semi-empirical backscatter model with C-band have not able to give much comprehensive result due to
fluctuating model parameters. The SCA estimation is not reliable for consideration, although the corelation with MODIS snow cover estimation is high. The C-band have been tried with Water Cloud
Model, which gave a better model with low RMSE and high coefficient of determination. The SCA
estimation (35 Km2) from this have show a good symmetry (97%) with the MODIS SCA estimation (36
Km2).
Key Words: Forest backscatter, snow backscatter, Water Cloud Model, Semi-empirical forest backscatter
model, SCA estimation.

i

ACKNOWLEDGEMENTS
@ Mr. Praveen Thakur: The research would not have been possible without his guidance and advice from
the first day to the last day of the work. Thank you sir.
@ Dr. Sarnam Singh: Very much obliged for his guidance, help and suggestions because of which the work
has gone in the right direction.
@ Mr. Gerrit Huurneman: Thank full for his time to time critical advice, evaluation and suggestions due to
which I able to improve on my work.
@ Mr. P.L.N.Raju: Thank you for providing all sorts of support and help during various ups and downs of
the course.
@ Dr. Nicholas Hamm: Thank you for his support and guidance all along the course.
@ Dr. P. S. Roy: Thank you for giving me the opportunity to study at his institute and providing all the
facilities for the research work.
@ Mr. Rahul, Mr. Manuj, Mr. Avdesh, Mr. Hemant (CMA staff members), Mr Ashish: Thank you for providing
prompt response and their service during the research period.
@ Mr. Gourav Misra, Mr. Vishnu Nandan, Miss Ruch Verma, Miss Priyanka Sharma (And to rest of my course
mates): Thank you for being with me and sharing the ideas and the help you rendered.

ii

TABLE OF CONTENTS
List of figures .................................................................................................................................................................v
List of tables ..................................................................................................................................................................vi
1. Introduction ...........................................................................................................................................................7
1.1.
1.2.
1.3.
1.4.
1.5.
1.6.
1.7

2.

Literature Review ............................................................................................................................................... 15
2.1.
2.2.
2.3.
2.4.

3.

Climate.....................................................................................................................................................................19
Surface water and drainage network...................................................................................................................20
Forest, flora and fauna..........................................................................................................................................20
Physiography..........................................................................................................................................................20
Geology and soli....................................................................................................................................................20
Radar imaging significance...................................................................................................................................21
Field data and forest inventory...........................................................................................................................22
Satellite data used in the study............................................................................................................................23

Methodology......................................................................................................................................................24
4.1.

4.2.
4.3.
4.4.
4.5.
4.6

5.

Wet Snow ................................................................................................................................................................... 13
Dry Snow.................................................................................................................................................................14
Forest backscatter Models.....................................................................................................................................14
Why L and C-band..................................................................................................................................................17

Study area and Data..........................................................................................................................................19
3.1.
3.2.
3.3.
3.4.
3.5.
3.6.
3.7.
3.8.

4.

Radar Remote Sensing ................................................................................................................................................8
Snow and Forest Backscattering..............................................................................................................................8
Problem Definition................................................................................................................................................. 9
Research Definition...................................................................................................................................................9
Research Objective....................................................................................................................................................9
1.5.1. Sub-Objective..............................................................................................................................................10
Research questions...................................................................................................................................................10
Innovation aimed at.................................................................................................................................................10

Data pre-processing...............................................................................................................................................24
4.1.1. Standard format conversion (Slant-to-ground range conversion)...................................................24
4.1.2. Amplitude image generation..................................................................................................................24
4.1.3. Amplitude to power image conversion, Multilooking.......................................................................24
4.1.4 Geocoding and Radiometric calibration..............................................................................................26
4.1.5. Backscatter image generation................................................................................................................27
4.1.6. Conversion from linear to decibel........................................................................................................28
Water Cloud Model for L-band/C-band...........................................................................................................30
Semi-empirical forest backscatter model for C-band.......................................................................................31
Snow cover area estimation by single reference image....................................................................................32
Accuracy assessment.............................................................................................................................................32
General proceedings..............................................................................................................................................33

Results, Analysis and discussion................................................................... .................................................35
5.1.
5.2.
5.3.
5.4.

Data quality analysis..............................................................................................................................................35
L-band Water Cloud Modelling and corresponding snow cover area estimation.......................................37
C-band Semi-empirical backscatter model and corresponding snow cover area estimation.....................43
C-band Water Cloud Modelling and corresponding snow cover area estimation.......................................51

iii

5.5
5.6

6.

Validation................................................................................................................................................................56
Discussion...............................................................................................................................................................57

Conclusion and recommendation....................................................................................................................58
6.1.
6.2

Conclusion..............................................................................................................................................................58
Recommendations.................................................................................................................................................59

List of references........................................................................................................................................................60
Appendix.....................................................................................................................................................................64

iv

LIST OF FIGURES
3.1.
3.2:
3.3:
4.1:
4.2:
4.3:
4.4:
4.5:
4.6:
4.7:
5.1:
5.2:
5.3:
5.4:
5.5:
5.6:
5.7:

5.8:
5.9:
5.10:
5.11:
5.12:
5.13:
5.14:
5.15:
5.16:

Study area of Manali sub-basin (Area: 350 km2) of Beas River.......................................................19
Alos Palsar , showing layover and shadow of the study area...........................................................21
Envisat ASAR, showing layover and shadow of the study area......................................................22
Multilooked Alos Palasr FBS HH 1.1, 18 February, 2011................................................................25
Multilooked Envisat ASAR APS, 11 March, 2008.............................................................................26
Geocoded, radiometrically calibrated Linear image of ASAR APS, 11 March 2008 ...................28
Geocoded, radiometrically calibrated Linear image of Alos Palasr FBS 1.1, 18 February 2011..28
Decibel image of ASAR APS, 11 March 2008.....................................................................................29
Decibel image of Alos Palasr FBS 1.1, 18 February 2011..................................................................29
Flow chart of methodology.....................................................................................................................30
Tree volume vs. Basal area graph of all the 14 in-situ plots...............................................................36
Graphical Residual Analysis of the L-band Water Cloud Model. a) Normal Probability Plot,
b) Residual Plot: e^(-βv) vs. Residual, c) Residual Plot: predicted backscatter vs. Residual..........39
L-band HH Alos Palsar forest minimised output of 18 February 2011 by water cloud model.....40
L-band HH Alos Palsar forest minimised output of 27 March 2008 by water cloud model...........41
Snow cover area inside the forest patch of the study area on 18 February 2011.
a) without forest minimisation b) after forest minimisation..................................................................42
Snow cover area inside the forest patch of the study area on 27 March 2008.
a) without forest minimisation b) after forest minimisation..................................................................43
Graphical Residual Analysis of the C-band Semi-empirical Backscatter Model. a) Normal
Probability Plot b) Residual Plot: Observed vs. Residual c) Residual Plot: Local incidence
angle vs. Residual d)Residual Plot: Tree volume vs. Residual e) Residual Plot: Local incidence
angle × Tree volume vs. Residual.............................................................................................................46
C-band VV Envisat ASAR forest minimised output of 11 March 2008 by Forest
backscatter model....................................................................................................................................47
C-band VV Envisat ASAR forest minimised output of 30 March 2008 by Forest
backscatter model....................................................................................................................................48
a) without forest minimisation b) after forest minimisation.............................................................49
Snow cover area inside the forest patch of the study area on 30 March 2008. a) without forest
minimisation b) after forest minimisation............................................................................................50
Graphical Residual Analysis of the C-band Water Cloud Model. a) Normal Probability Plot
b) Residual Plot: e^(-βv) vs. Residual c) Residual Plot: Calculated backscatter vs. Residual........52
C-band VV Envisat ASAR forest minimised output of 11 March 2008 by water cloud model...53
C-band VV Envisat ASAR forest minimised output of 30 March 2008 by water cloud model...54

v

LIST OF TABLES
2.1:
3.1:
5.1:
5.2:
5.3:
5.4:
5.5:
5.6:
5.7:
5.8:
5.9:
5.10:
5.11:
5.12:
5.13:
5.14:
5.15:

vi

Radar Bands and Wavelengths..................................................................................................................11
Microwave images used in the study........................................................................................................23
Model Parameters of Water cloud model for forest minimisation by L-band HH..........................38
Statistical observations of the model........................................................................................................38
Statistical observations of the model testing...........................................................................................39
Snow cover area below forest 18 February 2011...................................................................................42
Snow cover area below forest 27 March 2008.......................................................................................43
Model Parameters of forest backscatter model for forest minimisation by C-band VV.................44
Statistical observations of the model.......................................................................................................45
Statistical observations of the model testing..........................................................................................46
Model Parameters of Water cloud model for forest minimisation by C-band VV..........................51
Statistical observations of the model.....................................................................................................51
Statistical observations of the model testing..........................................................................................52
Snow cover area below forest 11 March 2008......................................................................................55

RETRIEVAL OF FOREST BACKSCATTER FROM SAR DATA TO IMPROVE SNOW COVER AREA ESTIMATION IN THE CONIFEROUS FOREST OF NORTH WESTERN
HIMALAYA CATCHMENT

1.

INTRODUCTION

Himalayan snow cover area is an essential parameter for environmental, meteorological, hydrological and
climatologically applications. Due to the hostile climate condition and remoteness of Himalaya, it is very
difficult to estimate the snow cover area. Conventional methods have limitations in measuring snow cover
area in this region especially during extreme weather condition. Remote sensing is an important tool for
snow cover area estimation in these zones. Space borne C-band microwave technology has proved to be
most ideal in determining snow cover area in the snow melt season [8]. The Himalayan glacier and snow
lines are very poorly surveyed and continuous monitoring is needed. Comprehensive measurement of
snow cover area has been made in major forested area around the world, but there remains a significant
gap in Himalayan snow cover research [1].
The current optical remote sensing methodology provides good result in cloud free and in snow starting
season over forest covered areas [2][3]. Moreover,100% cloud free image of the Himalayan catchment is
not available all the time [1] and also in the snow melt season, haze and fog hinders the capability of the
optical remote sensing resulting in decrease in temporal coverage of that area. Since both cloud and snow
has got similar spectral characteristics, the distinction between cloud and snow is the main difficulty in
identifying snow covered area by optical remote sensing.
Microwave remote sensing with its all weather and cloud penetration ability has already proven good result
in estimating snow cover area in forest and mountainous areas [4][5]. The space borne microwave
transmitter transmits its own source of radiation of longer wavelength compared to visible and infrared,
which is able to penetrate cloud, fog, rain as the longer wavelength is not susceptible to atmospheric
attenuation. It can operate even at night to get the image. Further studies has been made to enhance SAR
based snow cover area estimation in the forested zone [6][7]. But those studies have been done on
European region. Taking a view on aspect of Himalayas and coniferous forest characteristics - Snow
Cover Area determining methodology using SAR data has been intended for this region in this study. The
forest of the Himalayas is not the same like that of the European study area, the forest stem volume
differs, and the tree type differs. More over the Himalayan region is full of mountains and not flat plains
like that of the European snow areas. In Indian condition snow fall and subsequent snow accumulation
only takes place above 2300 m of elevation. Because of these factors which are different from the
European study zone, the same process cannot be used here.
The relation or the equation which estimates and simulates the real world situation from microwave
energy is known as a microwave scattering model. These microwave scattering models are the ways of
approximating the different radar scattering contribution from various objects on earth and getting
information about the scattering mechanism of the snow covered forest floor. With modelling, the forest
contribution and the ground contribution to the total backscatter will be estimated by a combination of
empirical and semi-empirical studies. Empirical models have been derived from experimentally obtained
data and observations. It does not consider the theoretically derived outcomes of the target interactions
and hence an empirical model might not capable enough of explaining a natural system fully. Any
empirical studies only gives relationship between the various derived parameters of the model under
consideration and the available data sets of that particular study. Whereas, a semi-empirical modelling

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HIMALAYA CATCHMENT

includes the previous research outcomes alongside the experimental and observed data of the study. The
semi-empirical model has been used to predict the contribution of forest backscatter in the SAR data from
a SAR sensor system. It has been done over the North West Himalayas temperate coniferous forest by
using parameters which has been derived from field based data.
Due to randomly oriented complex structure of various scattering particles, radar scattering from the earth
surface involves complicated electromagnetic wave interaction and hence it is impossible to deal with all
kinds of possible earth elements in a scattering model in general and also for a vegetation layer over a
snow cover area. Therefore an approximate scatter model for forest and snow will be required to estimate
the contribution in backscatter of these to the total backscattering at an area on the ground.
1.1.

Radar Remote Sensing

Microwave has got a large span of wavelengths from 1mm to 1m. Due to its larger wavelength compared
to optical sensors, it has the capability to penetrate cloud and even forest canopy in certain cases.
Microwave sensors emit electromagnetic wave that hits the earth surface and gets scattered in all possible
direction. Some portion of that wave reflects back to the sensor which generates the digital image. The
signal which is received by the sensor is the radar backscatter which determines the structural feature on
earth. The forest canopy penetration ability has been used here in this study.
1.2.
Snow and Forest Backscattering
Snow is a mixture of ice crystals, liquid water and air deposited over time on a place. The basic properties
of snow layer are the density and total thickness of the pack. With time the density of the snow pack
increases due to compactness by wind and gravity by the process of thermal metamorphosis. These
thickness and the density of snow determines the snow water equivalent (SWE) - which says the amount
of water would have formed from the snow pack had it been melted. The internal structure of the snow is
like grain or crystal. The presence of water in the snow determines whether it is dry or wet. Dry snow has
got no water in it - only ice crystals and air. While wet snow has got all the three - ice crystals, air and
water.
Radar reflectivity is high on forest and vegetation due to presence of moisture. Depending on the forest
type and their leaves, forest canopies have got large surface area coverage. Due to these factors, forests are
good reflector in the microwave spectrum. Forest acts like a group of volume scatters when microwave
interacts with the tree canopy. Volume scattering is predominant when short wavelength is used. The
mean length of the leaves and branches has to be smaller than the wavelength used. If this mean length is
larger than the wavelength, surface scattering from the canopy top takes place. At higher wavelength,
microwave penetrates the forest canopy and reaches the ground causing surface scattering from the
underlying ground. In those cases the ground below the forest acts like a surface scatters. The presence of
forest on any ground surface increases the volume scattering in comparison to surface scattering from the
forest floor [10]. The backscatter from the forest canopy increases with increase of stem volume [11].
Various snow surface scattering algorithms have been used to get snow cover area (SCA) from SAR
images. For majority of the algorithms, it follows that each and every pixel of the snow covered ground is
formed by contribution of wet snow, dry snow and snow free ground. The observed backscattering from
the snow depends on the dielectric property and wetness of the snow surface. There exists a relationship
between the measured backscatter and wetness of snow in each pixel. Eventually the classification of snow
cover area is based on best optimised threshold of snow wetness for a particular region. The forest

8

HIMALAYA CATCHMENT

backscatter contribution has been taken into consideration in the coniferous forest of Himalayas in order
to accurately measure snow area.
1.3.
Problem Definition
The Himalayan regions are covered with deep snow during winter, which becomes water eventually in
melting season. Therefore, it is essential to monitor the potential of these natural reserves for flood
forecasting and water management.
The limitation of detecting snow occurs in the mixed pixel situation where snow covers are obstructed by
dense forest. Many a times forest floor are not at all snow covered. Snow gets sublimated on the
coniferous tree canopy causing the snow invisible to the optical image even in continuous snow covered
area. Those snows that falls on to the ground through the canopy in the coniferous forest may not be
properly identifiable in the optical remote sensing. Sometimes even with high resolution imagery, it is
difficult to determine the actual percentage of area covered by snow below canopy. Off-nadir viewing of
optical sensor obscures snow even more.
The North West Himalayas temperate coniferous forest deteriorates the snow cover area estimation as
the level of backscattering and transmission through the forest canopy significantly varies [6]. Forest
obstructs the signal coming from the ground to the sensor, contributing to less information of ground. As
a result snow is mapped with low accuracy in forested area. The current prevailing method dose not
includes forest backscatter minimisation factor to detect snow cover area in the North West Himalayan
temperate coniferous forest.
Method dose not exists to determine the snow cover area of the North West Himalayas temperate
forested area, where major part of snow line fluctuates between forested areas. By answering the below
questions it has been tried to find a new approach to determine the snow cover area in the north west
Himalayas temperate coniferous forest zones of the Himalayan catchment. The process can also be
utilised on all those areas where the forest type and forest factors remains the same – like the dominant
tree species is Cedrus deodara.
It is necessary to develop a realistic model to address the interaction between major scattering
components of the Himalayan basin like backscatter from forest, ground and snow. Investigation of
backscattering property of North West Himalayas temperate forest and to improve the method of retrieval
of snow cover area by SAR data in the Himalayan catchment is required.
1.4.
Research definition
Applying a semi-empirical modelling approach to estimate the forest backscatter and improving the
estimation of the snow cover area in the North West Himalayas temperate coniferous forest zone using
SAR data.
1.5.

Research Objective

The main objective of the study is to develop a semi-empirical model which will be used to subtract the
forest backscatter contribution from the total backscattering such that only the snow backscattering is
remaining.

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HIMALAYA CATCHMENT

1.5.1.

Sub-Objective





1.6.

To find the radar response component of North West Himalayas temperate forest.
To study forest backscatter by semi-empirical modelling approach.
To improve snow cover area estimate by incorporating forest compensation factor.
To calculate the accuracy of snow cover area extraction

Research questions
 How can total backscatter be used to estimate the backscattering from temperate coniferous
forest and snow under the forest?
 How much backscatter is being contributed by temperate coniferous forest area?
 How does sensitivity differs between C and L band for better forest compensation factor to
improve SCA estimation?
 What

is

the

accuracy

of

semi-empirically

modeled

snow

cover

area

estimation?

 Is there any increase in the accuracy of snow covered area by model approach in the Himalayan
temperate coniferous forest region?
1.7.
Innovation aimed at
The determination of forest backscatter by the semi-empirical model and the backscatter from snow
covered ground below the forest canopy by the modified semi-empirical model will be the innovative part
of this study.

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HIMALAYA CATCHMENT

2.

LITERATURE REVIEW

Radio detection and Ranging (RADAR) was developed to detect the presence of earth objects using radio
waves and to determine their distance and angular position [12]. Radar remote sensing utilizes microwave
region of the Electromagnetic spectrum from 1 mm to 1.3 m [13] wavelengths. Radar is not only used for
detection and ranging applications but can also be used for imaging the earth surface. The high
penetration ability of radar waves enables the radar sensor to information on features present beneath the
ground. The radar waves can penetrate through clouds, light rain, smoke with limited attenuation up to a
particular level and serves as a weather independent remote sensing system. Radar waves at long
wavelengths such as L-band enables higher penetration capability through forest canopy making it very
useful to measure the bio-physical properties of forests.
The amount of backscatter received by the radar system from a particular earth feature is given by the
radar equation [13].
𝑊𝑅 =

𝑊𝑡 𝐺 𝑡
4𝜋𝑅 2

𝜎

1
4𝜋𝑅 2

𝐴𝑟

(2.1)

Here 𝑊 𝑅 is the received power, 𝑊𝑡 is the transmitted power, 𝐺 𝑡 is the gain of the transmitting antenna, R
is the distance from radar to the earth feature, 𝜎 is the effective backscatter co-efficient and 𝐴 𝑟 is the
effective aperture of the receiving antenna. The parameters which affect the backscatter are related to
different system and target parameters [14]. The different target parameters are surface roughness, dielectric constant, slope angle and orientation. The different system parameters are wavelength or
frequency, look angle, look direction, polarization and resolution [15].
Table 2.1: Radar Bands and Wavelengths [14]
Microwave bands
Ka
K
Ku
X
C
S
L
P

Wavelength (in cm)
0.75 - 1.10
1.10 -1.67
1.67 - 2.40
2.40 - 3.75
3.75 - 7.5
7.5 - 15.0
15.0 - 30.0
30.0 - 130

Frequency or wavelength is an important component since it influences the penetration of the EM wave.
As the wavelength increases, the level of penetration increases which helps to obtain information about
the surface below the earth up to a particular depth [13]. When the wavelength factor combines along with

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HIMALAYA CATCHMENT

different target parameters such as di-electric constant of the object, surface roughness, the radar
backscatter varies, depending on the remote sensing application [16].
Imaging of the earth surface using Real Aperture Radar (RAR) systems has limitations in the resolution by
the power and size of the footprint of the radar pulse, which depends mainly on the aperture size and
therefore RAR systems are used only for few remote sensing applications [17]. Whereas, in the case of a
Synthetic Aperture Radar (SAR) system, a large antenna is synthesized using Doppler effect principle in
the acquired data, using offline processing techniques. When compared to RAR systems, SAR uses signal
processing techniques and satellite orbital information providing a much higher resolution in both range
(across-track) and azimuth (along-track) directions. These systems generally help in understanding glacier
and ice movement giving better understanding on long term variation in climate, developing highly
accurate and detailed elevation maps, flood, oil spill and ecological monitoring, land use, land cover
change, soil moisture estimation, assessing the health of crops and forests and even in urban planning and
development applications [17].
Snow acts like an anisotropic reflector on the earth surface [18]. Fresh snow acts like a Lambertian
reflecting surface. The reflectance on it is maximum in the forward direction. As it metamorphoses,
forward scattering's specular component increases. In the visible part of the electromagnetic spectrum,
this reflectance of fresh snow is high and gradually starts decreasing in the near infra red spectrum, as the
grain size increases [18].
Snow with the presence of its albedo factor, maintains a good contrast between the other natural features
on the earth surface. This aspect of snow has been utilized by satellite imagery to measure the various
parameters of snow. TIROS-I weather satellite has used the albedo factor to map snow in 1960. The
duration of taking snow image is essential as snow melts away rapidly. The gap between two successive
image captures was reduced to a week following the launch of ESSA-3 satellite [19]. Advanced Vidicon
Camera Systems were mounted on the ESSA-3 satellite which works in the spectral range of 0.5 to 0.75
mm with a spatial resolution of 3.7 km at nadir looking [19]. Various types and quality of sensors were
utilized earlier to map snow effectively in the northern hemisphere. These include Scanning Radiometer
(SR) and Very high resolution radiometer (VHRR). Usually the snow parameters were analyzed from the
various polar-orbiting and geostationary satellite images obtained weekly. This weekly analysis used to miss
much of the snow information if the image is cloud covered. Snow covered maps were generated by hand
drawing and subsequently digitized so that it can be over-laid on stereographic maps. The Interactive
Snow and Ice Mapping System (IMS) came into being in 1997 which uses a combination of visible, nearinfrared, and passive microwave image to provide daily snow map at an approximation of 25 km [19]. In
the cloud cover and at night, snow was mapped by passive microwave sensor of the Nimbus satellite 5, 6
and 7 at an resolution of about 25 to 50 Km [19]. The Landsat Multispectral Scanner (MSS) with 80m
resolution and TM with 30m resolution were used to map snow successfully [19].
Because of the snow cloud discrimination confusion and improper estimation of snow area by optical
remote sensing, an alternate - Synthetic Aperture Radar (SAR) has been discussed as early as 1980 [20].
SAR has been recognised because of its advantage of high spatial resolution, day night operation capability
and the most important - all weather cloud penetration capability. But the SAR imagery are hampered by
the radar distortion effects like layover, foreshortening and shadow and also by speckle and geometric
distortion. Mountain area snow extent has been mapped considerably with SAR [21]. The monitoring of
snow by space borne SAR is most significant in the snow melt season. The logic to detect snow is based
on the difference in the backscattering property of the snow and the ground surface. The difference in
backscattering are prominent in wet snow and dry snow, and between wet snow and bare ground. These

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HIMALAYA CATCHMENT

differences in backscatter results in discriminating melt snow and no snow on the ground. The snow
surface remains wet in the melting season. The radar backscattering is lower from the wet snow compared
to dry snow or bare ground. With the approach of the snow melt season, snow starts melting slowly and
bare ground starts surfacing. Bare ground shows a much higher backscatter than to wet snow. With the
increase of bare ground, the radar backscattering increases. The highest level of backscattering was there
when all the snow melted away and only bare ground is visible. These changing factors of backscattering
were utilized in the SAR snow cover area evaluation.
For the various snow backscattering model, a brief discussion is given in [11]. Integral equation model
(IEM) is the most widely used model utilized for modelling the backscattering from snow-air interface.
The assumptions of this model are: 1) only single interface is important. 2) Fresnel power transmission
coefficient used to account transmission across top boundary and 3) Fresnel power reflection coefficient
used to calculate reflection at the lower boundary for the snow ground interface interaction. [11]. The
empirical Oh surface backscattering model is based on tower based scatterometer at L, C and X band
within an incidence angle of 10° to 70°. Snow volume scattering can be modeled with Dense Medium
Radiative Transfer (DMRT) model [11].
2.1.
Wet Snow
The various algorithms such as Baghdadi-algorithm [22], the Nagler & Rott algorithm [23], Koskinen [24]
has been used to extract the Snow Cover areas (SCA) from the SAR images. The Baghdadi-algorithm and
the Nagler-algorithm are similar type of change detection algorithms, where the current SAR-image
σws (from the melting period) are compared with a reference image σref from a period when the snow is
dry, or a period when the ground is not covered by snow. The ratio image is threshold, and they define the
pixels where (σws σref ) < -3dB as wet snow. The refinement in the algorithm is done for correcting the
topographic effects. A sub-pixel based classification scheme, similar to those implemented for optical
images [25], can also be implemented to improve the binary classification, which is a result of the
thresholding algorithm that is suggested by Nagler and Rott [26]. The basic assumption is that each pixel
can consist of a mixture of dry snow, wet snow, or snow free ground. The backscattering will also depend
on the wetness of the snow.
Koskinen [27] suggest an alternative algorithm which is optimized to detect snow in forested areas. They
conducted a pixel-wise comparison between the two reference images and the current image. The
algorithm was applied successfully to ERS-1 data over a forested area in northern Finland. The variation
in backscattering due to incidence angle and the need for frequent data acquisition makes both of the two
algorithms above difficult to apply. It is readily realized that an operational algorithm which applies all
available imaging modes and geometries of e.g. RADARSAT-1 will need large number of reference images
for each area under consideration. Envisat ASAR with polarimetric imaging are also used as reference
scenes. Koskinen [28] recently used temporal ERS-2 images for mapping SCA during spring snow melt
period in open and forested landscapes of Northern Finland.
Harnold, [29] derived wet SCA from RADARSAT-1 SacnSAR using change detection approach, where
comparison of the amplitude differences of the two SAR scenes is done using the ratio values i.e., dividing
the values of the winter scene with those of the summer scene.
Low [30] studied and attempted land use dependent snow cover retrieval using multi-temporal, multisensor SAR images to drive operational flood forecasting models, The ENVISAT performance is
simulated in a multi-temporal and multi-sensor attempt to delineate snow cover from RADARSAT and

13

HIMALAYA CATCHMENT

ERS imagery. the work was done on German catchments of the Ammer (~ 700 km²) and Neckar (~
14.000 km²) rivers. In a later stage, the developed algorithms and methods are transferred to the Binational Mosel watershed (~ 28.000 km²) to evaluate their regionalized applicability. For the retrieval of
snow covered area (SCA) by means of SAR, image rationing is used. It derives information on the areal
extent of wet SCA, which, due to its critical energetic stage, is a tremendously important indicator for
flood risk. To obtain ariel maps of SCA, a landuse-dependent thresholding for the difference between the
reference and the data take of interest is applied.
Luojus [31] used 24 ERS-2 SAR intensity images for boreal forest region of Northern Finland, for
establishing an extensive statistical accuracy analysis for the Helsinki University of Technology (TKK)developed SCA method.
Luojus [7] made an enhanced method for fractional snow-covered area (SCA) estimation for the boreal
forest zone of Northern Finland. The new approach is based on utilizing weather station data along with
space borne synthetic aperture radar (SAR).
2.2.
Dry Snow
Dry, weakly metamorphosed snow reflects most of the incoming shortwave radiation back into space.
During snowmelt the albedo decreases rapidly and may drop from about 80% to about 10% within few
weeks, completely changing the surface energy balance. Dry snow scatters most of the incident
electromagnetic waves as absorption is usually negligible. The propagation of electromagnetic waves in
snow is governed by the complex permittivity which is strongly dependent on the liquid water content.
Dry snow increases the backscatters signal, the increase is more pronounced for smooth ice than for
rough ice. Absorption is much higher than scattering in wet snow; therefore separate algorithms are used
for inferring the wet snow cover areas.
2.3.
Forest backscatter models
For the study of snow cover below forest canopy, various forest compensation models were reviewed
which were applied in various studies to simulate scattering from the vegetation and forest. Here, in this
study, the forest backscatter contribution are subtracted from the total backscatter such that only the snow
(below forest) backscattering is remaining.. A vegetation based microwave scattering model's primary
objective is to understand the mechanism of microwave scattering from forest canopies. The model
should be simple and complete enough to address all the natural probability factors. Microwave radar
backscatter has got a relation with the forest stand parameters - height, dbh [32]. The selection of
appropriate model for a specific forest stand and radar backscatter makes it easy to estimate forest
backscatter contribution. At lower wavelength, microwave data tends to saturate more with increasing
forest density. Longer wavelength microwave data has shown a better result in forest mapping [32].
The first to be mention is the Multilayer Radiative Transfer Model [33] which was designed for both the
snow and the forest canopy. The study was done to simulate the backscattering of the sub-Artic forest,
which is different for the Himalayan study area. The model includes many parameters of the nature. The
study was done with RADARSAT, C-band, HH polarisation, S1 and S2 mode at 20° to 50° incidence
angle. The backscattering from the snow was calculated by [33].
0
0
0
0
𝜎0
𝑠𝑛𝑜𝑤 = 𝜎 𝑠𝑢𝑟𝑓 + 𝜎 𝑣𝑜𝑙 + 𝜎 𝑔𝑟𝑜𝑢𝑛𝑑 + 𝜎 𝑣𝑔

14

(2.2)

HIMALAYA CATCHMENT

and the forest canopy was calculated by
0
0
𝜎0 = 𝛾2 𝜎0
𝑐𝑎𝑛
𝑠𝑛𝑜𝑤 + 𝜎 𝑣𝑒𝑔 + 𝜎 𝑖𝑛𝑡

(2.3)

where, 𝜎 0 = backscatter from snow, 𝜎 0 = backscatter from surface, 𝜎 0 = volume backscatter,
𝑠𝑛𝑜𝑤
𝑣𝑜𝑙
𝑠𝑢𝑟𝑓
0
0
2
𝜎0
𝑔𝑟𝑜𝑢𝑛𝑑 = ground backscatter, 𝜎 𝑣𝑔 =volume-ground interaction, 𝜎 𝑐𝑎𝑛 = canopy backscatter, 𝛾 =
0
Vegetation two way transmitting factors, 𝜎 0 = vegetation contribution and 𝜎 𝑖𝑛𝑡 = interaction between
𝑣𝑒𝑔
vegetation and snow. The model does include many parameters from the nature, but complex and
disadvantageous to implement. More over too many other mathematical interpretation are required and
also the forest on which the study was under taken is different from the forest here in this study area.

The Michigan microwave canopy scattering model [34] is a good one but it neglects the multiple scattering
effect. Also number of parameters is many. The model can be combined with the snow model at X and
Ku band. But the availability of satellite based X and Ku band data for the study area was not feasible.
Optimization of Polarimetric Contrast Enhancement (OPCE) [35] is a cross iterative method based on
optimal contrast polarisation state. It is used to optimize snow-covered surface response with respect to
forest. Comparatively the process is quite less complex. For this model to work, C-band is preferred in
presence of snow covered soil. Although the model can work both on C and L-band, the study not clearly
states whether the snow below the forest canopy can be optimized. It has got limitations in distinguishing
snow covered surface distinctly.
The Supervised Polarimetric Contrast Variation Enhancement (PCVE) [36] is the model which optimizes
snow covered surface and minimizes the influence of incidence angle. It requires C-band data alongside
another optical image of same area in summers. But cloud free image of summers of the Himalayan study
area is the not always feasible [8]. This model enhances the snow covered area response and effectively
discriminates snow over the rest of the image. But the below canopy discrimination is not sure from the
study.
The Santa Barbara microwave canopy backscatter model [37] was build from the surface backscatter (M s),
crown volume scattering (Mc), crown ground multiple path interaction (Mm) and double bounce from
trunk ground interaction (Md) of the forest backscatter. The mechanism is represented by 4×4
transformation matrices. A set of subcomponents was defined for each of these components by the
number of attenuating crowns in the forest of incident and return signals. The backscattering from each
subcomponents was weighted by the probability of its occurrence. Incoherent summation of the
subcomponents gave the component. The resultant component's incoherent summation results in the
total backscattering. The model's transformation matrix of the total backscatter is given by [37].
𝑀𝑡 = 𝑀 𝑠 + 𝑀𝑐 + 𝑀 𝑚 + 𝑀 𝑑

(2.4)

where, 𝑀 𝑡 = total backscattering, 𝑀 𝑠 = surface backscatter, 𝑀 𝑐 = crown volume scattering, 𝑀 𝑚 = crown ground multiple
path interaction and 𝑀 𝑑 = double bounce from trunk ground interaction.
The model requires stand parameters, crown constituents parameter, ground surface roughness parameter,
dielectric constants, regression equation of tree height on tree ‟dbh‟ (diameter at breast height), tree crown
depth at dbh, tree crown width at dbh, radar wavelength, polarization and incidence angle. The models
yields good result where forest canopies are discontinuous, like low to medium density forest where

15

HIMALAYA CATCHMENT

crown are narrow and do not interlock with each other. The model treats the individual trees as an
individual scattering element. At HH, HV and VV backscatter, the model underestimates the backscatter
under frozen condition for the forest stands and in the process over estimates the changes from thawed to
frozen.
C-band semi-empirical backscattering model [38] for forest and snow has studied extensively in the
European forest patches where snow accumulation below the forest canopy is high. The model divides the
total backscatter into two incoherent contributions: backscattering coefficient from the forest canopy and
backscattering coefficient from the ground.
𝑜
𝜎 𝑜 = 𝜎 𝑣𝑜 + 𝑡 2 𝜎 𝑏𝑔
𝑜

where 𝜎 = total backscattering coefficient,

𝜎 𝑣𝑜

(2.5)

= forest backscattering coefficient,

𝑜
𝜎 𝑏𝑔

= ground backscattering coefficient

and 𝑡 2 = two way transmissivity of canopy.
The model requires very less number of parameters stem volume and tree from in-situ measurement. Cband data sets are required for the model. But the study had been undertaken in the flat region of Europe
and there is no mention of mountain surface.
Helsinki University of Technology (HUT) forest scattering model [39] had been developed for the boreal
forest of European plains. The model takes into account the total stem volume of the forest stand and a
scalar variable defined by an empirical relationship.
𝜎° 𝑉, 𝜒 = σ° 𝑠𝑢𝑟𝑓 . 𝑒𝑥𝑝

−2𝐾 𝑒 𝐴 𝜒 . 𝑉
cos 𝜃

+

= 𝜎° 𝑠𝑢𝑟𝑓 . 𝑡 𝑉, 𝜒

σ° 𝑣

𝐵 𝜒

cos 𝜃

2𝐾 𝑒 𝐴 𝜒
2

+ 𝜎° 𝑐𝑎𝑛 𝑉, 𝜒

. 1 − 𝑒𝑥𝑝

−2𝐾 𝑒 𝐴 𝜒 . 𝑉
cos 𝜃
(2.6)

where 𝑉 = forest stem volume, 𝜒 = a scalar variable defined by the empirical relations A and B, 𝐾 𝑒 = canopy extinction
coefficient, 𝜎 𝑣𝑜 = forest canopy volume backscattering coefficient, 𝜎° 𝑠𝑢𝑟𝑓 = the backscattering coefficient of snow covered
terrain and θ = the angle of incidence.
The model requires C-band SAR data and two reference data, one of wet snow cover and another of
totally snow free of the same region. The semi-empirical model has got limited variability and can be
combined with both snow and forest in the equations. The model had been validated on the plane surface
of Norway.
In Water Cloud Model (WCM) [40], vegetation canopy was presented as a uniformly distributed water
particles like a cloud. The various parameters of the model were dependent on the forest type and
polarization state of the microwave data. The canopy-ground interaction was not taken into consideration
in the model. The model requires only stem volume from the stand and complexity is less compared to
other models. L-band data has been verified in the model which has got much higher penetration ability
in the forest compared to C-band due to its higher wavelength. The dielectric constant of dry vegetation
(1.5) is smaller than pure water, but greater than air (1.0), because of this, the model describes the
vegetation canopy as water cloud with in which water droplets are randomly distributed. Water cloud
model includes backscatter value as model parameters.

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HIMALAYA CATCHMENT

2.4.
Why L and C-band
Theoretically the dense forest of Himalayas requires L-band SAR data to penetrate the canopy and give
backscatter from the bottom. Because the wavelength of L-band is higher than C-band and hence it
penetrates the canopy. The shape of the canopy structure and the climatic condition also require L-band
because at C-band, backscatter increases or decreases depending on canopy condition. Also at C-band, the
effect of seasonal and weather condition effects the sensitivity of backscatter which can either be positive
or negative. C-band has proven to give good result for wet SCA and wetness. To analyse the ground
below canopy, two season image - no snow and wet snow image is required. Both L and C-band data is
being tried in the study to analyse the suitability of both the data sets in the two seasons.
L-band backscatter is more sensitive to the increase of forest stem volume than at C-band backscatter, i.e.
backscatter increases with increasing stem volume at L-band. L-band will produce better retrieval
accuracy for smaller area. At L-band the trunk ground reflection is a dominant contributor of backscatter.
But the physical validity of the semi-empirical model[39] for L-band is doubtful as the model does not
make a separation between extinction contribution between the tree trunks and the branches and needles.
This separation is essential at L-band as the contribution of trunk in backscatter is relatively higher than at
C-band. Also for this model, the experimental information on L-band, effective volume scattering
coefficient (σv) and forest canopy extinction coefficient (Ke) is not available. The unknown parameters of
the model using L-band needs multi temporal data set. Unfortunately, the PALSAR sensor failed on May
12, 2011, new imagery will not be available for future monitoring efforts.
Therefore to exploit the benefit of L-band, the available data from the archive (September 2007, June
2008, December 2007, February 2008) has been applied and Water Cloud Model has been selected for
modelling the L-band data.
The water cloud based canopy gap model had been tested in Boreal coniferous Scot pine and Norway
spruce dominated forest and has given satisfactory result in the study.[41] The tree species type and
physical structure of the trees in the western Himalayan study area are similar to the Norway spruce forest
type, and hence the applicability of this model seems to be good for use in this study.
The coniferous forest at the western Himalayas is dense but the structure of branches and needles makes
the backscatter of C-band a better comparison to L-band .The availability of C-band data makes it more
suitable for the study also. The radar response depends on weather and seasonal changing parameters
like total water content of canopy, snow cover and soil moisture. C-band is more sensitive to these
temporal changes than L-band. C-band will produce better retrieval accuracy for forest area larger than 20
Ha than at L-band. Also, the semi-empirical model has given better result in C-band than L-band [41]. The
WCM has also being tested for C-band for various forest related studies.
The C-band SAR data have been most widely available (ERS-1 and -2, Radarsat-1 and -2 and Envisat) and
therefore the largest body of snow investigations from space borne instruments and the developed
operational SAR-based snow monitoring system are based on C-band data [11]. The excellent feasibility of
C-band SAR for snow monitoring applications, C-band space borne SAR, which can provide reliable
snow cover information for hydrological simulation and forecasting applications for operational use on a
regional scale. The backscattering at lower frequencies, such as L- and S-band are less influenced by snow
cover and the main contribution comes from the snow-ground interface. In the case of wet snow the
contribution from the air snow interface is the dominating factor [11].

17

HIMALAYA CATCHMENT

The capability of C-band SAR to map fractional snow-covered area is based on the decrease of
backscattering coefficient with increasing snow liquid water content. This has been demonstrated using
repeat pass ERS-1 SAR observations for alpine regions by [42]. The method, utilizing a single reference
image for SCA estimation, has also been successfully used in operational applications.
some aspects make the C-band radar more prominent in the case of forests than in the general case:
1) when forests are typically relatively sparse, and, hence, the signal can partially penetrate through the
vegetation canopy layer causing the level of backscatter to depend on forest biomass; and 2) the high
seasonal changes of backscatter can benefit the estimation of forest biomass, if the backscattering
properties can be described by an appropriate model. The dominant tree species of the coniferous forest
in western Himalayas are Cedrus deodara. The medium-to-low average stem volume increase the
applicability of C-band radar for forest applications. For high to medium stem volume L-band data have
been used.
Therefore to overcome the limitations of C-band, L-band has been employed. The L-band has been
modeled with the water cloud model [41]. Due to limitations of L-band (availability) and advantage of
sensitivity of C-band to climatologically changes, C-band will be applied and modeled with semi-empirical
model. C-band gives better result than L-band in the semi-empirical model.[39].
The water cloud model in L-band over estimates the change under thawed condition. The smallest under
estimate has been found in HH backscatter modelling. L-band HH has been used since it has been found
out that the greatest improvement in the snow surface modelling backscatter has been in HH and least in
case of HV backscatter. HH polarisation of the Alos Palsar FBS has been done in the study [37].
In C-band, both HH and VV polarisation have similar capability of mapping snow. VV polarisation of Cband image have greater variation of backscatter which are not related to the target's backscattering
property [32].

18

HIMALAYA CATCHMENT

3.

STUDY AREA AND DATA

The study area which has been chosen is the Manali sub basin basin in the state of Himachal Pradesh,
India. It is located at 32°13'06" to 32°24'51" North latitude and 77°01'35" to 77°17'01" East longitude.
The altitude varies from 1800 m to 5900 m above sea level. The area of the watershed basin is around 350
sq Km and about 19 Km in length and 23 km width. The main river which contributes to the major part
of the river network is the Beas river, which originates from the Beas Kund glacier, 51 Km north of
Manali. The study area has got thick concentration of various variety of coniferous forest, out of which
the main dominating one is Cedrus deodara. The forest backscattering compensation modelling has been
done on these forests for snow cover area estimation below the canopy.

Fig. 3.1: Study area of Manali sub-basin (Area: 350 km2) of Beas River
3.1.
Climate
Dry and cold climatic condition prevails mostly in the region. The area has got three main seasons-winter
(October to February), summer (March to June) and rainy (July to September). Snowfall takes place in the
month of December and January. Temperature varies from -15°C to 0°C in winter and 20°C to 30°C in
summers. Maximum rainfall occurs in the month of July and August in these regions due to Western
disturbances. The meteorological data of the region are recorded by Snow and Avalanche study
Establishment (SASE), has got three meteorological stations inside the study area at Dhundi, Solang and

19

HIMALAYA CATCHMENT

Manali. The study area has been divided into three main climatic zone based on snowfall and temperature,
these are: lower, middle and upper climatic zones. The snowfall in lower zone is moderate to heavy and
temperature remains relatively high causing the zone with deeper and compact snow cover. The middle
zone has got lower temperature than lower zone but snowfall is less. This causes the region with powdery
and long lasting deep snow. The upper zone has got plenty of snowfall with lowest mean temperature
leads to shallow snowpack with formation of depth hoar crystals in the lower zones, which instigates
avalanche activities in the lower zones.
3.2.
Surface water and drainage network
The Beas river is the main source of surface water in the region. It's source lies in the Rothang Pass, 51
Km north of Manali at an altitude of 4350 m. During monsoon, the river gets plenty of water. The river
has got some prominent tributaries like-the Solang, the Hansa, the Parbati, the Manalsu and the Pin. All
these rivers contribute to the overall river network in the region and makes the Manali sub basin basin.
The town Manali lies on the right bank of the river Beas. The main course of the river Beas starts from
Beas Kund towards southwards up to Larji, then bends towards west. On its east bank, lies the larger
tributaries forming a fan based land form shapes. On its west bank lies the main water carrying tributaries,
the Solang, the Manalsu, the Phojal nullhas and the Surjoin. All the tributaries and the Beas has got the
highest flow of water in the month of June, July and August and the lowest in the month of December,
January.
3.3.
Forest, flora and fauna
The study area is nestled amidst the serenity of varied jungle and apple orchards. The woodland of Manali
constitutes of Pine (Pinus roxburghii), Fir (Abies pindrow), Poplar (Populus ciliata), Oak (Quercus
incana), Deodar (Cedrus deodara), Aesculus (Aesculus indica), Spruce (Pices smithiana), Maple (Acer
pictum), Bras (Rhododendron arborium), Fig (Ficus spp), and Walnut (Juglans regia). Among all these the
Deodar (Cedrus deodara) is the main predominant along the varies faces of the mountains.
The mountains and forest of Manali is a safe haven for Leopard, Barking deer, Musk deer, Snow leopard,
Black bear, Brown bear, Himalayan ibex, Porcupine and numerous other varieties. The avian variety
includes-Monal, Eurasian Sparrowhawk, Himalayan Griffon Vulture, Black Stork Western Tragopan,
Koklas, Kingfisher, Chakor, Grey Heron, White Stork, Snow pigeon and numerous other varieties.
3.4.
Physiography
The mountains of Manali composed of long high ridges with sharp crests and steep sides. These are lofty
on the north and in the east, the slope descends to the main streams. Forests covers the higher slopes,
particularly facing the north. The main mountain ranges of the region is the Pir Panjal Range.
3.5.
Geology and soil
The rocks of the area are made up of Precambrian crystalline schists and gnesisses of the Jutogh and the
Chail formations. These groups are central geneiss, Kullu formation, Banjar formation and Tourmalinc
granites. Central gneiss are presumabley the oldest rocks made over 1500 million years old. The
stratigraphic sequence is reversed because older rocks of the Jutogh lie over the Chail in the form of a
nappe called the „Kulu Nappe'. The Jutogh Formation is represented by graphite mica schist, quartz mica
schist, garnetiferous chloritic mica schist, and schistose quartzite bands, whereas the Chail Formation
comprises quartzite inter banded with phyllite, mylonitic gneiss, phyllite interbanded with limestone, and
metavolcanics interbanded with phyllite and quartzite.

20

HIMALAYA CATCHMENT

Structurally the Manali sub basin represents part of NNW-SSE trending Kulu syclinorium, which has at
least four overturned subsidiary anticlines and synclines. The western limb of the syncline is folded into
several anticlines and synclines. The Kulu Synclinorium is formed by two crystalline thrust sheets, i.e.,
Chail and Jutogh. The Chail consists of low-grade metamorphics, whereas the Jutogh represents highgrade metamorphic rocks. The thrust sheets have a common tectonic and metamorphic history and differ
from each other only in the grade of metamorphism, intensity of deformation and lithology.
There are three textural group of soil in these area-coarse loamy, loamy skeletal and rock outcrop. The soil
of the area has been widely influenced by the geology, landform, climate and vegetation. The lower parts
of the region are relatively flat with rich alluvial soil used for agriculture.
3.6.
Radar imaging significance
The radar images are often distorted due to foreshortening, layover and shadow in the mountainous
region. For any study with SAR, it is important that the image is free from these distortions. To develop a
model on forest compensation of Himalaya an accurate radiometric estimation required. The Manali sub
basin basin is comparatively free from foreshortening, layover and shadow (Fig 3.2, 3.3), due to selection
of high incidence angle image, therefore this area is suitable for the current study.

Fig. 3.2: Alos Palsar , showing layover and shadow of the study area

21

HIMALAYA CATCHMENT

Fig. 3.3: Envisat ASAR, showing layover and shadow of the study area
3.7.
Field Data and forest inventory
The filed data has been collected from the forest patch of the Manali basin in the month of November
2011, before the snow starting season. The sample plot has been chosen which are comparatively free from
shadow and layover, and also within the main vegetation cover of coniferous plantation. The forest
patches, which were feasible to reach in the mountainous terrain were chosen and marked before going to
the field from the image. The GPS coordinate of the corners of a sample plot has been recorded. Then plot
size has been demarcated with appropriate size of 25m at an average for all plots. Inside the plot, the total
number of trees and the tree types present has been recorded. Then the girth at breast height (gbh) and tree
height of each individual tree has been noted down. That GBH was converted to DBH (Diameter at breast
height). The DBH was used to get the stem volume utilizing an allometric equation from literature for
specific coniferous dominated trees of that geographic zone. The dominating tree species in the Manali
basin is the Cedrus deodara and the allometric equation which has been utilised to find the stem volume in
the region is as follows [43]:
Stem volume (allometric equation) = 0.167174-1.735312 × dbh + 12.039017 × (dbh)2
The total calculated stem volume was then converted to m3/ha.
3.8.
Satellite Data used in study
The satellite data used for the snow cover area estimation are the Alos Palasar FBS 1.1 for L band (23.5
cm) and the Envisat ASAR APS for C band (5.6 cm). The observed image are so chosen that it falls in the
snow melting season of the study area. The reference image are selected so that there is no snow (bare
ground) in the study area. The images and their corresponding dates are tabulated in table 3.1.

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Date
18 February 2011
27 March 2008
25 September
11 March 2008
30 March 2008
04 October 2008

Table 3.1: Microwave images used in the study
Product
Alos Palsar FBD 1.1
Observed
Alos Palsar FBD 1.1
Observed
Alos Palsar FBD 1.1
Reference
Envisat ASAR APS
Observed
Envisat ASAR APS
Observed
Envisat ASAR APS
Reference

Purpose

Apart from the microwave data, a digital elevation model (DEM) are also used in the study. Aster DEM
with 30 m resolution and +/- 25 m of accuracy has been utilised for topographical correction of the SAR
images. Modis images of the respective observed dates has been used for final SCA validation [19]. A LISS
III image of the study area are also used in the current study to make the forest mask.

23

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4.

METHODOLOGY

The total study has been divided into three parts - backscatter image generation, model training and wet
SCA estimation. Model parameters for the forest would be estimated from the backscatter measurements
of image and stem volume from ground data.
4.1.
Data pre-processing
The backscatter image generation from satellite data requires a few preprocessing steps. These are
necessary in order to produce a correct estimation of backscatter from an object on ground.
 Slant-to-ground range conversion.
 Amplitude image generation.
 Amplitude to power image conversion.
 Backscatter image generation.
 Conversion from linear to decibel.
4.1.1.

Standard Format Conversion (Slant-to-ground range conversion)

The radar system is such that it gives distance of the object by calculating the time taken by a signal to
travel from an antenna to the object and subsequently receiving the backscatter signal by the antenna. If
there is a difference in this time delay between two objects, then it can be determined how much
difference of distance is there on the ground between those two objects. By this distance mechanism of
the radar, the distance of the object from the sensor is measured along the slant range of the radar. This
slant range distorts, compresses the actual distance of the object at near range and compresses at far range.
The resultant image appears to be different at different places and the resultant scale of the image varies.
The problem is being solved by converting slant range image into ground range, which is the horizontal
distance along the ground [44].
The raw Alos Palsar FBS 1.1 and Envisat ASAR 1.0 data has been imported to standard format Single
Look Complex (SLC) data. SLC is a set of real and imaginary complex data, which retains the Doppler
information for subsequent image generation. The Alos Palsar data has been converted to the SLC format
with the CEOS leader file and the Envisat ASAR has been done with the appropriate date Doris file. Both
this CEOS and Doris acts as an header file for the corresponding data which stores satellite parameter
information needed for SLC data to image generation.
4.1.2.

Amplitude image generation

The real and imagery channels of the SAR data are combined to get the single intensity image. The pixels
of the single intensity image displays the corresponding amplitude values. The process to get a combined
single image from complex SAR data is to square the sum of the squares of the real and imaginary values.
𝐴 = 𝐼2 + 𝑄2
where A= amplitude, I = real channel, Q = imaginary channel.
The values of pixels of the final composite image is positive real value.
4.1.3.

Amplitude to power image conversion, Multilooking

The power image (pwr) is generated by multilooking which is the actual viewable image. It represents the
total power received by a SAR sensor. By multilooking speckle has been reduced at the expanse of spatial

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resolution. The SAR inherent speckle noise is reduced by combining several images of the same scene
from apparent different look angle. An averaged square pixel has been generated by multilooking, based
on the ground range spacing and azimuth spacing. The number of looks is then computed resulting the
image with approximately square pixel spacing along the ground. Without this, approximate square pixel
would not be possible. Once the SLC image has been generated, it is then multilooked. The grid size has
been fixed at 25 m for processing. The principal objective of multilooking is to filtering (reduce) the
speckle variance relative to image mean . The equivalent number of looks for a homogeneous region of an
image is defined to be the ratio of mean squared to the variance, both calculated by σ° radiometrics [44].
The speckle reduction procedure, multilooking of an image, leads to transformation of speckle statistics,
ie. multilooking changes the relative speckle level and leads to a predictable and verified increase in the
mean-squared to variance ratio [44]. Image with high spatial resolution even with speckle are preferred
over images with lower spatial resolution and more looks [44]. Since the grid size has been fixed at 25 m
for ASAR and Palsar, the following outcome has come out of the multilooking processing, which makes
square pixel.
Ground resolution of Alos Palsar FBS image from SLC image header file (SML):
pixel spacing slant range sin incident angle = 4.6843 sin 38.7439 = 7.485
Which is equivalent to 1 look in range.
Whereas at 25 m grid size, ground resolution comes to be : 22.4693, that means 3 times the actual range.
So, the range looks is 3. The multilooked factor required to make it an approximant square in azimuth
resolution is:
Ground resolution
7.485 × 3
=
= 7.35 ~ 8
pixel spacing azimuth
3.056
Therefore the final outcome (Fig 4.1) of the multilooking process are: Range: 22.4693, Azimuth 24.448,
Azimuth Looks 8, Range Looks 3.

Fig. 4.1: Multilooked Alos Palasr FBS HH 1.1, 18 February 2011
Ground resolution of Envisat ASAR image from SLC image header file (SML):

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HIMALAYA CATCHMENT

pixel spacing slant range
7.804
=
= 11.914
sin(incident angle)
sin(40.92)
This ground resolution corresponds to 1 look in range.
Whereas with 25 m grid size, ground resolution comes to be : 23.8404, that means 2 times the actual
range, so the range looks comes to be 2.
The multilooked factor required to make it an approximant square in azimuth resolution is:
Ground resolution
11.9144 × 2
=
= 6.1112 ~ 6
pixel spacing azimuth
3.8992
Therefore the final outcome of the multilooking process for Envisat ASAR are: Range: 23.8404, Azimuth
23.3953, Azimuth Looks 6, Range Looks 2.

VV
HH
Fig 4.2: Multilooked Envisat ASAR APS, 11 March 2008
Multilooking is done at the expense of spatial resolution to decrease the speckle in the image. The effect
of speckle has been reduced considerably and the output pixels are reasonably square.
Equivalent number of looks of Alos Palsar SLC image: (mean)2/variance = 0.1996
Equivalent number of looks of Alos Palsar PWR image: (mean)2/variance = 0.8747
After multilooking at 25m grid size of the Alos Palsar image, speckle statistics - the equivalent number of
looks has increased from 0.1996 to 0.8747, stating that the image has been better speckle reduced.
4.1.4.

Geocoding and Radiometric calibration

Large distortion in range direction takes place in the multilooked power image due to topographic
variations. Distortion increases as an object goes toward the far range from near range. The geometric
pattern of the ground in PWR image is inconsistent due to the far and near range concept. To make this

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HIMALAYA CATCHMENT

distortion small, terrain rectification has been done in the power image. The rectification has been
computed by incorporating the available ASTER DEM of 30 m resolution. Range Doppler
orthorectification has been adopted for geocoding the SAR data which uses the metadata information
(orbital parameters), slant to ground range parameters along with the DEM which is incorporated to get
the near real ground information. Within the forest patches, a 3×3 averaging of the pixels has been done
to accommodate the probable shift in the location due to DEM inaccuracy. Subsequently, the local
incidence angle (LIA) has been calculated radiometric calibration has been done based on this topographic
information. The ASTER DEM of 30 m with an rmse of +/- 25 m in x, y, z has been used to geocode the
_pwr (multilooked) image. Since the pixel size is of 22 m in range and 24 m in azimuth and the rmse of
the DEM is 25 m, therefore a maximum shift in the actual ground position could be of around two pixels.
The geocoding has been calibrated and processed into terrain corrected image in Geographic Lat/Long,
Zone 43 and WGS-84 datum projection by applying Aster DEM. The geocoding has been done to solve
the geometric rectification problem caused due to mountains. The Manali sub-basin study area is bounded
by high mountains. In the presence of the topography, the dominant scattering mechanism changes.
Because of this factor, backscatter contribution strongly depends on the local slope. With local incidence
angle steeper, resolution degrades, reducing the texture due to incorrect estimation of σ° from space.
Major effect of the local surface variation is the change of physical size of the scattering area, leading to
error in radiometric calibration. For this purpose the local incident angle is derived from the DEM.
The radiometric calibration has been done to correct the scattering area miss-match, antenna gain pattern
and the range spread loss. The scattering area of each resolution cell varies due to topography and the
incidence angle. This variation in the scattering area has been normalised for the real illuminated area by
radiometric calibration. Radar equation requires proper geometric parameters and to calculate the local
values- Aster DEM of the region has been given as input in the geocoding step itself where in all the
required parameters gets calculated. The geometric distortion in the calibrated SAR product has been
corrected by applying the Aster DEM and those pixels affected by layover and shadow effect are flagged
out so that those regions can be avoided for further analysis. In this study nearest neighbour re-sampling
has been used to reduce the further loss of information.
4.1.5.

Backscatter image generation

Backscattering is the portion of the total out-going radar signal that gets back to the sensor after getting
scattered from the target object. This backscattering is a measure of strength of the reflected signals from
the target. σ (Sigma) represents the scattering cross section towards the radar and this parameter describes
the reflectivity of the object. The return signal is normalized and measured producing a dimension-less
backscatter cross section (σ) - which is basically ratio of transmitted to received power level per unit area
[45]. The output image (Fig 4.3, 4.4) is a linear scale image and the values of the pixel represents
backscattering coefficient.

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VV
HH
Fig 4.3: Geocoded, radiometrically calibrated Linear image of ASAR APS, 11 March 2008

Fig 4.4: Geocoded, radiometrically calibrated Linear image of Alos Palasr FBS 1.1, 18 February 2011
4.1.6.

Conversion from linear to decibel

Usually the backscatter cross section (σ) is expressed in logarithmic form, i.e. decibel (dB) scale [45]. The
conversion from the linear to decibel scale is done by the following:
𝜎 ° 𝑑𝐵 = 10. log10 σ° 𝑙𝑖𝑛𝑒𝑎𝑟

(4.1)

Then finally the linear geocoded and radiometrically calibrated image is converted to dB image (Fig 4.5,
4.6). This dB image is utilized for the actual analysis. The study area boundary has been derived by Hydroprocessing of Aster DEM of the sub-basin and this boundary is used to subset the newly produced image
with grid size of 25m. The field vector points are overlaid on the subseted dB image. As stated in section
4.1.4, 2 pixel could be the shift on the ground, and therefore a pixel averaging of 3 × 3 pixels are taken
into consideration to avoid the error of geo-coding. The pixels which are lighter in shade in the total dB
image depicts higher value of backscatter, which signifies that these are forests. The rest of the dark pixels
in the dB image depicts lower value of backscatter, signifying that those are wet snow covered area or
other smooth surfaces. Vegetation acts like a rough surface due to which high backscatter takes place
resulting in lighter shade of pixel in dB image. Snow or other targets in the image are comparatively
behaves like a smooth surface compared to vegetation due to which small scattering takes place resulting
in darker pixel in the dB images.

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HIMALAYA CATCHMENT

VV

Fig 4.5: Decibel image of ASAR APS, 11 March 2008

HH

Fig 4.6: Decibel image of Alos Palasr FBS 1.1, 18 February 2011

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SAR data
Slant-to-ground range (SLC data)
Amplitude Image Generation
Amplitude to power & Speckle
reduction (Multi-looking)
Terrain
Geo-coding
and
radiometric calibration (Linear
backscatter image)
Linear to Decibel image

DEM

𝜎°

Water Cloud model to estimate forest
backscatter from L-band & C-band
Semi-empirical model training

Semi-empirical model to estimate
forest backscatter from C-band
In-Situ Data: Stem
Volume (V)

Calibration of parameters σ° , σ° , β
veg
gr
Estimation of forest and ground
parameter

Calibration of parameters a, 𝜎 °
𝑠𝑢𝑟𝑓
Validation of σ° , σ° ,
veg
gr
β by volume

Generation of forest compensated
image from total backscatter
Snow cover area (SCA) below forest
canopy.

MODIS data

Semi-empirical model training

Estimation of forest and ground
parameter
Generation of forest compensated
image from total backscatter

SAR reference data

Snow cover area (SCA) below forest
canopy.

Validation and comparison
Derived Wet Snow Cover Area map

Fig 4.7: Flow chart of methodology
Once the total backscatter dB image has been generated for both the L and C-band data, the second half,
i.e. respective modelling part begins.
4.2.
Water Cloud Model for L-band/C-band
There is a direct relation between the various forest parameters and the forest backscatter. This
relationship was being conceptualized in the Water Cloud Model [41]. Any such model which tries to
explain such relationship takes some assumption into consideration. The Water Cloud model makes an
assumption that forest acts like a homogeneous medium over a flat ground. The homogeneous forest is
full of uniformly distributed water droplets which acts like a scattering elements. Some parts of the
incoming microwave gets reflected back to the sensor, while a section of the microwave penetrates the
forest layer and reaches the ground. The part of the microwave which penetrates the forest canopy gets
attenuated by the vegetation mass. The Water Cloud Model says that all the scatters, coming from both

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the upper forest layer as well from the ground, has got the same property. Thus making both the total
attenuation cross section and radar cross-section same for all scatters. In this context the model presents
the total backscatter by an incoherent sum of energies which are scattered from every layer. The multiple
reflection and double bounce are not taken into consideration in the Water Cloud Model. Only the single
scattering from below canopy and canopy top are considered in the model.
The Water Cloud Model has been employed in the study to find the forest backscatter and the backscatter
from below the canopy layer. The backscatter from below the ground has been used to determine the wet
snow below the canopy. The relation which has been utilized to highlight the scattering components from
the ground and forest is [41]:
𝜎 ° = 𝜎 ° 𝑒 −𝛽𝑉 + 𝜎 °𝑣𝑒𝑔 1 − 𝑒 −𝛽𝑉
𝑔𝑟
𝑓𝑜𝑟

(4.2)

where, σ° is the total forest backscatter, σ° is the backscatter coefficient of ground, σ° is the
gr
veg
for
backscatter coefficient of vegetation layer, V is the forest stem volume and β is the two way transitivity.
The model training has been carried out to find the backscatter coefficient from the ground and the
backscatter coefficient of vegetation parameters. The model has been trained with the Alos Palsar FBS 1.1
HH data, Envisat ASAR VV and in-situ (volume) data. β is the empirical constant which varies accordingly
the forest canopy cover [41]. The training of the model has been done by iterative regression which
accurately estimates backscatter of vegetation (σ°veg) and backscatter from ground (σ°gr). These two
parameters has been retrieved by normal least square approximation.
𝑁
𝑖=1

𝜎 °𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

,𝑖

− 𝜎 °𝑚𝑜𝑑𝑒𝑙𝑒𝑑 ,𝑖

2

= 𝑚𝑖𝑛𝑖𝑚𝑢𝑚

(4.3)

The training of the model has been performed by non-linear least square minimization between the
observed backscatter and the modeled backscatter obtained from equation (4.1).
4.3.
Semi-empirical forest backscatter model for C-band
For the C-band data, the forest semi-empirical backscattering model [3,7,8] has been used to minimize the
forest effect from the total backscatter image. In the forest semi-empirical backscatter model, total
backscatter acts as a function of stem volume, collected from in-situ measurement.


 p1  a  V 
 p  a V
  p2  a  cos   1  exp  1
 cos  
 cos 




 0 V , a,  ,  surf    surf  exp 



  surf  t V , a,     can V , a,  ,
2




(4.4)

where V = forest stem volume [m³/ha]
𝜎°
𝑠𝑢𝑟𝑓 = backscattering coefficient of the ground or snow layer
θ = angle of incidence
t² = two-way transmissivity through the forest canopy
𝜎 ° = forest canopy backscattering contribution
𝑐𝑎𝑛
a = condition of forest canopy related to water content.
In the equation (4.3), p1 and p2 are polarization coefficients which depends on the frequency and
polarization state of the microwave. From previous studies, the value of p1 is -5.12 × 10-3 and p2 is 0.131

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for C-band VV polarization [11]. And for the C-band HH polarization, the values for p1 is -4.86 × 10-3 and
for p2 is 0.099 [11].
Using the forest semi-empirical model, the forest canopy backscatter contribution has been extracted by
using stem volume and SAR images. The process has been done by non-linear fit of the observed
backscattering coefficient. The parameter ”a” and σ°surf were optimized. The minimization which was
followed for the model is [11]
𝑚𝑖𝑛 𝑎,𝜎 °

𝑠𝑢𝑟𝑓

𝑛
𝑖=1

𝑤𝑖.

°
𝜎°
𝑣 𝑖 , 𝑎, 𝜎 °
𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑 ,𝑖 − 𝜎
𝑠𝑢𝑟𝑓

2

(4.5)

where, n is the number of stem volume classes, 𝜎 °
𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑 ,𝑖 is the mean observed backscattering
coefficient and σ° is the model predicted average backscattering coefficient. wi is the weighting factor for
unevenly distributed stem volume classes.
By employing these two models, forest contribution has been found out from both the C and L-band and
eventually snow below the forest canopy has been separated. The backscatter contribution of ground
image below forest canopy has been utilized to find the snow covered ground.
4.4.
Snow cover area estimation by single reference image
The difference between the backscatter coefficient of dry or bare ground has been compared with the wet
snow to estimate the snow cover area [26]. As described earlier, dry snow acts virtually invisible to
microwave due to its dielectric property and snow grain size. Thus, dry snow has also been included as a
reference image in the single image snow cover estimation. The reference image has also been forest
compensated, so that while comparing with the observed image, only the below canopy backscatter is
predominantly observed to get the snow cover area below forest cover. Where there has been an adequate
difference in backscatter coefficient, the corresponding pixel has been classified as wet snow. The process
can only classify wet snow or bare ground in the observed image depending on the condition of the
reference image [26]
°
𝐼f σ°
wet snow
obs σref < 𝑇𝑅
else
(bare ground)
(4.6)
where σ°obs is the observed backscattering image obtained from the previous two forest compensation
procedure for L and C-band. σ°ref is the reference backscatter image of bare ground or dry snow and TR is
the threshold for snow cover area estimation. From previous the studies the threshold has been given as 2.0 to -3.0 [42].
4.5.
Accuracy assesment
The accuracy of the model has been analysed by the statistical parameters like the coefficient of
determination (R2), sum of squared residual, root mean square error (RMSE), Residual standard deviation
and mean absolute error. These parameters has been defined as [46]:
Coefficient of determination:

𝑅2 = 1 −

𝑆𝑆 𝑒𝑟𝑟𝑜𝑟
𝑆𝑆 𝑡𝑜𝑡𝑎𝑙

(4.6)

where, 𝑆𝑆 𝑒𝑟𝑟𝑜𝑟 is the sum of the squared residual and 𝑆𝑆 𝑡𝑜𝑡𝑎𝑙 is the total sum of squares. Coefficient of
determination gives the goodness of fit of a model. It is a statistical measure which expresses how well the
regression line in the model matches with the real data.

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Sum of squared residual:

𝑛
𝑖=1

𝑥1 − 𝑥2

2

(4.7)

where, 𝑥1 is the observed value, 𝑥2 is the predicted value and n is the number of observations of the
model.
Root mean square error:

𝑛
𝑖=1

𝑥1 − 𝑥2 2
𝑛

(4.8)

where, 𝑥1 is the observed value, 𝑥2 is the predicted value and n is the number of observations of the
model. Root mean square error measures the difference between the observed and predicted value. The
less the value the better model fit is.
Residual standard deviation:

𝑆
𝑛−𝑝

(4.9)

where, S is the sum of squared residual, n is the number of data points and p is the number of parameters
of the regression model. Residual standard deviation gives the quality of the model fit with the size of the
residuals. In case of a good fit model residual standard deviation will be small and vice versa.
Mean absolute error:

𝑛
𝑖=1

𝐴𝐵𝑆 𝑥 1 − 𝑥 2
𝑛

(4.10)

where, ABS stands for absolute value, 𝑥1 is the observed value, 𝑥2 is the predicted value and n is the
number of observations of the model. It measures the average value of error in a set of predicted values in
the model.
Apart from the above statistical parameters, a set of graphical residual analysis has also been to test the
goodness of fit of the developed model. These are residual plot , normal probability plot and a regression
plot between volume and predicted backscatter. These are defined as follows:
Residual Plot: A scatter plot of the observed variables and the residual of the model are used to assess the
workability of the model. A scatter plot in which the residuals are randomly distributed indicates that the
model fits the data well. If there is a symmetry in the pattern of the residuals then it signified that there is a
scope of improvement in the model [46].
Normal Probability plot: It shows graphically whether or not a data set is well distributed. It is special case
where the points should be nearly linear pattern which indicates that the normal distribution is a good
model of the data set [46].
Regression plot between the stem volume and calculated backscatter gives the relation of the two in the
model [46].
4.6.
General Proceedings
The actual study boundary has been made from the Aster DEM and LISS III optical image by delineating
the watershed boundary. This vector polygon boundary has been used in all subsequent maps of the study.
Within this boundary on LISS III image, a NDVI has been generated to demarked the forest patches. The
forest patch polygons along with the field points has been used to generate a forest stem volume
interpolated map of the study area. This forest stem volume interpolated map has been utilized in the
actual modelling to generate forest compensated image.

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The Microsoft Excel 2007 Solver application has been used for optimising the nonlinear equations. It uses
the Generalised Reduced Gradient (GRG2) algorithm in the backend [47]. Excel Solver uses iterative
numerical methods. This method requires to give trial values for the adjustable cells. The results are
observed by constraint cells and optimum cell. This trial is the iteration in the spread sheet application.

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5.

RESULTS, ANALYSIS & DISCUSSION

5.1.
Data quality analysis
The quality of the field data has been evaluated and analysed. Each and every individual tree volume and
their corresponding basal area has been plotted in a scatter plot. The objective was to see whether the
stem volume which has been collected from the field is good for modelling. It can be verified from the
graph of tree volume and basal area. A linearity among the points explains that the individual tree volume
is in good co-relation with its respective basal area. All the 14 in-situ field plots has been plotted to analyse
its quality.

Basal Area (cm2)

Basal Area (cm2)

0.4
0.3
0.2
0.1

2.0
1.5
1.0
0.5
0.0

0.0

0
0.0

2.0

Tree Volume (m3)

1.0
0.5
0.0

Basal Area

10

15

0

10

20

30

Tree Volume (m3)

Plot ID: 5

Plot ID: 7

5

10

Tree Volume (m3)

0.2
0.0
0

Basal Area (cm2)
0

0.4

5

10

Tree Volume (m3)

0.3
0.2
0.1
0.0
0

1

2

3

4

Tree Volume (m3)

Plot ID :6

1.0
0.8
0.6
0.4
0.2
0.0

30

0.6

Plot ID: 4

2.0
1.5
1.0
0.5
0.0

20

0.8

20

Tree Volume (m3)

Plot ID: 3

(cm2)

5

10

Tree Volume (m3)

Plot ID: 2

Basal Area (cm2)

1.5

0

Basal Area (cm2)

6.0

Basal Area (cm2)

Basal Area

(cm2)

Plot ID: 1

4.0

0.8
0.6
0.4
0.2
0.0

15

0
Plot ID 8

5

10

Tree Volume (m3)

35

Basal Area (cm2)

Plot ID: 9

1

1.5

2

Tree Volume (m3)

0

0.2

0.4

0.6

0.8

Tree Volume (m3)

2.0
1.0
0.0

Plot ID: 14

10

20

30

Tree Volume (m3)

0.1
0.1
0.0
0

40

0.5

1

1.5

Tree Volume (m3)

0.2
0.1
0.1
0.0
0

0.5

1

1.5

Tree Volume (m3)

Plot ID: 13

3.0

0

0.2

Plot ID: 10

0.1
0.1
0.0
0.0
0.0

Plot ID: 11

Basal Area (cm2)

0.5

Basal Area (cm2)

0

Basal Area (cm2)

0.2
0.2
0.1
0.1
0.0

Basal Area (cm2)

Basal Area (cm2)

HIMALAYA CATCHMENT

1.0
0.8
0.6
0.4
0.2
0.0

Plot ID: 15

0

5

10

15

Tree Volume (m3)

Fig 5.1: Tree volume vs. Basal area graph of all the 14 in-situ plots
The individual graphs (Fig 5.1) of all the 14 in-situ plots has show a good linearity. A good straight line has
been maintained in all the plots. The tree volume data which has been recorded and utilised in the later
stage is of good consistency. The linearity explains that the each and every individual tree's tree volume
and basal area holds co-relation with each other. The stem volume per hectare (m3/ha) which has been
calculated from these field points are therefore good for use in future analysis.
Backscattering property of snow depends on the grain size, snow wetness, snow depth and ground surface
roughness [48]. The fraction of snow cover in every pixel is estimated by compairing the SAR images,
which are rectified using DEM. For forested terrain, the backscattering ratio gets smaller as the stem
volume increases [49].
The wetness in snow leads to higher attenuation, i.e. increased forward scattering and enhanced brightness
temperature [48]. Most of the variations is due to the variation of volume scattering in snow layer. For thin
snow layer (0.5m) the effect of ground scattering dominates and therefore, the total backscattering is lower
than that of a thicker snow layer where the volume scattering effect dominates [49]. The forest
backscattering models presented in the following sub-sections is very general and it does not considers
multiple scattering. Therefore they cannot not fully describe all the aspects of the backscattering from
snow covered ground.

36

RETRIEVAL OF FOREST BACKSCATTER FROM SAR DATA TO IMPROVE SNOW COVER AREA ESTIMATION IN THE CONIFEROUS FOREST OF NORTH WESTERN HIMALAYA CATCHMENT

RETRIEVAL OF FOREST BACKSCATTER FROM SAR DATA TO IMPROVE SNOW COVER AREA ESTIMATION IN THE CONIFEROUS FOREST OF NORTH WESTERN HIMALAYA CATCHMENT

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RETRIEVAL OF FOREST BACKSCATTER FROM SAR DATA TO IMPROVE SNOW COVER AREA ESTIMATION IN THE CONIFEROUS FOREST OF NORTH WESTERN HIMALAYA CATCHMENT