MINISTRY OF AGRICULTURE AND RURAL DEVELOPMENT
VIETNAM FORESTRY UNIVERSITY

STUDENT THESIS

COMPARING VEGETATION INDICES FOR MANGROVE
FOREST MAPPING USING REMOTELY SENSED DATA IN
KIEN THUY AND DO SON DISTRICT, HAI PHONG CITY
Major: Natural Resources Management (Advanced Curriculum)
Code: D850101
Faculty: Forest Resources and Environmental Management

Student: Ha Duc Thien

Student ID:1053091718

Class: K55 Natural Resources Management

Course: 2010 - 2014

Advanced Education Program
Developed in collaboration with Colorado State University, USA

Supervisor: Dr. Nguyen Hai Hoa

Hanoi, November 2015


ACKNOWLEDGMENTS

“People do not lack strength; they lack will”

Victor Hugo

I would like express my gratitude to my supervisor, Dr Nguyen Hai Hoa for his
support, guidance and encouragement throughout the process of this study.
My sincere appreciation to the members of my teammate whose loyalty and creative
endeavor made possible this paper


ABSTRACT
The use of vegetation indices of remote sensing data in vegetation mapping has
been long recognized. However, the accuracy of mapping through the use of vegetation
indices model has limitations, and has so far not been investigated. This study compared
performance of the vegetation indices (Normalized Difference Vegetation Index- NDVI,
Simple Vegetation Index-SVI, Soil-Adjusted Vegetation Index- SAVI) for mangrove
mapping in Kien Thuy district and Do Son county, Hai Phong city. Landsat Image was
used as a primary data to derive mangrove vegetation class from three vegetation indices
models. A total of three mangrove habitat categories were detected consisting of kandelia
Obovate and Sonneratia Caseolaris, Mixed - Kandelia Obovate and Sonneratia
Caseolaris, Mixed Sonneratia Caseolaris. The accuracy assessment of vegetation indices
were ranged from 68.3% to 75.8%. The results indicated that the SAVI was the best index
for mangrove mapping compared to other indices with accuracy of 75.8% and able to
determine three mangrove classes.

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KEY WORDS
Vegetation indices, Landsat Image, mangrove mapping performance, accuracy assessment

ACRONYMS

GIS

Geographic Information System

GPS

Global Positioning System

SVI

Simple Vegetation Index

NDVI

Normalized Difference Vegetation Index

SAVI

Soil-Adjusted Vegetation Index

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TABLE OF CONTENTS
ABSTRACT............................................................................................................................ i
KEY WORDS ........................................................................................................................ii
ACRONYMS .........................................................................................................................ii
TABLE OF CONTENTS......................................................................................................iii
LIST OF TABLES ................................................................................................................ vi
LIST OF FIGURES .............................................................................................................vii

Chapter I Introduction ........................................................................................................... 1
Chapter II Literature Review ............................................................................................... 4
2.1. Overview of using vegetation index for mangrove mapping ......................................... 4
2.2. Key vegetation indexes for coastal mangrove mapping ................................................. 5
2.3. Significance of study site .............................................................................................. 14
Chapter III Study goals, Objectives and Methodology....................................................... 15
3.1. Study goals and Objectives ........................................................................................... 15
3.1.1. Study goal .................................................................................................................. 15
3.1.2. Study objectives ......................................................................................................... 15
3.2. Study scope ................................................................................................................... 15
3.3. Methodology ................................................................................................................. 16
3.3.1. Investigation and determination of coastal mangrove species composition and its
habitat................................................................................................................................... 16
3.3.2. Calculation of different types of vegetation indices used to classify mangroves ...... 17

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3.3.2.1. Image pre-processing .......................................................................................... 17
3.3.2.2. Image processing ................................................................................................. 18
3.3.2.3. Calculating vegetation indices for mangrove classification ................................ 19
3.3.2.4. Accuracy assessment ........................................................................................... 20
3.3.2.5. Post classification ................................................................................................ 22
3.3.2.6. Mangrove mapping ............................................................................................. 22
3.3.2.7. Assessing and comparing different kinds of vegetation indices for
mangrove mapping ......................................................................................................... 22
3.3. Quantifying spatial dynamics of coastal mangroves in study areas during period 2010
– 2014 .................................................................................................................................. 22
Chapter IV STUDY SITE, NATURAL AND SOCIOECONOMIC FEATURES ............ 23
4.1. Natural characteristics................................................................................................... 23

4.1.1. Geographical location ................................................................................................ 23
4.1.2. Topography ................................................................................................................ 24
4.1.3. Climate ....................................................................................................................... 24
4.1.4. Hydrology .................................................................................................................. 24
4.1.5. Natural resources ....................................................................................................... 25
4.2. Socioeconomic conditions ............................................................................................ 25
4.2.1. Population .................................................................................................................. 25
4.2.2. Economy .................................................................................................................... 26
4.2.3. Ecological and economic values of mangroves ......................................................... 26

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Chapter V RESULTS AND DISCUSSION ....................................................................... 29
5.1. Spatial distribution and structures of coastal mangroves in study sites ........................ 29
5.2. Comparison of different kinds of vegetation indces for mangrove classification ........ 33
5.3. Dynamics of coastal mangroves during 2010- 2014 .................................................... 37
5.3.1. Thematic maps and dynamics of coastal mangroves ................................................. 37
5.3.2. Key drivers of coastal mangrove changes from 2010 to 2014 .................................. 41
Chapter VI GENERAL CONCLUSION, LIMITATION AND FURTHER STUDY ....... 43
6.1. Conclusions ................................................................................................................... 43
6.2. Limitations and further study ........................................................................................ 43
REFERENCES .................................................................................................................... 45

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LIST OF TABLES
Table.3.1. Landsat data used this study. .............................................................................. 18
Table 5.1: Synthesis of average mangrove structure characeteristics. ................................ 32

Table.5.2.1. Values of vegetation Indices for mangrove classification. .............................. 33
Table. 5.2.2. Accuracy assessment of image classified using SVI in 2014. ........................ 34
Table.5.2.3. Accuracy assessment of image classified using NDVI in 2014. ..................... 35
Table.5.2.4. Accuracy assessment of image classified using SAVI in 2014. ...................... 36
Table.5.2.5.Summarization of vegetation indices for mangrove classification ................... 37
Table 5.3.1: The extent of coastal mangroves in the study areas (ha) ................................. 38
Table 5.3.2: Dynamic of mangroves during period 2010 -2013 .......................................... 39
Table 5.3.3: Dynamic of mangroves during period 2013 -2014 .......................................... 39

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LIST OF FIGURES
Fig. 3.1: Clipped images of study sites in Hai Phong: (a) image in 2010, (b) image in 2013,
(c) image in 2014. ................................................................................................................ 19
Fig.4.1. Study sites in Hai Phong where: (a) Viet Nam map, (b) Hai Phong city map (c)
Selected sites as Kien Thuy District and Do Son County. ................................................... 23
Fig. 5.1: Species distribution of coastal magroves in Bang La and Dai Hop, Hai Phong. ............. 29
Fig.5.2. Mangrove species in different vegetation indices: (a) SVI – Simple Vegetation
Index; (b) NDVI- Normalized Difference Vegetation Index; (c) SAVI- Soil Adjusted
Vegetation Index. ................................................................................................................. 33
Fig. 5.3.1: Distribution of mangrove extents during the period 2010 - 2014. ..................... 38
Fig. 5.3.2: Spatial dynamics of coastal mangroves in study sites during two periods.................... 40
Fig 5.3.3: Fluctuation of mangroves area in study area. ...................................................... 41

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Chapter I
Introduction

Mangrove forests appear in the inter-tidal zones along the coast in most tropical
and semi-tropical regions (Tuan, Oanh et al., 2002).They are among the most important
and productive of ecosystems and provide habitat for wildlife (Wolanski, Brinson et al.,
2009). Mangroves play an important role in coastal zones and can reduce damage from the
effects of tsunamis .The most obvious evidence can be found from the Indian Ocean
tsunami of Dec, 2004 (Danielsen, Sorensen et al., 2005). Moreover, mangrove ecosystems
stabilize coastlines, clean water, protect land from erosion, and in many cases promote
coastal accretion, and provide a natural barrier against storms, cyclones, tidal bores and
other potentially damaging natural forces. For centuries, mangroves have contributed
significantly to the socioeconomic lives of coastal dwellers. In addition, they are a source
of timber for fire-wood and provide building materials, charcoal, tannin, food, honey,
herbal medicines, and other forest products (Hong and San, 1993). Importantly, mangrove
forests are amongst the most carbon-rich ecosystems in the tropics (Donato, Kauffman et
al., 2011) and are recognized as performing a vital role in climate change mitigation thanks
to “blue carbon“ storage (Pendleton L, 2012).
Despite their enormous socio-economic value, mangrove ecosystems are under
severe threats. High population growth, and migration into coastal areas, has led to an
increased demand for their products. This situation is further exacerbated by insufficient
governance, poor planning, and un-coordinated economic development in coastal zones.
Globally more than 3.6 million hectares of Mangroves have been lost since 1980, and Asia
has suffered the greatest loss of 1.9 million hectares (FAO 2007).

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Like many other countries in Southeast Asia, the mangrove areas in Vietnam have
decreased markedly. In Vietnam, it is estimated that the area of mangrove forests was
about 400,000 hectares in the early 20th century. However, this area has declined
dramatically during the past 50 years (Tuan, Munekage et al., 2003). In northern parts of
Vietnam, from Mong Cai to Do Son, throughout the periods 1964-1997, mangrove area

decreased by 17,094 ha. In the Red River plain, the loss of mangrove was 4,640 ha from
1975 to 1991 followed by a decrease of 7,430 ha in 1993 (NEA 2003). Despite government
and international efforts in mangrove restoration programs during the 1990„s, mangrove
forests on the Northern coast of Vietnam have declined significantly due to aquaculture.
Nevertheless, mangrove forests in the protected zone are well managed thanks to
community-based forest management (Dat and Yoshino et al., 2013).
Hai Phong city with a length of 125 km of sea dykes, is one of the potential area for
local mudflats and mangroves. From 1989 to 2013, Hai Phong lost 281 hectares of
mangrove forests over ten years and gained roughly 355 hectares after twenty four years.
The annual rate of mangrove loss in Hai Phong was approximately 23 hectares during the
first period 1989-2001. From 2001 to 2013, mangrove gained about 53 hectares per year.
This was due to the Doi Moi economic reform in Vietnam that was established in 1986.
The Vietnamese economy was transformed into a market economy. Shrimp farming for
export was encouraged and promoted by the government. As a consequence, mangrove
areas were converted to shrimp aquaculture because of the high benefit from shrimp
exports (Tuan et al., 2003). On the other hand, from 2001 to 2013, mangrove forests
increased slightly since some coastal districts had a good mangrove conservation system in
place thanks to community-based forest management in cooperation with local authorities.
This situation occurred in Hai Phong after three severe tropical cyclones named Washi,
Vin-centre, and Damrey attacked its coastal zone in 2005. In dense mangrove forested

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areas there was no damage; however, areas converted to shrimp aquaculture from
mangrove forest by local people were devastated in 2005. After 2005, people realized the
important role of mangrove in protecting the dyke system and their livelihood. They
planted mangrove again in vulnerable areas to defend against the typhoons (Dat and
Yoshino et al., 2013).
In order to support coastal management and planning programs, mapping mangrove

is a great need for effective measures on inventory of coastal mangrove areas and
aquaculture sites and change detection. Mangrove maps can be made from investigation insitu or analyzing from remotely sensing images and GIS techniques.
In fact, many studied results indicated that images of Landsat has been for
mangrove habitat mapping with different image processing techniques, including
Vegetation index (Ramsey and Jensen .,1996 ; Green et al .,1998; Wang et al .,2004 ;
Campbel , 1996 ; Baret and Guyot , 1991; Ismail et al., 2010) in which , the index most
commonly used is the Normalized Different Vegetation Index (Akamar et al., 2009), other
indices based on the same two spectral bands (NIR-near infrared and VIS-visible usually
red) have been developed , mostly with the aim of reducing the sensitivity of the index to
factors such as soil background , canopy architecture , atmospheric condition. However,
the accuracy of mangrove mapping through the use of vegetation index models has
limitations.
In this study, we propose three vegetation indices in comparison consisting of SVI
(Simple Vegetation Index), NDVI (Normalized Difference Vegetation Index) and SAVI
(Soil-adjusted Vegetation Index), in order to map mangrove forest for the Kien Thuy and
Do Son coasts, Hai Phong, Vietnam using multi-temporal image.

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Chapter II
Literature Review

2.1. Overview of using vegetation index for mangrove mapping
Since the launching of the fist remote sensing satellite in 1970‟s, there have been
tremendous efforts made in mapping mangrove areas. The advancement of image
processing established many techniques and models for mapping vegetation including
vegetation indices models approaches (Ismail et al., 2010). Vegetation index (VI) was
formed from combination of several spectral values that were mathematically recombined
in such a way as to yield a single value indicating the amount or vigor of vegetation within

a pixel (Campbel, 1996). Vegetation indices (VIs) are more sensitive than individual bands
to vegetation parameters (Baret and Guyot, 1991) and are strongly affected by two main
factors:
Effects of soil background on VI:
Until the soil is fully covered by vegetation, the soil background will influence the
VI. For incomplete canopies, the wetting of a previously dry soil can cause a change in VI.
The change is further complicated by the fact that transmission of light through vegetation
is considerably greater in the NIR than in the Red band. Both the ratio and the linear
combination classes of VI rely on the existence of the soil baseline in Red and NIR
wavelength space for soil normalization (Huete, 1998). The intercept of this line is close
to, but does not pass through the origin and there is usually some scatter of soil points
away from the principal soil line. Such secondary soil influences are most noticeable with
Red and yellow colored soils (Kauth and Thomas, 1976).These two factors affect the

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discrimination of low amounts of vegetation from bare soil, and are significant in arid
regions and in the early stages of vegetation growth (Huete et al., 1984).
Effect of canopy architecture on VI:
The architecture of a vegetation canopy determines the directions that radiation will
be reflected from plant surfaces. The vertical elements of an erectophile canopy trap
reflected radiation within the canopy, with a corresponding reduction in the amount
reflected vertically towards a nadir-oriented radiometer. The opposite is true for a
planophile canopy. The horizontal leaves reflect more in the vertical direction and less is
trapped within the canopy (Bunnik, 1978). A nadir-pointing sensor can receive 20-30%
more reflected radiation from a planophile canopy than from an erectophile canopy.
Canopy architectural effects on VI were examined by Jackson and Pinter (1986).
They showed that during the period of peak green vegetation densities, the RVI was about
30% higher for the planophile canopy. On the other hand, the PVI was about 30% higher

for the planophile canopy than for the erectophile canopy. Thus, within a scene that
contains similar amounts of vegetation, but different geometries, different VI will respond
differently to canopy architecture. At first glance, this complicating feature would diminish
the value of VI. In fact, it adds a new perspective that could possibly be exploited to
provide otherwise unobtionable information on canopy architectural feature within a scene.
2.2. Key vegetation indexes for coastal mangrove mapping
To understand how vegetation indexes are designed, it is essential to know some
concepts rerated to influence of soil and the use of the soil line and vegetation iso-line. At
this point is useful to introduce the different kind of vegetation indices that have developed
over the years. Some of the indices have developed considering that all vegetation iso-line
converge at a single point. These indices are called “ratio – based “and measure the slope
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of the line between the point of convergence and the soil line. When the vegetation iso-line
is considered parallel to soil line, the indices are called “perpendicular” vegetation indices.
Ratio - Based VIs: Ratio - based VI‟s computations are done using data acquired
in visible red and near IR bands. These values indicate both the status and abundance of
green vegetation cover and biomass. This is the first level of classification and aid in
delineating areas under vegetation from non-vegetation.
Simple Vegetation Index (SVI): RATIO vegetation indices (Rose et al., 1973)
separate green vegetation from soil background by dividing the reflectance values
contained in the near IR band (NIR) by those contained in the red band (R).
SVI = NIR / RED
This clearly shows the contrast between the red and infrared bands for vegetated
pixels with high index values being produced by combinations of low red (because of
absorption by chlorophyll) and high infrared (as a result of leaf structure) reflectance. Ratio
value less than 1.0 is taken as non-vegetation while ratio value greater than 1.0 is
considered as vegetation. The major drawback in this method is the division by zero. Pixel
value of zero in red band will give the infinite ratio value. To avoid this situation

Normalized Difference Vegetation Index (NDVI) is computed.
Normalized Difference Vegetation Index (NDVI): NDVI overcomes the
problem of Ratio method (i.e. division by zero). It was introduced in order to produce a
spectral VI that separates green vegetation from its background soil brightness (Rose et al.,
1974) and is given by,
NDVI = (NIR - RED) / (NIR + RED)

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This is the most commonly used VI due to the ability to minimize topographic
effects while producing a linear measurement scale ranging from –1 to +1.The negative
value represents non vegetated area while positive value represents vegetated area.
Ratio Vegetation Index (RVI): The ratio vegetation index is the reverse of the
standard simple ratio (Richerdson et al., 1977),
RVI= RED/NIR
The range for RVI extends from 0 to infinity. The ratio value less than 1.0 is taken
as vegetation while value greater than 1.0 is considered as non-vegetation area.
Perpendicular VIS: The main objective of the perpendicular vegetation index is
to cancel the effect of soil brightness in cases where vegetation is sparse and pixels contain
a mixture of green vegetation and soil back ground. These indices are evaluated on the
basis of soil line intercept concept. The soil line is a hypothetical line in spectral space that
describes the variation in the spectrum of bare soil in the image. The soil line represents a
description of the typical signatures of soils in red/near-infrared bi-spectral plot. It is
obtained through liner regression of the infrared band against the red band for sample of
bare soil pixels. Pixels falling near the soil line are assumed to be soils while those far
away are assumed to be vegetation. Equation of the soil lines is given below,
Y1 = 0.841333x + 10.781234 (red band independent variable)
Y2 = 0.985684x + 9.501355(infra-red band as independent variable)
Perpendicular Vegetation Index (PVI): PVI uses the perpendicular distance

from each pixel co-ordinate to the soil line and this was derived to define vegetation and
non-vegetation for arid and semi-arid region (Richerdson et al., 1977). The pixels, which
are close to soil line, are considered as non-vegetation while pixels, which are away from
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soil lines, represent vegetation. PVI values for data taken at different dates require an
atmospheric correction of data, as PVI is quite sensitive to atmospheric variations. This can
be defined as:
PVI= sin (a) NIR – cos (a) Red
Where, a = angle between the soil line and the NIR axis.
√
Where, (x1, y1) is the co-ordinate of the pixel and (x2, y2) is the coordinate of soil
line point that is perpendicular to pixel co-ordinate.
Perpendicular distance less than 7.0 is taken as non-vegetation area while greater
than 7.0 is taken as vegetation area.
Perpendicular Vegetation Index 1 (PVI1): It was noticed that original PVI
equation is computationally intensive and does not discriminate between pixels that fall to
the right or left of the soil line (i.e. water from vegetation). Given the spectral response
pattern of vegetation in which the infrared reflectance is higher than the red reflectance, all
vegetation pixels will fall to the right of the soil line. In some cases a pixel representing
non-vegetation (e.g. water) may be equally far from the soil line but it will fall left side of
the soil line.
In PVI the water pixel will be assigned a high vegetation index value. PVI1 assigns
negative values to those pixels, which can be delineated from vegetation. The
mathematical equation for PVI1 (Perry et al., 1984) is written as,
√

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Where, NIR: reflectance in the near infrared band, RED: reflectance in the red band
, a : intercept of the soil line, b : slope of the soil line.
Infrared band is taken as the independent variable and the red band as dependent
variable for regression analysis. Perpendicular distance less than 6.5 is taken as nonvegetation area while greater than 6.5 is taken as vegetation area.
Perpendicular Vegetation Index 2 (PVI2): In PVI2, Red band is taken as
independent variable over infrared dependent variable for regression analysis (Bannari et
al.,1996), given importance to the red band with the intercept of soil line. Mathematically
PVI2 can be represented as,
√
√
Where, a: intercept of the soil line, b: slope of the soil line.
Here, pixels having less than –95.0 are grouped as non-vegetation area.
Perpendicular Vegetation Index 3 (PVI3): PVI3 is improved version of PVI,
where red band is taken as independent variable on regression analysis and special
attention was given to avoid the negative results (Qi et al.,1994). PVI3 can be defined as,
PVI3= a pNIR – b pRED
Where, a: intercept of the soil line, b: slope of the soil line, pNIR: reflectance in the
near infrared band, pRED: reflectance in the visible red band.
Difference Vegetation Index (DVI): DVI weigh up the near-infrared band by the
slope of the soil line (Richerdson and Wiegand., 1977) and is given as :
DVI= gNIR – RED
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Where, g: the slope of the soil line.
Similar to the PVI1, with the DVI, a value of zero indicates bare soil, values less
than zero indicate non vegetation and greater than zero indicates vegetation.
Weighted Difference Vegetation Index (WDVI): Like PVI, WDVI is very
sensitive to atmospheric variations (Richerdson et al., 1977) and can be presented as,


Where, NIR : reflectance of near infrared band, RED: reflectance of visible red
band, g: slope of the soil line.
Although simple, WDVI is as efficient as most of the slope based VI‟s. The effect
of weighting the red band with the slope of the soil line is the maximization of the
vegetation signal in the near-infrared band and the minimization of the effect of soil
brightness.
Soil noise
Soil reflectance spectra depend on type of soil. The vegetation indices computed
earlier assume that there is a soil line, where there is a single slope in red-NIR space.
However, it is often the case that there are soils with different red-NIR slopes in a single
image. Also, if the assumption about the iso-vegetation line (parallel or intercepting at the
origin) is not exactly right, changes in soil moisture (which move along iso-vegetation
lines) will give incorrect answers for the vegetation index. The problem of soil noise is
most acute when vegetation cover is low. The following groups of indices like SAVI,
TSAVI1, TSAVI2, MSAVI1, MSAVI2 attempt to reduce soil noise by altering the
behavior of the iso-vegetation lines. All of them are ratio-based, and the way that they
attempt to reduce soil noise is by shifting the place where the iso-vegetation lines meet.

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These indices reduce soil noise at the cost of decreasing the dynamic range of the index.
These indices are slightly less sensitive to changes in vegetation cover than NDVI (but
more sensitive than PVI) at low levels of vegetation cover. These indices are also more
sensitive to atmospheric variations than NDVI (but less so than PVI).
Soil-adjusted Vegetation Index (SAVI): SAVI is intended to minimize the
effects of soil background on the vegetation signal by incorporating a constant soil
adjustment factor L in the denominator of the NDVI equation (Huete, 1988). L varies with
the reflectance characteristics of soil (i.e. color and brightness). The L factor chosen

depends on the density of the vegetation. For very low vegetation L factor can be taken as
1.0 while for intermediate it can be taken as 0.5 and for high density 0.25. The best L value
to select is where the difference between SAVI values for dark and light soil is minimal.
For L=0, SAVI equals NDVI. For L=100, SAVI approximates PVI. Mathematically SAVI
is defined as,
SAVI = {(NIR - RED) / (NIR+RED+ L)} * (1+L)
Where, NIR: near-infrared band, RED: visible red band, L: soil adjustment factor.
Multiplicative term (1+L) present in SAVI (and MSAVI) is responsible for
vegetation indices to vary from –1 to +1. This is done so that both vegetation indices
reduce to NDVI when the adjustment factor L goes to zero.
Transformed Soil-adjusted Vegetation Index (TSAVI 1): SAVI concept is exact
only if the constants of the soil line are a = 1 and b = 0, where a is slope of the soil line and
b is y-intercept of the soil line. As it is not generally the case some modification was
needed in SAVI. By taking into the consideration of PVI concept (Baret et al., 1989),
SAVI is modified as TSAVI1. This index assumes that the soil line has arbitrary slope and
intercept, and it makes use of these values to adjust the vegetation index and is written as:
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Where, NIR: reflectance in near infrared band, RED: reflectance in red band, a:
slope of the soil line, b: intercept of the soil line, X: adjustment factor which is set to
minimize soil noise.
Red band is taken as independent variable for regression analysis. Ratio value less
as non-vegetation while greater than –9.0 is taken than –9.0 is taken as vegetation. With
some resistance to high soil moisture, TSAVI1 could be very good candidate for use in
semi-arid regions. TSAVI1 was specifically designed for semi-arid region and does not
work well in areas with heavy vegetation.
Transformed Soil-adjusted Vegetation Index (TSAVI2): TSAVI2 is modified
version of TSAVI1 which was readjusted with an additive correction factor of 0.08 to
minimize the effects of the background soil brightness (Baret et al., 1989), and is given by,


Red band is taken as independent variable for regression analysis and is given
preference with soil line intercept.
Modified Soil-Adjusted Vegetation Index 1 (MSAVI1): The adjustment factor L
for SAVI depends on the level of vegetation cover being observed which leads to the
circular problem of needing to know the vegetation cover before calculating the vegetation
index which is what gives you the vegetation cover. MSAVI is the Modified Soil Adjusted
Vegetation Index (Qi et al.,), and provide a variable correction factor L. The correction
factor used is based on the product of NDVI and WDVI. This means that the iso-vegetation
lines do not converge to a single point. MSAVI1 is written as,
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Where, L: 1 - 2 * s * NDVI * WDVI, s: slope of the soil line, NDVI: Normalized
Difference Vegetation Index, WDVI: Weighted Difference Vegetation Index, 2: Used to
increase the L dynamic range, range of L = 0 to 1.
Modified Soil-Adjusted Vegetation Index 2 (MSAVI2): MSVI2 was derived
based on a modification of the L factor of the SAVI (Qi et al.,). SAVI and MSVI2 are
intended to correct the soil background brightness in different vegetation cover conditions.
Basically, this is an iterative process and substitute 1-MSAVI (n-1) as the L factor in
MSAVI (n) and then inductively solve the iteration where MSAVI (n) = MSAVI (n-1).
MSVI2 uses an inductive L factor to:
•Remove the soil “noise” that was not canceled out by the product of NDVI by
WDVI; and
•Correct values greater than 1 that MSAVI1 may have due to the low negative
value of NDVI * WDVI. Thus its use is limited for high vegetation density areas.
The general expression of MSAVI2 is,
√

Where, NIR: reflectance of the near infrared band, RED: reflectance of the red

band.
Pixel value less than 0.0 are under non-vegetation and pixel having greater than 0
are under vegetation.

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2.3. Significance of study site
Hai Phong is a coastal city with a total of 125km of seacoast. Coastal mangroves
not only reduce consequences from natural disaster but also bring huge economic benefit
for local people. Using Remote Sensing & GIS (Geographic Information System) as a tool
would enhance effectiveness of coastal mangroves management. Up to date, there is no
published document on applying vegetation index for coastal mangrove in Hai Phong city,
in particular in Kien Thuy and Do Son. Thus, this study would be significantly contribution
to technical enhancement for mangrove exploring and mapping in Hai Phong.

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Chapter III
Study goals, Objectives and Methodology

3.1. Study goals and Objectives
3.1.1. Study goal
The aim of the study is to propose a suitable vegetation index for coastal mangrove
mapping using multi-temporal Landsat image covering Dai Hop and Bang La communes
in Hai Phong. These findings provides good scientific foundation for enhancing mangrove
rehabilitation and restoration in the face of climate change and sea-level rise in Hai Phong.
3.1.2. Study objectives
In order to reach the main goal of study, three specific objectives are given as below:

Objective 1:To investigate the spatial distribution and structures of coastal
mangrove species in Dai Hop and Bang La communes, Hai Phong.
Objective 2: To determine different vegetation indices for classifying mangroves
in Dai Hop and Bang La communes, Hai Phong.
Objective 3: To quantify the spatial dynamics of coastal mangroves in study areas
during period 2010 - 2014.
3.2. Study scope
With period of five months (May to October 2014), the study focuses on
determining coastal mangrove classes in Dai Hop commune, Kien Thuy district and Bang
La ward , Do Son county, Hai Phong city.

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3.3. Methodology
3.3.1. Investigation and determination of coastal mangrove species composition and
its habitat
Field survey: The field survey was divided into two phases. Calibration data were
collected in May 2014, accuracy data in September 2014. A total of 335 sample sites in
square shape (30m * 30m - equal pixel size) were established Species composition, tree
height , tree density and crown diameter were recorded at all sites.
Species composition was visually estimated from a 5m2 (2 *2.5 m) plot marked by
a tape measure.
Tree height and crown diameter were measured using a 5 m telescopic pole.
Tree density was measured by counting the number of tree trunks at breast height.
When a tree forked beneath breast height (1.3 m) each branch was recorded as
a separate stem .The location of each field site was determined using GPS with a
probable circle error of 2-5 m.
Canopy closure/coverage (%): An estimation of canopy closure is made
using the densitometer approach. This device is made from piece of 2.5 cm diameter duct

pipe approximately 40 cm long with cross-hairs added at both ends using fine wire
threaded evenly across the diameter of the tube (Duke et al., 2010; Hai-Hoa et al., 2013).
The densitometer is held vertically and an estimate made of the percentage of the view
through the duct pipe covered by sky, or less or more than 50% of a leaf, branch and tree
trunk (recorded as 0, 0.5 or 1 respectively). Readings are taken every meter along the
transect length within a 100 m2 plot, resulting in about 100 readings.

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