REMOTE SENSING APPROACH IN WETLAND AND LAND DEGRADATION ASSESSMENT: A SCENARIO OF MODHUMOTI MODEL TOWN, SAVAR, BANGLADESH

Abstract

Due to rapid urbanization, people destroy core environmental elements such as water, land for habitation purpose without thinking about adverse environmental consequences. Modhumoti Model Town is such type of housing project which is situated at a river zone. Bangladesh Environmental Lawyers Association (Bela) filed a writ against the project and after three times (in 2004, 2005 and 2013) assembling the case, the High Court declared the project was illegal and should restore the wetland. Overruling the law, the development procedure is still constant which destroys the natural wetland and its surroundings. The study aims to assess the extent of water and land degradation due to human settlements using Geographic Information System (GIS) and Remote Sensing (RS) approaches. Modhumoti Model Town is selected as the study area which is situated at Amin Bazar, Savar. Landsat TM 4-5 and OLI 8 have been used to calculate several indices such before and after verdicts. Results indicate that NDVI, SAVI, NDWI were decreasing where NDBI was increasing sequentially. Now, both semi-pucca and pucca settlements are around 240 in amount and the project area completely blocks the free flow of water from river. Furthermore, the analysis captures the consequence of law violence and shows that imposing verdict is not enough to protect the environment if it would not be strictly maintained.

Full text

Faisal, AA; Hossain, MA; Haque, S;
Shaunak, MF; Kafy, AA
Remote Sensing Approach in Wetland and
Land Degradation Assessment
247
Research Paper
REMOTE SENSING APPROACH IN WETLAND AND LAND
DEGRADATION ASSESSMENT: A SCENARIO OF
MODHUMOTI MODEL TOWN, SAVAR, BANGLADESH
Save Wetlands, Remove Modhumoti Model Town
Abdullah-Al-FAISAL, Department of Urban & Regional Planning, Rajshahi University of
Engineering & Technology, Rajshahi-6204; Bangladesh
Muhammad Arif HOSSAIN, Department of Urban & Regional Planning, Rajshahi University of
Engineering & Technology, Rajshahi-6204; Bangladesh
Shajibul HAQUE, Department of Urban & Regional Planning, Rajshahi University of Engineering &
Technology, Rajshahi-6204; Bangladesh
Mir Fahim SHAUNAK, Junior Specialist, Remote Sensing Division, Center for Environmental
Geographic Information Services, Dhaka-1212, Bangladesh
Abdulla - Al KAFY, GIS Analyst, Rajshahi Development Authority; Bangladesh
Abstract
Due to rapid urbanization, people destroy core environmental elements such as
water, land for habitation purpose without thinking about adverse environmental
consequences. Modhumoti Model Town is such type of housing project which is
situated at a river zone. Bangladesh Environmental Lawyers Association (Bela) filed
a writ against the project and after three times (in 2004, 2005 and 2013)
assembling the case, the High Court declared the project was illegal and should
restore the wetland. Overruling the law, the development procedure is still constant
which destroys the natural wetland and its surroundings. The study aims to assess
the extent of water and land degradation due to human settlements using
Geographic Information S ystem (GIS) and Remote Sensing (RS) approaches.
Modhumoti Model Town is selected as the study area which is situated at Amin
Bazar, Savar. Landsat TM 4-5 and OLI 8 have been used to calculate several indices
such as Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation
Index (SAVI), Normalized Difference Built-up Index (NDBI) and Normalize Difference
Water Index (NDWI) in the year of 1998, 2004, 2010, 2014 and 2019 before and
after verdicts. Results indicate that NDVI, SAVI, NDWI were decreasing where NDBI
was increasing sequentially. Now, both semi-pucca and pucca settlements are
around 240 in amount and the project area completely blocks the free flow of water
from river. Furthermore, the analysis captures the consequence of law violence and
shows that imposing verdict is not enough to protect the environment if it would
not be strictly maintained.
Keywords
Environmental consequences, Writ, Overruling laws, GIS, RS
1. Introduction
Dhaka city was planned for l0 lakh people in 1959 (Hossain and Akther, 2011), whereas the
population of Dhaka Metropolitan Area (DMP) is about 8906039 (Statistics, 2011). The
population of Dhaka grows at an estimated rate of 4.2% per year, one of the highest among

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Asian cities (McGee, 2006). The continuing growth reflects ongoing migration from the rural
areas to the Dhaka urban region, which is accounted for 60% of the city's growth in the
1960s and the 1970s.In recent times, the city's population has also grown with the
expansion of city boundaries. The process added more than a million people in the town in
the 1980s (McGee, 2006). According to the Economic Review, Dhaka will become the home
of 25 million people by the year 2025 (Davis, 2006, McGee, 2006).
People are filling the low lying areas to meet the demands of land for residential,
commercial, industrial and so on (Kafy, 2018, Kafy et al., 2018). According to local news,
around 49 housing projects without approval have been identified which are inside the flood
flow zones by covering about 9,241 acres of land in Dhaka. Landfilling events are going on
even after the enactments of the "Wetland Conservation Act, 2000". In Dhaka, yearly loss of
wetland during 1989-1999 was 1.23 %, whereas, during 1999-2003, the damage was 5.67 %.
Dhaka is still left with 19.3 % of wetland. If the current rate of loss of wetland continues, by
the year 2037 all temporary wetlands of Dhaka will disappear (Zaman et al., 2010).
Remote sensing is a great medium of analysing environmental consequences. It has become
a great source of data as a scene cover a larger area with a lot of spectral information (Kafy
et al., 2019, Faisal and Khan, 2018). Moreover, to assess the environmental degradation rate
and its extent, some environmental indices has been developed. Hence, there are many
vegetation indices for detecting worth condition of vegetation cover, vegetation structure
and leaf distribution using satellite images (Yengoh et al., 2015). The most popular and
universally applied vegetation index is named as Normalized Difference Vegetation Index
(NDVI). NDVI relies on the red and near-infrared band combination (Gascon et al., 2016).
Additionally, In the assessment of water resources, the monitoring of water bodies
extraction has become a necessary task. In order to do so, Normalized difference water
index (NDWI) is an index that was developed by McFeeters to delineate the water features
using satellite images (Gao, 1996, McFeeters, 1996). Generally, to delineate water features
while reducing the appearance of vegetation and soil features, the NDWI uses near-infrared
(NIR) and middle infrared (MIR) radiation (Gao, 1996).
Moreover, urbanization is one of the most critical land cover change factors as it increases
the loss of agricultural lands by converting it to urban areas (Davis, 2006, McGee, 2006).
Information on urban built-up area is needed to detect land use/land cover changes (LULC)
(Singh et al., 2017). For detecting dynamics of urban built-up area, Normalized Difference
Built-up Index (NDBI) index is widely used (Kafy et al., 2018, Gascon et al., 2016). Basically,
the method named NDBI was introduced to evaluate urban zones from Landsat images
(Verbeiren et al., 2008). Besides, Soil Adjusted Vegetation Index (SAVI) another important
method used to minimize soil brightness influences from spectral vegetation using near-
infrared and red wavelengths (Gilabert et al., 2002).
In this research, the study team identifies the adverse environmental effects of Modhumoti
model town, Amin Bazar, Savar, Dhaka through combining GIS and Remote sensing
technologies. The study team quantifies vegetation, measures the volume of changes water
body, and differentiates urban zones measures the soil brightness influences through NDVI,
NDWI, NDBI and SAVI method. Combining all of these methods, the study team finds out the
overall environmental impact assessment because of Modhumoti Model Town.
2. Study Area Profile
Modhumoti model town is situated in Dhaka-Aricha highway, amin bazar, Savar, Dhaka. The
coordinate location of Modhumoti model town is 90°18'2.878"E and 23°47'12.302"N. This

Faisal, AA; Hossain, MA; Haque, S;
Shaunak, MF; Kafy, AA
Remote Sensing Approach in Wetland and
Land Degradation Assessment
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project is built without taking the permission of RAJUK. This project is declared illegal by the
high court. The authority was asked to regain the reservoirs as it was before. But overruling
the dictates, they began to continue the constructions work in there. It was first declared
illegal in 2005. They the Modhumoti Model Town’s authority appealed. But it was again
rejected and the high court declared it completely illegal in 2012.
Figure 1: Modhumoti Model Town
The figures show that there have some constructions though it was illegal. But it is a matter
of great sorrow that, the authority of this model town doesn’t pay heed to the high court
and they are continuously building new constructions. This figure shows that how they are
built construction illegally. The number of constructions is 10 times more than it was in
2014. This project is built on the bank of the river and most portion of the river are filled to
complete this project. Therefore, this eventually is influencing environmental impacts. The
study team finds out the environmental loss because of this illegal, inimical project.
As the Modhumoti Model Town is a relatively smaller area to analyse Landsat 30-meter
resolution images, the authors consider the study area as (5×5 sqkm) square. The square
covers project area with its surroundings that helps to evaluate the overall condition.
3. Methodology
Modhumoti residential area is a project which is fully athwart with environmental
sustainability. It has terrible effects on the environment which is destined not only here by
the study team through Geographic Information System (GIS) and Remote Sensing
approaches. The study team used NDVI, SAVI, NDBI and NDWI to evaluate the
environmental effects.
Normalized Difference Vegetation Index (NDVI): NDVI quantifies vegetation based on the
difference between red (R) and near-infrared (NIR) band values. Red (R) band refers to that
which vegetation absorbs and near-infrared (NIR) refers to that which vegetation strongly
reflects. According to Rouse et al., (1974) the NDVI equation is formulated as below (Rouse
Jr et al., 1974):
NDVI always ranges from -1 to +1. But there isn’t an exclusive boundary for each type of land
cover. High negative values of NDVI generally indicates that there is a massive possibility of
water and if the values near to +1, there’s a higher possibility of dense green leaves. But if

Faisal, AA; Hossain, MA; Haque, S;
Shaunak, MF; Kafy, AA
Remote Sensing Approach in Wetland and
Land Degradation Assessment
250
the NDVI is close to zero, it indicates that there aren’t green leaves and it might be an
urbanized area.
Soil Adjusted Vegetation Index (SAVI): In which areas the vegetative cover is low (i.e., <
40%) and the soil surface is manifested, the reflectance of light in the red and near-infrared
spectra can influence vegetation index values. The SAVI was established as a adjustment of
the NDVI to correct for the influence of soil brightness when vegetative cover is low.SAVI
calculation is based on the difference between R and NIR values with a soil brightness
correction factor (L) defined as 0.5 to accommodate most land cover types.
Normalized Difference Built-up Index (NDBI): NDBI is a process to convert satellite imagery
into a land cover map. It was introduced to extract urban zones from Landsat images
(Verbeiren et al., 2008).
For Landsat TM or ETM images, the calculation of NDBI is expressed below:
For Landsat 8 images, the calculation of NDBI is expressed below:
Normalize Difference Water Index (NDWI): NDWI index is the most appropriate used
method for water body mapping. It is developed to depict open water features and enhance
their presence in remotely-sensed digital imagery. The index uses the green and near Infra-
red bands of remote sensing images. According to Mishra et al., (2015) the calculation of
NDWI can be formulated below (Mishra and Prasad, 2015):
4. Results and discussions
The Modhumoti Model Town is a growing and illegal construction site which destroys the
whole ecological system of the riverine area. Several environmental indices indicate the
extent of environmental changes in the area.
The Metro Makers and Developer Limited company is the owner of the town which
developed a housing project by filling in 550 acres of wetlands. The wetland was identified
as floodplain in the master plan of 1997. On August 2004, Bela filed a writ petition as public
interest litigation against the project to the high court. The project did contradict with the
Environmental Conservation Act, Town Improvement Act and Rajuk (Rajdhani Unnayan
Kartipakkha) rules. The Bela challenged the legality of the project by addressing the laws. In
the petition, Bela addressed that the natural characters of the area will be destroyed if the
project continues. Also, to remain free flow of the water of the city, Bela appalled to the
apex court to pass the necessary orders. The high court continued the project's development
work after primary hearing.
On July 27, 2005, the high court declared that the Modhumoti Model Town project is
unauthorised, illegal and against public interest. The court also rejected a writ filed by the
owner Metro Makers. But high court also declared that interest of the purchasers is

Faisal, AA; Hossain, MA; Haque, S;
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protected. In 2006, Bela, Metro Makers Ltd, plot purchasers and Rajuk did file 5 separate
leave to appeal petition against separate portion of t he high court verdict with supreme
court. Metro Makers and plot purchasers appealed by addressing a lot of investment for the
plots and projects. Rajuk appealed for declaring the project as illegal.
On March 19, 2009, the Supreme Court upheld the high court verdict. But the court allowed
the company, plot purchasers and Rajuk to move regular appeals before it against the high
court verdict.
On August 7, 2012, the Appellate Division upheld the high court’s decision and allowed the
appeal of Bela. The court also dismissed Metro Makers, plot purchasers appeals and
disposed of the appeal of Rajuk as well.
Finally, on July 11, 2013, the Appellate Division of the apex court released the full text of its
159-page verdict. The verdict was directed to Modhumoti Model Town owner Metro Makers
and Developer Limited and the verdict was to restore within six months the wetland in
Bilamalia and Bailarpur moujas of Savar where it had developed the project.
Above discussion indicates several timelines which have direct linkage with writ, appeal and
verdict. Therefore, several year’s images have been chosen for evaluating action after
effects. The timeline of the images shows 1-year after-effects from different actions. These
years are 1998 (indicates initial stages), 2004 (indicates the situation of filing writ petition),
2010 (One year after effect after court verdict), 2014 (one year later effect after final verdict)
and 2019 (to evaluate the present situation).
As the project was running, and the natural riverbed was destroyed so that the area has
been experienced a huge environmental change. Environmental indices do help to assess the
environmental changes universally. The following sections evaluate the environmental
consequences of the area by evaluating environmental indices and additional analysis.
4.1 Assessment of water body:
The model town project was established by filling up the
surface water body of a river. In previous, the river had a free flow of water towards the
project area. The project area blocks the river channel and interrupts the free flow of water
by causing flood, waterlogging and environmental degradation as well. NWDI was used to
evaluate the overall water assessment. Figure 2 indicates the NDWI values of the project and
surrounding area. The hierarchy of colour red to blue indicates low to high surface water
availability. In 1998, the maximum NDWI value was 0.71 which shows higher surface water
availability. The maximum NDWI value drops drastically in 2004 as 0.55. The project area
was filled by sand and had experienced loss of water body. Most importantly, it destroyed
the flowing channel. In 2010, the area still faces loss of water availability as the project
verdict was hanging. After the final verdict, the area had experienced highest level of water
losses as the maximum NDWI value was 0.31. Now, in 2019, the maximum NDWI value
shows as 0.35 which is a little higher than 2014 but not enough at all. The whole NDWI value
indicates that the surface water availability and its flow direction are completely destroyed
and the water system is not restored even after verdict also.
The loss water system of the area can be visualized by the following figure 3. The figure
indicates extent of water body available indefinite time interval. The hierarchy from blue to
red indicates the water body found in 1998 to 2019, sequentially. The figure indicates that
the area losses around 5.34 km
2
from 1998 to 2014. Unfortunately, in 2019, the water body
has fallen around 2.23 km
2
from 10.54 km
2
. The area is a small area but has great impact and
significant loss in last 21 years. The project area is indicated as ‘Main-boundary’ in the figure.

Faisal, AA; Hossain, MA; Haque, S;
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Hence, it is clear that the project area was nothing but a river and the project area was
established by filling up the river.
NDWI_98
High : 0.71
Low : -0.38
NDWI_04
High : 0.55
Low : -0.38
NDWI_10
High : 0.43
Low : -0.38
NDWI_14.tif
High : 0.31
Low : -0.13
NDWI_19
High : 0.35
Low : -0.15
Figure 2: NDWI value of the study area
4.2 Assessment of vegetation and soil:
The project area was filled by sand causes
degradation of soil and vegetation properties. NDVI is a universal vegetation productivity
index that helps to evaluate the extent of productivity of vegetation. The whole area is
classified into three broad classes which are high (NDVI value 0.42-1), medium (0.08-0.42)
and lowly productive (-1-0.08) vegetation (Table 1). These categories are also classified into
several subcategories. The table indicates a clear vegetation overview of the area. In case of
high productive class, productivity was decreased from 1998 to 2004 at 1.8%, increased from
2004 to 2010 at 2.8 %, decreased from 2010 to 2014 at 2.93% and increased from 2014 to
2019 at 2.3%. The noticeable fact is that in the year of 2004 to 2010, t he high productive
vegetation was increased and the reason might be stopping the developing works in the
time interval. In case of medium productive classes, the overall shows increased
productivity.
90°21'0"E
90°21'0"E
90°19'30"E
90°19'30"E
90°18'0"E
90°18'0"E
90°16'30"E
90°16'30"E
23°48'0"N
23°48'0"N
23°46'30"N
23°46'30"N
´
0 420 840 1,260 1,680210
Meters
Figure 3: Overall water bodies of the model town and its surrounding area
The amount of land which is decreased from high productive class, they fall into the medium
productive classes. The highest change was found from 2014 to 2019, and the amount of

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change increasing change is around 28.24%. Moreover, in case of lowly productive class, all
the class shows that NDVI values were decreased all the time and that means, overall
vegetation impact was not well. The significant change shows from 2014 to 2019 and that is
around 30.6% of lowly productive land was decreased. That means, the NDVI values are
gone towards -1 which indicates increase of impervious layers.
Table1: Overall change in vegetation productivity coverage in percentage and km
2
.
Furthermore, to estimate the vegetation index more accurately SAVI index has been to
correct for the influence of soil brightness when the vegetative cover is low. Also, due to
increase of built-up area as well as impervious surfaces over time, NDBI index helps will
correlate the overall environmental condition of the area. Figure 4 shows environmental
indices over different time period. Every image value indicates degradation of vegetation
and up-gradation of hardscapes. In the meantime, the correlation coefficient indicates the
extent of influence for one factor to another (Table 2). In the case of 1998, the correlation
coefficient indicates that the 1-unit change of NDVI depends on 0.91-unit change of NDBI,
negatively. Here intercept value for NDVI with NDBI is 0.004. Besides, NDVI has a positive
relation with SAVI and the correlation coefficient value is 1.49. That means the 1-unit change
of NDVI depends on 1.49-unit change of SAVI, positively. Both SAVI and NDBI are negatively
correlated and the correlation coefficient is -1.63. That means the 1-unit change of NDBI
depends on 1.63-unit change of SAVI, negatively. Hence, built-up areas increase by reducing
vegetation covers of the study area. Similarly, all the correlation coefficients from different
timeline show more or less similar result which is built-up area takes over vegetation lands.
This analysis also shows that impervious surface that means built-up area, road, housing and
so on are increasing continuously which adversely affects vegetations. As government
declared to restrain the previous environmental condition such as free flow of water through
the area, enough green spaces by demolishing built structures but the developing of making
structures is continuous. Even after the final verdict, the housing project is still live and some
influential person overran the site illegally. Figure 5 indicates number of structures in four
different timelines.
Change in area coverage in percentage and km
2
From 1998 to 2004
Fr
om 2004 to 2010
From 2010 to 2014
From 2014 to 2019
Vegetation
Productivity Classes NDVI Values Percentage
Area
(km
2
) Percentage
Area
(km
2
) Percentage
Area
(km
2
) Percentage
Area
(km
2
)
High Productive
0.97
-
1
0.54
-
0.97
-
0.048
-
0.011
0.004
0.001
-
0.004
-
0.001
0.42
-
0.54
-
1.749
-
0.394
2.775
0.626
-
2.927
-
0.660
2.328
0.525
0.42
-
1
-
1.797
-
0.405
2.779
0.626
-
2.931
-
0.661
2.328
0.525
Medium Productive
0.34
-
0.42
-
1.334
-
0.301
5.969
1.346
-
9.415
-
2.122
21.491
-
2.122
0.29
-
0.34
1.354
0.305
3.574
0.
806
-
6.856
-
1.545
17.314
-
1.545
0.24
-
0.29
2.280
0.514
3.841
0.866
-
2.727
-
0.615
9.172
-
0.615
0.16
-
0.24
5.722
1.290
2.040
0.460
12.950
2.919
-
8.330
2.919
0.08
-
0.16
6.529
1.472
-
1.031
-
0.232
13.234
2.983
-
11.412
2.983
0.08
-
0.42
14.552
3.280
14.393
3.
245
7.186
1.619
28.235
1.619
Lowly Productive
0
-
0.08
5.415
1.220
-
0.196
-
0.044
8.706
1.962
-
18.508
1.962
-
1
-
18.170
-
4.096
-
16.976
-
3.826
-
12.961
-
2.921
-
12.055
-
2.921
-
1
-
.08
-
12.755
-
2.876
-
17.172
-
3.870
-
4.255
-
0.959
-
30.563
-
0.959

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(a)
NDVI_98
High : 0.62
Low : -0.43
NDVI_10
High : 0.54
Low : -0.26
NDVI_14
High : 0.35
Low : -0.05
NDVI_19
High : 0.48
Low : -0.06
NDVI_04
High : 0.45
Low : -0.24
(b) (c) (d) (e)(a)
NDVI_10
High : 0.54
Low : -0.26
NDVI_14
High : 0.35
Low : -0.05
NDVI_19.tif
High : 0.48
Low : -0.06
NDVI_04
High : 0.45
Low : -0.24
(b) (c) (d) (e)
(f)
NDBI_98
High : 0.38
Low : -0.71
NDBI_10
High : 0.38
Low : -0.43
NDBI_14
High : 0.13
Low : -0.31
NDBI_19
High : 0.15
Low : -0.35
NDBI_04
High : 0.38
Low : -0.55
(g) (h) (i) (j)
SAVI_10
High : 0.81
Low : -0.39
SAVI_14
High : 0.53
Low : -0.08
SAVI_19
High : 0.73
Low : -0.08
SAVI_04
High : 0.67
Low : -0.35
SAVI_98
High : 0.93
Low : -0.64
(k) (l) (m) (n) (o)
Figure 4: Environmental indices (a) NDVI in 1998, (b) NDVI in 2004, (c) NDVI in 2010, (d) NDVI in
2014, (e) NDVI in 2019, (f) NDBI in 1998, (g) NDBI in 2004, (h) NDBI in 2010, (i) NDBI in 2014, (j)
NDBI in 2019, (k) SAVI in 1998, (l) SAVI in 2004, (m) SAVI in 2010, (n) SAVI in 2014 and (o) SAVI
in 2019
Table 2: Correlation coefficient among NDVI, SAVI and NDBI of the study area
In 2004, structures were started building and the increasing number was continuous even
after verdict also. In present years, the total number of semi-pucca and pucca structures are
around 240. The graph represents continuous building of structures. The noticeable fact is
1998 NDVI NDBI SAVI
2004 NDVI NDBI SAVI
2010 NDVI NDBI SAVI
NDVI
1
NDVI
1
NDVI
1
NDBI -0.9128 1
NDBI -1.247 1
NDBI -1.0035 1
SAVI 1.4927 -1.6264 1 SAVI 1.5007 -1.2025 1 SAVI 1.4963 -1.4903 1
2014 NDVI NDBI SAVI
2019 NDVI NDBI SAVI
NDVI 1
NDVI 1
NDBI -0.9774 1
NDBI -0.9795 1
SAVI 1.4988 -1.5314 1 SAVI 1.498 -1.5281 1

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that total number of structures are built after 2014. That means, after the verdict, building
of new structures have not been stopped rather have been increased rapidly. The scenario is
happened only because of poor law maintenance. The figure 6 describes a clear overview of
past and present condition of Modhumoti Model town.
Figure 5: Number of structures in
Modhumoti Model Town
a) b)
d)c)
Z
0 370 740 1,110 1,480185
Meters
Pucca
Semi_pucca
Road
Main_boundary
Figure 6: Structures of Modhumoti Model Town in the year of (a)
2004, (b) 2010, (c) 2014 and (d) 2019
The above analysis indicates that the model town project is not a blessing for the
environment. It not only destroys the whole ecological system but also it is a proper example
of violation of laws.
5. Conclusion
A Residential Town is not always a blessing for a country. Modhumoti Model town is a
similar type of project which creates high facilities for a living, but in a broader sense it
destroyed the whole ecological system. Overall environmental assessments indicate that the
area has been lost its character. A live free-flowing water channel is completely destroyed
and that forces to make the river dead. NDWI value indicates that previously the overall
water condition was good but year after year due to the growing project the NDWI values
are decreased. That mean, the river has lost its own tone and that turns to loss of availability
of water. NDWI and SAVI indicate that amount of productive vegetation is decreased
overtime period. Impervious surface availability is increased rather than softscape which are
analysed using NDBI index. Most importantly, the case was hanging for several years and in
the meantime the development work did not stop. The noticeable fact is, in the final verdict,
the project was identified as illegal and ordered the owner to restore the wetland in the
previous state but the condition of developing hardscape remains continuous. Even recent
years, the model town has grown its full form. A complete community is living in the town
with continuous urbanizing of the site.Hence, it is completely understandable that imposing
law to an illegal development project is not enough yet. Practical maintenance, and forced
to admire the laws and verdicts are also necessary as well.

Faisal, AA; Hossain, MA; Haque, S;
Shaunak, MF; Kafy, AA
Remote Sensing Approach in Wetland and
Land Degradation Assessment
256
Acknowledgement: The authors would like to acknowledge CEGIS for giving an opportunity
to work with this interesting topic in the internship period.
References
Davis, M. 2006. Planet Of Slums. New Perspectives Quarterly, 23, 6-11.
Faisal, A. & Khan, H. 2018. Application Of Gis And Remote Sensing In Disaster Management: A
Critical Review Of Flood Management. International Conference On Disaster Risk
Mitigation.
Gao, B.-C. 1996. Ndwi—A Normalized Difference Water Index For Remote Sensing Of Vegetation
Liquid Water From Space. Remote Sensing Of Environment, 58, 257-266.
Gascon, M., Cirach, M., Martínez, D., Dadvand, P., Valentín, A., Plasència, A. & Nieuwenhuijsen,
M. J. 2016. Normalized Difference Vegetation Index (Ndvi) As A Marker Of Surrounding
Greenness In Epidemiological Studies: The Case Of Barcelona City. Urban Forestry &
Urban Greening, 19, 88-94.
Gilabert, M. A., González-Piqueras, J., Garcıa-Haro, F. J. & Meliá, J. 2002. A Generalized Soil-
Adjusted Vegetation Index. Remote Sensing Of Environment, 82, 303-310.
Hossain, N. S. M. & Akther, F. 2011. Impacts Of Private Residential Land Development Projects In
The Flood Flow Zones Of Dmdp Area: Case Study Of Tetuljhora Union. Journal Of
Bangladesh Institute Of Planners, 4, 57-70.
Kafy, A.-A. 2018. Importance Of Surface Water Bodies For Sustainable Cities: A Case Study Of
Rajshahi City Corporation. Bangladesh Institute Of Planners (Bip).
Kafy, A.-A., Islam, M., Khan, A. R., Ferdous, L. & Mamun, M. 2018. Identifying Most Influential
Land Use Parameters Contributing Reduction Of Surface Water Bodies In Rajshahi City,
Bangladesh: A Remote Sensing Approach.
Kafy, A., Hossain, M., Prince, A. A. N., Kawshar, M., Shamim , M. A., Das, P. & Noyon, M. E. K.
Estimation Of Land Use Change To Identify Urban Heat Island Effect On Climate Change:
A Remote Sensing Based Approach. International Conference On Climate Change (Iccc-
2019), 2019 Dhaka, Bangladesh. University Of Dhaka.
Mcfeeters, S. K. 1996. The Use Of The Normalized Difference Water Index (Ndwi) In The
Delineation Of Open Water Features. International Journal Of Remote Sensing, 17, 1425-
1432.
Mcgee, T. 2006. Urbanization Takes On New Dimensions In Asia's Population Giants. Population
Reference Bureau. Web: Http://En. Wikipedia. Org/Wiki/Dhaka. Retrieved, 09-27.
Mishra, K. & Prasad, P. 2015. Automatic Extraction Of Water Bodies From Landsat Imagery Using
Perceptron Model. Journal Of Computational Environmental Sciences, 2015.
Rouse Jr, J. W., Haas, R. H., Schell, J. A. & Deering, D. W. 1974. Monitoring Vegetation Systems In
The Great Plains With Erts.
Singh, P., Kikon, N. & Verma, P. 2017. Impact Of Land Use Change And Urbanization On Urban
Heat Island In Lucknow City, Central India. A Remote Sensing Based Estimate.
Sustainable Cities And Society, 32, 100-114.
Statistics, B. B. O. 2011. Statistical Yearbook Of Bangladesh. Statistics Division, Ministry Of
Planning, Dhaka, Government Of The People’s Republic Of Bangladesh.
Verbeiren, S., Eerens, H., Piccard, I., Bauwens, I. & Van Orshoven, J. 2008. Sub-Pixel Classification
Of Spot-Vegetation Time Series For The Assessment Of Regional Crop Areas In Belgium.
International Journal Of Applied Earth Observation And Geoinformation, 10, 486-497.
Yengoh, G. T., Dent, D., Olsson, L., Tengberg, A. E. & Tucker Iii, C. J. 2015. Use Of The Normalized
Difference Vegetation Index (Ndvi) To Assess Land Degradation At Multiple Scales:
Current Status, Future Trends, And Practical Considerations, Springer.
Zaman, A. K. M. H., Alam, K. M. T. & Islam, M. J. 2010. Urbanization In Bangladesh: Present Status
And Policy Implications. Asa Univ. Rev, 4.