LANDUSE AND LAND COVER CHANGE IN DHAKA ......2019/05/01 · Vegetation cover changes due to human...

LANDUSE AND LAND COVER CHANGE IN DHAKA METROPOLITAN AREA 1

Oriental Geographer

Vol. 60, No. 1&2, 2016

Printed in March 2019

LANDUSE AND LAND COVER CHANGE

IN DHAKA METROPOLITAN AREA DURING

1991-2022

MD. SOFI ULLAH1

MUHAMMAD ESMAT ENAN2

Abstract: The study aims at finding the changing pattern of landuse/cover of Dhaka

Metropolitan Area (DMA). Dhaka is one of the most populated megacities in the world.

The city built-up area is increasing day by day. As a result, the natural covers like water

body, green coverage and cultivable land is decreasing. To evaluate this change, different

geospatial indices like MNDWI, NDVI, and NDBI were used. Further, the Markov model

was used to predict the future state of landuse change in DMA. Among the indices

MNDWI was used to identify water body from satellite images, NDVI helps identify the

vegetation/green coverage and NDBI was used to find out the built-up area in DMA. The

result shows that, in 1991, the extension of built-up area was only 100 km2

and later, after

26 years in 2016, it was 220 km2 and will be 260 km

2 in 2022. But water body is

continuously decreasing which was 123 km2

in 1991 and later in 2016, it was 41.8 km2

and after six years it will be 20 km2. The similar trend shows in green coverage which is

decreasing alarmingly. The trend shows that in 1991 there was 58.6 km2

green coverage,

later in 2016 it stood 38 km2

and it will be 23 km2

in 2022. The natural covers like water

bodies and greeneries should be protected for the sake of having livable environment for

Dhaka city dwellers.

Keywords: Geospatial techniques, GIS, Remote sensing, Markov probability matrix,

Indices, NDVI, MNDWI, and NDBI.

INTRODUCTION

Landuse/cover change is one of the most significant indicators that explain the impact of

human activities on environment. Both natural and manmade factors are responsible for

changing landuse/cover in local and regional levels. Such changes through human

intervention are common, that has seriously affected the earth’s ecological systems

(Lambin et al., 2001). Landuse change (LUC) is the modification of land surface. This

change is based on the purposes of need, which is not necessarily only making the change

in land cover but also change in intensity and management (Verburg et al., 2000).

1 Md. Sofi Ullah is Associate Professor, Department of Geography and Environment, University of Dhaka, Bangladesh 2 Muhammad Esmat Enan is an MS Research Student, Department of Geography and Environment, University of Dhaka,

Bangladesh

2 ORIENTAL GEOGRAPHER

The International Geosphere-Biosphere Program (IGBP) and the International Human

Dimension Program (IHDP) initiated a joint program to study the landuse/cover change

(LULC) considering the enormous impacts generating from such changes of landuse/land

cover on human life and environment. It emphasized the necessity of understanding,

modeling and projections of landuse dynamics from global to regional scale with focus

particularly on the spatial explicitness of processes and outcomes (Geoghegan et al.,

2001).

Vegetation cover changes due to human intervention in the landscape have resulted a

variety of negative environmental consequences. Deforestation, for example, can affect

the vegetation composition and water balance and can increase soil erosion (Glade, 2003;

Ghimire et al., 2013). This leads to increased environmental risks, such as landslide and

can have strong impacts on the human wellbeing on a larger scale (Tasser et al., 2003;

Korner et al., 2005; Papathoma-Köhle and Glade, 2013). Besides, forest cover change

produces a net carbon flux to the atmosphere (Houghton and Skole, 1990; Alves and

Skole, 1996), which in turn contributes to global climatic change. Another big concern is

urban area expansion by converting rural lands to urban lands. Globally, a large number

of the people live in the urban areas and since 2007 the global urban population has

exceeded the global rural population (UN, 2014). This process has impacts on energy

flow, biogeochemical cycles, biodiversity and climatic conditions at local, regional and

even global levels (McDonnell et al., 1997; Baker et al., 2001; Green and Baker 2003).

These changes are significant in understanding the global change of environment

(Nagendra et al., 2004).

Remote sensing and GIS are important tools in detecting and identifying the

landuse/cover changes and their geographical dynamics and its association with human

activities (Jensen and Cowen, 1999). These tools however are often inadequate to derive

detailed landscape properties such as its composition and configuration (Narumalani et

al., 2004). Various landscape metrics are available in ArcGIS to calculate and quantify

spatial patterns of land cover (McGarigal and Marks, 1995).

During last few decades, a number of researchers have identified, detected and monitored

urban landuse/cover from remotely sensed data (Ridd, 1995; Yang and Lo, 2002; Haack

and Rafter, 2006; Yuan, 2010). These data from various satellites are very expensive

depending on their resolutions (Blodget et al., 1991), provides an excellent opportunity to

know the historical landuse/cover changes. Such changes can be related to other

environmental and human factors as well. Therefore, a large number of researches have

utilized satellite-derived landuse/cover data with GIS landscape metrics to detect the

impacts on landscape structure (Lausch and Herzog, 2002; Herold et al., 2002;

Southworth et al., 2002; Li et al., 2004; Seto and Fragkias, 2005; Yu and Ng, 2006;

Kamusoko and Aniya, 2007; Jat et al., 2008; Hargis et al., 1998; O’Nell et al., 1999;

Dramstad et al., 2001; Herold et al., 2002; Civco et al., 2002; Luck and Wu, 2002;

Kamusoko and Aniya, 2007; Tzanopoulos and Vogiatzakis, 2010). These studies further

open the door of comprehensive knowledge of landuse change and its impact on

environment ((Leitao and Ahern, 2002). Such knowledge can help to restore the


ecological degradation (Egan and Howell 2001) and can help to formulate appropriate

landuse planning (Berger, 1987).

Bangladesh has already experienced a rapid increase in urban population in the few

decades. The total urban population was 13.2 million in 1981, 20.8 million in 1991, 29.2

million in 2001 and 33.5 million in 2011 (BBS, 2014) (Figure 1). The urban livelihood

thus converts rural land to the built-up areas to meet their necessary demand. It is

estimated that every year more than 809 km2 of arable land is being converted to cities’

roads and infrastructures in Bangladesh (BBS, 1996).

Figure 1: Population Growth Trends of Dhaka City

Data Source: BBS, 2011; UN, 2014.

Dhaka is one of the fastest growing megacities in the world. In 1991, Dhaka SMA

(Statistical Metropolitan Area) has been raised to the status of Dhaka Megacity with a

population of 6.8 million (BBS 1998). Between 1990 and 2005, the population of Dhaka

City doubled in number to 12 million (Khondker, 2010). In 2010, Dhaka was a city of 15

million populations (World Bank, 2013). Dhaka will become the second most densely

populated city in the world by 2020 (World Bank, 2007). By 2025, the UN predicts

Dhaka will be the home of more than 20 million people, larger than Mexico City, Beijing

or Shanghai (UN, 2014). The landscape of Dhaka City is undergoing a continuous

changes and modifications due to rapid urbanization (WASA, 1991). As the fastest

growing megacity, Dhaka has been facing multifarious challenges and numerous

difficulties (Islam and Ahmed, 2011).

The environmental consequences associated with fast and unplanned growth of Dhaka

have considerable impacts on natural habitats and on land (Amin et al., 2008; Dewan and

Yamaguchi, 2009). Such stresses induce high rate of poverty, unplanned urbanization, the

growth of urban slums and squatters, traffic jams, environmental pollution and other socio-

economic problems on the one hand and on the other hand, the landuse of DMA is

changing rapidly.


Applications of geospatial technology enable us to explore the existing landuse condition of

a particular area. However, there is little attempt that has been made to employ geospatial

techniques to determine and detect landuse/cover changes in a fastest growing

metropolis, Dhaka. This paper aims at quantify land cover patterns of DMA for a specific

time period and predict the future scenario of land cover using remote sensing, GIS, and

metrics.

Considering these, the aims of this paper are to detect and identify the changes in

landuse/cover from 1991 to 2016 and predict it up to 2022. The specific objectives are:

To find out the landuse pattern of DMA from 1991 to 2016.

To analyze inter-sectoral changing pattern of landuse in DMA from 1991 to 2016

using Markov Matrix, and

To simulate landuse change using CA Markov Matrix of probability for the year

of 2022

MATERIALS AND METHOD

Study Area

Dhaka Metropolitan Area (DMA) has been chosen for the study. It lies between 23.55

and 24.18N latitudes and 90.18 and 90.57E longitude (Figure 2). Dhaka is surrounded

by six rivers, like a garland. Balu and Sitalakhya Rivers on the eastern side, Turag and

Buriganga on the western side, Tongi Khal in the north and Dhaleshwari in the south

(Figure 2). Tongi Khal connects Turag and Balu rivers. Dhaleshwari and Sitalakhya join

in the south of Narayanganj and flow into the Meghna River.

There are different administrative bodies in Dhaka city. According to BBS report 2001,

the area under DCC (both North and South City Corporation) was 276 km2. There are

other areas, about 120 km2 outside DCCs but within DMA. These areas are administered

mainly by Union Parishads (Rural Local Governments).

Topographically, the area is flat partly flood plain and partly alluvial terrace, popularly

known as the Modhupur terrace of the Pleistocene period (Miah and Bazlee, 1968). The

elevation of the surface of the area is between 1 and 14 m and most of the built-up areas

are located at the elevations of 6 to 8 m (FAP 8A, 1991).

Data Sources

Both primary and secondary data were considered in this study. Primary data ) generated

from toposheets (with a scale of 1:50,000), collected from the cartographic lab,

Department of Geography and Environment, University of Dhaka and satellite data

comprised with Landsat 4-5 TM ( for the year 1991, 2001and 2011) and Landsat

OLI_TIRS for 2016 were considered and collected from USGS GloVis (Table 1).


Figure 2: Study Area: Dhaka Metropolitan Area (DMA)

Table 1: Specification of Satellite Data

Satellite Sensor Path/Row Acquisition Date Resolution

Landsat 4-5 TM 137/44

16/01/1991

21/01/2001

30/01/2011 30M

Landsat 8 OLI_TIRS 137/44 17/01/2016

Source: USGS GloVis

All the images were collected in the month of January considering sky condition. Spatial

resolution of these images was 30 meters.

Method

A lot of indices are available to calculate and quantify land cover change and to

understand their geographical pattern. For example, Normalized Difference Vegetation


Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized

Difference Water Index (MNDWI), Normalized Difference Built-up Index (NDBI),

Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Red

Edge Index (NDRE), etc.

Erdas Imagine and ArcGIS software were used for extracting the landuse/cover data of

different periods. All images were rectified using geometric correction and radiometric

correction. To find out the green coverage, Normalized Difference Vegetation Index (NDVI)

has been used. NDVI is a widely used vegetation index and is basically the difference of

vegetation reflectance in Near Infrared and Red bands (Rouse et al., 1974). The NDVI is

expressed as follows, NDVI = (NIR - RED) / (NIR + RED) , where NIR is a near infrared

band (0.76-0.90m) such as TM band 4 and RED is RED band such as TM band 3 (0.63-

0.69m). NDVI values range from +1.0 to -1.0. Areas of barren rock, sand, or snow

usually show very low NDVI values (for example, 0.1 or less). Sparse vegetation such as

shrubs and grasslands or senescing crops may result in moderate NDVI values

(approximately 0.2 to 0.5). High NDVI values (approximately 0.6 to 0.9) correspond to

dense vegetation such as found in temperate and tropical forests or crops at their peak

growth stage.

The second Index that has been used in this study is Modified Normalized Difference Water

Index (MNDWI). MNDWI helps finding out surface water by suppressing errors

produced by built-up area. Since water features extracted using the NDWI include false

positives from built-up land, a modified NDWI (MNDWI) was developed in which the

middle infrared (MIR) band was replaced with the near-infrared (NIR) band. The MNDWI

extracts surface water while suppressing errors from built-up land as well as vegetation and

soil (Xu, 2006). Modified NDWI can be expressed as follows, MNDWI = (Green –MIR) /

(Green + MIR), where MIR is a middle infrared band, which is Shortwave Infrared

(SWIR-1) and Green is the GREEN band. The computation of the MNDWI will produce

three results: (1) water will have greater positive values than in the NDWI as it absorbs

more MIR light than NIR light; (2) built-up land will have negative values as mentioned

above; and (3) soil and vegetation will still have negative values as soil reflects MIR light

more than NIR light (Jensen 2004) and the vegetation reflects MIR light still more than

green light.

The third index, Normalized Difference Built-up Index (NDBI), has been used in this

study to calculate the built-up area. NDBI is a new method to automate the process of

mapping the built-up areas (Zha et al., 2003). Like previous two indices, this index also

helps us to separate built-up area from the images of different years according to

reflectance value. Normalized Difference Built Index can express as follows,

NDBI = (SWIR – NIR) / (SWIR + NIR). Finally, the outputs of all indices have merged

and found out the fourth type of landuse, the bare soil of Dhaka City. A total four

landuse/cover types have been identified in the study area, these are: water body, green

coverage, built-up area and bare soil (Table 2 and Figure 3).


Table 2: Landuse/Cover Types in Dhaka Metropolitan Area

Landuse/cover types Description

Bare soil Landfills and bare area

Built-up area Residential, commercial, service, transport, land prepared

for settlement, and communication

Green coverage Crop field, vegetation, natural and manmade forest

Water body Permanent and seasonal wetlands, low lands, marshy land,

river and khal, etc Source: Based on image analysis

Markov Model in Landuse/Cover Change

The classified landuse/cover (LU/LC) were used to create Markov Transitional Matrix of

landuse/cover change between 1991 and 2016. Then the Markov Matrix of Probability

was used to simulate landuse/cover change for the period of 2016 to 2022. Markov model

is a convenient tool for simulating LU/LC change when changes and processes in the

landscape are difficult to describe. Markov model describes LU/LC change from one

period to another and used this as the basis to project future changes (Logsdon et al.,

1996). Markov model has provided a simple methodology by which a dynamic system

could be analyzed and examined (Muller and Middleton, 1994; Dongjie et al., 2008;

Huang et al., 2008; Dadhich and Hanaoka, 2010). Researchers have tested the accuracy

of the Markov model (Jianping et al., 2005; Zhang et al., 2011).

Figure 3: Flow Chart of Methodology


Accuracy Assessment

To assess the accuracy, some 200 sample points were cross verified with the Google

Earth Pro. Finally, the overall accuracy for different year was calculated. According to

accuracy assessment in 1991, there was a 3.85 percent absolute error, which is 118.02

hectares of the total land area at 96.15 percent accuracy level with a Kappa coefficient of

0.92. In 2001, there was a 7.69 percent absolute error, which is 236.04 hectares of the

total land area at 92.31 percent accuracy level with a Kappa coefficient of 0.83. In 2011,

there was a 3.85 percent absolute error, which is 118.02 hectares of the total land area at

96.15 percent accuracy level with a Kappa coefficient of 0.92. Similarly, in 2016, there

was a 3.85 percent absolute error, which is 118.02 hectares of the total land area at 96.15

percent accuracy level with a Kappa coefficient of 0.92 (Table 3).

Table 3: Overall Accuracy Assessment for Land Cover Analysis

Year Absolute

Errors (percent)

Absolute Errors

(in Hectares)

Overall Accuracy

(percent)

Kappa Coefficient

(T)

1991 3.85 118.02 96.15 0.92

2001 7.69 236.04 92.31 0.83

2011 3.85 118.02 96.15 0.92

2016 3.85 118.02 96.15 0.92

Source: Satellite image and Google Earth Pro

RESULTS AND DISCUSSIONS

Landuse/Cover Change from 1991 to 2016

The spatial pattern of landuse/cover change in DMA for the year 1991, 2001, 2011 and

2016 is shown in Figure 4. It is revealed that in 1991, the city land cover was dominated

by water body. The spatial extent of this water body was covered the eastern part and

north-western part of the city, and it was replaced by green coverage in 2001. Similarly,

in 1991, the northern part of DMA (Uttarkhan and northern Uttara) was covered by green

coverage and the north-eastern portion was converted to the built-up area.

Furthermore, in 2001, the eastern part, which was low lying area, was filled-up with

plants temporarily. Later most of this area was converted into plain land through land

filling and turn to bare land area. The research findings show that, in 2011 the western

part, the vegetative area was converted into the built-up area and in the northern part

water and green coverage were narrowed down and shifted to built-up and bare land.

Finally, in 2016, the northern part of Uttarkhan, the Gazaria area and eastern part of

Matuail area were under green coverage. Similarly, in 2016, the water body has come

down to the lowest level (Figure 4).


Figure 4: Landuse/Cover Change in Dhaka Metropolitan Area from 1991 to 2016

Source: Extracted result from Landsat image analysis

Table 4 shows the water body was 123.4 km2

in 1991 which stood at 84 km2

in 2001,

50.39 km2

in 2011 and about 42 km2

in 2016. The decreasing rate of the water body is

strongly negatively correlated with the increase of built-up area and the rate of change

was -0.99. With this trend of change it can be speculated that in the near future, the water

body of the city will be nil if this rate continues. Similarly, the green coverage of DMA is

also decreasing. In 1991, City’s green coverage was 58.6 sq km which stood at 72 km2 in

2001, 49 km2 in 2011 and 38 km

2 in 2016. The change rate of the green coverage is -0.71,

which is also negatively correlated. Another sector of landuse/cover, the bare soil, has


also decreased between 1991 and 2016. In 1991, the bare soil was 24.3 km2, which stood

20 km2

in 2001, 14.8 km2

in 2011 and 6.3 km2

in 2016. Generally, the bare soil is rapidly

transformed into the built-up area. In DMA this is an excellent example of land

transformation. Finally, the dominated landuse/cover, the built-up area, which was 100

km2 in 1991, stood at 130.5 km

2 in 2001, 192 km

2 in 2011 (doubled between the year

1991 and 2011) and 220.5 km2

in 2016. The temporal increasing rate of the built-up area

is strongly positively correlated with urbanized land and the rate is 0.988 (Table 4). The

landuse/cover change trend is represented in Figure 5.

Table 4: Landuse/Cover Statistics of Dhaka Metropolitan Area from 1991 to 2016

Landuse Category Area (sq km)

Correlation (r) 1991 2001 2011 2016

Bare soil 24.34 20.24 14.87 6.32 -0.952

Built up area 100.43 130.57 192.32 220.53 0.988

Green coverage 58.64 72 49.27 38.16 -0.710

Water body 123.43 84.05 50.39 41.83 -0.993

Total 306.84 306.84 306.84 306.84

Source: Result of Landsat image analysis

Figure 5: Landuse/Cover Change Statistics in Dhaka Metropolitan Area from 1991 to 2016

Source: Result from Landsat image analysis

Proportional Area in Each Category of Landuse/Cover from 1991 to 2016

The proportional area in each of the landuse categories in DMA from 1991 to 2016 is

presented in Table 5. In 1991, the highest portion of the land cover was water body

(40%), the second highest was built-up area (33%), green coverage was 19 percent and

the smallest portion was bare soil, which was 8 percent only. In 2001, the built-up area

occupied 43 percent of the land and secured the first place among the land coverage


categories. In this period, water body was 27 percent, placed at the second position, green

coverage was 23 percent, and bare soil was 7 percent. In 2011, the built-up area

increased at 63 percent, the water body and green covers were the same in this period

(16%). In 2016, the built-up area covered about 72 percent of DMA and water body

covered at 14 percent. Green coverage was 12 percent only (Table 5).

Table 5: Proportional Area in Each Category of Landuse/Cover in Dhaka Metropolitan

Area from 1991 to 2016 (in percentage)

Landuse Category 1991 2001 2011 2016

Bare soil 08 07 05 02

Built up area 33 43 63 72

Green coverage 19 23 16 12

Water body 40 27 16 14

Grand Total 100 100 100 100

Source: Result from Landsat image analysis

Transitional Matrix of Landuse/Cover Change

In control theory, the state-transition matrix is a product with the state vector at an initial

time gives at a later time. The state-transition matrix can be used to obtain the general

solution of linear dynamic systems. A transitional matrix can represent changes from one state

to another state. According to the transition matrix, the LU/LC class will change to another class

in the future, given the present state of the class (Kumar et al, 2014). The matrix equation is as

follows:

On the basis of the given equation, the transition probability of a certain LU/LC type in 1991

was converted into the same LU/LC type in 2001. Similarly, the transitional probability of 2001-

2011 and 2011-2016 were calculated. Table 6 shows the transitional probability of 2011-2016.

Finally, the transitional probability of a certain LU/LC type in 2016 converted into the same

LU/LC type in 2022 by using Markov Matrix. Table 7, shows the primary transitional probability

of four landuse/cover types (bare soil, built-up area, green coverage and water body) during 2016-

2022.


Table 6: Transitional Matrix of Landuse/Cover between 2011 and 2016

Landuse Category Transitional Probability

Bare soil Built up area Green coverage Water body

Bare soil 0.0042 0.8846 0.0539 0.0572

Built up area 0.0054 0.5189 0.247 0.2287

Green coverage 0.0047 0.3591 0.2913 0.345

Water body 0.0209 0.6948 0.1545 0.1298


Table 7: Cellular Automata (CA) Markov Matrix of Probability to Simulate Landuse/Cover

Change from 2016 to 2022

Landuse Category Transitional Probability

Bare soil Built up area Green coverage Water body

Bare soil 0.070 0.814 0.050 0.066

Built up area 0.011 0.901 0.056 0.032

Green coverage 0.009 0.770 0.135 0.085

Water body 0.007 0.641 0.123 0.229


Simulated Landuse/Cover in Dhaka Metropolitan Area

The simulated result shows that the built-up area of DMA has been continuously

increasing. There will be 84.78 percent built-up area in the City in 2022. All other land

covers will decrease, such as 1.14 percent bare soil, 7.49 percent green coverage and the

6.6 percent water body in 2022 (Table 8 and Figure 6). Similarly, the simulated result of

the year 2028 and 2034 was also calculated using Markov model, which shows, the built-

up area will increase to 87.40 percent in 2028 and 88.46 percent in 2034.

Table 8: Landuse/Cover Change Prediction Using Markov Probability Matrix

Yea

r

Landuse/Cover Category

Bare soil Built-up area Green coverage Water body

Area ( sq km)

% Area ( sq km) %

Area ( sq

km) % Area ( sq km) %

1991 24.34 7.93 100.43 32.73 58.64 19.11 123.43 40.22

2001 20.24 6.59 130.57 42.55 72.00 23.46 84.05 27.39

2010 14.87 4.84 192.32 62.68 49.27 16.06 50.39 16.42

2016 6.32 2.06 220.53 71.87 38.16 12.44 41.83 13.63

2022 3.49 1.14 260.15 84.78 22.98 7.49 20.24 6.60



Figure 6: Trend of Landuse/Cover Change during 1991-2022 Source: Landsat image analysis and Markov simulation model

Markovian Model for Sectoral Change of Landuse/Cover

According to Markovian model, the sectoral movement of landuse change is always

towards built-up area (Table 9). The change of bare soil to bare soil, built-up area, green

coverage and water body has decreased during 1991 to 2016 and the same trend will

follow in 2022 (Table 9). Similarly, water body to water body, bare soil, and green

coverage has decreased during 1991 to 2016 and will decrease in 2022. The green

coverage has also decreased in all sectors (green to green, green to bare soil and green to

water) except green to the built-up area. The change between built-up to built-up was

very high during 1991-2016 which was not usual and this trend will continue in 2022.

Among this period the changing rate of built-up to built-up is 98 percent. The trend of

changing built-up to green coverage has been slightly increasing. In the recent time

people are motivated to planting trees in and around the compound of houses and

courtyards. May be this is the main reason to increase of built-up to green coverage. The

scenario of the landuse change within 32 years (between 1991 and 2022) gives us a

direction that the land of all categories is moving significantly. If this trend continues in

future, the City will be losing its ecological balance.

Table 9: Each Type of Landuse Change in Dhaka Metropolitan Area for Each Time Period

Landuse change

1991-2001 2001-2011 2011-2016 2016-2022

Area

(sq km) %

Area

(sq km) %

Area

(sq km) %

Area

(sq km) %

Bare soil bare soil 10.7963 3.52 8.6241 2.81 5.0073 1.63 0.4419 0.14

Bare soil built-up area 8.3576 2.72 9.139 2.98 6.7359 2.20 5.1458 1.68

Bare soil green coverage 4.0974 1.34 1.7373 0.57 0.9386 0.31 0.3191 0.10

Bare soil water body 1.0925 0.36 0.7355 0.24 2.1844 0.71 0.4173 0.14

Built-up area built-up area 86.4617 28.18 113.2691 36.91 170.135 55.45 198.7833 64.78


Landuse change

1991-2001 2001-2011 2011-2016 2016-2022

Area

(sq km) %

Area

(sq km) %

Area

(sq km) %

Area

(sq km) %

Built-up area bare soil 2.8206 0.92 2.5576 0.83 0.8163 0.27 2.375 0.77

Built-up area green coverage 9.2216 3.01 7.4575 2.43 10.3746 3.38 12.3752 4.03

Built-up area water body 1.9296 0.63 7.2839 2.37 10.997 3.58 7.0001 2.28

Green coverage green coverage 25.0659 8.17 17.365 5.66 12.1713 3.97 5.158 1.68

Green coverage bare soil 2.9279 0.95 1.9548 0.64 0.265 0.09 0.359 0.12

Green coverage built-up area 21.4854 7.00 38.9409 12.69 25.5703 8.33 29.4011 9.58

Green coverage ` water body 9.1657 2.99 13.7357 4.48 11.2673 3.67 3.2423 1.06

Water body water body 71.8613 23.42 28.6315 9.33 17.3829 5.66 9.5776 3.12

Water body bare soil 3.6912 1.20 1.7297 0.56 0.2354 0.08 0.3099 0.10

Water body built-up area 14.2633 4.65 30.9739 10.09 18.0923 5.90 26.8166 8.74

Water body green coverage 33.6116 10.95 22.7141 7.40 14.676 4.78 5.1276 1.67

Total 306.8496 100 306.8496 100 306.8496 100 306.8498 100

Source: Analysis result of Markov matrix

CONCLUSIONS

Dynamics of landuse/cover changes and the quantification of land cover patterns in the

Dhaka Metropolitan Area were shown in this paper by using geospatial techniques and

Markov model. Markov model helps to calculate sifting of sectoral landuse and

landuse/cover. The results show that City’s built-up area has been increasing from

1991and continuing in accelerating rate. In 1991, the built-up area was 100 km2, but in

2016 it was 220.53 km2 and it will increase to 260 km

2 in 2022. On the other hand, during

1991-2016, the green coverage has been decreased from 58 km2

to 38 km2

and will stand

at 23 km2

in 2022. Similarly, the water body has decreased unusually. In 1991, there was

123.4 km2 water body in the City, which stood at 41.8 km

2 in 2016 and it will be 20.2 km

2

in 2022. The result indicates that the land cover, especially, the water bodies, green

coverage is decreasing continuously, and Dhaka is losing its sustainability. Dhaka is

growing without any guidance of Master Plan or Detail Area Plan (DAP). If this

continues, Dhaka will be uninhabitable within a few years. It is necessary to find a way

by which the City development can be made sustainable.

REFERENCES

Alves, D. S., and Skole, D. L. (1996). Characterizing land cover dynamics using multitemporal

imagery. International Journal of Remote Sensing, 17(4), 835–839.

Amin, S. M. N., Dewan, A. M., & Kabir, M. H. (2008). Urban development and environmental

degradation: the case of Dhaka metropolitan, Bangladesh. Poster presented at Annual

Meeting of the Association of American Geographers, Boston, April 15–19.

Baker, L. A., Hope, D., Xu, Y., Edmonds, J., and Lauver, L. (2001). Nitrogen balance for the

Central Arizona-Phoenix (CAP) Ecosystems. Ecosystems, 4(6), 582–602.


BBS. (1996). Bangladesh Census of Agriculture. Bangladesh Bureau of Statistics (BBS), Statistics

and Informatics Division (SID), Ministry of Planning, Government of the People’s

Republic of Bangladesh.

BBS. (1998). Statistical Year Book, 1998. Bangladesh Bureau of Statistics (BBS), Statistics and

Informatics Division (SID), Ministry of Planning, Government of the People’s Republic

of Bangladesh.

BBS. (2001). Bangladesh Population Census 2001. Bangladesh Bureau of Statistics (BBS),

Statistics and Informatics Division (SID), Ministry of Planning, Government of the

People’s Republic of Bangladesh.

BBS. (2011). Bangladesh Population and Housing Census 2011. Bangladesh Bureau of Statistics

(BBS), Statistics and Informatics Division (SID), Ministry of Planning, Government of

the People’s Republic of Bangladesh.

BBS. (2014). Union Statistics, 2014. Bangladesh Population and Housing Census 2011.

Bangladesh Bureau of Statistics (BBS), Statistics and Informatics Division (SID),

Ministry of Planning, Government of the People’s Republic of Bangladesh.

Berger, J. (1987). Guidelines for landscape synthesis: some directions–old and new. Landscape

and Urban Planning, 14, 295–311.

Blodget, H., Taylor, P., and Roark, J. (1991). Shoreline changes along the Rosetta-Nile

Promontory: monitoring with satellite observations. Marine Geology, 99(1–2), 67–77.

Civco, D. L., Hurd, J. D., Wilson, E. H., Arnold, C. L., and Prisloe, S. (2002). Quantifying and

describing urbanizing landscapes in the northeast United States. Photogrammetric

Engineering and Remote Sensing, 68(10), 1083–1090.

Dadhich, P. N. and Hanaoka, S. (2010). Remote sensing, GIS and Markov’s method for landuse

change detection and prediction of Jaipur district. Journal of Geomatics and Geosciences.

4:9–15.

Dewan, A. M. and Yamaguchi, Y. (2009), Landuse and Land Cover Change in Greater Dhaka,

Bangladesh: Using Remote Sensing to Promote Sustainable Urbanization. Journal ofApplied

Geography, 29, pp.390-401.

Dongjie, G., Weijun, G., Kazuyuki, W. and Hidetoshi F. (2008). Landuse change of Kitakyushu

based on landscape ecology and Markov model. International Journal Geographical

Information Science 18:455–468.

Dramstad, W. E., Fry, G., Fjellstad, W. J., Skar, B., Helliksen, W. and Sollund, M. L. B. (2001).

Integrating landscape based values- Norweigenian monitoring of agricultural landscapes.

Landscape and Urban Planning, 57(3), 257–268.

Egan, D. and Howell, E. A. (2001). The historical ecology handbook. Washington DC: Island

Press.

FAP 8A. (1991). Flood Action Plan. Master Plan Study for Greater Dhaka Protection Project.

Dhaka: Japan International Cooperation Agency.

Geoghegan, J., Klepeis S.C.P., Mendoza, P.M., Yelena, O.R., Chowdhury, R., Turner II, B.L., and

Vance, C. (2001). Modeling tropical deforestation in the southern Yucatan peninsular region:

comparing survey and satellite data. Agriculture, Ecosystems and Environment, 85, pp. 25-46.

Ghimire, S., Higaki, D. and Bhattarai, T. (2013). Estimation of soil erosion rates and eroded

sediment in a degraded catchment of the Siwalik Hills, Nepal. Land 2:370–391.

Glade. T. (2003). Landslide occurrence as a response to landuse change: a review of evidence

from New Zealand. Catena 51:297–314.


Green, D. M., and Baker, M. G. (2003). Urbanization impacts on habitat and bird communities in

a Sonoran desert eco-system. Landscape and Urban Planning, 63(4), 21–37.

Haack, B. N. and Rafter, A. (2006). Urban growth analysis and modeling in the Kathmandu

valley, Nepal. Habitat International, 30(4), 1056–1065.

Hargis, C., Bissonette, J., and David, J. (1998). The behavior of landscape metrics commonly used

in the study of habitat fragmentation. Landscape Ecology, 13(3), 167–186.

Herold, M., Clarke, K. C., and Scepan, J. (2002). The use of remote sensing and landscape metrics

to describe structures and changes in urban landuse. Environmental Planning A, 34(8),

1443–1458.

Houghton, R. A., and Skole, D. L. (1990). The earth as trans-formed by human action. In W. C.

Clark, R. W. Kates, J. F. Richards, J. T. Mathews, W. B. Meyer, & B. L. Turner (Eds.),

The earth as transformed by human action (pp. 393–408). Cambridge: Cambridge

University Press.

Huang, W., Liu, H., Luan, Q., Bai, M. and Mu, X. (2008). Monitoring urban expansion in

Beijing, China by multi temporal TM and spot images. IEEE Proc, IGARSS. 4:695–698.

Islam, M. S. and Ahmed, R. (2011). Landuse change prediction in Dhaka city using GIS aided

Markov chain modeling. Journal of Life Earth Science. Vol. 6, pp. 81-89.

Jat, M. K., Garg, P. K., and Khare, D. (2008). Modeling urban growth using spatial analysis

techniques: A case study of Ajmer city (India). International Journal of Remote Sensing,

29(2), 543–567.

Jensen, J. R., and Cowen, D. C. (1999). Remote sensing of urban/suburban infrastructure and

socioeconomic attributes. Photogrammetric Engineering and Remote Sensing, 65(5),

611–622.

Jensen, J.R. (2004). Introductory Digital Image Processing: A Remote Sensing Perspective. 3rd

Edition, Prentice-Hall, Inc., New York, 316.

Jianping, L,I., Bai, Z. and Feng, G. (2005). RS-and-GIS-supported forecast of grassland

degradation in southwest Songnen plain by Markov model. Geo-spatial Information

Science. 8:104–06.

Kamusoko, C., and Aniya, M. (2007). Landuse/cover change and landscape fragmentation

analysis in the Bindura District, Zimbabwe. Land Degradation and Development, 18(2),

221–233.

Khondker, H. H. (2010). “Dhaka: Megacity Despair”, Global Dialogue, Vol. 1, Issue 2,

Bangladesh

Korner C, Ohsawa M, Spehn E et al (2005) Mountain systems. In: Hassan R, Scholes R, Ash N

(eds) Ecosystem and human well being: current state and trends: findings of the

Conditions and Trends working group. Millennium Ecosystem Assessment, pp 681–716.

Kumar, S., Radhakrishnan, N. and Mathew, S. (2014). Landuse change modeling using a Markov

model and remote sensing, in Geomatics, Natural Hazards and Risk, 2014,Vol. 5, No. 2,

145–156.

Lambin, E. F., Turner, B. L., Geist, H. J., Agbola, S. B., An-gelsen, A., Bruce, J. W., Coomes, O.

T., Dirzo, R.,Fischer, G., Folke, C., George, P. S., Homewood, K., Imbernon, J.,

Leemans, R., Li, X., Moran, E. F., Morti-more, M., Ramakrishnan, P. S., Richards, J. F.,

Ska˚nes, H., Steffen, W., Stone, G. D., Svedin, U., Veldkamp, T. A., Vogel, C., and Xu,

J. (2001). The causes of landuse and land cover change moving beyond the myths.

Global Environmental Change, 11(4), 261–269.


Lausch, A., and Herzog, F. (2002). Applicability of landscape metrics for the monitoring of

landscape change: Issues of scale, resolution and interpretability. Ecological Indicators,

2(1–2), 3–15.

Leitao, A. B., and Ahern, J. (2002). Applying landscape ecological concepts and metrics in

sustainable landscape planning. Landscape and Urban Planning, 59(2), 65–93.

Li, Z., Li, X., Wang, Y., Ma, Y., and Wang, J. (2004). Landuse change analysis in Yulin

prefecture, northwest China using remote sensing and GIS. International Journal of

Remote Sensing, 25(24), 5691–5703.

Logsdon, M. G., Bell, J, E. and Westerlund, V,F. (1996). Probability mapping of landuse change:

a GIS interface for visualizing transition probability. Computer Environment Urban

Systems. 20:389– 398.

Luck, M., and Wu, J. (2002). A gradient analysis of urban landscape pattern: A case study from

the Phoenix metropolitan region, Arizona, USA. Landscape Ecology, 17(4), 327–339.

McDonnell, M. J., Pickett, S., Groffman, P., & Bohlen, P. (1997). Ecosystem processes along an

urban-to-rural gradient. Urban Ecosystem, 1(1), 21–36.

McGarigal, K. and B. Marks. (1995). FRAGSTATS: Spatial analysis program for quantifying

landscape structure. USDA Forest Service General Technical Report. PNW-GTR–351.

Miah, M. M., and Bazlee, B. L. (1968). Some aspects of geomorphology of the Madhupur Tract.

Oriental Geographer, 12, 39–48.

Muller, R.M. and Middleton J. (1994). A Markov model of land-use change dynamics in the

Niagara region, Ontario, Canada. Landscape Ecology. 9:151–157.

Nagendra, H., Munroe, D. K., and Southworth, J. (2004). From pattern and process: Landscape

fragmentation and the analysis of landuse/cover change. Agriculture, Ecosystem and

Environment, 101(2–3), 111–115.

Narumalani, S., Mishra, D. R. M., and Rothwell, R. G. (2004). Change detection and landscape

metrics for inferring anthropogenic processes in the greater EFMO area. Remote Sensing

of Environment, 91(3–4), 478–489.

O’Nell, R. V., Ritters, K. H., Wickham, J. D., and Jones, K. B. (1999). Landscape pattern metrics

and regional assessment. Ecosystem Health, 5(4), 225–233.

Papathoma-Köhle M, Glade, T. (2013). The role of vegetation cover change for landslide hazard

and risk. In: Renaud FG, Sudmeier-Rieux K, Estrella M (eds) The role of ecosystems in

disaster risk reduction. UNU-Press, Tokyo, pp 293–320.

Ridd, M. K. (1995). Exploring a VIS (vegetation-impervious-surface soil) model for urban

ecosystem analysis through remote sensing: Comparative anatomy for cities. Inter-

national Journal of Remote Sensing, 16(12), 2165–2185.

Rouse, J. W. Haas, R. H., Schell, J. A. and Deering, D. W. (1974). Monitoring vegetation systems

in the Great Plains with ERTS. Third Earth Resources Technology Satellite-1,

Syposium. Volume I: Technical Presentations, NASA SP-351(I), pp. 309-317.

Seto, K. C. and Fragkias, M. (2005). Quantifying spatiotemporal patterns of urban landuse change

in four cities of China with time series landscape metrics. Landscape Ecology, 20(7),

871–888.

Southworth, J., Nagendra, H., and Tucker, C. (2002). Frag-mentation of a landscape:

Incorporating landscape metrics into satellite analysis of land cover change. Landscape

Research, 27(3), 263–269.


Tasser E, Mader M, Tappeiner U (2003) Effects of landuse in alpine grasslands on the probability

of landslides. Basic and Applied Ecology. 4:271–280.

Tzanopoulos, J., and Vogiatzakis, I. N. (2010). Processes and patterns of landscape change on a

small Aegean island: The case of Sifnos, Grecce. Landscape & Urban Planning, 99(1),

58–64.

UN. (2014). World Urbanization Prospects, United Nations, Department of Economic and

Social Affiars, Population Devision. New York. USA.

Verburg, P.H., Chen, Y., Soepboer, W. and Veldkamp, A. (2000).GIS-based modeling of human-

environment interactions for natural resource management.Applications in Asia. Proc. 4th

International.conference.Integration. GIS Environment. Modeling (GIStEM4): Problems,

Prospectsand Research Needs, Banff, Alberta, Canada, Sept. 2 - 8, 2000, pp. 1-18.

WASA. (1991). “Dhaka regional groundwater and subsidence model,” Dhaka Water Supply and

Sewerage Authority (WASA), 1991.

World Bank. (2007). Dhaka: Improving living conditions for the urban poor. South Asia Region:

World Bank, Sustainable Development Unit.

World Bank. (2013). World Bank Annual Report 2013. The World Bank. Washinton, D.C. USA.

Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water

features in remotely sensed imagery.International Journal of Remote Sensing.2006, 27,

pp. 3025-3033.

Yang, X., and Lo, C. P. (2002). Using a time series of satellite imagery to detect landuse and cover

changes in the Atlanta, Georgia. International Journal of Remote Sensing, 23(9), 1775–

1798.

Yu, X., and Ng, C. (2006). An integrated evaluation of landscape change using remote sensing and

landscape metrics. International Journal of Remote Sensing, 27(6), 1075–1092.

Yuan, F. (2010). Urban growth monitoring and projection using remote sensing and geographic

information systems: A case study in the Twin Cities Metropolitan Area, Minnesota.

Geocarto International, 25(3), 213–230.

Zha, Y. Gao, J and NI, S. (2003). Use of normalized difference built-up index in automatically

mappingurban areas from TM imagery. International Journal of Remote Sensing.Vol. 24,

N0, 3, pp.583 -594.

Zhang, R., Tang, C., Ma, S., Yuan, H., Gao, L. and Fan, W. (2011). Using Markov chains to

analyze changes in wetland trends in arid Yinchuan plain, China. Math Computer

Modelling 54:924–930.

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