Applyying gis and remote sensing technology to detect mangrove forest cover change in ha an district, quang yen town, quang ninh province in 2000 2014

MINISTRY OF AGRICULTURE AND RURAL DEVELOPMENT
VIETNAM FORESTRY UNIVERSITY

STUDENT THESIS

APPLYING GIS AND REMOTE SENSING TECHNOLOGY TO
DETECT MANGROVE FOREST COVER CHANGE IN HA AN
DISTRICT, QUANG YEN TOWN, QUANG NINH PROVINCE
IN 2000 - 2014

Major: Advanced Curriculum in Natural Resources Management
Code: D850101
Faculty: Forest Resources and Environment Management

Student: Nguyen Vu Bach

Student ID: 1054010901

Class: K55 Natural Resources Management

Course: 2010 – 2014

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

Supervisor: Asso.Prof. Tran Quang Bao Ph.D.

Ha Noi, November 2014


TABLE OF CONTENTS

INTRODUCTION ................................................................................................................. 1
I. LITERATURE REVIEWS ............................................................................................... 3
1.1. Climate change: .............................................................................................................. 3
1.2. Roles of Mangrove forest: .............................................................................................. 3
1.3. Remote sensing: .............................................................................................................. 4
II. OBJECTIVES AND METHODOLOGY ......................................................................... 7
2.1. Objectives ....................................................................................................................... 7
2.2. Scopes ............................................................................................................................. 7
2.3. Methods: ......................................................................................................................... 7
2.3.1. Data Sources: ............................................................................................................. 7
2.3.2. Field survey method: .................................................................................................. 8
2.3.3. Image classification method..................................................................................... 10
2.3.4. Method mapping fluctuations mangrove forests:..................................................... 10
2.3.5 Characteristics of Landsat images for the study area: ................................................ 10
2.3.6 Fluctuations of mangrove forests in 2000 – 2014: ..................................................... 14
III. RESULTS ..................................................................................................................... 17
3.1.Processing mangrove forest area ................................................................................... 17
3.2. Mapping mangrove forest in Ha An over time ............................................................. 18
3.3. Evaluating the accuracy of Landsat image interpretation methods .............................. 19
3.4. Fluctuations mangroves in 2000 – 2014 ....................................................................... 20
3.3. Cause of fluctuations mangrove period 2000 – 2014: .................................................. 22
IV. DISCUSSION ............................................................................................................... 23
V. CONCLUSION ............................................................................................................... 24


LIST OF ACRONYMS
Notation

Meaning

NDVI

Normalized Difference Vegetation Index

OLI

Operational Land Imager

TIRs

Thermal Infrared Sensor

LIST OF TABLES
Table

Name

Page

2.1

Landsat Data used for data analysis

7

2.2

The specifications of the Landsat images

12

3.1

Mangrove forest area each year in the study area

19

3.2

Evaluation accuracy table in 2014

19

3.3

Fluctuation mangrove in 2000 - 2005

20

3.4

Fluctuation mangrove in 2005 – 2010

20

3.5

Fluctuation mangrove in 2010 - 2014

20

LIST OF FIGURES
Figure
2.1

2.2

Name
Location of studied areas: Viet Nam, Quang Ninh province, Quang
Yen town, Ha An district.
Overview classification methods and image processing of Landsat
remote sensing

Page
8

9

3.1

Landsat 8 satellite image of study area in 2014

13

3.2

Studied area in year 2000, 2005, 2010 and 2014

14

3.3

NDVI of studied area in each year

15

3.4

NDVI of mangrove in each year

16

3.5

Distribution of coastal mangroves overtime in Ha An district, Quang
Yen Town, Quang Ninh

17

3.6

Location of 20 points in the field to check the accuracy in 2014

18

3.7

Maps of mangroves fluctuation periods 2000 - 2005, 2005 - 2010, 2010 - 2014

21

3.8

Fluctuations of mangroves period 2000 – 2014

21

1


ACKNOWLEDGMENT
With the permission of the Vietnam Forestry University, Faculty of Forest Resources
and Environment Management, I have completed the thesis: "Applying GIS and remote
sensing technology to detect mangrove forest cover change in Ha An district, Quang Yen
town, Quang Ninh province in 2000 – 2014”.
To perform this topic, I have received the enthusiastic support of teachers from
Vietnam Forestry University, the Institute for Forest Ecology and Environment, local
officials and the rangers of Ha An district, Quang Yen town, Quang Ninh province.
After completion of thesis, I would like to deeply thank supervisor Asso.Prof. Tran
Quang Bao Ph.D. I would also like to thank MSc. Pham Van Duan who has guided and
helped me in the process of analyzing and processing data.
Because of my private limitations in term of expertise knowledge, surely, there are
some certain shortcomings and inadequacies in my thesis. Therefore, I truthfully expect to
receive active and frank responses from lectures as well as contributing opinions from
friends so that I can promote my research later.
I sincerely thank you!
Hanoi, 10 November 2014
Students

INTRODUCTION
With a coastline of over 3,260 kilometers of territory on the mainland, Vietnam has a
large mangrove area ranked second in the world after the mangrove forest at the mouth of
the Amazon River (South America). According to statistics from the Ministry of
Agriculture and Rural Development, Vietnam had over 400 thousand hectares of
mangroves in 1943. However, for over six decades ravaged by war coupled with
overfishing, by 2006, Vietnam only about 155 thousand hectares of mangroves. It must be
said that in the years after the war, the shrimp farm is one of the main reasons that
mangroves disappearing. Image analysis of the Mekong Delta in 2011, the area of
mangroves typical show, in 1973-2008, more than half of mangroves have been converted
into shrimp farms, causing serious erosion.
Vietnam was the first country in Southeast Asia accede to the Ramsar Convention on
wetlands waters, and as of 2013, Vietnam had five wetlands are recognized as a Ramsar
site, including 4 mangrove forests, which is Xuan Thuy National Park - Nam Dinh Bau
Sau in Cat Tien National garden belongs - Dong Nai, Tram Chim National Park, Tam
Nong district, Dong Thap and Ca Mau Cape National Park, Ngoc Hien district, Ca Mau
Province.
In the current period, together with the strong development of science and
technology, satellite imagery and remote sensing technology is the way to bring about
tremendous changes in the management of resources. With the introduction of a series of
satellite remote sensing power supply with increasing resolution, remote sensing
techniques have made great progress in almost every field is applied. In forest industry,
remote sensing techniques have been used for about 30 years to build the kind of forest
status map and classify forest state, the partition key forest fires, monitoring changes forest
resources.

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Coastal mangrove forest play an important role for biodiversity conservation, coastal

habitat protection, and prevention negative effects of sea level rise. However, due to global
warming, pressure of economic development in poor country. Spatial coveraged mangrove
has reduce significantly in recent decades both in regional and national scale.
For the purpose of further understanding of how mangrove forest change overtime and
give some fact evidence of mangrove change at specific area. With support from Remote
Sensing and GIS technology. We have implemented the thesis: "Applying GIS and remote
sensing technology to detect mangrove forest cover change in Ha An district, Quang Yen
town, Quang Ninh province in 2000 – 2014”.

2


I.

LITERATURE REVIEWS

1.1. Climate change:
The warming of the climate system is unequivocal, as is now evident from
observations of increases in global average air and ocean temperatures, widespread melting
of snow and ice, and rising global mean sea level. The Earth’s average surface temperature
has risen by 0.76° C since 1850. The global average surface temperature is likely to rise by
a further 1.8-4.0°C this century, and by up to 6.4°C in the worst case scenario. Nowadays
there are so many factories that exhale really destructive substances and pollute the air. We
all know very well that air is something we can’t live without. When we breathe the
polluted air, we can get seriously ill. Another issue are the greenhouse gasses. They are
gasses which trap heat in the atmosphere. Greenhouse gases such as carbon dioxide occur
naturally and maintain a gabitable planet but excess concentratión emitted solely through
human activities. For example carbon dioxide is entering the atmosphere because of human
activities like burning of fossil fuels (oil, natural gas, and coal). All vehicles exhale too
much damaging substances. Another huge problem is that the sea levels are rising

worldwide. Also the expansion of ocean water is caused by warmer ocean temperatures.
Mountain glaciers and small ice caps are melting as well as Greenland’s Ice Sheet and the
Antarctic Ice Sheet. The temperature is rising which means that ice is melting faster and
faster.
1.2. Roles of Mangrove forest:
Mangroves are also known as rich centers of biodiversity as they provide a home and
shelter for many species including fish, birds, frogs, snakes, insects and several endangered
crocodiles. Mammals also occupy these forests ranging from small animals like swamp
rats and monkeys to large carnivores like tigers, that use the dense foliage as cover.
Mangroves are also important nursery areas for many species of fish. Overfishing is a

3


global problem and we are fishing at an accelerated rate without allowing fish stocks to
recover. Mangroves are a vital resouurces in providing breeding grounds for fish. Along
with protecting coastlines from erosion by acting as a natural barrier and flood defense,
mangroves also filter pollutants from river run-off and prevent harmful buildup of
sedimentation from reaching the oceans and nearby marine habitats such as coral reefs.
Mangroves and coral reefs have a symbiotic (mutual beneficial) relationship – the reef
protects the coast where the mangroves grow from being eroded by the sea, and the forest
traps sediment washed from the land preventing it reaching the reef. Both mangrove forests
and coral reefs found in coastal areas provide protection and breeding grounds for fish – a
key source of income and nutrition for people in these regions.
1.3. Remote sensing:
Remote sensing has flourished over the last three decades when providing digital
imagery, from satellites in Earth's orbit since 1960s. The development of remote sensing
techniques associated with the development of imaging techniques. In 1859
G.F.Tounmachon - French scientist used hot air balloon at a height of 80 meters for aerial
photography, it is considered the birth of digital remote sensing industry. In 1894, Aine

Laussedat began a program guide using images for purposes of topographic mapping
(Thomas, 1999). The development of the aviation industry has created a great tool for
aerial photography selections and control. The first photo was taken from the plane by

Wilbur Wright in 1910 on region Centocal, Italy. The automatic cameras have high
precision, take gradually in to replace the camera shutter by hand. In 1929, Soviet Union
established Institute aerial photographs Leningrad, image was used to study landforms,
vegetation and soil. On 23/07/1972 U.S. launched first Landsat satellites gives possibility
to acquire global information about the planets (including Earth) and the surrounding
environment. And since then, NASA has launched six observation satellites more: Landsat

4


2 (1975), Landsat 3 (1978), Landsat 4 (1982), Landsat 5 (1984), Landsat 6 (1993), Landsat
7 (1999) and Landsat 8 (2013). United States also launched meteorological satellite NOAA
3rd generation after Trios. NOAA, NOAA 7 ... NOAA 12; NOAA-1 (1992) and NOAA – J
(1993) has provided photos by updates mode with spatial resolution 1.1 km. Remote
sensing now provides aggregate information used a variety aat problems such as natural
disasters and monitoring changes of the resource recovery.
Remote sensing technology was first applied in Vietnam began in the 1980s, and in the
subsequent years of the twentieth century promote efficiency in many sectors of the
national economy including natural resource management, forecasting weather, pollution
monitoring, current use of land, cartography, disaster prevention, monitoring of forests,
fisheries and aquaculture, urban planning and traffic management. The period 1990 - 1995,
the industry has applied remote sensing technology in fields such as meteorology,
cartography, geology minerals, and forest resources management and has obtained the
visible results. Remote sensing technology combined with geographical information
system has been applied to perform scientific research and projects related to survey
natural conditions and natural resources, expertise environmental monitoring, reduce to a

minimum the number of natural disasters in some regions. Many sectors, and agencies
already equipped with powerful software popular in the world as the software ENVI,
ERDAS, PCI, ER Mapper, OCAPI to build geographic information system. Vietnam had
the National Remote Sensing Center, which is the basis of research and technological
advancements take telecommunications expedition to the application of professional work,
such as Remote Sensing Center General administration, Remote Sensing Division of forest
inventory and Planning Institute of the Ministry of Agriculture and Rural Development.
However, the application of remote sensing in the study of coastal mangrove forests in
Vietnam took place late on a smaller scale and distribution of forest land. Since early 1989,

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Vietnam became the 50th member of the world and the first country in Southeast Asia
signed the International Convention on Wetlands (Ramsar Convention).
The advent of satellite imagery remote sensing and GIS technology has greatly
supported the study of fluctuations in the natural environment, and has aided in propose
measures for environmental management and natural resources remotely to them. In the
field of environment, remote sensing technique used to investigate the variation of soil and
the coatings, research the process of desertification..., also in the forestry, remote sensing
technique used to the study of forest classification survey, forest fire research division...
However, when using the remote sensing images have low resolution, lack professionalism
in image interpretation will cause interpretation to misleading results for the study area.
Therefore, the researchers use satellite images as high-resolution Landsat has practical
significance in research and assessing the quality of natural resources and forest resources.
Effective forest resources management is one of the issues being of particular concern. The
management, protection and development of forest resources is considered to be one of the
key tasks in the development of the economy - society in Vietnam. One of the
requirements for successful implementation of this task is to have the appropriate
mechanisms to attract the active participation of local communities in the management,

protection and development of forests. However, there are several causes of forest
resources dwindling and strong variation: population pressure on forests region have
increased, poverty and difficult economic circumstances, people’s livelihood are based
primarily on exploitation of forest resources, educational level are low in remote areas,
indigenous knowledge has not been promoted, extension inefficient, state policies on
community forest management is inadequate, the traditional structure of society has
changed a lot.

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II.

OBJECTIVES AND METHODOLOGY

2.1. Objectives
- Research mangrove fluctuations over periods 2000 - 2005, 2005 - 2010 and 2010 - 2014
in Ha An district, Quang Yen town, Quang Ninh province.
- Identify causes of mangrove fluctuation.
2.2. Scopes
- Mangrove forests in Ha An district, Quang Yen town, Quang Ninh province.
- This thesis used images Landsat 5 (1993 &2003), Landsat 7 (2003 & 2014) and
Landsat 8 (2014).
2.3. Methods:
2.3.1. Data Sources:
This thesis used images Landsat 5 (1993 &2003), Landsat 7 (2003 & 2014) and
Landsat 8 (2014) to detect fluctuations of mangrove forests.
Table 2.1. Landsat Data used for data analysis
Year

Image code

Date captured

Resolution

Path/Row

2000

LT51260462000310BJC00

05-Nov-2000

30 m

126/46

2005

LE71260462005331EDC00

27-Nov-2005

30 m

126/46

2010

LE71260462010057EDC02

26-Feb-2010

30 m

126/46

2014

LC81260452014268LGN00

25-Sep-2014

30 m

126/45

Source:
This thesis was conducted in 3 phases: (1) data collection, analysis and data
processing and interpretation conduct, (2) established the current map status in 2000, 2005,
2010 and 2014 maps of mangroves fluctuation periods 2000-2005, 2005-2010, 2010-2014,
(3) summary statistics and evaluation results.

7


2.3.2. Field survey method:

Figure 2.1. Location of studied areas: Viet Nam, Quang Ninh province, Quang Yen town,

Ha An district.
Forests are affected by many biological, physical processes, and are highly impacted
by society. Researchers need to gather information, collect data with surveys in the field,
process analyze data obtained, using specialyzed equipment, tools and software. Steps of
overall study is shown in Figure 2.2.

8


Identifying the topic, areas of
research
Defining the suitable object,
remote sensing
Data collection

Statistics report

Remote
sensing data

GIS data

Field survey data

Analysis, image processing

Evaluate the accuracy

Map of mangrove forest

Select and input the
necessary data

Map of fluctuations mangroves

Identify causes of mangrove
fluctuation

Report

Figure 2.2. Overview classification methods and image processing of Landsat remote sensing
The first, step was to survey the research area to identified a list of research
objectives and scopes. Then, combined with satellite images and map the research area,

9


surveyed the area, confusing objects in the image, establish the key for satellite image
interpretation of the study area. Finally, check the accuracy of the image classification at
the sample point as a basis for mapping the current state of forest resources. Sample
collected information on natural characteristics, socioeconomic by interviewing with local
people to find the causes of mangrove fluctuation in the study area.
2.3.3. Image classification method
Thesis using the method: Using high-resolution images (Google maps, Google
Earth) to model key for handling low-resolution images (Landsat). The process of visual
image interpretation show accurate results with some clear recognizable objects or distinct
spectral features. Some objects however have similar spectral characteristics close to each
other, the classification process auto usually inaccurate results. To response content
mapping fluctuations mangrove forests, some object to the visual image interpretation
incorrect results and combined with supporting documentation to identify these objects.

Specifically, using GPS to check the accuracy of these objects. Method category with
selected range sample execute the above ENVI 4.5 combined with software other
applications are ArcGIS 10.1.
2.3.4. Method mapping fluctuations mangrove forests:
After determining the accuracy of thematic maps for each period, GIS layers were
overlayed to detect the fluctuations of mangroves in each period. Mangrove map images
from 2000, 2005, 2010 and 2014, were composed using ArcGIS 10.1 software. Principles
for assessing changes in classification of the two images is based on the change matrix
2.3.5 Characteristics of Landsat images for the study area:
Documentary images, maps and specifications of Landsat image:
 Documentary images:

10


Satellite images used in the thesis are Lansat5, Landsat7 and Landsat8 with 30 m
spatial resolution. The image has been taken at the time of 2000, 2005, 2010 and 2014. The
material was transferred to the images coordinate system VN-2000 at level 3 and was a
combination of natural color.
 Features and specifications of the Landsat image:
From 1972 to now, NASA has launched 8 satellites observing resources (Landsat);
3 first satellite (1972 - Landsat 1, 1975 - Landsat 2, 1978 - Landsat 3) only multispectral
sensors equipped MSS (Multispectral Scanner System) with a resolution of 80m. In 1982,
NASA launched Landsat 4, Landsat 5 in 1984 was put into orbit; 2 are both equipped with
new sensors TM (Thematic Mapper) image with 7 bands, spatial resolution is 30m to 120m
resolution for the visible spectrum is infrared heating solutions. Landsat 6 and 7 was
launched in 1993 and 1999 with a new sensor ETM (Enhanced TM). Landsat 8 was
launched in 2013, carrying two sensors: the image acquisition surface (OLI - Operational
Land Imager) and thermal infrared sensor (TIRS - Thermal Infrared Sensor). These sensors
are designed to improve performance and reliability over previous Landsat sensors.

The specifications of the Landsat images are summarized in Table 3.1.

11


Table 2.2. The specifications of the Landsat images
Band

Instrument

Satellite

RBV

1, 2, 3

MSS

1 , 2, 3, 4, 5

4
5
6
7

TM

1 , 2, 3, 4, 5

ETM

6

ETM+

7

OLI
TIRs

8

1
2
3
4
5
6
7
1
2
3
4
5
6
7
8. Band
Panchromatic
1
2

3
4
5
6
7
8
1
2
3
4
5
6
7
8
9. Cirrus
10. Thermal
Infrared (TIR) 1
11. Thermal
Infrared (TIR) 2

Name

12

Wave (µm)
0,475 - 0,575
0,580 - 0,680
0,690 - 0,830
0,505 - 0,750
0,5 - 0,6

0,6 - 0,7
0,7 - 0,8
0,8 - 1,1
10,4 - 12,6
0,45 - 0,52
0,52 - 0,60
0,63 - 0,67
0,76 - 0,90
1,55 - 1,75
10,4 - 12,5
2,08 - 2,35
0,45 - 0,52
0,52 - 0,60
0,63 - 0,67
0,76 - 0,90
1,55 - 1,75
10,4 - 12,5
2,08 - 2,35

Resolution
(m)
80
80
80
30
79/82
79/82
79/82
79/82
240

30
30
30
30
30
120
30
10
10
10
10
10
60
10
2.5

0.45-0.52
0.52-0.60
0.63-0.69
0.77-0.90
1.55-1.75
10.40-12.50
2.09-2.35
0.52-0.90
0.433 - 0.453
0.450 - 0.515
0.525 - 0.600
0.630 - 0.680
0.845 - 0.885
1.560 - 1.660

2.100 - 2.300
0.500 - 0.680
1.360 - 1.390
10.3 - 11.3

30
30
30
30
30
60 (30)
30
15
30
30
30
30
30
30
30
15
30
100

11.5 - 12.5

100




Source map:
Maps used in this study:
- Commune boundaries map in 2013.


Merge, crop the image to the boundary study (Figure 3.1)

Figure 2.3. Landsat 8 satellite image of study area in 2014

13


2.3.6 Fluctuations of mangrove forests in 2000 – 2014:
2.3.6.1. Processing studied area image from Landsat image
I used different Landsat image in each year, depending on image quality and satellites.

Figure 2.4. Studied area in year 2000, 2005, 2010 and 2014.

14


2.3.6.2. Calculating NDVI vegetation index
Thesis used Image Analysis Tool in ArcGIS 10.1 to calculate NDVI vegetation index for
studied area in each year.
Nearly all satellite Vegetation Indices employ this difference formula to quantify
the density of plant growth on the Earth — near-infrared radiation minus visible radiation
divided by near-infrared radiation plus visible radiation. The result of this formula is called
the Normalized Difference Vegetation Index (NDVI). Written mathematically, the formula
is: NDVI = (NIR — VIS)/(NIR + VIS)

Calculations of NDVI for a given pixel always result in a number that ranges from
minus one (-1) to plus one (+1); however, no green leaves gives a value close to zero. A
zero means no vegetation and close to +1 (0.8 - 0.9) indicates the highest possible density
of green leaves.

Figure 2.5. NDVI of studied area in each year

15


2.3.6.3. Evaluating the accuracy of Landsat image interpretation methods:

Figure 3.6. Location of 20 points in the field to check the accuracy in 2014

To evaluate the accuracy of classification methods, this study used GPS to check
the accuracy of the visual image interpretation. Randomly selected 20 points on Google
Earth, then use GPS to check the accuracy of the object in the field.

16


III. RESULTS
3.1.Processing mangrove forest area
Mangrove development and growth in coastal areas, salt water, outside dike. so I used
dike to make the boundary divide mangrove and mainland flora in Ha An.

Figure 3.1. NDVI of mangrove in each year

17

3.2. Mapping mangrove forest in Ha An over time

Figure 3.2. Distribution of coastal mangroves overtime in Ha An district, Quang Yen
Town, Quang Ninh
Research results of the analysis showed that mangrove distributed stretches in
coastal area of Ha An.

18


Table 3.1. Mangrove forest area each year in the study area
Year

Mangrove area (ha)

2000

298

2005

300

2010

60

2014

146

3.3. Evaluating the accuracy of Landsat image interpretation methods
Table 3.2. Evaluation accuracy table in 2014
Point

Coordinate
Longitude

Landsat

Field

image

Latitude

Accuracy

1

106°52'18.98"E 20°54'27.21"N Road

Road

Right

2

106°52'46.23"E 20°54'29.23"N Mangrove

Mangrove

Right

3

106°52'47.15"E 20°53'57.18"N Dike

Dike

Right

4

106°52'40.27"E 20°53'53.16"N Water

Mangrove

Wrong

5

106°52'3.76"E

20°54'6.90"N

Water

Right

6

106°52'4.45"E

20°53'39.69"N Mangrove

Mangrove

Right

7

106°52'10.81"E 20°53'26.12"N Mangrove

Mangrove

Right

8

106°52'20.68"E 20°53'32.20"N Dike

Dike

Right

9

106°52'47.70"E 20°53'37.81"N Mangrove

Mangrove

Right

10

106°52'11.76"E 20°52'56.18"N Water

Water

Right

11

106°52'5.12"E

20°52'31.85"N Mangrove

Mangrove

Right

12

106°51'33.36"

20°52'17.84"N Mangrove

Grassland

Wrong

13

106°51'14.47"E 20°52'27.86"N Mangrove

Mangrove

Right

14

106°51'1.78"E

20°53'15.40"N Mangrove

Mangrove

Right

15

106°50'57.38"E 20°53'35.20"N Mangrove

Mangrove

Right

16

106°51'23.23"E 20°53'25.10"N Land

Water

Wrong

17

106°51'16.22"E 20°54'4.63"N

Water

Mangrove

Wrong

18

106°51'15.78"E

20°54'8.54"N

Road

Mangrove

Wrong

19

106°50'9.35"E

20°54'55.17"N Mangrove

Mangrove

Right

20

106°49'47.41"E 20°55'31.04"N Mangrove

Mangrove

Right

Water

Accuracy = 15/20 * 100 = 75 %

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Using high resolution images for verification of low-resolution images and visual
interpretation methods result indicate approximately 75% accuracy. These result are likely
lower than expected. Because of the use of moderate resolution of Landsat, the
perturbation of spectral images, and the effect of angle photography. However, the method
can be used in the analysis, and image interpretation.
3.4. Fluctuations mangroves in 2000 – 2014

Table 3.3. Fluctuations mangroves in 2000 – 2005
Fluctuation
Year

Mangrove

2000

2005

298

300

Area (ha)

%

+2

+0.1

Table 3.4. Fluctuations mangroves in 2005 – 2010
Fluctuation
Year

Mangrove

2005

2010

300

60

Area (ha)

%

-240

-80

Table 3.5. Fluctuations mangroves in 2010 – 2014
Fluctuation
Year

Mangrove

2010

2014

60

146

Area (ha)

%

+86

+143.3

After I have finished mangrove classes in each year, I used the merge tool in Arc Toolbox
to determine the fluctuation of mangroves.

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Figure 3.3. Maps of mangroves fluctuation periods 2000 - 2005, 2005 - 2010, 2010 - 2014

Fluctuaion of mangrove
150
100
50
0
-50

2000 - 2005

2005 - 2010

2010 - 2014
Fluctuaion of mangrove

-100
-150

-200
-250

-300

Figure 3.4. Fluctuations of mangroves period 2000 – 2014

21