Application of remote sensing and GIS for forest cover change detection in ngoc hien district ca mau province 1990 2016

Therefore, the detection of forest cover change annually is very necessary in order to have sensible management methods.. Nowadays, the development of science and technology, especially

ACKNOWLEDGEMENT

The success and result of this project required a lot of guidance and assistance from many people and I am extremely fortunate to have this all along the completion of my project work Whatever I have done is only due to such guidance and assistance and I would not forget to thank them

A special thanks to Assoc Prof PhD Phung Van Khoa, who advised us throughout this study Without his enthusiasm and advice, it is unlikely this work would have been completed and for that we are truly grateful

I am thankful to and fortunate enough to get constant encouragement, support and guidance from teachers in Vietnam National University of Forestry

I would like to thank the staffs and foresters in the Dat Mui and Nhung Mien

Mangrove Forest Management Board for their support and assistance

Students:

Trinh Nam Phong

ABSTRACT

Ngoc Hien is known as the southernmost rural district of Ca Mau, Vietnam with the extensive mangrove forest areas and availability Therefore, the detection of forest cover change annually is very necessary in order to have sensible management methods Through researching, we recognized that the flora in Ngoc Hien is quite abundant and mostly is mangrove species The mangrove forest area increases continuously through periods 1990 –

1995 – 2016 The forest lost area decreased significantly from 1995 to 2016 It proves that local people in Ngoc Hien have had more awareness about protecting forest The application

of RS and GIS to detect mangrove forest cover changing is very useful It helps us to shorten the time to research and give us the high accurate results

Keywords: Remote sensing; Forest Cover Change; application of GIS; construct

map…

CONTENTS

CHAPTER I INTRODUCTION 1

CHAPTER II LITERATURE REVIEW 3

2.1 General definition about Remote Sensing and GIS 3

2.2 Application of Remote Sensing and GIS 3

CHAPTER III OBJECTIVES, CONTENTS AND METHODOLOGY 6

3.1 Goal and objectives: 6

3.1.1 Goal: 6

3.1.2 Objectives: 6

3.2.Contents: 6

3.2.1 Collecting materials related to the status of forest cover in Ngoc Hien (Ca Mau) in the period (1990 - 1995- 2016) 6

3.2.2 Construct map and detect forest cover change in Ngoc Hien in the period (1990 - 1995- 2016) 6

3.2.3 Propose some solutions for effective and sustainable forest managing and using in Ngoc Hien (Ca Mau) 7

3.3.Methodology: 7

3.3.1.Collecting materials related to the study 7

3.3.2.Image Acquisition and Pre-processing 7

3.3.3.Software 8

3.3.4.Image Interpretation 8

3.3.5.Fieldwork 21

CHAPTER IV NATURAL AND SOCIOECONOMIC CHARACTERISTICS OF THE STUDY SITE 24

4.1 Natural characteristics 24

4.1.1 Geography 24

4.1.2 Terrain 25

4.1.3 Climate 25

4.1.4 Hydrology 25

4.1.5 Natural Resources 25

4.2 Socioeconomic characteristics 26

4.2.1 Social characteristics 26

4.2.2 Economic characteristics 26

4.3 Mangroves and roles of mangroves in Ngoc Hien- Ca Mau 26

Chapter V RESULTS 28

5.1 Results of fieldwork combine with interpret Landsat image 28

5.2 Forest cover change map in periods 1990 – 1995 – 2016 37

5.2.1 Forest cover change map in 1990 37

5.2.2.Forest cover change map in 1995 38

5.2.3 Forest cover change map in 2016 40

5.3.Reason causing forest cover change in Ngoc Hien district and solutions 41

5.3.1 Reason causing forest cover change in Ngoc Hien district 41

5.3.2.Propose solutions 42

Chapter VI CONCLUSION, LIMITATION AND FUTHER STUDY 44

6.1 Conclusion 44

6.2 Limitation and further study 44 REFERENCES

LIST OF TABLES

Table 3.1 Landsat Satellite Images used in the study 7

Table 3.2 Image Classification 17

Table 3.3 Matrix to assess the accuracy 22

Table 5.1 Area of lost and exploited forest Statistic in 1990 37

Table 5.2 Area of lost and exploited forest Statistic in 1995 38

Table 5.3 Area of lost and exploited forest Statistic in 2016 40

LIST OF CHARTS

Chart 5.1 Forest cover change in 1990 37 Chart 5.2 Forest cover change in 1995 39 Chart 5.3 Forest cover change in 2016 40

LIST OF FIGURES

Figure 3.1 Defaults setup 9

Figure 3.2 Import data 9

Figure 3.3: Change the default and add bands created 10

Figure 3.4 Modify images 11

Figure 3.5 Layer stacking 12

Figure 3.6 Process of Creating Subset Image 13

Figure 3.7 Image of the study area after cutting 14

Figure 3.8 Edit signature 15

Figure 3.9 Assessing the quality of image choosing 16

Figure 3.10 Image before purifying 18

Figure 3.11 Image after purifying 19

Figure 3.12 Exclude subjects out of the study area 20

Figure 3.13 Processing of changing from Raster into Vector 21

Figure 3.14 Overall steps to collect information 22

Figure 3.15 Overall steps to process information 23

Figure 4.1 The Study Area 24

Figure 5.1 Forest Cover Change detection in 1990 38

Figure 5.2 Forest Cover Change detection in 1995 39

Figure 5.3 Forest Cover Change detection in 2016 41

LIST OF PHOTOS

Photo 5.1 Photo in the field 1 28

Photo 5.2 Photo in the field 2 29

Photo 5.3 Photo in the field 3 30

Photo 5.4 Photo in the field 4 31

Photo 5.5 Photo in the field 5 32

Photo 5.6 Photo in the field 6 33

Photo 5.7 Photo in the field 7 34

Photo 5.8 Photo in the field 8 35

Photo 5.9 Photo in the field 9 36

CHAPTER I INTRODUCTION

Besides getting benefits from forest exploitation and using, human activities such as agriculture, mining, deforestation and construction have caused many impacts on natural resource and environment Urban growth and its associated population increase is a major factor, which has altered natural vegetation cover This has resulted in a significant effect on local weather and climate Nowadays, we are facing with regression of natural resources and environment Economic development combining with protecting environment and natural resources to develop sustainably is an urgent problem To do this mission well, investigation - monitoring - and assessment task is one of the most important task Although each year there are some reports about status of forest cover change, almost these reports based on constructing map by rudimentary methods It is very complex and time consuming and we cannot find out most recent information from them because time for summarizing and constructing is long, then the information in map is backward and not exact While a good map is required exact form, quick time and updating information Therefore, we need new methods to make good maps

Nowadays, the development of science and technology, especially the births of Geographical Information System (GIS), Remote Sensing (RS) and satellite images, helps us

so much to research forest cover change, study and propose some methods to manage the natural resources and environment without direct contacts Remote Sensing (RS) information with many advantages like synchronous and updating information, broad covering ability and covered everywhere in the Earth… and with rapid development of technology such as supply information rapidly, exactly… brings to us authentic values of mapping methods in researching relationships and reciprocal impacts of some phenomenon, subjects such as forest cover change, forest resources change… Geographical Information Systems (GIS) can collect,

update, manage and analyze, represent geographical data in order to service applied mathematics relating to geographical points of some subjects on the Earth It is a better-supported tool for natural resources and environment management, and planning

In addition, Ca Mau is the coastal province in the south of Vietnam, where has a huge ecological tourism potential Along with abundant natural resources, Cau Mau is facing with many disadvantages such as salt water intrusion, pollution, erosion… occurring in the whole province Especially, in recent years, forest cover change is the urgent problem in Ca Mau in general and in Ngoc Hien in particular

From these above reasons, we chose the topic: “Application of Remote Sensing and GIS for Forest Cover Change Detection in Ngoc Hien district, Ca Mau province (1990 - 2016)”

CHAPTER II LITERATURE REVIEW 2.1 General definition about Remote Sensing and GIS

Remote Sensing is the science of obtaining information about objects or areas from a

distance, typically from aircraft or satellites without any direct contact

Geographical Information System (GIS) is a system designed to capture, store, manipulate, analyze, manage & present all types of geographic data GIS can show many

different kinds of data on one map This enables people to more easily see, analyze, and understand patterns and relationships

Change detection is the process of identifying differences in the state of an object or

phenomenon by observing it at different times Essentially, it involves the ability to quantify temporal effects using multi-temporal data (Singh 1989) Remote sensing provides a viable source of data from which updated land-cover information can be extracted efficiently and cheaply in order to inventory and monitor changes effectively Thus, change detection has become a major application of remotely sensed data because of repetitive coverage at short intervals and consistent image quality (Mas, 1999)

2.2 Application of Remote Sensing and GIS

The use of GIS and remotely sensed data in mapping different natural resources management and environmental modeling are gaining mass momentum in recent years Majority of work in remote sensing was mainly focused on environmental studies in the last few decades The implication of Remote Sensing and Geographic Information System to forest cover change and urban planning is now getting attention and interest among GIS and remote sensing professionals The techniques are becoming an important part of watershed management, urban planning, hydrological modeling, drought prediction, and forest cover mapping Remote sensed data provide advantages like synoptic coverage, consistency in data,

global reach and readability, precision and maximum accuracy in data provision (Lambing, E.F., 1997) Geographical Information System and Remote sensing has been efficiently and widely used much in single thematic analysis such as land use and land cover change mapping, forest monitoring , watershed management and forest fire management (Kachmar and Sánchez-Azofeifa, 2003), and forest strategy appraisal…

There are many research projects applying RS and GIS in the world Such as:

“Deforestation: Change Detection in Forest Cover using Remote Sensing” of Soraya

Violini (Argentina, 2013)

“Forest Cover Change Detection Using Remote Sensing and GIS – A Study of Jorhat and Golaghat District, Assam” of Shukla Acharjee, Mayuri Changmai, Smita Bhattacharjee and Junmoni Mahanta (India)

“Application of Remote Sensing and GIS for Forest Cover Change Detection (A case study of Owabi Catchment in Kumasi, Ghana)” of Adubofour Frimpong (Ghana)

“Application of Remote Sensing and GIS in Forest Cover Change in Tehsil Barawal, District Dir, Pakistan” of A Sajjad et al (Pakistan)

And many other projects These projects use main tools that are RS and GIS combine with other software like ERDAS, ENVI…

In Vietnam, Remote Sensing has been known since 1980, when Vietnam joined in the International Space Station Interkosmos (Nguyen Xuan Lam, 1999) However, before 1990 the application of satellite images was limited because the condition of technique and economy is poor Since 1990 up to now, we have recognized the important roles of satellite images so many ministries and branches like Ministry of Agriculture and Rural Development, Ministry of Natural Resources and Environment, Department of Hydrology and Meteorology… invested satellite images, equipment, trained people, and usually applied this technology to service for many purposes in researching as well as daily life

Up to now, there have been many scientific research projects applying RS and GIS of ministries, branches, doctors, professors… even college students in Vietnam

“Applying Remote Sensing and GIS to construct map of forest cover status in Cao Ky, Cho Moi, Bac Kan” of Hua Phuc Hoang (2012) The author used SPOT 5 image in 2010 combined with ERDAS 9.1 software to interpret

“Applying Remote Sensing and GIS to detect mangrove forest change” of Pham Viet Hoa (2012) The author used SPOT 5 image in periods and NDVI to assess forest change

“Applying Remote Sensing and GIS to detect land use change in Vinh Trai, Lang Son (2003-2008)” of Le Thi Thuy Van (2010) The author used SPOT 5 image with a resolution of 2.5×2.5m and combined with ENVI software

“Applying Remote Sensing and GIS to detect forest area change in Dao Tru, Lap Thach, Vinh Phuc” of Nguyen Thi Tho (2009)

In general, we can recognize that Remote Sensing and GIS are used widely in many different fields, especially in Natural resources and environment management However, because the system of forest maps in Vietnam were constructed in different time and referred

to many different information and image sources such as Landsat MSS, TM, SPOT, Aster, and Radar…, there are inconsistent data It is very difficult for users, especially in detecting forest area change in periods

CHAPTER III OBJECTIVES, CONTENTS AND METHODOLOGY 3.1 Goal and objectives:

Objective 3: To propose some solutions for effective and sustainable forest managing and

using in Ngoc Hien (Ca Mau)

- Collect forest status maps in Ngoc Hien of periods (1990 - 1995- 2016)

- Collect Landsat Satellite images in periods (1990 - 1995- 2016)

3.2.2 Construct map and detect forest cover change in Ngoc Hien in the period (1990 - 1995- 2016)

- Classify, interpret visually Landsat Satellite images

- Analysis data

- Construct map of forest cover change in Ngoc Hien- Cau Mau (1990 - 1995- 2016)

3.2.3 Propose some solutions for effective and sustainable forest managing and using in Ngoc Hien (Ca Mau)

- Refer to materials and current situation in Ngoc Hien to find out reasons and propose some solutions after surveying and constructing forest cover changing map

3.3 Methodology:

3.3.1 Collecting materials related to the study

- Collect maps: forest status maps, forest exploitation maps… of the Nhung Mien and Dat Mui Mangrove forest Management Board in 2016

- Inherit materials, reports about natural, socioeconomic condition in Ngoc Hien, Ca Mau

3.3.2 Image Acquisition and Pre-processing

For change detection over a period of time we required temporal satellite imagery of same time period Landsat Satellite images of periods (1990-1995-2016) were downloaded from the website http://earthexplorer.usgs.gov/ Landsat L8 OLI/TIRS, Landsat L8 OLI/TIRS Pre-WRS-2 and L4-5 TM satellite images were used in this study

Date required

The Landsat Satellite Images must satisfy some standards as follow:

- Images must have high homogeneousness of color or position…

- When collecting data, we have to make sample plots in where we sample, measure, describe forest status and land use characteristics…

3.3.3 Software

The processing of the satellite imagery during this research was carried out by using the ERDAS Imagine 2014 software Some specific image processing operations were done using the ArcGIS software version 10.1

3.3.4 Image Interpretation

Image interpretation comprises some steps below:

 Step 1: Setup some map defaults (input and output links after processing…):

- Start software ERDAS 2014

- In Main menu we choose Preferences/ Preference Editor

- Default Data Directory: Input file/ Default Data Directory: Output file

- Viewing/ Viewer/ Fit to Frame and Background Transparent/ Save to save default

setup process

Simulation in Figure 3.1

Figure 3.1 Defaults setup

 Step 2: Import data from storage equipment (USB, CD-ROM) into software:

- On main menu choose Manage Data/ Import Data/ In Format we must choose the exactly form of input Landsat image which is TIFF/ Input file/ ; choose Output file

which have default is img

- Add band 1 to band 6 in turn

Simulation in Figure 3.2

Figure 3.2 Import data

Right Click in 2D View #1→ Open Raster → change the default to file img → add

bands created

Simulation in Figure 3.3

Figure 3.3: Change the default and add bands created

 Step 3: Modify images:

a Intensify image quality:

- Intensify image quality: is modify action to increase the readability and

understandability of images for interpreters

- Stack up layer: in order to visual interpret more clearly and exactly, we need to stack

up colors according to steps:

+ Raster/ Spectral/ Layer Stack

Input file: open all of color Bands (.img) from Band 1 to Band 6

Output file: Name as and navigate folder destination after stacking up bands/ OK

Simulation in Figure 3.4

Figure 3.4 Modify images

+ Choose color bands: Color Infrared (vegetation) red, green, blue (RGB)

+ Raster/ Multispectral: Red: band 5/ Blue: band 4/ Green: band 3

Simulation in Figure 3.5

Figure 3.5 Layer stacking

 Step 4: Create Subset Image in the study area:

A Landsat image often has large size causing high capacity So it is so difficult to process and interpret images Layer stacking helps to create a concrete image in the study area and easy to process and interpret images Concrete steps as follow:

- On main menu, choose Raster/ Subset & Chip/ Create Subset Image/

Input: Name of origin file

Output File: Destination of the image after cutting

→ Click in Ignore Zero in Output Stats

- Raster/ Drawing/ click on Polygon symbol and draw

- On Box Subset, click on AOI tab/ Viewer/ OK

Simulation in Figure 3.6

Figure 3.6 Process of Creating Subset Image

Figure 3.7 Image of the study area after cutting

+ On main menu, choose Raster/ Supervised/ Signature Editor

+ Click on the zigzag symbol (black circle on the Figure 3.8) to add in

+ Name as: Name/save file *.sig

Simulation in Figure 3.8

Figure 3.8 Edit signature

- Assess the quality of image choosing:

+ In the dialog box Signature Editor, choose Evaluate/ Reparability

+ Distance measure/ Transformed Divergence

+ Output form/ Cell Array

+ Which Listing/ Best Average/ OK

If the value ranges from 1000-2000, the image is good

Simulation in Figure 3.9

Figure 3.9 Assessing the quality of image choosing

Table 3.2 Image Classification

- Choose classification method:

Base on the classified group of basic status to process the supervised classification The order of steps as follow:

+ Raster/ Supervised classification

+ Input files (Name of image that is need to classify); Output (Name as the image that is classified)

+ Signature File (File classified in the previous step)

+ Attribute option: mean

+ Parametric rule: Maximum likelihood/ OK (Non – Parametric Rule: None)

- Purify images after classification:

Images after classification often have small pixels of land and forest types that are alternate This alternateness may be right in fact but it is very difficult to check and apply We cannot determine the boundary among those small plots Therefore, we need to exclude them

by the follow steps:

+ On main menu, choose Raster/ thematic tool/ Neighborhood Function

+ Input file (Image that is need to purified); Output file (Name as the file after purifying) + Mark: File; Output: unsigned 8 bit

+ Function: Majority; size: 3 x 3/ OK Process 5 times