Using remote sensing indices for mapping and monitoring spatialtemporal changes in coastal mangrove extents in thanh hoa province during 2005 2018

i PUBLICATION Hai-Hoa, N., Huu Nghia, N., An, L.T., Ngoc Lan, T.T., Khanh Linh, D.V Simone Böhm 2018: Using remote sensing indices for mapping and monitoring spatialtemporal changes i

i

PUBLICATION

Hai-Hoa, N., Huu Nghia, N., An, L.T., Ngoc Lan, T.T., Khanh Linh, D.V

Simone Böhm (2018): Using remote sensing indices for mapping and

monitoring spatialtemporal changes in coastal mangrove extents in Thanh Hoa province during 2005- 2018 Jounal of Geo-spatial Information Science

(Submited and accepted to be reviewed)

ACKNOWLEDGEMENTS

This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 105.08-2017.05

With the consent of Vietnam Forestry University, Ministry of Agriculture

and Rural Development faculty, we conducted the study “Using remote sensing indices for mapping and monitoring spatial-temporal changes in coastal

mangrove extents in Thanh Hoa province during 2005- 2018”

With this study, I am extremely grateful for the guidance, advice and the support of many people First, I would like to thank most sincerely and deeply to

my mentor – Associate Professor Dr Hai-Hoa Nguyen, who gave helpful

advices and strong supports during the implementation and completion of this study

Also, I would like to thanks for the encouraging words, and suggestions

of the teachers of the Forest Resources and Environment Management Faculty, Vietnam Forestry University that helped me complete the study with the best quality

The study could not be finished and achieved the results without the

enthusiastic help, friendliness, and hospitality of the local government and

residents of two districts, namely Nga Son and Hau Loc, I would like give a big thanks and extreme appreciation to them

I also would like to thanks to our family and friends who always

supported and, encouraged me to perform and complete the study

Because of the limited study duration as well as lacking awareness and knowledgewe are looking forward to receiving the comments, evaluation and feedback of teachers and friends to raise the quality of study and improve not only the professional knowledge but also the lacking skills of us in this study

I sincerely thank you!

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TABLE OF CONTENTS

PUBLICATION i

ACKNOWLEDGEMENTS ii

TABLE OF CONTENTS iii

LIST OF FIGURES vi

LIST OF TABLES vi

ABBREVIATION vii

CHAPTER I INTRODUCTION 1

CHAPTER II LITERATURE REVIEW 4

2.1 GIS and Remote sensing data 4

2.1.1 Concept of GIS and remote sensing 4

2.1.2 Landsat image 5

2.1.3 Sentinel Satellite Image 6

2.1.4 Image classification approach 8

2.1.4.1 Vegetation indices 8

2.1.4.2 Supervised classification 9

2.1.4.3 Unsupervised classification 9

2.2 Overview of coastal mangrove 10

2.2.1 In the world 10

2.2.2 In Vietnam 11

2.3 Remote sensing application to mangrove management 12

2.3.1 In the world 12

2.3.2 In Vietnam 14

CHAPTER III RESEARCH OBJECTIVES AND METHODOLOGY 17

3.1 Goal and objectives 17

3.1.1 Goal 17

3.1.2 Specific objectives 17

3.2 Scope of study 18

3.3 Material and methodology 18

3.3.1 Material description 19

3.3.1.1 Satellite Imagery 19

3.3.1.2 Secondary data collection 19

3.3.2 Fieldwork and data collection 19

3.3.2.1 Ground truthing 20

3.3.2.2 Interviews 20

3.3.2.3 Soil organic carbon sampling 21

3.3.3 Data analysis 21

3.3.3.1 Determining organic carbon 21

3.3.4 Image processing 22

3.3.4.1 Data pre-processing 22

3.3.4.2 Images classification 23

3.3.4.3 Accuracy assessment 25

3.3.4.4 Thematic maps construction and mangroves dynamics 25

CHAPTER IV NATURAL AND SOCIO-ECONOMIC CONDITIONS 27

4.1 Natural characteristics 27

4.1.1 Geographical conditions 27

4.1.2 Topography, climate, hydrology and natural resources 28

4.1.2.1 Topography 28

4.1.2.1 Climate 29

4.1.2.2 Hydrology 29

4.1.2.3 Natural resources 29

4.2 Socioeconomic and cultural conditions 31

4.2.1 Economic conditions 31

4.2.2 Social and cultural conditions 32

4.3 Roles of coastal mangroves 32

CHAPTER V RESULTS AND DISCUSSIONS 34

5.1 Current status and the management scheme of mangrove 34

5.1.1 Historical background of mangrove dynamics in study sites 34

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5.1.2 Current status of mangrove forests 28

5.1.3 Management scheme in mangrove forests 30

5.2 Thematic maps using Image Classification 32

5.3 Quantification of coastal mangrove extents and drivers of changes 35

5.4 Soil organic carbon estimation 39

5.4.1 Soil organic carbon mapping 39

5.4.2 Evaluate accuracy of carbon stock map in Nguyen Binh: 40

5.4.3 Carbon price 42

5.5 Solutions for sustainable management 43

5.5.1 Shortcomings of mangrove management 43

5.5.2 Solutions for sustainable mangrove management 44

CHAPTER VI GENERAL CONCLUSION AND FURTHER STUDY 47

6.1 General conclusions 47

6.2 Limitation 48

6.3 Futher research 49

REFERENCES 50

APPENDIX 50

LIST OF FIGURES

Fig.3.1: Flow chart of constructing status maps and coastal mangrove extent 18

Fig.3.2: Plots for soil sample 21

Fig.5.1: Spatial distribution of coastal mangroves in study areas in 2018 28

Fig.5.2: Mangrove extents by different classification methods in April 2018 33

Fig.5.3: Mangroves cover changes over period 2015-2018 37

Fig.5.4: Soil organic carbon by IDW 40

LIST OF TABLES Table 2.1: Sentinel 2 Radiometric and spatial resolutions 7

Table 3.1: Remote sensing image collected in study 19

Table 3.2: Equations of vegetation indices used for estimating mangrove cover 24

Table 5.1: Past and present mangrove projects in Thanh Hoa province 27

Table 5.2: Accuracy assessment of map using different classification methods 29

Table 5.3: Structure characteristics of coastal mangrove forests 30

Table 5.4: Accuracy assessment of map using different classification methods 34

Table 5.5: Mangroves cover changes over period 2005-2018 36

Table 5.6: Soil organic carbon 40

Table 5.7: Accuracy assessment of map using IDW 41

Table 5.8: Absorbed and commercial value of carbon in study areas 43

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ABBREVIATION

CARE Cooperative for Assistance and Relief Everywhere

CMMB Community- Based Mangrove Management Board

DRC Danish Red Cross

ERTS Earth Resource Technology Satellite

GIS Geographic Information System

IDW Inverse distance weighted

JRC Japanese Red Cross

LULC Land Use/Land Cover

MMS Multimission Modular Spacecraft

MSI Multi-Spectral Instrument

PFES Payment for forest environment services

REDD+

Reduce emissions from deforestation and forest degradation, and foster conservation, sustainable management of forests, and enhancement of forest carbon stocks

RS Remote sensing

SLC Scan Line Corrector

SWIR Shortwave Infrared

TM Thematic Mapper

VIs Vegetation Indices

VNIR Visible and Near-Infrared

VNRC Viet Nam Red Cross

CHAPTER I INTRODUCTION

The coastal mangrove forests are one of the most productive ecosystems

on this planet (Ramdani et al., 2018) that provide a wide range of ecological and economic products and services, and also support a variety of others coastal and marine ecosystems (Van Ieperen, 2012, Salem and Mercer, 2012, Viswanathan

et al., 2011) Over the past five decades, discussions of mangrove ecosystems and management have focused on: the ability to fix, store and mineralize carbon; their nursery functions; shoreline protection, and their land-building capacity (Peneva-Reed, 2014, Lee et al., 2014, Viswanathan et al., 2011, Naciones et al.,

2004, Field et al., 1998) Mangroves can be disturbed by natural events, such as typhoons and floods However, nearly all of the mangroves have experienced significant losses in recent decades under the economic and population pressure

to meet the major demand for aquaculture and fishing In addition, it is estimated a world population growth with a peak of 9.22 billion in 2075 (Naciones et al., 2004), which leads to increasing pressures from human activities including over-harvesting, aquaculture, and coastal development interventions (Viswanathan et al., 2011) Besides, despite existing rules, regulations and policies, the management, and protection of mangrove resources are still poor and lack linkage among sectored, and studies have not brought specific solutions and practices It is estimated that about one-fifth of all mangroves have been lost since 1980 and nowadays, many remaining mangrove forests are considered degraded (Van Ieperen, 2012) Although the annual global rate of mangrove forest loss declined from just over 1% in the 1980s to 0.66% between 2000 and 2005, it is nevertheless still 3-5 times higher than the average rate of loss of all forest (Duke et al., 2014) The Vietnam War (1962-1971) resulted in the destruction of nearly 40% of mangrove forests in Southern Vietnam (Hong and San, 1993)

Thanh Hoa province is located in the North Central region of Vietnam

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with 102 km of the coastline that has total wetlands and coastal mangrove forests about 1195.53 ha in 2018 included 967.53 ha of mangroves Thanh Hoa province is also considered to have a high potential for planning, restoration, and development of mangroves, thus promoting local people‟s livelihoods However, due to the shortage of investigation and studies in the mangrove sites, there are still a few comprehensive documents and information about mangroves

in this study site

In the last three decades, spatial technology has evolved dramatically to include a suite of sensors operating at a wide range of imaging scales with potential interest (Rogan and Chen, 2004) and this technology is widely applied not only in people „s lives today but also in the scientific research The remote sensing and Geographic Information System (GIS) technologies are a powerful tool that enables to capture, store, analyze and manage referenced data of the different objects in the Earth surface spatially (Zaman, No date) Particularly, remote sensing (RS) data is the primary source and a powerful application in investigating the change in forests (Singh, 1989, Lu et al., 2004) It has been identified as an effective tool to study, which otherwise makes it difficult to reach and difficult to penetrate mangroves along coastal areas (Green et al.,

1998, Dat et al., 2000, Wang et al., 2003) In a few studies, a GIS and remote sensing data have been used for wetland cover mapping in Vietnam (Dat et al., 2000) This study is to quantify the extents of mangrove forest in the whole coastline of Thanh Hoa province for four different years (2005, 2010, 2015 and 2018) to understand mangrove dynamics over the last 13 years using freely available multi-temporal Landsat, Sentinel imageries and Land Use/Land Cover (LULC) maps by using Supervised classification, Unsupervised classification, NDVI, SAVI, MSAVI, IPVI, DVI, GNDV, BNDV, OSAVI, TVI, and EVI

(Table 03) Therefore, by using Landsat and Sentinel imageries between 2005

and 2018, this study tends to quantify the change in mangroves extends based on the method, which has the highest accuracy and reliable for the study site The

study then identifies drivers of changes in mangrove extents and suggests feasible solutions for enhancing mangrove management activities

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CHAPTER II LITERATURE REVIEW

2.1 GIS and Remote sensing data

2.1.1 Concept of GIS and remote sensing

GIS is a system of synthesizing and analyzing hardware, software, and data to “capture images”, manage, manipulate, and present all sorts of geographic data (Iyengar, 1998) It is a powerful tool for military trainers,

environmentalists, and natural resource planners

Remote Sensing means obtaining information about an object, area or phenomenon without coming in direct contact with it It can be understood as the practice of deriving information about the Earth‟s land and water surfaces using images acquired from an overhead perspective, using electromagnetic radiation in one or more regions of the electromagnetic spectrum, reflected or emitted from the Earth‟s surface (Shravan et al., 2013) There are different types of

RS technologies These include visual, optical infrared, electmicrowave, radar, satellite, airborne, and acoustic remote sensing systems The different applications of the systems are geology, mineral exploration, oceanography, agriculture, forestry, land degradation, environmental monitoring, and so on In order to take advantage of remote sensing data, meaningful information needs to

be extracted Much interpretation and identification of targets in remote sensing imagery are performed manually or visually, i.e by a human interpreter Recognizing targets is the key to interpretation and information extraction Observing the differences between targets and their backgrounds involves comparing different targets based on any or all of the visual elements of tone, shape, size, pattern, texture, shadow, and association

The integration between RS and GIS is considered a great application

The temptation to take advantage of the opportunity to combine ever-increasing computational power, modern telecommunications technologies, more plentiful and capable digital data, and more advanced algorithms has resulted in a new round of attention to the integration of remote sensing and GIS as well as with GPS for environmental, resources, and urban studies (Wang and Cheng, 2010) These tools will facilitate all levels of processing remotely sensed raster data, from pixel-based to per-parcel approaches and context-based approaches in an object-oriented environment This could subsequently lead to a full implementation of spatial operators („GIS-operators‟) in remote sensing software (Blaschke et al., 2000) In particular, such technologies assist in monitoring the change in LULC

2.1.2 Landsat image

Landsat satellites have provided multispectral images of the Earth continuously since the early 1970s Landsat data have been used in a variety of government, public, private, and national security applications Examples include land and water management, global change research, oil and mineral exploration, agricultural yield forecasting, pollution monitoring, land surface change detection, and cartographic mapping

Landsat 8 is the latest satellite in this series which began in 2002 The first

was launched in 1972 with two Earth-viewing imagers - a Return Beam Vidicon (RBV) and an 80-meter, 4-band Multispectral Scanner (MSS) Landsat 2 and Landsat 3 launched in 1975 and 1978, respectively, were configured similarly

In 1984, Landsat 4 was launched with the MSS and a new instrument called the Thematic Mapper (TM) Instrument upgrades included improved ground resolution (30 meters) and three new channels or bands In addition to using an updated instrument, Landsat 4 made use of the Multimission Modular Spacecraft, which replaced the Nimbus-based spacecraft design employed for Landsat 1- Landsat 3 Landsat 5, a duplicate of Landsat 4, was launched in 1984

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and returned scientifically viable data for 23 years - 28 years beyond its 5-year design life Landsat 6, equipped with an additional 15-meter panchromatic band, was lost immediately after launch in 1993 Finally, Landsat 7 was launched in

1999 and performed nominally until its Scan Line Corrector (SLC) failed in May 2003 Since that time, L7 has continued to acquire useful image data in the

“SLC-off” mode All L7 SLC-off data is of the same high radiometric and geometric quality as data collected prior to the SLC failure

2.1.3 Sentinel Satellite Image

The first satellite which was designed to monitor the Earth's surface is called ERTS (Earth Resource Technology Satellite- techniques probe the Earth), also known as Landsat-1 was launched on 7/23/1972 by NASA It was designed

as an experiment to test the feasibility to collect data from the Earth Until today, the satellite image is still the best tool to help people to observe the Earth and other planets in the solar system as well as serving for research monitoring and data collection Sentinel mission is based on a constellation of two satellites to fulfill revisit and coverage requirements, providing robust datasets for Copernicus Services Sentinel-2 is a polar-orbiting, multispectral high-resolution imaging mission for land monitoring to provide, for example, the imagery of vegetation, soil and water cover, inland waterways, and coastal areas Sentinel-2 can also deliver information for emergency services Sentinel-2A was launched

on 23 June 2015 and Sentinel-2B followed on 7 March 2017 The Sentinel-2 Multi-Spectral Instrument acquires 13 spectral bands ranging from Visible and Near-Infrared (VNIR) to Shortwave Infrared (SWIR) wavelengths along a 290-

km orbital swath The spatial resolution is dependent on the particular spectral band:

 4 bands at 10 meter: blue (490 nm), green (560 nm), red (665 nm), and near-infrared (842 nm)

 6 bands at 20 meter: 4 narrow bands for vegetation characterization (705 nm, 740 nm, 783 nm, and 865 nm) and 2 larger SWIR bands (1,610

nm and 2,190 nm) for applications such as snow/ice/cloud detection or vegetation moisture stress assessment

 3 bands at 60 meter: mainly for cloud screening and atmospheric corrections (443 nm for aerosols, 945 nm for water vapor, and 1375 nm for cirrus detection)

Table 2.1: Sentinel 2 Radiometric and spatial resolutions

Bands

Number

Central wavelength (nm)

Bandwidth (nm)

Central wavelength (nm)

Bandwidth (nm)

Sources: Gatti and Bertolini (2013)

Data acquired after December 5 in 2016 including a full resolution Color Image as an RGB (red, green, blue) composite image created from bands

True-4, 3, 2 Color combinations of images are used to set up the current state of forest maps There are many color bands together for the figures in different colors Each combination has its own advantages and disadvantages But this study just gives the characteristics of the combination of bands 432-RGB color

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which created a full resolution True-Colour Image as an RGB (red, green, blue)

2.1.4 Image classification approach

a fully grown green plant without any biotic or abiotic stress is generally in the range of 0.96–0.99 and is more often between 0.97 and 0.98 On the contrary, for dry plants, the emissivity rate generally has a larger range going from 0.88 to 0.94 Vegetation emissivity in the near and mid-infrared regions has been widely studied within plant canopies Different applications are dependent on the reflectivity peaks or overtones for specific compounds within the visible and near/mid-infrared regions of light spectra This study uses NBR, SR, NDVI, GNDVI, BNDVI, SAVI, OSAVI, MSAVI, DVI, IPVI, EVI2, TVI for

mangrove mapping

2.1.4.2 Supervised classification

According to Richards and Richards (1999) supervised classification is the technique most often used for the quantitative analysis of remote sensing image data At its core is the concept of segmenting the spectral domain into regions that can be associated with the ground cover classes of interest to a particular application In practice, those regions may sometimes overlap A variety of algorithms is available for the task, and it is the purpose of this chapter to cover those most commonly encountered Essentially, the different methods vary in the way they identify and describe the regions in spectral space Some seek a simple geometric segmentation while others adopt statistical models with which to associate spectral measurements and the classes of interest Some can handle user-defined classes that overlap each other spatially and are referred to as soft classification methods; others generate firm boundaries between classes and are called hard classification methods, in the sense of establishing boundaries rather than having anything to do with the difficulty in their use Often the data from a set of sensors is available to help in the analysis task Maximum likelihood classification is one of the most common supervised classification techniques used with remote sensing image data and was the first rigorous algorithm to be employed widely

Supervised classification can be very effective and accurate in classifying satellite images and can be applied at the individual pixel level or to image objects (groups of adjacent, similar pixels) and for the process to work effectively, the person processing the image needs to have a priori knowledge (field data, aerial photographs, or other knowledge) of where the classes of interest (e.g., land cover types) are located or be able to identify them directly from the imagery

2.1.4.3 Unsupervised classification

Unsupervised classification is where the outcomes (groupings of pixels

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with common characteristics) are based on the software analysis of an image without the user providing sample classes The computer uses techniques to determine which pixels are related and groups them into classes The user can specify which algorism the software will use and the desired number of output classes but otherwise does not aid in the classification process However, the user must have knowledge of the area being classified when the groupings of pixels with common characteristics produced by the computer have to be related

to actual features on the ground (such as wetlands, developed areas, coniferous forests, etc.) Overall, unsupervised classification is the most basic technique Because you don‟t need samples for unsupervised classification, it‟s an easy

way to segment and understand an image

2.2 Overview of coastal mangrove

2.2.1 In the world

“Mangrove” is an ecological term referring to a taxonomically diverse association of woody trees and shrubs (Ball, 1996) which have common morphological, biochemical, physiological and reproductive adaptations that allow them to colonize and develop in saline, hypoxic environments (Alongi, 2018) In addition, the term mangroves denote a group of 68 woody halophytic plant communities (Basha, 2018) which have the ability to adapt to the extreme transitional zone, which embodies divergent habitats between marine and terrestrial environment Mangrove is one of the highest yielding ecosystems, which constitute just 0.1 % of the earth‟s continental land surface, they are responsible for 10 to 11 % of the total export of terrestrial C to the ocean and for

8 to 15 % of the C deposited in coastal sediments (Liu et al., 2014), mangroves play an important role in the global carbon cycle They are one of the most carbon-rich ecosystems (Donato et al., 2011) because of their high productivity, rapid sediment accretion, and low respiration rates

The structure and functions of mangroves diverge significantly depending

on the topography, substrate, latitude, and hydrology Mangroves are classified into four major associations of different structure in relationship with features and the environment in which they exist (Lugo and Medina, 2014) They are fringe/coastal mangroves; riverine /estuarine mangroves; basin mangroves; and dwarf scrub mangroves

Mangrove species have unique adaptations for survival Noteworthy

amongst this survival includes gas exchange through the stilt roots and

pneumatophores Some species re-sprout while others fill vacant growing spaces

in response to canopy disturbances (Kauffman and Cole, 2010)

2.2.2 In Vietnam

Like many other countries in Southeast Asia, the mangrove area in Vietnam has decreased markedly In Vietnam, it is estimated that the number of mangrove forest was about 400,000 hectares in the early 20th century However, this number declined dramatically over 50 years (Tuan et al., 2003) In Northern parts of Vietnam, from Mong Cai to Do Son, throughout the periods from 1964 to1997, mangrove area decreased by 17,094 ha In the Red River plain, the loss

of mangrove was 4,640 ha from 1975 to 1991 then followed by a decrease of 7,430 ha in 1993 (NEA, 2003) The coastal zone of Southern Vietnam witnessed minor decrease of mangroves (from 250,000 ha to 210,000 ha) during 1950 -

1960 yet, the figure reduced to 92,000 ha of mangroves in 1975 due to the spraying of warring herbicides by the American force (1962 – 1972) (Tuan et al., 2002) Therefore, it is necessary to monitor mangrove forest, and mapping of mangroves is important in order to support coastal zone management and planning programs

The Viet Nam Red Cross (VNRC) has been at the forefront of these activities since 1994 when Thai Binh launched the community-based MP/ DRR project with the support of the Danish Red Cross (DRC) In 1997, after a series

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of successes, the project was expanded to include another seven coastal provinces in Northern Vietnam In support of VNRC‟s implementation of the project, the DRC expanded its coverage to Nam Dinh province, while the Japanese Red Cross (JRC) initiated funding to six provinces (Ha Tinh, Hai Phong, Nghe An, Ninh Binh, Thanh Hoa, and Quang Ninh) through the IFRC

By the early 2000s, the focus of the project was broadened to include disaster

preparedness training and afforestation with bamboo and Casuarina trees in

communes along the rivers In 2005, DRC finished its part of the project, and JRC has funded activities in all eight provinces since then

2.3 Remote sensing application to mangrove management

2.3.1 In the world

LULC have become one of the key issues in sustainable development and global environmental changes (Guan et al., 2011, Halmy et al., 2015) Up-to-date, adequate and reliable LULC changes information from the past to present together with the future plausible changes is vital to understand and evaluate several social, economic and environmental consequences of these changes (Foley et al., 2005) In recent years, the attention of researchers has been directed towards GIS and RS techniques for monitoring LULC changes The RS data and GIS techniques have been increasingly applied in the land use map extraction, so LULC changes analysis and urban development investigation due

to their cost-effectiveness and high efficiency (Parsa and Salehi, 2016)

Recent efforts in spatial and temporal data models and database systems have attempted to achieve an appropriate kind of interaction between the two areas Since the integration of spatial and temporal database models into spatio-temporal database models, a number of new approaches have been proposed, and reviews of this work have classified and compared to the existing spatio-temporal models One of the most significant contributors to the domain has

been (Kucera, 1992) who first looked at the aspects of the time in GIS (GISs) Although Langran‟s work has emerged since Frank (1992)was one of the first to attempt to present the poor theories and methods of reasoning in the time-varying spatial space, while a bibliography of spatio-temporal databases was published until 1994 in Al-Taha et al (1994), which contains interesting pointers for further reference More fruitful reviews of the domain were available in the forthcoming years, Yuan (1996), Renolen (1995), Abraham and Roddick (1999), Sellis (1999), Peuquet (2001), and especially in Pavlopoulos (1998), where the classification of spatio-temporal database models used in the current survey of the area is introduced over many of the most important issues

of spatio-temporal systems, a number of new proposals

Currently, domain experts are trying to achieve more effective integration

of the spatial and temporal aspects providing practical, unified spatio-temporal data modeling, and clarifying the direction for further research and development One of the simplest spatio-temporal data models is the snapshot model (Langran, 1988) Temporal information has been incorporated into this spatial data model by timestamping layers In this model, every layer is a collection of temporally homogeneous units of one theme It shows the states of a geographic distribution at different times without explicit temporal relations between the layers Another closely related approach was presented by Armenakis (1992), where spatio-temporal data are looked at with respect to storage, retrieval and update efficiency He compared three approaches, which he called „„estimation methods‟‟ to describe time-varying spatial information The aim of his investigation was to see whether these methods have the ability to reconstruct complete geographical states, offer functionality for comparisons between states, and describe the events that lead to changes between states

Concluding, early work on spatio-temporal database modeling attempted

to capture the state of real-world objects or the physical events upon them, on the timeline However, investigations in real-world applications brought new

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directions and requirements for further development Thus, the processes of continuous change on the timeline, changes in the description, size, position, the extent of entities were investigated and models to capture them were proposed

At the same time, various types of spatio-temporal attributes were defined and the importance of enhanced relationship operations was recognized The models

on a later stage are more concerned with conceptual notation of spatio-temporal data The next step in the spatio-temporal database development is the testing stage, where the models proposed to run on different applications, to identify further requirements and research directions

Cuc and Chinh (2014) were also involved in topic research functional and services of mangrove at Dai Hop, Kien Thuy, and Hai Phong as the results indicated the ability to protect sea dike, as well as direct economic benefits of

mangroves In addition, the project also pointed out the ecological benefits through CO2 aborting function of mangroves This is one of the extremely valuable researches in the management and efficient exploitation of mangroves

in Dai Hop commune, Kien Thuy and Hai Phong

Moreover, many different researches using remote sensing technique to research mangrove issues such as Khanh (2008) conducted the project of research and application of remote sensing and GIS technology build a map of the current state of natural resources serving planning provincial environmental protection As a result, shows that the selection of remote sensing and GIS technology is the optimal solution for the work of mapping the current state of natural resources in service the planning and environmental protection Remote sensing tool allows collecting information of objects on a wide area and in a short time with high accuracy which met the requirements of information synchronized Phuong et al (2014) conducted the project of applying remote sensing to map vegetation land-cover in Nghe An province This study indicates that based on a combination of vegetation index and the heat allow the establishment of vegetation cover map province Nghe An in 2000, 2010 and

2013 quickly and accurately This method solves the lack of field data verification inception maps of vegetation cover during the period from 2000 to

2013

Hoa (2016) worked on using Landsat imagery and vegetation indices differencing to detect mangrove change in Thai Thuy district in Thai Binh province This research used multi-temporal Landsat data and GIS technology to quantify changes in coastal mangroves then, proposed a scientific foundation for better mangrove management Vegetation indices, such as NDVI, SAVI, IPVI, DVI, SR, RVI were adopted as a suitable method to quantify and monitor the extents of mangrove, this study and NDVI showed the most accurate in comparison with others

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In short, remote sensing and GIS technology have currently been applied

in a wide range of natural resources and environmental management However, there are obstacles preventing Vietnam from catching up with the world and there are few researches in Thanh Hoa province Using remote sensing data to monitor spatial-temporal changes in extents of coastal mangroves in Thanh Hoa province plays an important role in an effective mangrove mapping and management

CHAPTER III RESEARCH GOAL, OBJECTIVES AND METHODOLOGY

3.1 Goal and objectives

3.1.1 Goal

This study aimed to provide a good scientific basis for better management of coastal

mangroves through using multitemporal remote data approach in Vietnam

3.1.2 Specific objectives

In order to meet the overall goal of this study, the following are the objectives:

Objective 1: To analyze the current status and management scheme of coastal

mangroves in the study area, this objective answered the questions of how spatially coastal mangrove forests have distributed in the study area and how was mangrove managed

Objective 2: To construct thematic maps using multi-temporal remote sensing

data, this objective answered the question of which method is the most suitable for quantifying coastal mangroves extents

Objective 3: To quantify the changes in extents of coastal mangrove and

determine the drivers of changes in mangrove extents in each periods (2005-2010, 2010-2015, 2015-2018, this objective answered the question of how much coastal mangrove forests have changed over the last 13 years and what are the main causes of changes in mangrove

Objective 4: To estimate organic carbon of mangrove from a field-based

plot survey and Inverse distance weighted (IDW)-based interpolation approach

in studied sites, this objective answered the questions of how much carbon mangroves can store and its price

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Objective 5: To propose solutions for better management of coastal mangroves

in study sites, this objective answered the questions of what applicable solutions should be given for sustainable management of coastal mangroves in study sites.

3.2 Scope of study

Spatial scale: This study is conducted in Nga Son and HauLoc districts,

Thanh Hoa province In addition, coastal mangroves are selected in this study

Temporal scope: The research was conducted from 2005 to April 2018

3.3 Material and methodology

The flow diagram showing the sequence and method employed in

embarking on the research as shown in Fig.3.1.

Fig.3.1: Flow chart of constructing status maps and coastal mangrove extent.

3.3.1 Material description

3.3.1.1 Satellite Imagery

The multi-temporal Landsat images (2005-2015) and Sentinel image (2015-2018) were used to detect mangrove cover and change Sentinel image in the year 2018 was used for accuracy assessment of Landsat image in combination with the field survey because, Sentinel image offers a higher spatial resolution (10 x 10) than Landsat data, but it is only available since 2015 Sentinel image was used in this study and downloaded from the website earthexplorer.usgs.gov/ or sentinel.esa.int The information of the image is

shown in Table 3.1

3.3.1.2 Secondary data collection

To study about distribution, the structure of coastal mangroves and management scheme, inherited data methods were used for this study related to remote sensing, GIS technology, and its applications to study mangroves The study reviewed the documents from the previous research, legal documents, books, journals, and magazine etc in relation to mangrove management and development in the study site

3.3.2 Fieldwork and data collection

An extensive fieldwork was carried at study area to collect GPS point for

Table 0.1: Remote sensing image collected in study.

be used to support many classification methods with high accuracy (Svatonova, 2016) Some classes were spectrally confused and could not be separated well

by supervised classification and hence visual interpretation was required to field survey 573 GPS points were collected from the field, including 220 points for mangroves, 108 points for water body and 172 points for others types of land use

3.3.2.2 Interviews

Primary data in this study was mainly obtained from semi-structured interviews and open questions which were used to identify the challenges and opportunities from mangrove for local livelihood Randomly 50 households were selected to collect information in Nga Son and Hau Loc in a diverse group

of People‟s Committee at district as well as village levels and local people who have been living nearby mangroves for a long time In addition, mangrove rangers and local guards also were interviewed to obtain the additional information of coastal mangroves, such as mangrove species names and historical spatial distribution

3.3.2.3 Soil organic carbon sampling

This study conducted field survey measurements along the Nga Son and Hau Loc coast in July 2018 with help and permission from local authorities Plots were selected using a stratified random sampling method in which strata were determined based on a pre-survey involving local people to ensure the

range of biomass values would be valid for the entire mangrove forest (Fig.3.2 )

To take the soil sample in the central plot, the soil was taken at a depth of 100

cm from the surface Each soil sample was equally divided in to 5 layers: 0 ÷ 20 cm; 20 ÷ 40 cm, 40 ÷ 60 cm, 60 ÷ 80cm and 80 ÷ 100 cm Then, the soil samples were cover by a plastic bag and preserve in the suitable condition until

it is sent to the testing and practice center

Fig 3.2: Plots for soil sample

3.3.3 Data analysis

3.3.3.1 Determining organic carbon

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The method used to determine total organic carbon in the soil which was adapted from national standard TCVN 9294: 2012 and based on the Walkley-Black method In this process, organic material was oxidized by using a redundancy amount of potassium dichromate solution in the sulfuric acid environment, using heat by dissolving the concentrated sulfuric acid into the dichromate solution, then titrating the redundant of dichromate by iron (II) solutions, thus deducting the organic carbon content To calculate the amount of carbon in the soil sample, the following formula was used:

C = Soil organic carbon (%) = ( )

By using the specific bulk density of the soil sample, the underground carbon stock in an area was calculated as follows:

Landsat images: Atmosphere correction is applied to remove errors and

to increase accuracies using Spatial Analyst Tools in Arc GIS 10.3 indicated as below (Hoa, 2016)

DN values to TOA reflectance = Band-speciic reflectance_Mult_Band x

DN value + Reflectance_Add_Band

Correct for Sun Angle = TOA Reflectance/sin (Sun Elevation)

ArcToolbox => Spatial Analyst Tools => Map Algebra => Raster Calculator

Composite bands: Sentinel and Landsat data conclude difference bands,

so the combination is necessary for image interpretation This study combines band 2, 3, 4, 8 and band 1, 2, 3, 4, 5 for Sentinel and Landsat, respectively

Arc Toolbox => Data Management tools => Raster => Raster Processing => Composite bands

Clip: Sentinel image based on the research area: Usually, a screen of

remote sensing image has very large area compared to the specific research area that would be determined In order to raise efficiency, the following steps were performed:

Arc Toolbox => Data Management tools => Raster => Raster Processing =>Clip

3.3.4.2 Images classification

The visual interpretation, vegetation indices (SR, NBR, NDVI, SAVI, MSAVI, GNDVI, BNDVI, TVI, OSAVI, DVI, IPVI, EVI), supervised and

unsupervised classification methods (Table 3.2) were used to map the coastal

mangroves in 2018 In the combination with data collection in the field, the accuracy can be assessed using ground truthing approach The method with the highest accuracy was used to map the coastal mangroves extents in 2005, 2010,

2015, and 2018 Two map layers then were overlaid to achieve dynamic maps over different periods Two layers were added together in ArcGIS 10.3, and the final result was the dynamics map of coastal mangroves in five periods, namely 2005-2010, 2010-2015, 2015-2018 with four main categories classified as no

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forest, forest loss, forest gain, stable forest Besides, raw data after processing with Microsoft Excel was used to draw the graph to illustrate the trends and changes of different variables in this study

Table 0.2: Equations of vegetation indices used for estimating mangrove cover

1

SR (Simple Ratio) (Batadlan et al., 2009,

Kongwongjan et al., 2012, Pham et al., 2018)

NIR/RED

2

NDVI ( Normalised Difference Vegetation

Index) (Saleh, 2007, Pham et al., 2018,

Ramdani et al., 2018)

(NIR-RED)/(NIR+RED)

3

GNDVI (Green Normalised Difference

Vegetation Index) (Muhsoni et al., 2018)

(NIR-GREEN)/(NIR+GREEN)

4

BNDVI (Blue Normalised Difference

Vegetation Index) (Wang et al., 2007)

(NIR-BLUE)/(NIR+BLUE)

5

SAVI (Soil Adjusted Vegetation Index)

(Kongwongjan et al., 2012, Pham et al., 2018)

RED)/(NIR+RED+L)]*(1+L), L

[(NIR-= 0.5

6

OSAVI (Optimised Soil Adjusted vegetation

Index) (Batadlan et al., 2009)

(1+0.16)*[(NIR-RED)/ (NIR+RED+0.16]

7

MSAVI (Modify Soil Adjusted Vegetation

Index) (Kongwongjan et al., 2012, Pham et al.,

2018)

RED)/(NIR+RED+L)]*(1+L), L

[(NIR-= 0.5

8

DVI (Difference Vegetation Index ) (Batadlan et

al., 2009, Kongwongjan et al., 2012, Pham et

NIR - RED

2.5*[(NIR-11 NBR (Normalised Burn Ratio) (Li et al., 2013) (NIR-SWIR)/(NIR+SWIR)

12

TVI ( Triangular Vegetation Index )

(Kongwongjan et al., 2012, Muhsoni et al.,

3.3.4.4 Thematic maps construction and mangroves dynamics

After accuracy assessment, the map with the highest accuracy was used for mangrove area extents determination The multi-temporal Landsat images (2005-2015) and Sentinel images (2015-2018) were used to detect mangrove

26

cover and change The mangrove dynamic was made as the following step:

In 2015, set the value of mangroves is 1 and non-mangroves is 2

In 2018, set the value of mangroves is 5 and non-mangroves is 10

Use GIS software: Arc Tool box => Spatial analyst tools => Map Algebra

=> Raster calculator

The value 6 is mangroves unchanged, 7 are non-mangroves change to mangroves, 11 are mangrove changed to non-mangroves and 12 are non-mangroves unchanged

CHAPTER IV NATURAL AND SOCIO-ECONOMIC CONDITIONS

4.1 Natural characteristics

Nga Son and Hau Loc are two districts belonging to Thanh Hoa province

so natural and economic-social conditions of Thanh Hoa are representative for both Nga Son and Hau Loc districts Moreover, Nga Son and Hau Loc districts are the two contiguous communes thus they are similar in some below conditions

Fig.4.1 Study sites

4.1.1 Geographical conditions

Thanh Hoa is located in the North Central, 150 km south of Hanoi, 1,560

km from Ho Chi Minh City It borders Son La, Hoa Binh and Ninh Binh provinces in the north, Nghe An province in the south, and Hua Phan province

in the west, and the Gulf of Tonkin in the west

Thanh Hoa is located in the area affected by the key economic areas of the

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North, Northern provinces of Lao PDR and the central key economic region, located at the gateway linking the North with the Central Convenient transportation, such as Trans-Vietnam Railway, Ho Chi Minh Road, national highways 1A, 10, 45, 47, 217; Nghi Son deep-water seaport and a convenient river system for the North-South traffic, with provincial areas and international destinations Currently, Thanh Hoa has Sao Vang airport and is planning to open

an international airport near the sea serving for Nghi Son economic zone and tourists

The geographic location has become one of the favorable conditions for the socio-cultural and economic development of Thanh Hoa province

4.1.2 Topography, climate, hydrology and natural resources

The delta has a natural land area of 162,341 ha, accounting for 14.61% of the province's area, which is accreted by the Ma River, Song Bang, Yen River, and Hoat River The average elevation ranges from 5 to 15 m, alternating with low hills and independent limestone mountains The Ma River Delta has the third largest area after the Mekong Delta and the Red River Delta

The coastal area has an area of 110,655 ha, accounting for 9.95% of the province's area, with a coastline of 102 km, the terrain is relatively flat Running along the coast are estuaries The coastal sandy beach has an average height of

3-6 m, with famous Sam Son beach and other resorts such as Hai Tien (Hoang Hoa) and Hai Hoa (Tinh Gia); There are large areas of land suitable for aquaculture and the development of industrial parks and marine economic services

4.1.2.1 Climate

Thanh Hoa is located in the tropical monsoon climate with 4 distinct seasons The average annual rainfall is about 1600-2300mm, each year about 90-130 days of rain Relative humidity is from 85% to 87%, with average sunshine hours of 1600-1800 hours The average temperature is 230C - 240C, the temperature decreases as the high mountain The prevailing winds of the winter are the Northwest and the Northeast, the summer is the East and the South East Climatic conditions with heavy rainfall, high temperature, and abundant light are favorable conditions for the development of agriculture, forestry, and fishery

4.1.2.2 Hydrology

Thanh Hoa has four main river systems: Hoat, Ma, Bong and Yen rivers with a total length of 881 km and a total catchment area of 39,756 km2 Total annual water volume is 19.52 billion m3 The Thanh Hoa River flows through many complex terrains, which are great potential for hydropower development Groundwater in Thanh Hoa is also abundant in reserves and types because of its full range of sedimentary, metamorphic, macular and eruptive rocks

4.1.2.3 Natural resources

Land resources: Thanh Hoa has an area of 1,112,033 ha, of which

agricultural land is 245,367 ha; land for forestry production is 553,999 ha; land for aquaculture is 10,157 ha; 153,520 ha of unused land with appropriate land categories for the development of food crops, forest trees, and industrial crops

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and fruit trees

Forest resources: Thanh Hoa is one of the provinces with large forest

resources with 484,246 hectares of forest land and reserves of about 16.64 million m3 of timber and can exploit 50,000 - 60,000 m3 annually The forest is mainly broad-leaved forest with a rich flora; this area is the largest province in the country with an area of over 50,000 ha Thanh Hoa is also home to many species of animals such as deer, deer, gibbons, monkeys, wild pigs, reptiles and birds Especially in the southwest of the province, there are forests Ben En National Park, the Northwestern Pu Hu, Pu Luong and Xuan Lien Natural Reserves, are special-use forests where rare and precious plant and animal sources are stored and protected

Marine resources: Thanh Hoa has 102 km of coastline and 17,000 km2 of territorial waters, with large fish farms and shrimp farms Along the coast, there are 5 large creek gates, convenient for fishing boats to go in These are the fisheries centers of the province In the mouth of creeks are sandy mudflats of thousands of hectares, which are favorable for aquaculture, seagrass planting, wave-stopping, and salt production The surface of the salt water in the sea of

Me island, Bien Son can be cultured fish, pearl oyster, lobster and tens of thousands of hectares of coastal water is favorable for mollusk shellfish such as clams

Mineral resources: Thanh Hoa is one of the few provinces in Vietnam

with abundant mineral resources; There are 296 mines and mineral points with

42 different types, many of which have large reserves compared to the whole country such as granite and marble (reserve of 2-3 billion m3), limestone for cement (over 370 million tons) cement (85 million tons), chromium (about 21 million tons), iron ore (2 million tons), serpentine (15 million tons), dolomite (4.7 million tons) and other minerals

4.2 Socioeconomic and cultural conditions

4.2.1 Economic conditions

Gross Provincial Product (GRDP) in 2017 at constant prices in 2010 is estimated to increase 8.26% compared to 2016; Of which agriculture, forestry and fishery increased by 1.81%; industry and construction increased by 11.98%; service sector rose 8.10%; import tax, product tax excluding subsidies increased

by 6.57% In 8.226% growth in 2017, agriculture, forestry, and fishery contributed 0.34 percentage point; industry, construction contributed 4.31 percentage points; Services sector contributed 3.33 percentage points; import tax, product tax minus 0.28 point percentage contribution

The figures show that the industry and construction sector grew by 11.98%, contributing the most to overall growth; of which, industrial added value increased 11.38% (processing and manufacturing industry increased 11.30%); Construction increased by 12.97% compared to 2016

Agriculture, forestry, and fishery increased by 1.81%; Of which, agricultural added value increased by 0.83%; forestry increased 5.08%; Aquaculture increased 5.96% compared to 2016

Services sector increased by 8.10%; Some sectors accounted for a large proportion in the service sector, with a corresponding increase compared with the same period of last year: Accommodation and food services increased by 12.10%; wholesale, retail 10.66%; transportation, warehousing increased by 10.09%; Financial, banking and insurance activities increased by 7.81%; real estate business increased by 4.03% compared to 2016

In terms of economic structure in 2017, the agriculture, forestry and fisheries sector accounts for 17.68%, down by 2.43%; Industrial and construction sector accounted for 36.68%, up 1.33%; service sector accounted for 41.37%, up 1.16%; import tax, product tax minus product subsidy, 4.27%,

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down 0.07% over 2016 Gross output at current prices per capita in 2017 is estimated at VND34 million; equivalent to about $ 1,540

4.2.2 Social and cultural conditions

In 2010, the province has an estimated population of 3,412,612, accounting for approximately 35% of the population in the North Central region and 4.41% of the population The average population density is 307 people /

km2 There are 7 ethnic groups living: Kinh, Muong, Thai, H'mong, Dao, Tho, Hoa Ethnic minorities live mainly in mountainous and border districts The population of working age is about 2.16 million, accounting for 58.8% of the province's population The labor force of Thanh Hoa is relatively young and well educated The trained labor force accounts for 27% of the total, of which 5.4% are college-educated workers

4.3 Roles of coastal mangroves

Mangrove afforestation is a promising option to protect both the community and the sea dike From 1989, the JRC, Save the Children, and government collaborated to plant mangrove seedlings in offshore areas bordering Thanh Hoa After testing several different mangrove species, they

selected Kandelia candel and Sonneratia However, survival rates proved

disappointing; in some instances, only 15%-20% survived within a year of planting

In 2005, Typhoon Damrey inflicted serious damage on Thanh Hoa The sea dike failed to protect the commune except where mangroves remained to buffer the storm In these sheltered areas, agricultural land suffered less seawater intrusion, whereas elsewhere seawater swept several kilometers inland, destroying settlements and livestock, and taking human lives (Buffle et al.,

2011, Kempinski and Cuc, 2009) But, the long-term impacts on agriculture and freshwater supplies are still being felt

Mangrove forests provide essential functions and services to coastal communities These include acting as carbon sinks thereby mitigating the effects

of climate change, providing nutrients for marine life and enhancing protection

to coastal communities from associated storm surges and erosion, by capturing soil during periods of heavy precipitation thus stabilizing shoreline sediments Additionally, mangroves serve as a nursery and breeding ground for many organisms, while they have also been sustainably used for food production, medicines, fuelwood and construction materials In an attempt to mitigate the impact of disasters, restoration and rehabilitation of mangrove forests have been

a central focus of both governmental and non-governmental actors in the region