Estimation of above ground biomass and carbon stocks of mangrove forests from remote sensing and field data in mong cai city quang ninh province during 2016 2019

Propose solutions to enhance carbon stocks of mangrove forests in Mong Cai district, Quang Ninh province .... Status of mangrove forests and management scheme in Mong Cai district, Quang

DURING 2016 – 2019

Major: Natural Resources Management

Code: D850101

Faculty: Forest Resources and Environmental Management

Supervisor: Assoc Prof Dr Hai-Hoa Nguyen

Student: Pham Van Bang Student ID: 1553090753 Class: K60 Natural Resources Management Course: 2015 – 2019

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

Ha Noi, 2019

I sincerely thank teachers in Faculty of Forest Resources and Environmental Management for the encouragements and suggestions and for helping me to improve the quality of my study

I would also like express our gratitude to the staffs of Mong Cai -Forest Protection Department and local people who living there for the enthusiastic support during my works

Due to the limited knowledge and experiences, this report is not finalized yet

I’m looking forward to receiving feedback from teachers and friends to improve this scientific research

I sincerely thank you all!

Ha Noi, 30, September, 2019

Author

Pham Van Bang

ABBREVIATION

GIS: Geographic Information System

FAO: Food and Agriculture Organization

UNEP: United Nations Environment Program

ITTO: International Tropical Timber Organization

ISE: Society for Mangrove Ecosystems

UNESCO: United Nations Educational Scientific and Cultural Organization AGB: Above-ground Biomass

AGC: Above-ground Carbon

VIs: Vegetation Indices

DBH: Diameter at Breast Height

RS: Remote Sensing

REDD+: Reduce Emission from Deforestation and forest Degradation GPS: Global Position System

NDVI: Normalized Difference Vegetation Index

SPSS: Statistical Package for the Social Sciences

PFES: Payment for Forest Environment Services

PES: Payment for Forest Services

CONTENTS

ACKNOWLEDGEMENTS……….i

ABBREVIATION ii

LIST OF TABLES v

LIST OF DIAGRAMS v

LIST OF FIGURES vi

CHAPTER 1 INTRODUCTION 1

CHAPTER 2 LITERATURE REVIEW 3

2.1 Overview of mangroves and remote sensing data 3

2.1.1 Overview of mangroves 3

2.1.2 Mangrove biomass and coastal services 6

2.2 Mangroves biomass and carbon stock in the worldwide 7

2.2.1 Mangroves biomass in the world 7

2.2.2 Mangroves biomass in Vietnam 8

2.3 Application of GIS and remote sensing to estimate mangrove biomass and carbon stock 9 CHAPTER 3 GOAL, OBJECTIVES AND METHODOLOGY 12

3.1 Overall Goal 12

3.2 Specific objectives 12

3.3 Equipments 12

3.4 Methods 13

3.4.1 Current status of mangrove forest in Mong Cai district, Quang Ninh province 13

3.4.2 Estimate above-ground biomass and carbon stocks of mangrove forest 14

3.4.3 Quantify changes in above-ground biomass and carbon stocks of mangrove forests 20

3.4.4 Propose solutions to enhance carbon stocks of mangrove forests in Mong Cai district, Quang Ninh province 21

CHAPTER 4 NATURAL AND SOCIO-ECONOMIC CONDITIONS 22

4.1 Features of natural conditions 22

4.1.1 Geographical location 22

4.1.2 Topography and hydrology 22

4.2 Social-economic condition 23

CHAPTER 5 RESULTS AND DISCUSSIONS 24

5.1 Status of mangrove forests and management scheme in Mong Cai district, Quang Ninh

province 24

5.1.1 Status of mangrove forests 24

5.1.2 Status map of mangrove forests 25

5.2 Above-ground biomass and carbon stocks of mangrove forests 30

5.2.1 Above-ground biomass and corresponding NDVI value of sub-plots in 2019 30

5.2.2 Development of regression models for biomass and carbon stock 30

5.3 Changes in above-ground biomass and carbon stock of mangrove forests during 2016 – 2019 in Mong Cai city, Quang Ninh province 38

5.3.1 Changes in above-ground biomass 38

5.3.2 Changes in above-ground carbon stocks 40

5.4 Solutions for mangrove management towards PFES in Mong Cai city, Quang Ninh province 42

5.4.1 Implementation of carbon payments for mangrove forests 42

5.4.2 Mechanism and policy solutions 44

5.4.3 Technical solutions 44

CHAPTER VI CONCLUSIONS, LIMITATIONS AND FURTHER STUDY 45

6.1 Conclusions 45

6.2 Limitations and further study 45

REFERENCES 47

LIST OF TABLES

Table 2.1 Previous estimate of global extent of mangroves. 5

Table 2.2 About wood density of tree in mangrove forest. 8

Table 3.3 Wood density of mangrove species. 17

Table 3.4 Regression models proposed for testing. 19

Table 5.1: Area of mangrove forests in Mong Cai city (ha). 24

Table 5.2 Mangrove species distributed in Mong Cai city, Quang Ninh. 25

Table 5.3 Accuracy assessment of classified map in 2019. 27

Table 5.4 Accuracy assessment in 2016. 29

Table 5.5 Mangrove forest areas in different years. 29

Table 5.6 Above-ground biomass and corresponding NDVI of sub-plots in 2019. 30

Table 5.7 Summary of models. 30

Table 5.8 Above-ground biomass and carbon stocks of study site in 10/2016 and 8/2019. 38

Table 5.9.Total above-biomass and carbon stock in 8/2019. 43

LIST OF DIAGRAMS Diagram 3.1 Methods of contrition mangrove maps using NDVI. 18

Diagram 3.2 Steps to improve map of change in biomass/carbon stocks. 20

LIST OF FIGURES

Fig 3.1 Spatial distribution of field plot survey. 15

Fig 3.2 Design of sampling plot for mangrove structures. 16

Fig 4.1 Map of study site: (a) Vietnam; (b) Quang Ninh; (c) Mong Cai. 22

Fig 5.1 Status map of mangrove forests in Mong Cai city, Quang Ninh province (Landsat 8, 15/08/2019). 26

Fig 5.2 Distribution of mangrove forests in Mong Cai city, Quang Ninh province 28

(Landsat 8, 09/10/2016). 28

Fig 5.4 Spatial distribution of above-ground biomass of Mong Cai City, Quang Ninh province in 2019. 34

Fig 5.5 Spatial distribution of above-ground biomass of Mong Cai City, Quang Ninh province in 2016. 35

Fig 5.6 Spatial distribution of carbon stocks of Mong Cai City, Quang Ninh province in 2019. 36

Fig.5.7 Spatial distribution of carbon stocks of Mong Cai city, Quang Ninh province in 2016. 37

Fig 5.8 Above-ground biomass changed in the period between 2016 and 2019. 39

Fig 5.9 Above-ground carbon stocks changed in the period between 2016 and 2019. 41

CHAPTER 1 INTRODUCTION

Mangroves are a group of plants that occur in the coastal intertidal zones of tropics and the subtropics Vietnam’s mangroves have been larger and rich and they play an important role for forest ecosystems, biodiversity and local livelihoods and development, fighting natural disasters, flooding, environmental protection of coastal areas, climate regulation and development economic and social of the country (Vashum, et al., 2012) Mangroves absorb a lot of CO2 by industrial activities and daily life discharge and they also produce lots of oxygen and help the air clean In Viet Nam, the effect of climate change is the one of the most affected countries from rising sea levels and the continuous storms which have caused serve damage to people and assets Mangroves bring high economic values and provide many products for human such as woods, building materials, sea foods and medicine

Quang Ninh is a coastal province in Northeast Vietnam The province is seen as a miniature Vietnam because it consists of sea, islands, plain, midland, mountain and border As planned, Quang Ninh belongs to key economic region of both Northern region and Red River Delta This is a major coal mining province of Vietnam with Ha Long Bay, the natural heritage and natural wonders of the world Quang Ninh has a large area of mangroves (over 23,000 ha), mainly concentrate in localities such as Mong Cai, Tien Yen, Quang Yen and Van Don Quang Ninh's mangroves play an important role in ensuring ecological balance and dyke protection, as well as sheltering many species of seafood, serving people's lives Currently, the province is focusing on protecting and developing mangroves, thereby contributing to environmental protection, disaster mitigation and climate change prevention

One of the environmental management tools is common in the world today is Geographic Information systems However, in recent years, new GIS just been developed in Vietnam This is to introduce topics and more widespread tool for field GIS environment sector Here, the subject will introduce one of GIS tools that vegetation index separated from the near-infrared band, infrared and red strip intermediate parameters from which could see the different characteristics of vegetation, such as biomass, index leaf area, photosynthetic capacity, total biomass products of seasonal plants can create These properties are related and depended greatly on the type of plant cover and weather, physiology, biochemistry and insect Technology for monitoring approximate characteristics of different ecosystems that allow

GIS-multi-resolution including very large, repeat the cycle time to help gather information quickly, sync, objectivity is deal for monitoring a number of factors affecting the forests in Quang Ninh as temperature and humidity This is a technical one rather strange for users in Vietnam

Stemming from this reality our group choose the project name is “Estimation of

above-ground biomass and carbon stocks of mangrove forests from remote sensing and field data in Mong Cai city, Quang Ninh province during 2016 – 2019”

CHAPTER 2 LITERATURE REVIEW 2.1 Overview of mangroves and remote sensing data

2.1.1 Overview of mangroves

Roles and function of coastal significance

Mangrove ecosystem is important coastal ecosystem with many functions which are not limited to ecological but also economical functions Although previous studies related to mangrove forests were not regarded as important, transitional communities with low productivity, most ecologists today view them as important ecosystem with high ecological productivity (Hsieh, et al., 2015) The major role of mangrove swamps is contribution to soil formation and stabilize coastline, mangroves act as filters for upland runoff, mangrove systems serve as the habitat of marine organism, mangrove produces large amount of detritus that may contribute to productivity in coastal waters In addition, mangrove forests also serve

as protection for coastal communities against storms, serve as nurseries and refuge for many marine organisms that are commercial or sport value, serve as habitat of threatened species, serve as aesthetics and tourism (Hsieh, et al., 2015) Mangroves are among the most carbon-rich forests in the tropics which containing 1,023 MgC/ha in average (Johnson, et al., 2016)

“Blue carbon” is a term to explain the sequestered carbon in vegetated coastal ecosystems, especially mangrove forests, seagrass beds, and salt marshes (Arias-Ortiz, et al., 2018)

 Definition of mangroves: Mangrove forest is a type of forest with tress growing in

the big estuaries where the salt water mixes with the fresh water When the tide comes in, a part of the mangroves forest or the whole forest will be submerged; when the tide goes out,

the forest will appear in its entirely (According to National Defense Magazine)

 Roles and function of mangroves: Mangrove has the roles of ecological, economic,

and social issues are very important in supporting the development of coastal areas In Biodiversity, home to an incredible array of species, mangroves are biodiversity hotspots They provide nesting and breeding habitat for fish and shellfish, migratory birds, and sea turtles An estimated 80% of the global fish catch relies on mangrove forests either directly or indirectly Livelihoods, the rural communities we work with are fishers and farmers who depend on their natural environment to provide for their families Healthy mangrove ecosystems mean healthy fisheries from which to fish, and healthy land on which to farm

surrounding vegetation, they filter and trap sediments, heavy metals, and other pollutants This ability to retain sediments flowing from upstream prevents contamination of downstream waterways and protects sensitive habitat like coral reefs and seagrass beds below

Mangroves are the first line of defense for coastal communities They stabilize shorelines by slowing erosion and provide natural barriers protecting coastal communities from increased storm surge, flooding, and hurricanes In 2003, it was estimated that a quarter of the world’s population lived within 100 kilometers of the coast and at 100 meters of sea level Robust mangrove forests are natural protection for communities vulnerable both to sea level rise and the more intense and frequent weather events caused by climate change

Especially, Carbon storage, Mangroves “sequester carbon at a rate two to four times

greater than mature tropical forests and store three to five times more carbon per equivalent area than tropical forests” like the Amazon rainforest This means that conserving and restoring mangroves is essential to fighting climate change, the warming of the global climate fueled by increased carbon emissions, that is already having disastrous effects on communities worldwide At the same time, mangroves are vulnerable to climate change as sea level rise pushes ecosystems inland

Mangrove at world scale

Mangroves are commonly found along sheltered coastlines in the tropics and tropics where they fulfil important socio-economic and environmental functions These include the provision of a large variety of wood and non-wood forest products; coastal protection against the effects of wind, waves and water currents; conservation of biological diversity – including a number of endangered mammals, reptiles, amphibians and birds; protection of coral reefs, sea grass beds and shipping lanes against siltation; and provision of habitat, spawning grounds and nutrients for a variety of fish and shellfish, including many commercial species

sub-High population pressure in coastal areas has, however, led to the conversion of many mangrove areas to other uses, including infrastructure, aquaculture, rice and salt production Numerous case studies describe mangrove losses over time, but information on the status and trends of mangrove area extent at the global level is scarce

The first attempt at estimating the total mangrove area in the world was undertaken as part of the FAO/UNEP Tropical Forest Resources Assessment in 1980, where the world total was estimated as 15.6 million hectares More recent estimates range from 12 to 20 million ha (refer to Table 2.1) For many of these studies, countries with small areas of mangroves were excluded due to lack of information and because their combined area of mangroves would not significantly affect the world total

Table 2.1 Previous estimate of global extent of mangroves

Number of countries Total area (ha) Reference

All national level estimates for each country are presented along with a short description of the mangrove vegetation and graphs illustrating the trend in mangrove area changes over time

Mangroves in Vietnam

Mangroves of Vietnam also called “green lung”, mangroves are more than just remarkable natural environments They are essential for the Vietnamese biodiversity and the society

The Vietnam mangrove, with his incredible flora and wildlife, can be observed in Can Gio, only about 2h from Ho Chi Minh City The mangrove forest is an exceptional natural

environment, which rests upon a particular ecosystem that can be found in few other Asian countries as Indonesia, Malaysia, Birmanie, India, and Philippines…Unique characteristics are the presence of swamps and countless natural canals with mix of freshwater and saltwater This has a surprising effect on the wildlife inhabiting it, where monkeys can be observed eating crabs under giant trees with big appearing roots More than being just astonishing natural surroundings, mangroves also have an important economic value and help

to create “green walls” to rising sea level and tsunami Vietnam as one of the most concerned countries regarding the rising sea level has developed a national strategy for climate change

by emphasizing on restoring and developing mangroves in the country In 1943, Vietnam used to possess more than 400 000 ha of mangroves However, most of it got wipe off and got reduced to 155 000 ha in 2006 because of the war

Recognized by UNESCO as a worldwide biosphere in 2000, the Can Gio mangrove forest is considered as the “Green lungs” of Saigon’s population Acting as huge natural filters

to clean the air and waste water coming from industrial zones along Dong Nai and Sai Gon rivers, mangroves are essential for the everyday well-being On an experimental level, the government made a decision a few years ago to share the benefits from the development, management and long-term protection of mangroves Thanks to this agreement, people have now the right to exploit and use these resources, grow plants and raise animals there but they have to protect and develop mangroves in return

2.1.2 Mangrove biomass and coastal services

Biomass is the amount of organic matters in an area at the certain period and calculated by ton per hectare The forest biomass is classified as the above-ground and below-ground biomass Above-ground biomass, is the fresh terrestrial biomass included: stems, roots, branches, barks, seeds, and leaves Below-ground biomass is all living biomass off root trees (Hossain, et al., 2012)

Main part of the biomass is focused on the continent with advantage in the favor of plant biomass The volume of biomass in the biosphere estimate n =1014-2.1016 tons Earth biomass accounts for very small percentage compared to the weight of the whole earth and very little compared to the hydrosphere, lithosphere However, in the long geological period, since they have appeared in 3 billion years ago Earth’s biomass sequestration is that practicing a powerful change a large amount of material on the earth Biomass is almost sediment rocks, metamorphic and sediment minerals, from the earth in the form of organic materials

2.2 Mangroves biomass and carbon stock in the worldwide

2.2.1 Mangroves biomass in the world

Canell (1982) has published the research: “World forest biomass and primary production data” In these, composed of 600 study has been published about the dry biomass

of stems, branches, leaves, and other parts, primary products of more than 1,200 mangrove

forest in Thailand included species Soneratia, Rhizophora, Bruguinera The results showed that the Rhizophorapiculata is the highest biomass approximately 701,9 tons per hectare, the second one is Bruguiera is 234,75 tons per hectare and the lowest one is Ylocarpus is only

20,1 tons per hectare

G.Saena and D.S.Kamat (India,1956) have introduced their research: “Access biomass

by application of remote sensing”, mentioned generally the issue of biomass products and evaluate the biomass by satellite image (Yingbin, et al., 2012)

Komiyama eand his colleagues (1987) have studied the biomass and the size of roots below-ground with the total biomass approximately 437,5 tons/hectare; the percentage of

biomass on the ground of Da Voi (Ceriopstagal) is 1,05 tons/hectare and the biomass of stem:

53,35 tons/hectare, branches: 23,61 tons/hectare, leaves: 13,29 tons/hectare, roots: 1,99 tons/hectare and below-ground is 87,51 tons/hectare Two scientist: Clough and Attiwil (1982) have discovered percentage biomass of root vary 14-64 percent in the total of biomass

To define the above-ground biomass, we use the structure of Komiyama (2005)

In which: P: wood density

DBH: Diameter at breast height

Table 2.2 About wood density of tree in mangrove forest

Vẹt trụ Bruguiera cylindrical (L) Blume 0.668 ± 0.057 (n=6)

Vẹt tách Bruguieraparviflora (Roxb) Wright & Griff 0.6256 ± 0.031 (n=3)

Dà vôi Ceriopstagal (Perr) C.B.Rob 0.6952 ± 0.028 (n=6)

Mấm Đen Avicenniaofficialis (L) Blume 0.5362±0.051 (n=6)

Su Ôi Xyclocarpusgranatum Koenig 0.5894 ± 0.021 (n=6)

Su Sung Xyclocarpusmoluccensis Lam 0.5495 ± 0.034 (n=6) Cui Biển Heritieralittolaris (Dryand) Aiton 0.6010 ± 0.105 (n=6)

Source: Santini, et al., (2012)

2.2.2 Mangroves biomass in Vietnam

Ngo Dinh Que (1971) estimated the biomass of Pine forest in Lam Dong province withn density if 2500 trees/heactare, valued at 330 tons/hectare In 1986, Nguyen Hoang Tri adopted the medthod of “Tree model” to estimate the biomass yield of Duoc Doi forest

communities (Rhizophoraapiculata) coastal mangrove in Minh Hai This is a highly

significant contribution in both theory and practice for study of ecology and coastal mangrove

forests Finding of forest biomass and Duoc Doi communities (R.apiculata) in the jungle,

natural regeneration forest and forest plantations 7 years in Ca Mau of the author showed the total of three forest types respectively 119,335 dry tons/hectares; 33,159 dry tons/hectare; 34,853 dry tons/hectare For the mature mangrove forests, the total of biomass is 276,892 kilograms/ hectare In which include stem: 158034,12; bark: 8990,09 kilograms/hectare (12,33%), root bark:4767,12 kilograms/hectare (1,72%); leaves: 9304,52 kg/hectare (3,36%); buds: 812,36 kg/hectare (0,29%); flowers and fruits: 6771,91 kg/hectare (2,44%) and below ground root: 19701,60 kg/hectare (7,11%) (Hanh, N.T.H., et al., 2016)

Ha Van Tue (1994) also used the method of “Tree model” of Newboul D.J (1967) studied productivity, biomass od some pulpwood plantations in permanently endowed Midlands in Vinh Phu (Ly Nhu Quynh,2007), research: “evaluate the growth, development,

biomass and forest production of Pinus KeysiaRoillexGordm) in Da La-Lam Dong province

by “Le Hong Phuc (1996), Nguyen Trong Binh (1996) discovered the rule of biomass development and structure of facts stem biomass development Percentage of fresh and dry biomass of parts of trees include stem, branches, leaves, roots, amount of falling, the total biomass of individuals and communities After that, he created some programs which showed the interrelation between biomass and other parts have 3m in diameter

According to Ly Nhu Quynh (2007), biomass and carbon sequestration capability of Mangletia conifer Dandy in Tuyen Quang, Phu Tho was estimated that biomass structure consists of 4 parts retail leaved stems, leaves and root, in which fresh biomass respectively 60%, 8%, 7%, 24%, of the total fresh biomass of a hectare plantation Manglietia conifer Dandy ranges from 53.400 – 30.9689 kg/ ha (in which conclude 86% is biomass wood floors, 6% is biomass shrub vegetation and 8% a biomass falling objects) (Hanh, et al., 2016)

In addition, several other research projects as Vien Ngoc Nam and Nguyen Duong Thuy (1991) studied forest biomass in the mangrove forest in Can Gio, Nguyen Van Be (1999) studied mangrove biomass in Ben Tre; Dang Trung Tan (2001) with the work

“Mangrove forest biomass” identified a total dry biomass of mangrove forest in Ca Mau is

to and established for natural resources management Currently, RS is widely used to collect information regarding forest AGB and vegetation structure as well as to monitor and map vegetation biomass and productivity on large scales by measuring the spectral reflectance of the vegetation However, optical RS does not directly assess above-ground forest biomass, and radiometry is sensitive to vegetation structure (i.e., crown size and tree density), texture, and shadow, which are correlated with AGB, particularly in the infrared bands RS data are now considered to be the most reliable method of estimating spatial biomass in tropical regions over large areas RS technology has been applied to biomass assessment in many

studies because it can obtain forest information over large areas at a reasonable cost and with

acceptable accuracy based on repetitive data collection with minimal effort

Estimating of above ground biomass of mangroves

In general, estimating the AGB in mangroves is a challenging task because of their complex forest structure and diverse species Many studies have shown that the method of determining relationships between field measurements and RS data and then extrapolating these relationships over large areas is very useful (Fu, et al., 2011) To determine the relationship between above-ground field biomass and RS data, researchers have used linear regression models with or without log transformations of field biomass data and multiple regressions with or without stepwise selection Artificial neutral networks, semi-empirical models, nonlinear regression, and nonparametric estimation techniques have also been used Although no model can determine this complex relationship absolutely, researchers continue

to use multiple regression models as one of the best options Vegetation index models are generally used to estimate biomass in many studies

Vegetation indices (VIs) are calculate from mathematical transformations of the original spectral reflectance data and can be used to interpret land vegetation cover VIs are applied to remove the variations caused by spectral reflectance measurements while also measuring the biophysical properties that result from the soil background, sun view angles, and atmospheric conditions Many previous studies have shown significant positive relationships between biomass and VIs; however, other studies have shown poor relationships Many methods can be used to map and estimate above-ground forest biomass for different land-cover types; one such method is the use of Landsat Imagery (medium-resolution satellite image) to estimate the attributes of forests through direct correlations or stepwise regression analyses with spectral bands, band ratios, or VIs In general, land-cover change mapping cannot be accurately performed based on low and medium-resolution satellite images However, the used of Remote Sensing Landsat is commonly used for many applications because it can be obtained for free or at low cost

A combination of many data sources (e.g., forest inventory, land use, elevation, and

RS data) can be used to predict vegetation variables over large areas A hybrid supervised/unsupervised classification approach couple with a geographical information systems (GIS) analysis has been employed to improve land use/cover mapping for Landsat data In tropical regions, forest plot-based field measurements have been correlated with RS data, and these measurements have been used to estimate that carbon stocks are limited, particularly in Southeast Asian countries The present study seeks to characterize the carbon

stock of mangrove forest types using forest-plot-based field measurement and RS data to develop a simple RS-based methodology The field-based measurement and RS approach might also help to improve forest carbon estimation in order to reduce emissions resulting from deforestation and forest degradation (REDD+) and to design incentive programs; furthermore, this approach might improve forest management with regard to climate change mitigation (Vicharnakorn, 2014)

CHAPTER 3 GOAL, OBJECTIVES AND METHODOLOGY 3.1 Overall Goal

This study aims to provide an additional scientific foundation of estimating ground biomass and carbon stocks of mangrove forests based remote sensing and field survey data

above-3.2 Specific objectives

- Objective 1: To investigate the status of mangrove forests and management scheme in

Mong Cai district, Quang Ninh province

This objective tends to answer the questions of how much mangrove forest extents, where the mangrove forest has spatially distributed and how the status of mangrove forests is, how many areas, how many species there are and how the current management scheme of mangrove forest works in this study site

- Objective 2: To estimate above-ground biomass and carbon stocks of mangrove forest

based on remote sensing and field survey data

This objective tends to answer the questions of how much above-ground biomass and carbon stocks per hectare in this area we have, where carbon stocks of mangrove forest are the highest or the lowest, and what the main influential drivers of carbon stock fluctuation

- Objective 3: To quantify changes in above-ground biomass and carbon stocks of mangrove

forests during 2016- 2019 in Mong Cai district, Quang Ninh province

This objective tends to answer how above-ground biomass and carbon stocks of mangroves have been changed during selected periods, and what drivers of change there are

- Objective 4: To propose solutions to enhance carbon stocks of mangrove forests in Mong

Cai district, Quang Ninh province

This objective tends to answer the question of what the solutions should be given to enhance carbon stocks of mangrove forests in this study area

3.3 Equipments

Devices and software required:

+ GPS Garmin 76CSX: to mark locations

+ Computer installed software such as ArcMap 10.4, Google Earth, Excel, SPSS: easy

to analyze data

Thesis used Landsat 8 Images in 2016, 2019 with resolution 30:30 to design maps

3.4 Methods

3.4.1 Current status of mangrove forest in Mong Cai district, Quang Ninh province

NDVI classification used with ISODATA clustering algorithm will be used Users choose their own initial estimates of class means, and then each pixel is assigned in classes in processing Number of randomly generated classified points have been chosen, then compared with ancillary data to accuracy assessment

Remotely sensed images are used in this study in the table below:

Table 3.1 Remote sensing data used this study

Status of coastal mangrove management

The first job is to conduct direct interviews households contracted for forest protection

in coastal mangroves, targeting at those who were bore and are living very closely to coastal mangroves Therefore, they have a very good understanding of coastal resources in studies site Also, interviews are conducted with households who are assigned to protect and manage mangrove forest In addition, interviews with personnel are directly involved in the management of mangrove protection in areas such as ranger stations, social forestry officials, officials of the contracting unit to protect the forest in the area

Interviews content generally refers to coastal mangroves that they have clearly known about mangroves, their local species and species appearance Recognize species such as

Kandelia obovata, Rhizophora stylosa, Aegiceras coniculatum can provide a clue for

obtaining a high accuracy when classifying images Also, this study gave the color photo or color drawings to farmers for further clarity in the document; to identify species in order to

provide more information about the venue, the growth of the species they are not aware of the consultation of experts

Processing Image Landsat 8

Step 1:

+ Download Lansat8 Image in 2016 & 2019

Using link: https://earthexplorer.usgs.gov/ to download

+ Drawing polygon of mangrove forest area of Mong Cai City

Using Google Earth Software to draw boundary and ensure that no mangrove areas are missed in different years

Step 2: Open ArcMap to Pre-processing Landsat Image

+ Clip band 4 (RED) and band 5 (NIR) of satellite data by polygon created

(ArcToolbox => Data Management Tools => Raster => Raster Processing => Clip) + Calculate NDVI value for study site by using band 4 clipped and band 5 clipped based on the formula:

Step 3: Add band 1, 2, 3, 4, 5, 6 of Landsat8 Image in 2016 and 2019

Step 4: Composite band (from band 1 to band 6) then change to true color image

(band 432) (ArcToolbox => Data Management Tools => Raster => Raster Processing => Composite Bands)

Step 5: Add boundary layer by attribute table => Choose Mong Cai City Boundary Step 6: Clip Landsat8 Image by using Mong Cai boundary

(ArcToolbox => Data Management Tools => Raster => Raster Processing => Clip)

Step 7: Apply NDVI classification method

Step 8: Classify objects by using NDVI classification method

Step 9: Using GPS to collected ground-control-point, including: mangrove forest,

water, other vegetation, road, etc Accuracy assessment bases on the selected control point

Step 10: Reclassify classes into three categories: mangrove, bare land, water, and

others, and calculate area of each land cover type

Step 11: Classify mangrove object, then create NDVI map of mangrove

3.4.2 Estimate above-ground biomass and carbon stocks of mangrove forest

To obtain this objective, the study uses combination of field plot survey with remote sensing approach

3.4.2.1 Field plot survey

Study tends to select 10 sample plots in Mong Cai district with dimension of 30m x 30m (equivalent 900m²) Location of each plot was illustrated in Fig 3.1 below:

Fig 3.1 Spatial distribution of field plot survey

Table 3.2 Location of field plot survey

Mong Cai city

Within each plot, there are 3 sub-plot with dimension of 10m x 10m have been set up

to measure forest structure, including: species, total height, diameter-at-breast-height (DBH), status Soil sampled plot is established at the center of sample plot

Fig 3.2 Design of sampling plot for mangrove structures

Sample 01: Mangrove forest structure survey

Data is recorded in the sheet below:

Coordinate: Lat:………

Species Status Perimeter at 1.3m

b) Data analysis

Kauffman and Donato (2012) provided “Protocols for the measurement, monitoring and reporting of structure, biomass and carbon stocks in mangrove forests” In which, biomass and carbon stock of each components are estimated separately, include: living trees, dead trees, lianas, forest floor vegetation and liter, etc

Above-ground biomass and carbon mass of living tree:

AGB is estimated by general equation:

B = 0.251*ρ*(D) 2.46 (Komiyama et al., 2005)

The original data was in Asia The equation has high correlation with R² = 0.98 (maximum DBH = 49cm, and sample size = 104 trees) In which, B is AGB (kg), D is diameter at breast height (cm), ρ is wood density (gram/cm³)

Wood density of common mangrove species are provided by Simpson (1996) and the World Agroforestry Centre (2011):

Table 3.3 Wood density of mangrove species

Source: Agroforestry Centre (2011)

Carbon mass of living wood can be converted from biomass by multiplying 0.46 – 0.5

c) Biomass mapping and Carbon stock mapping

Using vegetation indices is one convenient approach to estimating biomass and carbon stock

Mapping biomass and carbon stock in study site illustrate in the diagram below:

Diagram 3.1 Methods for mangrove mapping, biomass and carbon stocks estimation

 Step 1: Calculating NDVI of mangrove in study site

Each sub-plot has dimension of 30m x30m, which equal to the size of a pixel of the NDVI map Therefore, each sub-plot had a corresponding NDVI value

Field survey

Preprocessing of Landsat 8 images

Image Classification: Normalized Differences

Vegetation Index (NDVI)

Thematic mangrove map in 2016

NDVI values of sub-plot were obtained automatically from NDVI map of mangrove forest

 Step 2: Improve regression equation between biomass/carbon stock value at plot

location with appropriate NDVI value

NDVI and AGB value of each sub-plot were acquired to develop regression model In which, AGB played as a dependent variable and NDVI value was an independent variable The 9 types of regression model were acquired for the determining which one had the highest correlation coefficient SPSS software was used for the test

Table 3.4 Regression models proposed for testing

Source: Formula in SPSS software

Note: AGB (Above-ground biomass); NDVI (Normalized Difference Vegetation Index)

 Step 3: Choose the regression with the highest correlation and mapping biomass and

carbon stock of study site bases on the regression and NDVI map

 Step 4: Calculate AGB for each pixel of map

The allometric equation of corresponding chosen regression model was used to calculate AGB for each pixel of NDVI map of mangrove forest in both 2019 and 2016

 Step 5: Calculate total AGB of study site

The biomass of the study area was calculated by the total biomass of the all pixels

3.4.3 Quantify changes in above-ground biomass and carbon stocks of mangrove forests

Mapping the change in biomass and carbon stock in study site illustrate in the Diagram 3.2 below:

Diagram 3.2 Steps to improve map of change in biomass/carbon stocks

- Step 1: From appropriate images, improving NDVI map for study site in 2016 and

2019

- Step 2: From the NDVI map in 2016, and 2019 and the regression equation that has

been improved in objective 2, building biomass map and carbon stock map for study site in that year

-Step 3: Using “Raster Calculation” command in ArcGIS for map of change in

biomass/carbon stock as following equation:

“Map of change” = “Map in later” – “Map in earlier”

- Step 4: Reclassify value in new raster into three categories:

<0: biomass/carbon stock decrease

= 0: unchanged

>0: biomass/carbon stock increase

- Step 5: Mapping the change of biomass/carbon from 2016-2019

Remotely sensed image of study site

Map of changes in biomass and carbon

3.4.4 Propose solutions to enhance carbon stocks of mangrove forests in Mong Cai district, Quang Ninh province

To propose some possible solutions, this study was based on desire of local inhabitants and local administrators through interviews It included solutions about strategies to manage mangroves and other methods such as capital, communication