Biomass and carbon stock estimation of coastal mangroves using data based remote sensing and field survey in kien thuy and do son hai phong city

VIETNAM NATIONAL UNIVERSITY OF FORESTRY FOREST RESOURCES & ENVIRONMENTAL MANAGEMENT FACULTY STUDENT THESIS BIOMASS AND CARBON STOCK ESTIMATION OF COASTAL MANGROVES USING DATA-BASED REM

VIETNAM NATIONAL UNIVERSITY OF FORESTRY

FOREST RESOURCES & ENVIRONMENTAL MANAGEMENT FACULTY

STUDENT THESIS

BIOMASS AND CARBON STOCK ESTIMATION OF COASTAL MANGROVES USING DATA-BASED REMOTE SENSING AND FIELD SURVEY IN KIEN

THUY AND DO SON, HAI PHONG CITY

Major: Natural Resources Management (Advanced Curriculum) Code: D850101

Faculty: Forest Resources & Environmental Management

Student: Le Thanh An Student’s ID: 1453091055

Class: 59B-Natural Resources Management Course: 2014-2018

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

Supervisor: Assos.Prof Dr Hai Hoa Nguyen

HA NOI, 2018

PUBLICATION

Hai-Hoa, N., An, L.T., Huu Nghia, N., Ngoc Lan, T.T., Khanh Linh, D.V (2018)

Biomass and carbon stock estimation of coastal mangroves at Hai Phong city using data- based Sentinel 2A and field survey in Dai Hop and Bang La district, Hai Phong city, Vietnam Journal of Geo-spatial Information Science (Submitted and Under review)

ACKNOWLEDGEMENTS

This research is funded by Vietnam National Foundation for Science and

With the consent of Vietnam National University of Forestry, Ministry of

Agriculture and Rural Development faculty, we perform the study: “Biomass and carbon

stock estimation of coastal mangroves using data- based remote sensing and field survey in Kien Thuy and Do Son, Hai Phong city”

I would like to express my sincere respect to my supervisor - Assoc Prof Dr Hoa Nguyen for his enthusiastic and patient support with invaluable comments In addition, the study could not be finished and achieved the result without the enthusiastic help, friendliness, and hospitality of the local authorities and residents of Dai Hop commune and Bang La district

Hai-Also, I would like to thanks for the encouraging words, and suggestions of the lecturers of the Forest Resources and Environmental Management Faculty, Vietnam National University of Forestry that helped me complete the study with the best quality

I also would like to thank to my friends and family who always supported and, encouraged me to perform and complete the study

Because of the time limitation as well as the lack knowledgewe, the study still has had some mistakes, I look forward to receiving the comments, evaluation and feedbacks of lecturers and friends to enhance the quality of the study and improve not only the professional knowledge but also the lack of skills in this study

I sincerely thank all of you!

TABLE OF CONTENTS

PUBLICATION i

ACKNOWLEDGEMENTS ii

TABLE OF CONTENTS iii

ACRONYMS vi

LIST OF TABLES vii

LIST OF FIGURES viii

CHAPTER I 1

INTRODUCTION 1

CHAPTER II 3

LITERATURE REVIEW 3

2.1 GIS and satellite image 3

2.1.1 The concept of GIS, remote sensing and GPS 3

2.1.2 Sentinel-2A satellite image 4

2.2 Overview of estimating of biomass and above carbon stock by using remote sensing 6

2.2.1 In the world 6

2.2.2 In Vietnam 10

2.2.3 Method to estimate above carbon stocks and biomass in previous studies 12

2.3 Overview of estimating SOC by using remote sensing 14

2.3.1 In the world 14

2.3.2 In Viet Nam 16

CHAPTER III 19

GOAL, OBJECTIVES AND METHODOLOGY 19

3.1 Study goal and objectives 19

3.1.1 Overall goal 19

3.3 Materials 20

3.3.1 Remote sensing data 20

3.3.2 Equipment 20

3.4 Study contents 21

3.4 Methodology 22

3.4.1 Investigate current status and management scheme 22

3.4.2 Estimate the biomass, carbon stocks and SOC 25

3.4.3 Construct thematic map of biomass, carbon stock and SOC 31

3.4.4 Propose the feasible solution for a better mangroves management in Bang La district and Dai Hop Commune 31

CHAPTER IV 33

NATURAL, SOCIO-ECONOMIC CONDITIONS 33

4.1 Natural, Socio-Economic condition 33

4.1 1 Natural characteristics 33

4.1.2 Socioeconomic and cultural conditions 34

4.2 Roles of mangroves to local people in study area 35

CHAPTER V 37

RESULTS AND DISCUSSION 37

5.1 Current status and management scheme of mangroves forest management in Hai Phong 37

5.1.1 Spatial distribution and species composition of mangroves 37

5.1.2 Characteristics of some forest measurement parameters 39

5.1.3 Management scheme and policies related to mangroves forest management in Bang La and Dai Hop 42

5.1.4 Current status map of mangroves in the study areas 44

5.2 Estimation of biomass, above carbon stocks in Hai Phong 48

5.2.1 Biomass and carbon stocks estimation-based field survey 48

5.2.2 Construct the biomass map based on Inverse Distance Weight (IDW) 50

5.2.3 Estimation of above carbon stocks- based IDW interpolation 51

5.3 Estimation of SOC in Hai Phong 53

5.3.1 Estimation of total SOC- based IDW interpolation 53

5.3.2 Estimation of SOC- based IDW interpolation at various depths 56

5.4 Solutions for better management of mangroves in study area 59

5.4.1.Basic information about the policy for PFES 59

5.4.2 Scientific basis for PFES 61

5.4.3 Evaluating the commercial value of total carbon stocks in Bang La district and Dai Hop communes 62

CHAPTER VI 66

CONCLUSION, LIMITATIONS AND FURTHER STUDY 66

6.1 Conclusion 66

6.2 Limitations 67

6.3 Further study 67

REFERENCES 68

APPENDIX 73

Appendix 1: Pictures in the field survey 73

Appendix 2: Semi-structure questionnaire for coastal mangrove management scheme 74

Appendix 3: Coordinate of marked points 77

Appendix 4: Coordinate of marked points 78

ACRONYMS

NAFOSTED Vietnam National Foundation for Science and Technology Development

LIST OF TABLES

Table 2.1: Spectral bands for the SENTINEL-2 sensors (S2A & S2B) 5

Table 2.2 Carbon content in mangrove soil in Thailand 15

Table 2.3 Carbon content in mangrove soil in Ca Mau and Can Gio 17

Table 3.1: Satellite image 20

Table 3.2: Forest inventory form 26

Table 5.1: Forest structure characteristic of 17 plots in study area 41

Table 5.2 Accuracy assessment of different methods 47

Table 5.3 Forest structure of 17 plots in Bang La and Dai Hop commune, Hai Phong city 49

Table 5.4 Accuracy assessment of IDW method for biomass estimation 51

Table 5 5 Accuracy assessment of IDW method for Carbon stocks estimation 52

Table 5.6 The proportion of different carbon stocks depth in study area 52

Table 5.7 SOC in different plots 53

Table 5.8 Accuracy assessment of IDW method for SOC estimation 55

Table 5.9 Proportion of different SOC depth in study area 55

Table 5.10 Accuracy assessment of IDW method for SOC in difference depths 58

Table 5.11: Absorbed carbon and commercial value of study areas 62

LIST OF FIGURES

Fig 3.1 Flowchart of methodology used in this study 22

Fig 3.2 Plot layout for forest structure and soil sampling 25

Fig 5.1 Provincial institution structure for coastal mangroves management in Dai Hop and Bang La Commune 43

Fig 5.2 Current status map of mangroves extents in 2018 by using Supervised classification method 45

Fig 5.3.Current status map of mangroves extents in 2018 by using Un-supervised classification method 45

Fig 5.4 Current status map of mangroves extents by using NDVI 46

Fig 5.5 Biomass estimation based on IDW method 50

Fig 5.6 Carbon stocks of mangroves extents by using IDW method 51

Fig 5.7 Total SOC by using IDW method 54

Fig 5.8 Interpolated SOC in different soil depth 57

CHAPTER I INTRODUCTION

Climate change is now a global challenge that does not respect national borders (Beck, 2010) Human has experienced significant impacts of climate change, which include changing weather patterns, rising sea level, and extreme weather events (Patz, Campbell-Lendrum, Holloway, & Foley, 2005) The greenhouse gas emissions caused by human activities are the key factors of climate change and continue to rise to the highest level in history (Moss et al., 2010) During the pre-industrial period, the carbon dioxide concentration in the atmosphere has increased from about 280 ppm at the beginning of the period to approximately 390 ppm in 2012 (Vashum & Jayakumar, 2012) Consequently, solutions had to be found in an international frame (Altamimi, Collilieux, & Métivier, 2011) The introduction of REDD+ has eliminated global greenhouse gas emissions by building a carbon footprint in which developed countries would meet their carbon reduction goals by buying carbon credits from developing countries like Vietnam (Corbera, Estrada, & Brown, 2010)

There are many researches about the roles of terrestrial forests as a source and sink

of greenhouse gases, but recently, the attention has focused on the high rates of annual carbon sequestration in vegetated coastal ecosystems such mangroves ecosystem Indeed, the carbon sequestration in mangroves is strong and sustainable in above-ground and underground carbon sink The research has shown that the annual carbon sequestration in coastal mangrove forest was much higher than in the same latitude of tropical forests (Pham, Yoshino, & Bui, 2017) However, the carbon sequestration differs significantly between live biomass and sediment By measuring soil carbon in the Indo-Pacific region, scientists found that organic-rich soils ranged from 0.5  m to more than 3  m depth and accounted for 49–98% of carbon storage in these systems (Donato et al., 2011) Moreover, the coastal mangrove forests are extremely productive ecosystems that provide numerous

goods and services, both to the marine environment and people such as (1) their nursery function, (2) shoreline protection, and (3) their land-building capacity (Donato et al., 2011)

Hai Phong is a city in the North East of Vietnam that had 4.742 ha of mangrove areas (in 2012) with 125 km coastline long (Pham & Yoshino, 2016) Rising sea levels and tropical cyclones associated with climate change, which are forecasted to become more severe due to the impact of climate change in not only Hai Phong but Viet Nam (Engels et al.) With its natural conditions, Hai Phong is considered as great potential capacity to build the planning, restoration, and development of mangroves, then, promote local people’s livelihood However, due to complex terrain, there have been a few comprehensive researches and information about mangroves forests in the study sites

Recently, with the development of remote sensing and image interpretation technology, users are enables users to capture, store, analyze, and manage spatially referenced data of the different objects in the Earth surface In addition, the remote sensing technology has been a powerful application in investigating the change in forest and assessment in the mangrove carbon sequestration Previous studies have shown that the accumulation of carbon estimates is the most accurate when performed on the domesticated forest (Hanh, 2016), based

on local conditions, which was very difficult to do because the regenerated species were intermingled However, the potential contribution to GHG fixation and storage by these ecosystems becomes obviously, but the comprehensive study about the exact amount of stored carbon is limited and still an attractive area of research, especially in Vietnam Moreover, the management does not have practical and scientifical significance with the development, protection, and management of mangrove resource Thus, this study was conducted primarily for the purpose of estimating the accumulation of above and underground carbon stocks of

Sonneratia Caseolaris and Kandelia Obovata in mixed plantation forest in Hai Phong city

CHAPTER II LITERATURE REVIEW 2.1 GIS and satellite image

2.1.1 The concept of GIS, remote sensing and GPS

Remote sensing: is the process of acquiring information about an object or

phenomenon without making actual physical contact with it, as opposed to onsite observation or onsite sensing This often requires the use of aerial sensor technologies such

as those used in reconnaissance airplanes and satellites in order to detect and analyze objects on the Earth, usually on the surface

GIS (Geographic Information System) that origin from three concepts geography,

information, and system

“Geography”: is related to spatial characteristics They can be physical, cultural,

and economic and so on in nature

“Information”: refers to data managed by GIS It is the data about attributes and

space of the object

“Systems”: is a GIS system constructed from modules Creating modules helps

conveniently in management and consolidation

GPS (Global Positioning System): is a satellite navigation system used to determine

the ground position of an object GPS technology was first used by the United States military in the 1960s and expanded into civilian use over the next few decades

NDVI (Normalized Difference Vegetation Index): a numerical indicator that uses

the visible and near-infrared bands of the electromagnetic spectrum, and is adapted to analyze remote sensing measurements and assess whether the target being observed contains live green vegetation or not NDVI is based on the principle of spectral difference that based on strong vegetation absorbance in the red and strong reflectance in the near-infrared part of the electromagnetic spectrum (Chellamani, Singh, & Panigrahy, 2014)

Supervised classification method: which is the user specifies the various pixels

values or spectral signatures that should be associated with each class This is done by selecting representative sample sites of known cover type called Training data The computer algorithm then uses the spectral signatures from these training areas to classify the whole image Ideally the classes should not overlap or should only minimally overlap with other classes (Liu, 2005) Supervised classification requires close attention to development of training data If the training data is poor or not representative the classification results will also be poor

Un-supervised classification: 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 (Wang & Cheng, 2010)

2.1.2 Sentinel-2A satellite image

Sentinel-2A is an Earth observation mission developed by ESA as part of the Copernicus Programmed to perform terrestrial observations in support of services such as forest monitoring, land cover changes detection, and natural disaster management In addition, it consists of two identical satellites built by Airbus DS, Sentinel-2A and Sentinel-2B, with two additional satellites being constructed by Thales Alenia Space The two satellites will work on opposite sides of the orbit The launch of the first satellite, Sentinel-2A, occurred 23 June 2015 at 01:52 UTC on a Vega launch vehicle Sentinel-2B was launched on 7 March 2017 at 01:49 UTC, also aboard a Vega rocket

The Sentinel-2A mission has the following capabilities:

Multi-spectral data with 13 bands in the visible, near infrared, and short wave infrared part of the spectrum

Systematic global coverage of land surfaces from 56° S to 84° N, coastal waters, and all of the Mediterranean Sea

Revisiting every 5 days under the same viewing angles At high latitudes,

Sentinel-2 swath overlap and some regions will be observed twice or more every 5 days, but with different viewing angles

The spatial resolution of 10 m, 20 m, and 60 m

290 km field of view

Free and open data policy

The Sentinel-2 satellites will each carry a single multi-spectral instrument (MSI) with 13 spectral channels in the visible/near infrared (VNIR) and short wave infrared spectral range (SWIR), as follows:

Table 2.1: Spectral bands for the SENTINEL-2 sensors (S2A & S2B)

Bandwidth (nm)

Central wavelength (nm)

Bandwidth (nm)

Spatial resolution (m)

- Sets 432-RGB color: The color combination is good-looking, clear water and plants layer and can identify with the water by blue It is the method using a combination

of false color to distinguish vegetation and aquatic systems

In this study, the true color of combinations 432-RGB was used, then this will be easier to see color combinations for us to interpret the transportation, residential, roads, and the mangroves Moreover, it is easy for the devices to filter out because they are colors close to the human eye

2.2 Overview of estimating of biomass and above carbon stock by using remote

sensing

2.2.1 In the world

The method uses electromagnetic radiation as a means to investigate the characteristics of the object (Lillesand & Kiefer, 1994) It has been a valuable source of information for many centuries and will be an important source of information in the future So far in the world, there is a lot of remote sensing data used in forestry, some of

the images are commonly used today such as scientific satellite images, SPOT satellite images, LANDSAT satellite images, MODIS satellite images, etc

In a research of Sandra Brown in 2002 have shown the current status and future challenges of measuring carbon in forest and assert that future measurements of carbon storage in forests may rely more on remote sensing data, and new remote data collection technologies are in development (Brown, 2002)

The current trend is to use remote sensing images not only to map overlays but also

to monitor forest inventory factors including density, stock, biomass, forest carbon The IPCC (2003) argues that the remote sensing method is particularly suitable for land use change analysis, land use mapping, forest carbon estimation, and aboveground biomass monitoring This method provides complete and available reference data including forest resource factor estimates (Tanabe & Wagner, 2003)

The Kyoto Protocol requires that signatory countries reduce their human-induced emissions of CO2 by at least 5% below their emission levels of 1990 by 2008–2012 Then all of the member countries must estimate above carbon stocks in 1990 and any changes since 1990 from all afforestation, reforestation and deforestation activities Therefore, they must estimate carbon stocks in 1990 and any changes since 1990 from all afforestation, reforestation and deforestation activities In the UK, although some data are already available, the Protocol will require additional monitoring However, to address this, the research of Genevive in 2004 provides a quantitative assessment of remote sensing approaches for: (1) land cover discrimination to monitor deforestation; and (2) above-ground forest carbon stocks estimation (Patenaude, Milne, & Dawson, 2005) The research stresses the need for a synergetic use of approaches and for the launch of satellite missions designed especially for terrestrial carbon stock monitoring and also highlight future requirements for improving the current forest inventory scheme

In 2005, the report of Dengsheng asserts that remotely sensed data have become the primary source for biomass estimation In the document, his literature review has demonstrated that biomass estimation remains a challenging task, especially in those study areas with complex forest stand structures and environmental conditions Furthermore, the combination of spectral responses and image textures improves biomass estimation performance More researches are needed to focus on the integration of the use of multi‐source data, and the selection of suitable variables and algorithms for biomass estimation at different scales (Lu, 2006)

A recent research about Mangroves biomass by using sentinel 2 of Jose Alan in

2017 aimed to demonstrate encouraging results in biomass mapping of mangroves and other coastal land uses in the tropics using the freely accessible and relatively high-resolution Sentinel imagery As a result, the model based on biophysical variable Leaf Area Index (LAI) derived from Sentinel-2 was more accurate in predicting the overall above-ground biomass In contrast, the model which utilized optical bands had the lowest accuracy Overall, Sentinel-1 SAR and Sentinel-2 multispectral imagery can provide satisfactory results in the retrieval and predictive mapping of the above-ground biomass of mangroves and the replacement non-forest land uses, especially with the inclusion of elevation data (Castillo, Apan, Maraseni, & Salmo III, 2017)

In India 2007, an national-level carbon databank is envisaged for all types of forest

in India to study the temporal change and carbon sequestration potential for better management of forests As a pilot study, carbon stock in a natural forest area of Kolli hills, part of the Eastern Ghats of Tamil Nadu, India has been estimated using geospatial technology The total biomass, both above and below ground, is calculated and the total carbon stock estimated Likewise, the sequestered soil organic carbon (SOC) is also estimated The biomass carbon estimated is 2.74 Tg and the soil carbon is 3.48 Tg The lesser SOC indicates that the forest area is severely affected by degradation due to various

need-based forestry practices and anthropogenic disturbances (Ramachandran, Jayakumar, Haroon, Bhaskaran, & Arockiasamy, 2007)

Brown, (2002) argues that future forest carbon stock measurements may be based only on remote sensing data with new techniques by growing satellite imagery (Brown, 2002) Although biomass cannot be measured directly in space, remote sensing data is related to biomass measured directly on the ground so that forest carbon biomass can be estimated from this relationship by mathematical models (Change, 2003; Dong et al., 2003)

With the need for rapid carbon sequestration in the forest to participate in the forest environmental services payment scheme, the World Agroforestry Center (Van Noordwijk

& Hairiah, 2007) has developed methods for forecasting carbon sequestration through land-use change monitoring by remote sensing analysis, biomass sample plot design and cumulative carbon estimation These methods should be inherited and considered more appropriately applied to the forest ecosystem of Vietnam, in which the study aims to establish a sample plot for collecting biomass data, the amount of carbon accumulated with the kernel Forest inventory, ecology is the scientific basis and easy to apply (Nguyễn, 2006)

The Sentinel-2A satellite was successfully launched on 23 June 2015, up to now,

innovative wide-swath and high-resolution imager is going to offer unprecedented perspectives on our land and vegetation In 2016, a study of Markus was published in aim to assess the suitability of Sentinel-2 data for typical land cover classifications in agriculture and forestry using a supervised Random Forest (RF) classifier The two cases study were in summer crop and deciduous and coniferous tree species in Germany The Sentinel-2 data assessment, crop and tree species maps were produced at 10 m spatial resolution by combining the ten S2 spectral channels with 10 and 20 m pixel

size As a result cross-validated overall accuracies ranged between 65% (tree species) and 76% (crop types) (Immitzer, Vuolo, & Atzberger, 2016) This result has shown a very high applicability of sentinel satellite image in order to detect and monitor the vegetation cover In addition, the study confirmed the high value of the red-edge and shortwave infrared (SWIR) bands for vegetation mapping Also, the blue band was important in both study sites

2.2.2 In Vietnam

In Vietnam, remote sensing application in the forestry sector has been applied for a long time by the Forest Inventory and Planning Institute to map the forest status and store the map database in GIS software Previously, the investigation used mainly Landsat imagery, which recently used higher resolution images such as sentinel, SPOT4 and 5 However, the use of the image is primarily a matte mapping, with an image-visual interpretation method in combination with GCP (Ground Control Points) training sites for use of verified image classification Database mapping is mainly stored in MapInfo software with VN2000 coordinate system At the provincial level, there are no national regulations for the use of remote sensing imagery in forest classification, estimation of reserves, biomass, and carbon through photos Vietnam ratified the United Nations Framework Convention on Climate Change on 16 November 1994 and the Kyoto Protocol

on 25 September 2002, which is considered one of the most active countries in the world to enter the Kyoto Protocol at the earliest However, in the area of research on Clear Development Mechanism (CDM), studying the carbon sequestration of forests, calculating the value of forests is a relatively new issue which has been studied in recent years Forest carbon sequestration is mainly focused on plantation forest species to participate in the CDM (Zomer, Trabucco, Bossio, & Verchot, 2008)

Ngo Dinh Que (2005), when researching and developing criteria for afforestation under the clean development mechanism in Vietnam, has assessed the actual CO2

absorption capacity of some plantation species in Vietnam Acacia, Acacia, A auriculiformis and Uro in different ages The results showed that the CO2 absorption capacity of different stands, depending on the yield of the stands at certain ages To accumulate about 100 tons of CO2 per hectare, pine needles aged from 16 to 17, pine and pine blossom at age 10, acacia hybrid 4 -5 years, A mangium 5 - 6 years, year old This result has been very important as a basis for the zonation planning and development of CDM reforestation projects The author has developed a correlation-regression equation between the annual CO2 content absorbed by wood yield and biological productivity From that, we can calculate the actual CO2 absorption capacity in our country for 5 species

N.T.H.Hạnh (2017) carbon quantification in mangrove forest planted in the North Coast of Viet Nam published at the Natural Science and Technology Publishing House The research has shown carbon sequestration in mangroves and has developed a model for calculating carbon on and under the ground for some of the plant species characteristic of the mangroves, thereby evaluating the cumulative potential carbon of different plants in the mangrove forest

In recent years, there have been some studies on forest carbon mapping as studied

by K.T.T Ngoc and T.T.Kien (2013); K.T.T Ngoc and T.T.Kien (2013): Spatial Mapping

of Mangrove Ecosystem Services in Ca Mau The results showed that the total above carbon stock in 2005 was higher than that in 2010, which has correlated with the decline of mangrove in 2010 compared to 2005 due to the conversion of forest land into aquaculture

Pham Van Cu and Le Quang Toan (2011), the results showed that the application

of RADAR data in band C and field data to calculate mangrove forest biomass in the Northern Delta is feasible and for the main relatively high for mangrove forests with a biomass value of fewer than 150 tons/ha

N.T.H.Hạnh (2009) research on carbon sequestration of Trang (Kandelia Obovata)

planted in coastal Giao Thuy district, Nam Dinh province (Ph.D thesis in Hanoi Pedagogic

University) The subject has given the carbon calculus for Trang, and the general model for quantifying carbon stocks for mangroves

Tran Thi Bich Thuy (2013) studied the environmental movement mangrove areas Beach Mac-Dinh Vu Family, Hai Phong using remote sensing technology This result indicated that besides normal classification method based on electromagnetic spectrum values of the objects on samples of mangrove vegetation cover when combined classification with NDVI will give us better results, accuracy is also higher

Nguyen Hai Hoa (2016) researched about use of remote sensing data to conduct the

biomass and carbon stock of Acacia hybrid in Yen Lap district, Phu Tho province The

above-ground dry biomass of plantation forest was estimated from 147÷192 ton/ha at the

trees was 296.64 ton/ha which create a good base for PFES and provide sustainable local livelihood (Nguyen, 2016)

Tran Quang Bao (2013) researched about the estimation of biomass and carbon stock of different forest types in Kim Boi district, Hoa Binh province, as a combination of remote sensing and field survey, the total carbon absorbed by the forest in Kim Boi district

is 2.3Mton The highest carbon storage is in medium forest accounting for 68%; fallow land and regeneration forest account for 24% and the rest is grassland, agriculture, and plantation (Tran, 2013)

2.2.3 Method to estimate above carbon stocks and biomass in previous studies

The concept of biomass is defined as all living and dead organic matter in trees and below ground (Brown, 1997; Ponce-Hernandez, 2004)

Biomass is the unit for assessing the productivity of a stand On the other hand, to obtain data on carbon sequestration, capacity, and dynamics of forest carbon sequestration, one must calculate the biomass of the forest Therefore, the survey of biomass is also an

investigation into the absorption of forests (Ritson & Sochacki, 2003) Methods for determining biomass and soil carbon sequestration are presented below

2.2.3.1 Estimation forest of above carbon stocks and biomass-based biomass density

Total biomass on the ground can be calculated by multiplying the area of a stand with the corresponding biomass density Carbon is usually calculated from the biomass by multiplying the conversion factor by a factor of 0.5 Therefore, choosing the conversion factor plays a very important role in the accuracy of this method The biomass density of the forest depends mainly on the composition of the tree species, soil fertility, and forest age Due to the large variability of this method, it is often used for estimation in rapid national forest inventory

2.2.3.2 Estimation forest of carbon stocks and biomass-based forest inventory

Investigating the biomass and carbon sequestration of forests based forest inventory

is a directly measurement in a numbers of plot which the sample size is large enough for different forest types to give the reliable results In addition, on surveyed, trees with no commercial value or small trees are not often measured

2.2.3.3 Estimation forest of carbon stocks and biomass-based on-field measure

Most of the researches so far on biomass and carbon sequestration are based on the results of individual tree studies, including the carbon content in parts of the plant In this method, the biomass of individual trees is determined from its relationship with other survey factors of individual trees such as height, the diameter of the breast, cross-section, volume or combination of these factors of the tree

Determination of biomass on the ground for mangrove trees using (Ritson & Sochacki, 2003)

AGB=0.251* * Where: AGB: above ground biomass (kg); DBH: diameter at 1.3m (cm); : wood

trees (AGC) is calculated using the default coefficient 0.47

Convert from carbon stocks (ton/ha) to CO2 (ton/ha):

C*44/12 (Hanh, 2014)

Estimation of biomass, carbon stocks in Vietnam:

The model of Associate professor Bao Huy (2009) calculated carbon in average tree trunks (stem, bark, leaf, and stem):

AGB=0.0428*DBH 2.4628 , R 2 =0.9378 (Hanh, 2014)

Where: AGB: above ground biomass (kg); DBH: diameter at 1.3m (cm)

The conversion from biomass to carbon is calculated by multiplying of biomass with 0.5 (Gifford 2000)

AGC=AGB*0.5 (kg) (Hanh, 2014)

Limitations of the biomass estimation method:

- The definition of DBH varies widely between countries, for example Australia (1.3m); New Zealand (1.4m), United States (1.37m), Vietnam (1.3m),

- Selectively measure the sample plot

- Not enough samples needed

- Relational Model: the subjective tendencies in choosing mathematical models often do not provide the best accuracy for estimation

This method is very popular in the world, so it is important to build relationships in the stands to determine the carbon sequestration of the forest

2.3 Overview of estimating soil organic carbon by using remote sensing

2.3.1 In the world

deeply on the carbon cycle in tropical coastal ecosystems, the role of mangroves in carbon sequestration in soil and in CO2 reduction plants - One of the major greenhouse gases Studies by Batjes, et al (2001), the carbon sequestration of mangrove in Senegalese

mangroves and results in the accumulation of carbon in mangrove soil is 90 - 257 tons/ha/year In 2003, Bouillon S et al studied carbon stocks accumulated in mangrove sediments in the Godavari River deltas, India, and southwestern Srilanka, indicating that the carbon content accumulated in the mangrove sediments 0.6 to 31% dry weight, sometimes up to 75%

In 2000, Fujimoto K and his colleagues studied some mangroves in Thailand and calculated carbon content in soil at different depths:

Table 2.2 Carbon content in mangrove soil in Thailand

Study site Forest type Soil depth (cm) Total carbon

The results of Table 2.2 showed that the amount of carbon stored in mangrove soil

decreases with the depth of soil due to the sulphation of organic matter and the anaerobic respiration of the soil The amount of carbon accumulated in the Khlong Thom mangrove

forest at depths of 0 cm - 90 cm ranged from 464.7÷627.0 ton/ha, at depths of 0 - 230 cm ranged from 1093.5÷1126.1 ton/ha, while in satin mangrove soil at depth (0cm - 150cm), the accumulated carbon content ranged from 218.4÷460.1 ton/ha, in depth (0cm - 210cm) Between 460.1÷633.9 ton/ha At the same time, Fujimoto's research also shows that the amount of carbon stored in mangroves depends on the type of forest The domesticated

Rhizophora Apiculata has higher carbon sequestration than other forest types The results

of Fujimoto's research are consistent with the results of Nguyen Thanh Ha et al (2002) The amount of carbon stored in the soil in some mangroves in southern Thailand was

19.5÷11,881 ton/ha with the highest value found on R.Apiculata forest The high

productivity of old mangrove forests indicates the importance of mature forest for term accumulation and storage (Kuenzer, Bluemel, Gebhardt, Quoc, & Dech, 2011)

long-The results also show that carbon sequestration in mangroves depends on species Studies by Matsui N et al (2000) on carbon sequestration in mangroves in southern Sawi

of southern Thailand were estimated at 1208 ton/ha (up to 8.5 m depth) The organic

carbon content of Acrostichum sp with a depth of 40 cm was 347 ton/ha, Ceriops sp with

45 cm in depth was 312 ton/ha, Rhizophora sp with a depth of 40cm was 312 ton/ha,

Avicennia sp with a depth of 50cm was 45 ton/ha The organic carbon content of R stylosa

in Australia ranged from 140 to 330 ton/ha and A.Marina from 120 to 360 ton/ha (Alongi,

2003)

2.3.2 In Viet Nam

In Vietnam, the researches on carbon sequestration in the mangrove forest are not popular In 2000, Fujimoto K and colleagues also studied the carbon footprint of mangrove forest and plantation forests in Ca Mau and Can Gio, southern Vietnam

Table 2.3 Carbon content in mangrove soil in Ca Mau and Can Gio

suggest that the carbon stored in the forest land decreases with the depth of the soil, which

is due to organic sulfate and anaerobic respiration of the soil

Nguyen Thi Hong Hanh (2009) with a doctoral dissertation "Research on the

accumulation of carbon of Kandelia Obovata Sheue, Liu & Yong, coastal plantation in

Giao Thuy district, Nam Dinh province" showed the SOC in soil at 0 - 100 cm depth increases with age (Hanh, 2015) The lowest forest yield was 69,337 ton/ha, the 5-year-old forest was 76,058 ton/ha, the 81-year-old forest was 81,644 tons/ha and the 98,815 ton/ha

was 8 years old 9-year-old forest accumulated 108,043 ton/ha The results also show that the accumulation of carbon and nitrogen in the soil depends on many factors such as the age of the forest, the type of crop, the resolution of the organic matter in the soil and the tidal inundation In particular, root biomass and frequent flooding of the tide are important factors affecting the amount of carbon stored in the soil, while the amount of nitrogen accumulated in the soil depends primarily on sedimentation and sedimentation

In 2014, to compare the results of carbon sequestration and the effects of factors on

the accumulation of Sonneratia caseolaris and other forest species, Nguyen Thi Hong Hanh et al Study on the accumulation of carbon in Sonneratia caseolaris at 4,3,2 years old

in Nam Hung commune, Tien Hai district, Thai Binh province Research results show that the amount of carbon stored in forestland decreases with the age

To assess the carbon sequestration of mixed mangrove plantation of two mixed species, from June 2013 to December 2014, Nguyen Thi Hong Hanh and her colleagues

conducted a quantitative study of carbon in the mixed forest Kandelia Obovata and

Sonneratia caseolaris at 13 years old, 11 years old and 10 years old in Nam Phu commune,

Tien Hai district, Thai Binh province The results show that the amount of carbon stored in the forest depends on the species, age and density of the mangroves

Most recently, in 2015, Nguyen Thi Hong Hanh and her colleagues continued to

study carbon accumulation in Kandelia Obovata forest at 13, 11, 10 years old planted in

Giao Lac commune, Giao Thuy district, Nam Dinh province Research results show that plantation forests affect the accumulation of carbon in the soil Carbon sequestration in the soil is not only dependent on age, tidal inundation, but also on species, tree density and natural conditions Therefore, forest carbon sequestration is a process that accumulates over time, tends to increase with the development of forest trees

CHAPTER III STUDY GOAL, OBJECTIVES AND METHODOLOGY 3.1 Study goal and objectives

Objective 1: To investigate the current status and management schemes of coastal

mangroves in selected coastal sites of Hai Phong city

Objective 2: To estimate the biomass, carbon stock and SOC based on a field

survey

Objective 3: To construct thematic maps of coastal mangrove biomas, carbon stock

and SOC remote sensing data in selected coastal sites of Hai Phong city

Objective 4: To propose solutions to enhance management of coastal mangroves

based on carbon payment schemes in the study areas

3.2 Study object and scope

- Using IBM SPSS software to find out the relation between measured parameter

3.3 Materials

3.3.1 Remote sensing data

Study used Sentinal_2A satellite image in 2018 with a resolution of 10x10m to establish a map of current status, determination of biomass distribution and mangrove

forest reserve in the study areas as shown in Table 3.1

Table 3.1: Satellite image

Source: https://earthexplorer.usgs.gov/

3.3.2 Equipment

 Equipment and software used in the field:

- Administrative map of Dai Hop commune and Bang La district, Hai Phong province

- GPS Garmin 76CSX: used to determine plot coordinates

- Software: GIS software Google Earth, Excel, IBM SPSS

- Survey questionnaire, pen, ruler,

- Roll the tape measure, pile, tape measure divided into D1.3 and 1.5 m long

- Other materials: tarpaulins, paint, pens, plastic bags, plastic wires, poles and survey cards, etc

- Drill

- Weigh (2kg)

- Plastic bag and knives for soil samples

 Laboratory equipment:

- Dedicated shakers or speeders

- Drying cabinet with ventilating fan

- Sieve, with a sieve size of 0.05 mm to 2 mm

- Measuring cup, 1000ml

- The sampling mixer is a circular disk of rubber with a diameter suitable for measuring tubes, on a face with eight holes and 10 holes of diameter 4 mm to 5 mm

- Timer

- Pipette, with horizontal vacuum holes of 20 ml or 25 ml

- Pipette, capacity 5; ten; 20 ml

- Thermometer

- Cups or cans, of a capacity of 35 ml to 50 ml for drying

- Jet

- Desiccant

- Electronic scales, precision 0.01mg

- Technical scale, precision 0.1g

- Heater

- Heat-resistant glassware, with a capacity of 300 liters; 500 ml

3.4 Study contents

- Determination of the current status and management schemes of coastal mangroves

in selected coastal sites of Hai Phong city and investigation of the distribution and structures of coastal mangroves in Kien Thuy and Do Son district, Hai Phong

- Estimating biomass, carbon stock and SOC based on field survey

- Estimating biomass, carbon stock and SOC based on IDW interpolation

- Proposing feasible solutions for enhancing the efficiency of coastal mangrove classification in study areas

3.4 Methodology

Fig 3.1 Flowchart of methodology used in this study

3.4.1 Investigate current status and management scheme

Secondary data collection: To perform this study and improve efficiency, science

and inheritance of the study, the study collected secondary data from the study about remote sensing and GIS technology, and applications of it in the study mangroves in general and the training samples for classifying coastal mangroves in particular By selectively used from multiple documents, legal documents, scientific data, essays, projects, scientific research in the country and abroad to accurately give information and

Field data collection Considering of location, subjects and study method Secondary data collection and idea development

closest to reduce the amount of information included in the study but did not diminish the quality of the research

Study use inheritance to collect data with the following information:

dissertations, projects relating to the use of remote sensing and GIS technology to study forest mangrove in Vietnam and in the world

- The researching, reports, thesis relating to distribution and structure, training the sampling for classifying coastal mangrove, dynamic coastal mangrove in Vietnam and in the world

- The natural conditions, economic and social of the study area

Assessment of management by interview method:

Using the PRA method and interview tools 10 households near the study area and local officials to collect information:

Social information

Information about benefit and mangroves forest valuables

Information in management and development of mangroves forest policies

The dominant species in study area

The local people awareness and knowledge about PFES for mangroves forest The interviewees should be representative and equally distributed, the interview was taken in 6 villages of 2 communes (totally 50 people including local people in different age and local authorities)

Constructing the current status map

Survey method:

The study has conducted a preliminary investigation, selecting the field points to assess the accuracy of the classification method Random selection was chosen to determine the scores for the subjects across the study area The location of the survey sites

is determined by the global positioning system (GPSmap 78s) As a result, 70% of sites in the field for classification purposes and 30% of points used for evaluating the accuracy of the classification method 510 points were identified in the 2 communes

- Steps for mapping mangrove forest situation in the study area:

Step 1: Image processing Sentinel-2A

+ Composite bands: When collecting remote sensing images from images located

in different spectral channels and in black and white So for the sorting and image interpretation, we have to make a combination of bands to contribute to the interpretation

of images is easier

ArcGIS procedure: Arctoolbox / Data Management tools / Raster / Raster

Processing / Composite Bands

Clip: Usually a sentinel image can cover a large area of the field, so that the

volume of data is very large, cutting through the study area and reducing the time spent working as well as interpretation of a picture in an easier way

ArcGIS procedure: Arctoolbox / Data Management tools / Raster / Raster

processing / Clip

Step 2: Classifying and analyzing

Using unsupervised, supervised classification and NDVI to classify satellite imagery The result of this analysis is that satellite imagery is divided into different target groups, each containing a set of homologous spectral features that can be classified by eye before accessing the accuracy The most commonly used isotropic algorithm is used to generate large numbers of objects with similar universes Use Isodata to filter out layers of information for the level of detail of the map

Step 3: Constructing the current status map

ArcGIS procedure: Arctoolbox/ Spatial Analyst tools/ Reclass/ Reclassify

Step 4: Accuracy assessment

+ Post-Classification Accuracy: Used to evaluate the quality of the satellite image

to be interpreted or to compare the reliability with the results of different methods in the classification of remote sensing images

+ Post-Classification: After classifying, we need to carry out classifying procedures

to create layers that can be mapped out by generalizing the information

Accuracy =

3.4.2 Estimate the biomass, carbon stocks and SOC

 Method for constructing the thematic map of biomass, carbon stock and SOC

- Establishment of plot for forest inventory:

In this research, sample plots were randomly selected by the following method: + Based on the area of the plot, dividing the survey area on the map into a grid of cells per square meter

+ Number the cells in a grid from 1 to N

+ Based on the number of plots to be surveyed, using a random plot or drawing method to determine the number of plots to be surveyed

+ Finally, creating a map of the sample plots to determine the location of the plots to

be surveyed in the field

were collected in the plots included: tree high, D0, D13, canopy cover, soil sample

into 9 sub-plots as following:

Fig 3.2 Plot layout for forest structure and soil sampling

Forest inventory was conducted on sub-plots A, B, C and soil sample were taken in subplot

E For the purposes of forest structure inventory, the indicators were collected in sub-lot A,

B, C and included species, DBH, tree height (H total), height under, canopy Diameter and canopy cover

Table 3.2: Forest inventory form

Htotal (m)

DBH (m)

Canopy diameter (m)

Canopy cover (%)

DBH: Diameter at breast height 1.3 m: Calculated from the base by tape to 1.3m in struck

Canopy diameter: The diameter was measured using calibrated meters, by 2 direct W-E and N-S and measure the largest canopy of trees

Canopy Cover: Calculated by the tube had diameter 2cm and judged by the visual eye through the investigating experience

After 47 sample plots, we investigated the status of mangroves in the field through GPS to define coordinates of the points Then, we synthesize the survey was, process the data to match and put on ArcGIS to conduct mapping the current state of the status of mangrove forests in the study area

- Soil sampling and processing:

Step 1: Collecting soil sample

In the standard plot set up as the original, we conducted soil sampling at the center of the sub-plot E The soil samples in the central plot were taken at a depth of 100cm from the soil surface Each soil sample was equally divided into five layers (0 ÷ 20 cm; 20÷40 cm, 40÷60 cm, 60÷80 cm and 80÷100 cm) Then, determining the fresh soil weight (dt = g / cm3) and weighing the land mass by electronic scales for each soil layer

Step 2: Determination of carbon content in soil

In this study, because of the mangrove soil, it is necessary to remove the chloride and sulfide prior to the chemical analysis

 Qualitative test for sulfide:

Apply 5 g of the test sample to a 250-ml triangular flask, then add 250 ml of 25% hydrochloric acid

Strain the ground container on a weak (low) fire stove, and check the presence of sulfur in the soil by placing a piece of lead acetate on the pot Paper will turn black if the soil contains hydrogen sulfide, then the sample should be washed to remove sulfur from the soil before the determination of organic matter content; If no sulfur (paper does not turn black) then skip this operation

 Remove sulfur and chloride from the soil sample

Apply the soil sample to a 250-ml triangular flask Add 1mol/l sulfuric acid solution

to the sample container and mix, until no more hydrogen sulphide in the soil (when the lead acetate is acidic) until no black colored of paper is seen on the jar

Wet the filter paper and place it in a hopper of about 10 cm in diameter, so that it covers the inside of the hopper Use a jar and distilled water to transfer the soil sample in a triangular flask to the filter hopper and wash the sample with distilled water Be careful to retain all solid particles; Wash the sample until acid is out (use blue litmus paper for testing)

Continue using the distilled water on the filter hopper to remove the chloride from the soil, until it is no longer opaque when using a droplet of silver nitrate to test the filtered water at the hopper

Dry the filter paper and the soil on it for 3 to 4 hours at 450C to 500, then let it cool down in the desiccator Then, carefully peel the filter paper, collect the soil and grind it all through the 0.25 mm hole sieve (use a soft brush to sweep the filter paper, mortar and pestle to collect the fine particles of soil)

Weigh up to 0.01 g of soil mass after removal of sulfur and chloride (Gr), then mix thoroughly, sampling representative moisture, W (% mass), according to TCVN standard 4196: 2012, preserve the remaining soil in plastic bags and seal the mouth with a rubber ring to determine the organic content

Determination of the SOC based on Walkley-Black method:

Using the Ferroin indicator, during color titration the solution turns from green to

lengthen the titration step, during the titration, the solution turns from violet to green

Requirement chemicals:

K2Cr2O7 0.2N solution in H2SO4 (1: 1): Add 50 ml of a K2Cr2O7 0.4N solution

100ml, H2SO4 must be cooled to a normalized flask with water

Morn 0.2 N salt solution: Weigh 7.840 g (NH4)2SO4.FeSO4.6H2O dissolved in water,

of this Morh salt solution was titrated with 0.2 N K2Cr2O7 solution

ammonium hydrate (C12H8N2.H2O) dissolved in 100ml of distilled water

Process:

Step 1: Sample analysis

The soil is then dried at room temperature and then sieved using 0.3 mm sieve

Step 2: Determination of drying coefficient

Dry balance to constant weight then let cool and weigh (m0)

+ Approximately 1 g of soil is sieved into the weighing cup and weighed (m1)

Step 3: Titration

Using analytical balance, weigh exactly 0.5 grams of soil sample, put into a 100ml

triangular flask (avoid soil sample clinging to the jar)

shaking the flask, avoiding the sample on the flask Then, cover the hopper and cook on

matter in the sample is completely decomposed However, boiling time and boiling temperature must be controlled to avoid the decomposition of the substance

distilled water) Add a few drops of feroin indicator and titrate with 0.2N salts (the

= 0.202N) until when the solution turns from blue to red brown, the titration stops, recording the morh salt consumption

Step 4: Calculate results

Where: V0 (ml) is the volume of Morh used for the titration, V1 (ml) is the volume of Mohr used to titrate the environment

K is coefficient of dryness, conversion from dry air to dry land

1,742 is Experiment coefficient (Conversion coefficient from carbon content to organic matter content)

W (g) is the weight of soil at the beginning

By using the specific bulk density of the soil sample, the underground carbon stock

in a specific area was calculated as follows:

A(H) = a(h) x dh a(h) = c(h) x T(h)/100

C(H) = A(H) x 100

Sources: (Donato et al., 2011)

Where: dh [cm] is The depth of a soil layer

H [cm] is The soil depth

c(h) [%] is Carbon content at the depth h

T(h) [g/cm3] is The density of the soil or the volume of soil at the

depth h

a(h) [g/cm3] is The accumulation of carbon in the soil at depth h

A(H) [g/cm2] is The accumulation of carbon in the soil at depth H

C(H) [ton/ha] is The accumulation of carbon in the forest soil at depth H

The total accumulation CO2 (ton/ha) =Total SOC x 3,67 (Donato et al., 2011)

3.4.3 Construct thematic map of biomass, carbon stock and SOC

To constructing biomass, carbon stock and SOC thematic map, this study used the Inverse Distance Weighted (IDW) interpolation method incorporating with field surveys in biomass estimation and more reliable carbon stocks IDW method determines the value of the unknown by computing the weighted average of the values of known points in the vicinity of each pixel The points that are far from the point of calculation need less to affect the computed value

ArcGIS procedure: Arctoolbox / Spatial Analyst Tools / Interpolation / IDW

3.4.4 Propose the feasible solution for a better mangroves management in Bang La district and Dai Hop Commune

Field survey and data collection methods

The research is conducted by using survey methods such as semi-structured interviews and opened questions to local authorities, forest management agencies and local people As a result of interviews, we would gather information about the situation of forest resources, people's living environment, and situation of the management and protection of forest resources

The final results of field surveys and collecting documents on payments for forest