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 databased 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).

i


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 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. HaiHoa 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.
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!

ii



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

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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

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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

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ACRONYMS
DARD

Department of Agriculture and Rural Development


DBH

Diameter at Breast Height

DN

Digital Number

DONREs

Department of Natural Resources and Environment

ERDAS

Earth Resources Data Analysis System

GIS

Geographic Information System

GHG

Green House Gasses

GPS

Global Positioning System

IDW


Inverse Distance Weight

NASA

National Aeronautics and Space Administration

JRC

Japanese Red Cross

LULC

Land Use Land Cover

MARD

Ministry of Agriculture and Rural Development

MDM

Minimum Distance to Mean

MERC

Marine Environment Research Center

ML

Maximum Likelihood


MONRE

Ministry of Natural Resources and Environment

NAFOSTED

Vietnam National Foundation for Science and Technology Development

NDVI

Normalized Difference Vegetation Index

NGOs

Non-Government Organizations

ODA

Official Development Assistance

ppm

Parts per million

SOC

Soil Organic Carbon

SID


Spectral Information Divergence

RGB

Red-Green-Blue

WB

World Bank

VAFS

Vietnamese Academy of Forest Sciences

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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

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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

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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
1


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.

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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 nearinfrared part of the electromagnetic spectrum (Chellamani, Singh, & Panigrahy, 2014).
3


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
4


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, Sentinel2 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).
S2A

Band
Number

S2B

Central

Central
Bandwidth

wavelength

Bandwidth

Spatial

(nm)

resolution (m)

wavelength

(nm)

(nm)

(nm)

1

443.9

27

442.3

45

60

2

496.6

98

492.1

98

10


3

560.0

45

559

46

10

4

664.5

38

665

39

10

5

703.9

19


703.8

20

20

6

740.2

18

739.1

18

20

7

782.5

28

779.7

28

20


5


8

835.1

145

833

133

10

8a

864.8

33

864

32

20

9

945.0


26

943.2

27

60

10

1373.5

75

1376.9

76

60

11

1613.7

143

1610.4

141


20

12

2202.4

242

2185.7

238

20

Under the research objective of allocating specific spatial distribution mangroves
forest. This research only uses 4 bands in the bands of numbers limited, included band 2,
band 3, band 4, and band 8. With those bands, there is true color of combinations:
- 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

6


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) aboveground 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.


7


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 highresolution 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
8



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,
not many researches have been done with this satellite. But this satellite carries an
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
9



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
10



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
11


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
density of 33 stems/100m2 and average DBH. As a result, the average CO2 absorbed by
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

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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
density (g/cm3), proportion of trees 0.701± 0.033 g/cm3; carbon accumulation in standing
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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*DBH2.4628 , R2=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
From the beginning of the 21st century, many scientists have researched more
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
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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)

(Rhizophora apiculata

(ton/ha)

0 - 155

773.1

(Rhizophora apiculata –

0 – 175

852,0

Xylocarpus sp.)

0 – 230

1093.5


0 – 90

627,0

0 – 230

1126.1

0 – 140

496.6

(Ceriops tagal (Perrtter)

0 – 150

460.1

Robinson)

0 – 210

633.9

0 - 120

484.8

Blunme)


Khlong

Total carbon

Thom
(Rhizophora apiculata –
Lumnitzera littorea (jack)
Voigt.)
(Rhizophora apiculata –
Bruguiera cylindrica (L.)
Brume)
Satun

(Rhizophora apiculata –
Xylocarpus molucensis
(Lam) Roem.)
Source: Fujimoto, (2000).
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
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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 longterm accumulation and storage (Kuenzer, Bluemel, Gebhardt, Quoc, & Dech, 2011)
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.

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