INTRODUCTION
Mangroves are a unique and vital ecosystem found in estuaries and coastal tropical regions, known for their rich biological diversity They play a crucial role in preventing soil erosion, stabilizing sediments, and defending against wave action, thereby protecting coastlines from erosion and flooding Additionally, mangroves act as natural biofilters, improving water quality by filtering pollutants, while providing essential nutrients for aquatic species Their presence helps maintain ecological balance in coastal environments, making them indispensable for sustainable coastal management.
Research on biomass and carbon in forest ecosystems dates back to the early 19th century, focusing on understanding the carbon cycle to enhance forest productivity and management In recent years, the capacity of forests to absorb carbon has gained critical importance amid growing concerns over climate change, which poses severe threats such as disease, poverty, habitat loss, and biodiversity decline globally While climate change is a natural phenomenon, the recent surge in atmospheric CO2 emissions—primarily caused by human activities—has accelerated global warming Evidence indicates that greenhouse gases like CO2 and CH4 significantly impact climate change, and deforestation diminishes the forest’s ability to sequester carbon, worsening the problem According to Christopher Field (2007), "The amount of carbon stored in forest ecosystems accelerates as atmospheric CO2 increases," highlighting the importance of forest carbon management As of 2006, statistics from BAS show nearly 10 billion tons of CO2 in the atmosphere, a 35% increase since 1990 To mitigate and adapt to climate change, initiatives like REDD and REDD+ aim to reduce greenhouse gas emissions from deforestation and forest degradation, with Vietnam participating to conserve biodiversity, reduce emissions, and promote sustainable development through financial support from developed countries.
Carbon in tree biomass originates from atmospheric CO2 absorbed during plant growth, making forests essential carbon sinks When vegetation is lost through burning or decomposition, carbon is released back into the atmosphere as CO2 or methane Mangroves, in particular, are highly efficient at accumulating surface-level carbon compared to other forests (Ong, 1995), playing a vital role in coastal ecosystem carbon reservoirs (Kristensen, 2007) They store carbon primarily through photosynthesis, with biomass accumulation in plant parts, but also release carbon via respiration and microbial decomposition When carbon sequestration exceeds emissions, forests, including mangroves, can help reduce atmospheric CO2, making mangrove planting projects under the Clean Development Mechanism (CDM) a viable and effective climate mitigation strategy.
The study of biomass and productivity of mangroves have also havebeen interested in many countries since 1978 (Phan Nguyen Hong, 1991) (Cited from Vien Ngoc Nam) [2]
Numerous researchers have conducted large-scale and national studies on biomass, including Clough, BF, Scott, K (1989), Clough, BF (1998), and Clough, BF, Dixon, P., and Dalhaus, O (1997) In Australia, extensive research focuses on mangrove biomass, particularly studies evaluating the correlation between variables to assess the regression of above-ground biomass in mangrove trees (cited from Phan Nguyen Hong) Additionally, research by Christen, B (1978), and Ong, J E et al., has explored both natural and plantation mangrove biomass, providing valuable insights into biomass distribution and estimation methods.
Research on mangrove biomass highlights the significant carbon sequestration potential of these ecosystems, with studies indicating that mangrove forests, such as Rhizophora apiculata, store substantial amounts of carbon both above and below ground The Center for International Forestry Research (CIFOR) reports that mangrove ecosystems in the Indian-Pacific region serve as carbon sinks twice as effective as rainforests and temperate forests In Thailand, soil carbon accumulation ranges from 19.5 to 1158.1 tons per hectare, influenced by flooding, forest age, and species composition Indonesian studies in Tanjung Puting, Segara Anakan, and Bunaken reveal carbon storage values from 437 to 2,186 tons per hectare, with underground carbon (soil and roots) accounting for 72%–99% Additionally, mangroves exhibit the highest carbon stocks at approximately 987 tons per hectare, followed by average mangroves at 623 tons per hectare, and low mangrove areas at 381 tons per hectare, underscoring their critical role in climate change mitigation.
Scientific studies indicate that mangrove forest biomass can store approximately 177 tons of carbon per hectare, highlighting the significant role of these ecosystems in carbon sequestration However, most research focus on quantifying total ground biomass at the forest stand or type level, leaving a gap in species-specific data Different mangrove species and locations exhibit unique characteristics that influence carbon storage capacity, emphasizing the need for more targeted, species-specific studies to better understand their ecological and carbon storage potential (Cited from Vien Ngoc Nam)
Several studies in Vietnam have explored biomass and carbon accumulation in mangrove forests For instance, Phan Nguyen Hong and Nguyen Hoang Tri (1984) evaluated plant biomass and primary productivity in Ca Mau mangrove forests, highlighting the significance of biomass assessment Further research by Phan Nguyen Hong (1991) examined terrestrial biomass in 10-year-old mangrove plots subjected to herbicide treatment In addition, a review by Nguyen Hoang Tri et al (2001) focused on the net productivity of Kandelia candel leaves Regarding carbon accumulation, Le Tan Loi's research indicated that mangrove trees predominantly store carbon in the trunk (54-80%) and roots (14-30%), with minimal amounts in leaves (1-6%) The soil’s carbon content decreases with depth, and roots account for approximately 66% of carbon storage within trees, while litter and fallen vegetation contain less than 0.5% Specifically, the cumulative carbon stock in Rhizophora apiculata has been quantified, emphasizing its vital role in carbon sequestration in Vietnamese mangrove ecosystems.
The study highlights that the average carbon accumulation in trees reaches 97.26 t/ha, with the highest ratio of carbon stored in the stems, which increases as the diameter of the trees increases Conversely, the proportion of carbon in leaves decreases, while the ratios in stems and roots remain relatively stable Various methods can be used to measure carbon accumulation, including laboratory chemical analysis techniques such as Loss on Ignition (LOI) and Chiurin methods Additionally, recent approaches allow for calculating plant carbon content by converting biomass units and values, ensuring consistent estimation of carbon storage According to Nguyen Hoang Tri (2006), converting plant biomass into carbon forms and subsequently relating it to CO₂ is crucial for accurate carbon accounting.
Current research on biomass and carbon accumulation in Vietnam predominantly focuses on global patterns, with limited and scattered studies on Rhizophora stylosa Griff This species has been understudied and lacks a systematic approach, highlighting the urgent need for focused research to better understand its biomass and carbon sequestration potential Conducting comprehensive studies on Rhizophora stylosa Griff is essential for advancing knowledge of its role in carbon storage and supporting sustainable forest management practices.
The study area is situated in Quang Yen (Yen Hung), in the southwest coastal region of Quang Ninh Province within the Red River Delta This area is characterized by extensive mangrove forest systems that play a vital role in protecting local communities from coastal hazards Additionally, these mangrove forests significantly contribute to the livelihoods and income of local residents, supporting sustainable development in the region.
Researching and quantifying the increase in biomass and carbon accumulation in mangroves is essential for establishing fair payments for environmental services and developing effective forest management and protection strategies These efforts play a crucial role in mitigating the effects of climate change by enhancing carbon sequestration Studies focusing on biomass and carbon accumulation in mangrove forests help clarify the extent of their contribution to climate mitigation and support sustainable conservation practices.
“BIOMASS AND CARBON ACCUMULATION IN RHIZOPHORA STYLOSA Griff
FOREST AT HOANG TAN COMMUNE, QUANG YEN TOWN, QUANG NINH PROVINCE “ being conducted in order to clarifying these issues
NATURAL CONDITIONS OF STUDY AREA
Geographic and topography conditions
The study area is located in Quang Yen town, southwest coast of Quang Ninh Province, with geographic coordinates ranging from 20º45’06” to 21º02’09” North latitude and 106º45’30” to 106º00’59” East longitude It borders the mountainous Northeast region and the Red River Delta, featuring diverse topography with numerous rivers, creeks, and small islands The Chanh River, a major tributary of the Bach Dang River, runs through the area, dividing Quang Yen into two distinct parts The Ha Bac region, on the left bank of the Chanh River, is characterized by low hills and a midland landscape sloping toward the sea.
The complex topography poses significant challenges for soil improvement, as well as the construction of transportation and irrigation systems However, most of the land comprises flatlands, which are highly suitable for agriculture and aquaculture.
Climate characteristics
The study area is a coastal delta characterized by a tropical monsoon climate, experiencing hot, humid, and rainy conditions influenced by the sea It receives an average of 1,700 to 1,800 hours of sunshine annually The region's average annual temperature is 22.9°C, with highs reaching up to 38°C and lows dropping to 5°C; the temperature variation between day and night ranges from 5 to 7°C The highest average temperature is 26.3°C, while the lowest is 20.5°C Annual rainfall averages around 2,000mm, predominantly occurring from July to December, while average evaporation is approximately 980.2mm Humidity levels are high, averaging 82%, and wind speeds are generally gentle at 2.6 m/sec, with stronger winds occurring in July through October.
Quang Yen's moderate climate makes it ideal for agriculture, tourism, and resorts, attracting visitors year-round However, its flat, low-lying terrain near the seaside and estuaries makes it vulnerable to natural disasters such as hurricanes, floods, and tidal surges On average, the region experiences 3-5 storms annually, leading to significant damage to life and property The risk of inundation increases considerably when heavy rainfall coincides with high tide, especially if sea dikes are compromised, posing a serious threat to local communities.
The southern area of Quang Yen is protected by a 146 km long groyne system, including Ha Nam Island (33.6 km), Hoang Tan (53.3 km), and aquaculture swamps (79 km) However, most of these dikes have relatively low heights, except for Ha Nam, making them vulnerable to storm surges and flooding Historically, breaches in the dikes at Hoang Nam and Hoang Tan have caused significant damage and losses to local communities Strengthening and heightening these dikes are crucial to ensure better protection against severe weather events.
Land and Water resoureces
Quang Yen town covers a total natural area of 333.92 km², representing 5.5% of the province’s land area The soil characteristics primarily consist of alluvial river and sea sediments, heavily influenced by marine conditions, especially in the northern upland transition zone of the northeastern mountains The main soil types in Quang Yen include feralit soil, terraces, sloping soil, eroded soil, salinity and acidity-affected soil, coastal alluvium soils, sandy soil, and saline soil, with major groups being eroded soil, saline and acidic soil, coastal alluvium, and sandy soil.
Figure 2.1 The map of Quang Yen town, Quang Ninh province
STUDY OBJECTIVES AND METHODS
Study objectives
Objectives of this study are to
(1) Identify some biomass and carbon accumulation characteristics and of R stylosa Griff forest at the study area
(2) Determine the relationship between biomass and carbon accumulation in R stylosa Griff mangrove forests
(3) Propose rapid methods for biomass identification of the studied forest
Method
The document related to natural conditions (topography, climate, soil,…) and social economic in study sites
The results of some related studies (biomass, cumulative carbon of mangroves status)
Research methods of biomass standard tree and forest
- Sample plots were established along transect from the dyke to the sea
A total of 30 sample plots were established, each measuring 100 m² (10m x 10m) Within each plot, five sub-plots of 4 m² (2m x 2m) were designated, with four sub-plots positioned at the corners and one centrally located, enabling comprehensive data collection across the study area.
In sample plots of 100 m², identify all trees with a diameter at breast height (DBH) of 6 cm or more Conduct measured surveys to record critical data, including DBH and tree height (Ht), ensuring accurate assessment of forest structure and health.
DBH is measured by Palme ruler and Ht is measured by Blume Leiss with the distance of 15m far from the root to measuring point
Results after measuring are denoted in the Appendix 01
* Destructive sampling of the standard trees:
In each sample plot, trees are selected and removed based on their average DBH and Ht values to ensure that the remaining forest stand maintains consistent DBH and Ht characteristics This targeted approach helps optimize forest stand structure by aligning tree dimensions with the overall stand averages.
After harvesting, the collected plant materials were classified into stems, leaves, roots, and their respective parts, and their individual weights were recorded as fresh biomass These results are documented in Appendix 02, titled "Fresh Biomass of Rhizophora stylosa Griff Species." Subsequently, approximately one sample was taken from each individual part for further analysis.
0.5kg and is labeled on the bag for drying and determining the carbon content in the laboratory Trunk form, taking 01 of about 3cm thick wooden and cutting position from 1.3m and weigh up Thus, the total amount we will be 30 samples of stem , 30 samples of branch,
30 samples of leaves and 30 samples of roots
To determine the dry biomass of Rhizophora stylosa Griff., a 100g sample was collected from each part of the tree The stem samples were dried at 105°C, while other parts were dried at 85°C until reaching a constant dry weight The final dry weight measurements were recorded and documented in Appendix 03 – Dry Biomass of Rhizophora stylosa Griff Species.
3.2.3.1 Determining the biomass of individual trees and populations
To accurately convert fresh biomass into dry biomass, it is essential to determine the conversion coefficient (P) This coefficient is calculated based on laboratory analysis of biomass samples, using a specific formula that relates fresh biomass weight to dried biomass weight Establishing this conversion coefficient ensures precise measurement and assessment of biomass resources, optimizing processes such as bioenergy production and biomass management.
Where: Wki is drying up the volume of the sample i
Wti is fresh sample volume prior to drying i
-After obtaining the ratio between fresh and dry biomass (Pi) The determination of the biomass of each individual plant parts as follows:
Determining the dry biomass of individual tree parts, such as stems, branches, leaves, and roots, involves calculating the ratio of fresh biomass to dry biomass This is done by multiplying the fresh biomass of each part by the corresponding dry/fresh ratio (Pi), which accounts for the differing proportions of fresh and dry matter observed in laboratory analyses Accurate identification and measurement of these biomass components are essential for reliable assessment of tree biomass and carbon stock estimation.
The equation W(Sk, Brk, Lk, Rk) = W(St, Brt, Lt, Rt) × Pi illustrates the relationship between dry biomass and fresh biomass of individual tree parts Specifically, W(Sk, Brk, Lk, Rk) represents the dry biomass of various plant components such as stems and roots, while W(St, Brt, Lt, Rt) denotes the fresh biomass of these tree parts This formula highlights how dry biomass can be calculated by multiplying the fresh biomass by a specific coefficient, Pi, emphasizing the importance of understanding biomass conversion for accurate ecological measurements.
Dry biomass of individual tree: dry biomass of individual trees is calculated by the total biomass of individual plant parts (including dry biomass of stem,branch, leave, root)
The biomass of the populations is calculated by the total biomass of the trees standards and 1 ha Calculate the density of the forest: 10000
3.2.3.2 Determine the amount of carbon accumulation
The Japan International Forestry Promotion and Cooperation Center (JIFPRO) estimates accumulated carbon based on dry biomass, applying a conversion factor of 50% Specifically, the amount of carbon stored is calculated by multiplying the dry biomass by 0.5, which represents 50% of the dry biomass volume (Trinh Xuan Thanh, Do Huu Thu) This method provides an effective way to quantify the carbon sequestration capacity of forestry resources for environmental impact assessments and carbon accounting.
3.2.3.3 Developing a relationship model between biomass and carbon accumulation capacity
This model is designed to estimate biomass and carbon accumulation for individual trees by leveraging the strong relationship between biomass and the plant growth factor It aligns with natural growth patterns and utilizes interpolation techniques to simplify the measurement of complex indicators, making the assessment of biomass and carbon storage more accurate and efficient.
The model is based on the equation to apply analytic trees to estimate biomass and some other factors that are not cut down the trees
Database to establish the correlation model for individual trees including DBH data (DBH) total fresh biomass, dry biomass total, total carbon accumulation
When utilizing SPSS 16.0 software for selecting equations, it is essential to prioritize models with the highest correlation coefficient and the smallest error to ensure the most accurate predictions This approach is most effective when analyzing the existence of the regression equation and when the regression coefficients are statistically significant at a probability level of 0.05, which is the default setting in SPSS 16.0 These relationships are crucial for establishing the reliability and validity of the regression model, leading to more precise and meaningful analytical outcomes.
- The relationship between the total dry biomass of individual trees to survey factors: DBH
- The relationship between total carbon accumulation of the individual tree survey factors: DBH
The correlation equation illustrating the relationship between variables is established using the Analyze > Regression > Curve Estimation command in SPSS The study tests various correlation functions, including Linear and Logarithmic models The best-fitting equation is determined based on the highest correlation coefficient and the lowest standard error, ensuring accurate and reliable results.
3.2.3.4 Recommended identified some applications faster biomass estimation
From the results of the study, subjects given to conduct the process for determining the biomass of important issues are:
- Construction of conversion coefficient from fresh biomass into dry biomass, dry biomass to accumulate carbon
- Based on the correlation equation established application proposed equation to determine the biomass.
RESULTS AND DISCUSSIONS
Biomass characteristics of individual tree and populations
Forest biomass results from the growth and development of individual trees within forest populations, serving as a fundamental measure for assessing overall biomass Studying individual tree biomass is essential for understanding population characteristics and biomass estimation Each tree, as an individual, contributes uniquely to the population, with biomass reflecting its growth and the synthesis of organic materials in the tree Furthermore, tree biomass is closely related to key physical attributes such as diameter and height, which are important indicators of a tree’s overall size and biomass potential.
4.1.1 Structure of fresh biomass of individual trees
The analysis of fresh biomass from 30 sample trees reveals that the stem accounts for the highest proportion, constituting 55.1% of the total biomass, with a variation of 9.5-73.9% Branches contribute 23.6%, ranging from 3.4-61.9%, while leaves represent 11.1%, varying between 3-27.3% The remaining parts, including the stilt root, make up at least 10.2% Although overall biomass tends to increase with the tree's diameter, the proportion of biomass in parts such as stems, branches, leaves, and roots does not consistently increase with tree size, indicating uneven growth among different tree components influenced by population distribution.
Figure 4.1 The propotion of fresh biomass in different parts of R Stylosa Griff
4.1.2 Structure of dry biomass of individual trees
Similar to the calculation results and discuss fresh biomass, dry biomass of plant parts
The dry biomass of stems and stilt roots shows significant fluctuations, with these components comprising 89.64% of the total average dry biomass, indicating that water content in woody tissues is relatively low compared to leaves The highest proportion of dry biomass in individual trees is found in the stems, averaging 55.51% and ranging from 9.38% to 74.44%, followed by dry branches at 22.83%, varying from 0.19% to 62.5% Root biomass accounts for approximately 10.36%, with a range of 3.7% to 23.44%, while leaf biomass contributes about 11.3%, with volatility between 2.78% and 27.34%.
4.1.3 Structure of the populations biomass
Historically, the forestry industry has focused on forest productivity, measuring yield based on total standing volume per unit area throughout the forest’s life cycle Nowadays, attention has expanded to include the environmental services that forests provide, emphasizing both productivity gains and ecological value Therefore, understanding the biomass of forest populations is essential for forest managers to develop technical strategies that enhance both forest productivity and environmental sustainability.
This article presents a method to calculate individual tree biomass, including stem, branch, leaf, and root components, to estimate the overall mangrove biomass within a population By summing the biomass of all plant parts, the total biomass of the mangrove population can be determined accurately The results of these biomass calculations for the population are summarized and detailed in Table 4.1, providing valuable insights into mangrove forest biomass distribution.
Table 4.1 Structure of dry biomass populations
DBH(cm) Ht(m) Density(N/ha) Wsk Wbrk Wlk Wrk TAGB t/ha % t/ha % t/ha % t/ha % ton/ha
The results in Table 04 showthat biomass proportion of parts to the total biomass in parts large fluctuations Specific:
The dry biomass distribution within the plant populations shows that approximately 56.6% of the biomass is concentrated high on the stem, with a range from 21% to 95% Branch biomass accounts for an average of 19.5%, exhibiting variability between 4% and 58% Leaf biomass constitutes about 10.7% of the total, with a variation from 1% to 27.4% Additionally, root biomass makes up around 13.2%, fluctuating from 2.34% to 23.2%.
The average biomass of the populations is approximately 0.011 tons per hectare Tree density varies significantly and influences forest biomass, with a minimum density of 100 trees per hectare resulting in about 0.1 tons per hectare, and a maximum density of 3,900 trees per hectare reaching approximately 3.3 tons per hectare Additionally, a plant density of 3,200 trees per hectare corresponds to a biomass of 4.6 tons per hectare However, higher tree density does not necessarily equate to greater biomass due to the spatial arrangement, which affects nutrient distribution and utilization within the forest.
In Figure 05 also shows the relative majority of biomass plant and 3 parts (branch, leaves, roots) in tiny amounts
Figure 4.3 The proportion of dry biomass parts of R Stylosa Griff population
Amount of carbon accumulation in different parts of individual trees and populations
4.2.1 Carbon accumulation in the individual parts of plants
The results indicate the distribution of carbon accumulation across different parts of the plant, as shown in the table Higher biological productivity correlates with increased carbon stocks in forest biomass and tree communities, highlighting their significant carbon absorption capacity.
On average, 3.6 cm diameter trees accumulate approximately 0.6 kg of carbon in their biomass The majority of this carbon is stored in the stem, averaging 0.33 kg per tree and representing 55.4% of the total carbon reserves Branches contribute about 0.14 kg C per tree, accounting for 23.4%, while leaves contain around 0.06 kg C per tree, or 11.1% of the total carbon The remaining carbon is stored in the roots, with an average of 0.07 kg per tree, making up 10.1% of the total biomass carbon reserve.
Table 4.2 The ability to accumulate carbon in the individual parts of plants
No DBH(cm) Ht(m) Cst Cbr Cl Cr C(AGB) kg % kg % kg % kg % kg
Figure 4.4 The proportion of accumulation of carbon stocks in individual trees
Figure 06 illustrates the distribution of carbon across different plant parts, highlighting clear differences among stems, branches, leaves, and roots The data shows that 55.4% of carbon sequestration occurs in the stem, while branches account for 23.4% Leaves contribute approximately 11.1%, and roots hold at least 10.1% of the total carbon.
4.2.2 The ability to accumulate carbon in the population
This study estimates biomass and carbon stocks within populations using the individual tree carbon estimation model The results reveal the proportion of carbon accumulated in biomass and different plant parts, with detailed findings presented in Table 4.3.
No DBH(cm) Ht(m) Density(N/ha) CSt CBr Cl Cr TAGC t/ha % t/ha % t/ha % t/ha % ton/ha
The study indicates that population density in the area ranges from 100 to 3,900 trees per hectare The carbon stored in biomass varies from 0.05 to 2.9 tons of carbon per hectare, with an average of 1.03 tons C/ha The carbon concentration within biomass is approximately 55.51%, with the main contribution coming from relative biomass at 0.6 tons C/ha Branch biomass accounts for 23.46% at 0.2 tons C/ha, while leaf biomass contributes 11.3% at 0.12 tons C/ha Remaining root biomass has a carbon amount of 0.11 tons C/ha, representing 9.73% of total biomass carbon.
The data in the chart indicates that the average carbon accumulation across the plant parts is uneven, with the stem exhibiting the highest accumulation at 55.51% This is followed by the relative parts at 23.46%, roots at 11.3%, and leaves at 9.73% These findings highlight the significant role of the stem in carbon storage, underscoring its importance in biomass and carbon cycle studies.
Correlation models between biomass and carbon accumulation capacity
4.3.1 Estimation model of the total dry biomass of individual trees
Total dry biomass is an annual measure of tree productivity at specific times, indicating the amount of plant material produced It is determined by removing water content from fresh biomass through drying samples of various plant parts such as branches, leaves, and roots Since this process can be time-consuming, mathematical modeling techniques are often used to estimate dry biomass based on easily measurable factors, allowing for quicker and efficient biomass calculations in forest studies.
From testing the correlation equation used SPSS correlation between dry biomass and survey factors D1.3 have the following results:
Table 4.4 Model estimates the total dry biomass of individual trees
It was found that the correlations are quite tight R2 = 0193 and S are not too large S form of the equation Y = a + b * X is always less than S of the equation form Y = a + b * ln
X So we use the equation Y = a + b*X in the table 07
Figure 4.6 The diagram describes the correlation between dry biomass and survey factors D1.3
4.3.2 Model estimates of carbon stocks individual tree
The correlation between carbon sequestration and survey factors, specifically Diameter at Breast Height (DBH), is detailed in Table 05 The selected models effectively illustrate the relationship between carbon storage and growth factors such as DBH, highlighting the significant influence of tree diameter on carbon sequestration potential These models provide valuable insights for understanding how forest growth metrics impact carbon storage capacity, supporting the development of sustainable forest management strategies. -Boost your SEO with expertly rewritten, concise content on carbon sequestration and DBH—let us streamline your article for clarity and impact.
Table 4.5 Model estimates of carbon stocks estimate individual tree
Figure 4.7 The diagram describes the correlation between carbon stocks and factors investigated DBH
It was found that the correlations are quite tight R2 = 0193 and S are not too large S form of the equation Y = a + b * X is always less than S of the equation form Y = a + b * ln
X So we use the equation Y = a + b * X in Table 08.
Propose solutions for quickly biomass identification
For individual trees, the application is recommended as follows:
- Identify individual tree fresh biomass under DBH indicators
- Determination of dry biomass of individual trees under DBH indicators
To determine fresh and dry biomass, we measure the growth parameter of individual trees by their Diameter at Breast Height (DBH) Using the established correlation equation between dry biomass and DBH outlined in section 3.1, we can accurately estimate biomass Additionally, applying the appropriate conversion factor allows for precise calculation of biomass, supporting effective construction financing assessments.
From fresh biomass was measurement can determine the dry biomass of individual trees through conversion coefficients Calculation results of biomass conversion coefficient shows that:
- Dry biomass of individual trees can be calculated by: Fresh biomass of individual trees can multiply with conversion ratio is 0.193
CONCLUSIONS AND RECOMMENDATIONS
Conclusion
The study examines the biomass and carbon accumulation across different parts of individual plants and populations On average, trees with a diameter of 3.6 cm store approximately 0.6 kilograms of carbon in their biomass This highlights the significant role of small diameter trees in carbon sequestration, emphasizing their contribution to overall forest carbon storage Understanding biomass distribution and carbon content in various plant components is essential for effective forest management and climate change mitigation efforts.
Total biomass and carbon accumulation exhibit significant fluctuations based on tree density, diameter, and height On average, the total biomass of the populations is 0.011 tons per hectare, with forest biomass carbon stock averaging 1.03 tons of carbon per hectare These findings highlight the importance of tree growth parameters in influencing forest carbon storage potential.
Through the process of analysis and correlation functions, we can easily estimate based either on equation: Y = 0.693X + 0.347 or equation Y = 0.833 X + 0.347
Recomemendation
- The thesis has study biomass and carbon stocks on the ground of planting 1987, have not done at differents ages to have an overview over
- The study of the relationship of biomass, carbon stocks and other growth indicators not mentioned outside the 2 parameter (DBH and Ht)
- Results identifiable only recommended for initial reference should have the research and subsequent testing conducted for different ages
- Conduct research in soil carbon stocks to work determining the amount of carbon absorbed in mangrove plantations are fuller
- Conduct research in soil carbon stocks to work determining the amount of carbon absorbed in mangrove plantations are fuller
REFFERENCES 30 APPENDIX
1 Lê Tấn Lợi (2015) “Phương pháp nghiên cứu sự tích lũy carbon trong hệ sinh thái rừng ngập mặn theo CIFOR, Trường Đại học Cần Thơ”
Nghiên cứu của Viên Ngọc Nam (2004) tập trung vào sinh khối và năng suất sơ cấp của quần thể mắm trắng (Avicennia alba BL) tự nhiên tại Cần Giờ, TP Hồ Chí Minh Báo cáo này được thực hiện tại Viện Khoa học Lâm nghiệp Việt Nam, cung cấp dữ liệu quan trọng để thúc đẩy phát triển bền vững các hệ sinh thái mắm trắng trong khu vực.
Trần Xuân Thành và Đỗ Hữu Thu (2012) đã nghiên cứu khả năng hấp thụ CO2 của rừng trồng keo tai tượng tại xã Ngọc Thanh, thị xã Phúc Yên, tỉnh Vĩnh Phúc Nghiên cứu này cho thấy rừng keo tai tượng đóng vai trò quan trọng trong việc giảm thiểu khí nhà kính và bảo vệ môi trường Kết quả cho thấy, rừng keo tai tượng có khả năng hấp thụ lượng lớn CO2, góp phần thúc đẩy các hoạt động phát triển bền vững Các kết quả từ nghiên cứu này cung cấp thông tin hữu ích để xây dựng các chiến lược bảo vệ rừng và ứng phó với biến đổi khí hậu.
4 Komiyama,A.,Ong,J.E và Poungparn ,S.(2008),”Allometry, biomass, and productivity of mangroves forests:A review, Aquatic Botany”
5 Nguyen Hoang Tri (2006).’Khôi phục bảo tồn hệ sinh thái rừng ngập mặn và cơ chế phát triển sạch., Nhà xuất bản Nông nghiệp”
Nguyễn Thanh Tiến (2012) đã thực hiện nghiên cứu khả năng hấp thụ CO2 của các trạng thái rừng thứ sinh phục hồi tự nhiên sau khai thác kiệt tại tỉnh Thái Nguyên Luận án tiến sĩ nông nghiệp này đóng vai trò quan trọng trong việc đánh giá hiệu quả bảo vệ môi trường của rừng thứ sinh tự nhiên, góp phần thúc đẩy phát triển bền vững và ứng phó biến đổi khí hậu Nghiên cứu cung cấp số liệu quan trọng về khả năng hấp thụ CO2 của các khu rừng phục hồi tự nhiên, từ đó đề xuất các giải pháp bảo tồn rừng hiệu quả hơn trong khu vực.
Bài viết của Nguyễn Thị Kim Cúc, Phan Nguyên Hồng và Hoàng Thị Sản (2015) trình bày về công tác phục hồi và quản lý hệ sinh thái rừng ngập mặn trong bối cảnh biến đổi khí hậu, nhằm nâng cao khả năng ứng phó và bảo vệ các hệ sinh thái này Nghiên cứu tập trung vào các phương pháp bồi đắp, bảo tồn và phát triển rừng ngập mặn phù hợp với diễn biến khí hậu ngày càng phức tạp, góp phần duy trì đa dạng sinh học và các dịch vụ sinh thái quan trọng Báo cáo cũng nhấn mạnh tầm quan trọng của việc kết hợp các chính sách bảo vệ môi trường với hoạt động nghiên cứu khoa học để đảm bảo sự bền vững của hệ sinh thái rừng ngập mặn trong tương lai.
8 Nguyễn Thanh Tiến (2008) ”Bài giảng Phân loại &Điều tra rừng, Đại học Nông
Nghiên cứu sinh khối và năng suất sơ cấp của rừng Đước trồng tại Cần Giờ, Thành phố Hồ Chí Minh do Viên Ngọc Nam thực hiện năm 1996, đã cung cấp những hiểu biết quan trọng về tiềm năng phát triển bền vững của loài cây này ở khu vực phía Nam.
APPENDIX Appendix 01 The mean growth parameters of trees in studied sample plots
No Plot D1.3(cm) Ht(m) Density(N/ha) Note
Appendix 02 Fresh biomass of Rhizophora stylosa Griff Species
No DBH(cm) Ht(m) Wst Wbrt Wlt Wrt Wtt kg % kg % kg % kg % kg
Appendix 03: Dry biomass of Rhizophora stylosa Griff species
No DBH(cm) Ht(m) Wsk Wbrk Wlk Wrk Wtk kg % kg % kg % kg % kg
Appendix 04 The correlation between dry biomass and survey factors D1.3 Linear
Std Error of the Estimate
The independent variable is Wtt (kg)
Squares df Mean Square F Sig
The independent variable is Wtt (kg)
Std Error of the Estimate
The independent variable is Wtt (kg)
Squares df Mean Square F Sig
The independent variable is Wtt (kg)
The dependent variable is ln(1 / D1.3 (cm))
Appendix 05 The correlation between the carbon accumulation factor of the D1.3 Linear
Std Error of the Estimate
The independent variable is Wtt (kg)
Squares df Mean Square F Sig
The independent variable is Wtt (kg)
Std Error of the Estimate
The independent variable is Wtt (kg)
Squares df Mean Square F Sig
The independent variable is Wtt (kg)
The dependent variable is ln(1 / D1.3 (cm))
Std Error of the Estimate
The independent variable is C (AGB)
Squares df Mean Square F Sig
The independent variable is C (AGB)
Std Error of the Estimate
The independent variable is C (AGB)
Squares df Mean Square F Sig
The independent variable is C (AGB)
The dependent variable is ln(1 / D 1.3 (cm))