Estimation of above ground carbon stocks of mangrove forests from remote sensing and field data in hai ha district, quang ninh province during 2016 2019

VIETNAM NATIONAL UNIVERSITY OF FORESTRY FOREST RESOURCES & ENVIRONMENTAL MANAGEMENT FACULTY ========================= STUDENT THESIS ESTIMATION OF ABOVE-GROUND CARBON STOCKS OF MANGROVE FORESTS FROM REMOTE SENSING AND FIELD DATA IN HAI HA DISTRICT, QUANG NINH PROVINCE DURING 2016 – 2019 Major: Natural Resources Management Code: D850101 Faculty: Forest Resources and Environmental Management Supervisor: Assoc Prof Dr Hai-Hoa Nguyen Student: Vu Hong Son Student ID: 1553090678 Class: K60 Natural Resources Management Course: 2015 – 2019 Advanced Education Program Developed in collaboration with Colorado State University, USA Ha Noi, 2019 ACKNOWLEDGMENTS This research is supported by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 105.08-2017.05 During the study period, I received a lot of valuable help and support that guide and inspire me to overcome all difficulties and complete my study Firstly, I would like to express my gratefulness to the School Board of the Vietnam National University of Forestry and the Faculty of Forest Resources and Environmental Management for the favorable conditions to carry out my study Secondly, I sincerely thank to Assoc Prof Dr Hai-Hoa Nguyen, my supervisor who gave me aplenty of useful guidance and advice that help me to finish my study Thirdly, I would like to express my gratitude and appreciation to the Managing Board of Forest Protection Department of Hai Ha district because of the permission and enthusiasm support for me to implement the field survey Last but not least, I am greatly thankful to my group members for accompanying me in the hard but meaningful time In short, I really thank to all people helping me to finish this study Hanoi, September 25th, 2019 Author Vu Hong Son i CONTENTS ACKNOWLEDGMENTS Chapter I INTRODUCTION Chapter LITERATURE REVIEW 2.1 Overview of coastal mangrove 2.1.1 Status and distribution of mangrove forest in the world 2.1.2 Significance of mangroves carbon stock 2.1.3 Status and distribution of mangrove forest in Viet Nam 2.1.4 Status and distribution of mangrove in Hai Ha, Quang Ninh 2.2 Application of remote sensing data and GIS to mangrove mapping and carbon stocks estimation 2.2.1 Mangrove biomass and carbon pools estimation approach in the world 2.2.2 Advantages of applying remote sensing and GIS in mangrove forest studies 11 2.2.3 Application of remote sensing data and GIS to mangrove studies in the world 12 2.2.4 Application of remote sensing data and GIS to mangrove studies in Viet Nam 14 Chapter GOAL, OBJECTIVES AND METHODOLOGY 15 3.1 Goal 15 3.2 Objectives 15 3.3 METHODOLOGY 16 3.3.1 Remote sensing data 16 3.3.2 Investigating the status of mangrove forests and management scheme in Hai Ha district, Quang Ninh province 16 3.3.3 Calculating above-ground biomass and carbon stocks of mangrove forests during 2013- 2019 in Hai Ha district, Quang Ninh province 19 3.3.4 Quantify the changes in above-ground biomass and carbon stocks of mangrove forests during 2016 - 2019 in Hai Ha district, Quang Ninh province 23 Chapter NATURAL AND SOCIO-ECONOMIC CONDITIONS 25 4.1 Study site 25 ii 4.2 Natural conditions 25 4.3 Socio-economic conditions 26 Chapter RESULTS AND DISCUSSIONS 28 5.1 Status of mangrove forests in Hai Ha district, Quang Ninh province 28 5.1.1 Spatial distribution of Hai Ha mangrove forests 28 5.1.2 Species identification in study area 34 5.1.3 Current management scheme of Hai Ha mangrove forests 34 5.2 Above-ground biomass and carbon stocks of mangrove forests during 2016- 2019 in Hai Ha district, Quang Ninh province 35 5.2.1 Above-ground biomass and corresponding NDVI value of sub-plots in 2019 35 5.2.2 Development of regression models 35 5.2.3 Above-ground biomass and carbon stocks of study area 37 5.3 Changes in above-ground biomass and carbon stocks of mangrove forests 42 5.3.1 Changes in above-ground biomass during 2016 – 2019 42 5.3.2 Change in above-ground carbon 42 5.4 Solutions to enhance carbon stocks of mangrove forests in studies sites 45 5.4.1 Mechanism and policy solutions 45 5.4.2 Technical solutions 45 5.4.3 Implementation of carbon sequestration payment mangrove forests 46 Chapter CONCLUSION, LIMITATION AND FURTHER STUDY 48 6.1 Conclusion 48 6.2 Limitations and further study 48 REFFERNCES 49 iii LIST OF TABLES Table 2.1: Mangroves extent in the world (ha) Table 2.2: The 15 mangrove-rich countries Table 2.3: Mangrove forest distribution in Hai Ha district, Quang Ninh province in 2017 Table 2.4: Mangrove afforestation projects in Hai Ha district from 1999 to 2016 Table 2.5: Allometric equations for biomass calculation of mangrove trees Table 3.1: Satellite images information 16 Table 3.2: Field investigation plots 20 Table 3.3: Field data collection tables 21 Table 3.4: Wood density of mangrove species studied 22 Table 3.5: Regression models acquired for the test 23 Table 5.1: Accuracy assessment of land cover map in 2019 28 Table 5.2: Above-ground biomass and corresponding NDVI of sub-plots in 2019 35 Table 5.3: R-square values and parameters estimated of regression model tested 36 Table 5.4: Above-ground biomass and above-ground carbon of study area in 8/2019 and 12/2016 37 Table 5.5: Money from C-PFES for above-ground carbon of mangrove forest in Hai Ha district on 12/2019 46 iv LIST OF FIGURES Fig 2.1 : Comparison of mangrove C storage (mean ±95% confidence interval) with that of major global forest domains Fig 3.1: Polygon of study area 17 Fig 3.2: Spatial distribution of field investigation plots 20 Fig 4.1: Map of study area 25 Fig 5.1: Land cover map of study area in 2019 29 Fig 5.2: NDVI values for Hai Ha mangrove forests in 2019 30 Fig 5.3: Land cover map of study area in 2016 32 Fig 5.4: NDVI values for Hai Ha mangrove forests in 2016 33 Fig 5.5: Quadratic regression model of biomass and corresponding NDVI value 36 Fig 5.6: Spatial distribution of above-ground biomass of Hai Ha mangrove forests in 2019 38 Fig 5.7: Spatial distribution of carbon stocks of Hai Ha mangrove forests in 2019 39 Fig 5.8: Spatial distribution of above-ground biomass of Hai Ha mangrove forests in 2016 40 Fig 5.9: Spatial distribution of carbon stocks of Hai Ha mangrove forests in 2016 41 Fig 5.10: Above-ground biomass changed from 2016 to 2019 of Hai Ha mangrove forests 43 Fig 5.11: Carbon stocks changed from 2016 to 2019 of Hai Ha mangrove forests 44 LIST OF DIAGRAMS Diagram 3.1: Establishment processes of land covers and NDVI map 17 Diagram 3.2: Total above-ground biomass calculation process 19 Diagram 3.3: Processes of quantifying the changes in above-ground biomass 24 and carbon stocks 24 Diagram 5.1: Mangrove management scheme in Hai Ha district, Quang Ninh province 34 v Chapter I INTRODUCTION As the result of climate change, sea level rise has been a global issue According to The Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC), global sea level will rise by up to ~60 cm by 2100 in response to ocean warming and glaciers melting However, the recently identified accelerated decline of polar ice sheet mass (Allison et al., 2009; Rignot et al., 2008; Velicogna, 2009) raises the possibility of future sea-level rise by more than m in 2100 (Pfeffer et al., 2008; Lowe et al., 2009) The coastal areas are considered as the most vulnerable areas by sea level rise (Nicholls et al., 2010) The immediate effect is that flooding occurs frequently and higher intensity, as well as saltwater intrusion of surface waters Meanwhile, longer-term effects also occur as the coast adjusts to the new conditions, including soil erosion and saltwater intrusion into groundwater Coastal wetlands such as saltmarshes and mangroves will also decline unless they have a sufficient sediment supply to keep pace with sea level rise These physical impacts in turn have both direct and indirect socioeconomic impacts, which appear to be overwhelmingly negative (Nicholls et al., 2007) With over 3,260 kilometers of coastline and about 50 percent of the population living in lowland areas (General Statistics Office of Vietnam, 2018), Vietnam is considered as one of the most vulnerable and being negatively impacted by sea level rise In 2007, the World Bank estimated that a one-meter rise in sea level could affect 10 percent of Vietnam’s population, with the GDP loss about 10 percent Mangroves are considered one of the most effective solutions to deal with the effects of sea level rise due to their important roles (Kathiresan, 2012) Specifically, mangroves have important roles in minimizing the fury of cyclones and tsunami, controlling the flood, preventing the coastal erosion, trapping the sediments and recycling nutrient Moreover, mangrove forests also play very important role in climate change mitigation due to the ability to absorbing huge amount of CO2 from atmosphere Over the past five decades, Vietnam has lost 51% (about 408.000 ha) of mangrove areas compare to 1943 (Hoa et al., 2018) Large areas of mangrove forest have been cut down and converted into other land use purposes, such as aquaculture, industrial zone, infrastructures, port, etc Moreover, several consequences of climate change, especially sea level rise and increasing salinity level, are also considered as the main factors which lead to decreasing mangrove areas and mangrove degradable in Vietnam Fortunately, there are many great efforts to increase mangrove areas from Vietnamese government and local authorized in recent years Besides, Vietnam has been supported by many organizations around the world in the restoration and development of mangroves, such as JICA, Japanese Red Cross, Denmark Red Cross, etc It is important to assess the growth of mangrove forests in order to assess the efficiency of those efforts Currently, remote sensing is very common and useful tool to detecting mangrove forests extent and calculating carbon stock There are a lot of researches working in the topic of mangrove forests and carbon stocks of mangrove forests which using the remote sensing technique These researches have been conducted in many northern provinces of Vietnam, but just a few case researches in Quang Ninh province Therefore, the research of “Estimation of above-ground carbon stocks of mangrove forests from remote sensing and field data in Hai Ha district, Quang Ninh province during 2016 – 2019” was conducted with the expectation that it could provide important data for the locality, mangrove development projects and information for future studies Chapter LITERATURE REVIEW 2.1 Overview of coastal mangrove 2.1.1 Status and distribution of mangrove forest in the world The term mangrove has been discussed by experts and scientists for many years (Tomlison, 1986) It is commonly used to identify trees and shrubs that have developed morphological adaptation to the tidal environment (e.g aerial roots, salt excretion glands and vivipary of seeds), as well as the ecosystem itself Mangrove forests are distributed in the inter-tidal region between the sea and the land in the tropical and subtropical regions of the world between approximately 30° N and 30° S latitude (Giri et al., 2010) Their global distribution is believed to be delimited by major ocean currents and the 20° C isotherm of seawater in winter (Alongi, 2009) The world's first research of the total area of mangroves was carried out by FAO and UNEP in 1980 In the report, they estimated that the total area of worldwide mangrove forests were 15,642,673 In the following years, many other studies have been conducted to estimate the area of mangroves worldwide The results of these researches were presented in the Table 2.1 Table 2.1: Mangroves extent in the world (ha) Number of countries 51 Estimated total area (ha) 15 642 673 FAO, UNEP (1981) 65 16 221 000 Saenger et al (1983) 56 16 500 000 FAO (1994) 87 19 847 861 Groombridge (1992) 54 12 429 115 ITTO/ISME (1993) 91 19 881 800 Fisher and Spalding (1993) 112 18 100 077 Spalding et al (1997) No References Source: FAO (2007) The recent research about the distribution of mangrove forests was conducted in 2010 by Giri, et al In the report, total area of mangrove forests worldwide in 2000 was estimated about 137,760 km2 in 118 countries and territories The largest extent of mangroves was found in Asia (42%) followed by Africa (20%), North and Central America (15%), Oceania (12%) and South America (11%) Approximately 75% of mangroves were concentrated in 15 countries (Table 2.2) Table 2.2: The 15 mangrove-rich countries No Country Area (ha) Indonesia 3,112,989 % of global total 22.6 Australia 997,975 7.1 Oceania Brazil 962,683 7.0 South America Mexico 741,917 5.4 Nigeria 654,669 4.7 Africa Malaysia 505,385 3.7 Asia Myanmar 494,584 3.6 Asia 480,121 3.5 Oceania Asia Papua New Guinea Region Asia North and Central America Bangladesh 436,570 3.2 10 Cuba 421,538 3.1 11 India 368,276 2.7 Asia 12 Guinea Bissau 338,652 2.5 Africa 13 Mozambique 318,851 2.3 Africa 14 Madagascar 278,078 2.0 Africa 15 Philippines 263,137 1.9 Asia North and Central America Source: Giri et al (2011) The mangrove forests of the world is less than half of what it once was (Spalding et al., 1997; Spiers, 1999) and much of what remains is in a degraded condition (UNEP, 2004) Coastal habitats across the world are under heavy population and development pressures, and are subjected to frequent storms The continued decline of the forests is caused by conversion to agriculture, aquaculture, tourism, urban development and overexploitation (Alongi, 2002; Giri et al., 2008) About 35% of mangroves were lost from 1980 to 2000 (MA, 2005), and the forests have been declining at a faster rate than inland tropical forests and coral reefs (Duke et al., 2007) Relative sea-level rise could be the greatest threat to mangroves (Gilman et al., 2008) Predictions suggest that 30–40% of coastal wetlands (IPCC, 2007) and 100% of mangrove forests (Duke et al., 2007) could be lost in the next 100 years if the present rate of loss continues Fig 5.8: Spatial distribution of above-ground biomass of Hai Ha mangrove forests in 2016 40 Fig 5.9: Spatial distribution of carbon stocks of Hai Ha mangrove forests in 2016 41 5.3 Changes in above-ground biomass and carbon stocks of mangrove forests 5.3.1 Changes in above-ground biomass during 2016 – 2019 The Fig 5.18 illustrated the change of above-ground biomass of Hai Ha mangrove forest during 2016 to 2019 and the unit was expressed by kilogram per pixel (kg/pixel) Overall, total above-ground biomass increased about 45676,9 tons and the average of increase was 21.1 (ton/ha)(Table5.3) However, it witnessed a decrease in some particular areas through the study period The decline in above-ground biomass could have been explained by the loss or degradation of mangroves In which, above-ground biomass decreased from higher to lower and to zero were the signs of degradation and loss of mangroves respectively On the other hand the increase of biomass could be understood that the mangroves have been appearing or growing 5.3.2 Change in above-ground carbon The Fig 5.19 demonstrated the change of above-ground carbon of Hai Ha mangrove forest throughout studied period The biomass and carbon of mangrove forests were positively correlated with the ratio as AGC = 0.47*AGB In other words, causes of increase and decrease of carbon are the same for biomass Therefore, total above-ground carbon increased about 21468.1 tons and average of increased was 9.9 (ton/ha) from 2016 to 2019 42 Fig 5.10: Above-ground biomass changed from 2016 to 2019 of Hai Ha mangrove forests 43 Fig 5.11: Carbon stocks changed from 2016 to 2019 of Hai Ha mangrove forests 44 5.4 Solutions to enhance carbon stocks of mangrove forests in studies sites 5.4.1 Mechanism and policy solutions Government policies play a key role in mangrove management and development However, some limitations of mangrove management in Hai Ha district, Quang Ninh province were found from this study Therefore, this study also provided some solution to solve these limitations in order to enhance carbon stocks of mangrove forest in study areas - Implementing overall investigation and assessment of mangroves, thereby developing a master plan for the mangroves in the district as soon as possible This work should be assigned to a department which has forestry expertise For the mangrove areas, which have been under the management of commune People's Committee, should be assigned to Protection Forest Management Board after completing the master plan for mangroves Thereby, the mangrove management and protection will be implemented effectively - Projects related to mangroves and conversion of mangroves to other purposes should be strictly controlled Also, regulations on Land Law, Law on Forest Protection and Development, regulations on Protection Forest Management, regulations on Alternative Afforestation and other relevant legal documents need to be strictly implemented - Natural mangrove areas should not be allocated to organizations and individuals for aquaculture and projects affecting existing mangrove ecosystems should be carefully considered Instead, mangroves can be assigned to households to protect and people can harvest valuable aquatic species from the forest 5.4.2 Technical solutions Since 1999, afforestation and development of mangroves in Hai Ha district has been concerned and this brought the economic as well as environmental benefits to local communities However, the projects have not high effective, especially in areas with difficult site conditions such as sandy soils, rocky soils, deep tidal inundations and areas with unstable foundations In addition, unsuitable seedlings are also one of the main reasons for the inefficiency in afforestation Therefore, the study has proposed a number of solutions to increase the effectiveness of afforestation projects - Mangrove seedling nurseries should be established in the local area Then, good quality mother trees will be selected to provide a stable source of seed, good quality and suitable for specific local conditions - Seedling species should be suitable with the natural conditions of each area such as tidal regime, salinity, soil properties Also, seedlings older than months should be chosen to increase survival 45 - Mixed forest should be planted instead of pure forest because the mixed forest has high density and many canopies, which will reduce the impact of waves and wind In addition, mixed forests ensure the biodiversity of the mangrove ecosystem 5.4.3 Implementation of carbon sequestration payment mangrove forests Under the Decision No 1586/VPCP-NN of the Prime Minister was promulgated on 26/02/2019, the payment of forest environmental services (PFES) for forest carbon sequestration (C-PFES) is going to be implemented in Vietnam Then, Ministry of Agriculture and Rural Development organized a workshop on 7/8/2019 on piloting for C-PFES In particular, four provinces were selected as pilot, included Quang Ninh province In addition, the payment for one ton of CO2 sequestered was set at $ 3.35, which equivalent 77 720 VND ($1 = 23 200 VND, updated to October 17, 2019) However, this payment is lower than the World Bank's expected for the North Central region ($5/ton of CO2) (World Bank, 2019) and lower than the cost of sequestering ton of CO2 into the forest ($ 11.13) Potential of C-PFES implementation in the mangrove forests of Hai Ha district, Quang Ninh province Ratio of CO2 to carbon (C) could be calculated based on the atomic weights of each molecule: = 3.67 Then, above-ground carbon could be converted to CO2 by multiply by 3.67 There by, above-ground carbon of mangrove forest in Hai Ha was: 93790.1 * 3.67 = 344209.6 (tons) Next, the amount of money from C-PFES for above-ground carbon of mangrove in Hai Ha district was estimated by multiply number of tons CO2 by the price and the results were presented in Table 5.5 Table 5.5: Money from C-PFES for above-ground carbon of mangrove forest in Hai Ha district on 12/2019 Estimated by the price of Estimated by the price of MARDa World Bankb U.S dollar 1,153,102.0 1,721,048.0 VND 26,751,970,280.0 39,928,313,600.0 Unit a CO2 price according to workshop of MARD on 7/8/2019: 3.15 USD per ton of CO2 b CO2 price according to the expected of World Bank: USD per ton of CO2 From the above results, it can be seen that the mangroves in Hai Ha district have great potential in applying C-PFES because of their large carbon stock 46 Advantages of applying C-PFES for mangrove protection and development Currently the budget for mangrove protection and development is mainly provided by the afforestation and development projects because the CPC does not have enough funds Consequently, this budget is limited and unsustainable, thus, it cannot guarantee the maintenance of mangrove protection and development In addition, there was no specific policy to encourage local people to participate in mangrove protection and development However, applying C-PFES to the mangroves in Hai Ha district can solve above issues because of its two main advantages Firstly, a large budget will be provided to the Communes People’s Committee to conduct investigations and assessments for sustainable forest protection and development Secondly, a part of the budget can be allocated to local people to encourage them to actively protect and develop forests Especially for households with difficult economic conditions, livelihoods related to harvesting mangrove products Since then, it can both prevents deforestation and provides additional income for people 47 Chapter CONCLUSION, LIMITATION AND FURTHER STUDY 6.1 Conclusion Total mangrove area of Hai Ha district, Quang Ninh province in 8/2019 was about 1376.1 and 1241.8 in 12/2016 It has been managed directly by commune people's committees with the assistance of Forest Protection Department of Hai Ha district Next, the land cover map, which included land cover types as water, bare land, mangrove and other vegetation, in 2019 and in was created with the accuracy of 84.78% Then, total AGB and total AGC of mangrove were 199553.4 tons and 93790.1 tons in 2019, respectively These results in 2016 were 153876.5 tons and 72321.9 tons In additional, there was a fluctuation of above-ground biomass in both 2019 and 2016 among the sites Overall, total AGB and total AGB were increased about 45676.9 tons and 21468.1 tons from 12/2016 to 8/2019 correspondingly However, they witnessed the decrease in some particular areas through the study period due to deforestation and forest degradation Thus, the study suggested some solutions to enhance biomass and carbon stock of Hai Ha district Of which, mechanism and policy solutions, technical solutions and approach to CPFES solutions were proposed 6.2 Limitations and further study Total biomass and carbon of mangrove in Hai Ha district has not been overall estimated because the study just focused on above-ground carbon, meanwhile the dominant of carbon stock is below-ground biomass Thus, below-ground carbon calculation should be involved in the study Also, the characteristics of the mangroves have not been fully described because the number of surveyed plots was not much Therefore, the number as well as the representative of plots should be increased Furthermore, studies of carbon stocks by age group, height, diameter and type of forest (natural forest, plantation forest or rehabilitation forest) should be conducted to increase the specificity and accuracy of carbon stock calculation Last but not least, the study on annual growth and carbon sequestration has not been conducted because of time and budget limitation 48 REFFERNCES Alongi D.M (2009) Introduction in the energetics of mangrove forests Springer Science and Business Media BV, New York Alongi D M (2016) Climate Regulation by Capturing Carbon in Mangroves Tropical Coastal & Mangrove Consultants, Annandale, QLD, Australia, DOI 10.1007/978- 94- 007-6172-8_236-5 Amira, S 2008 An estimation of Rhizophora apiculata Bl biomass in mangrove forest in Batu Ampar Kubu Raya Regency, West Kalimantan Undergraduate Tesis, Bogor Agricultural University, Indonesia Allison I., Alley R B., Fricker H A., Thomas R H., Warner R C (2009) Antarctic Sci 21, 413 Barcelete Jr R.C., Palmero E.M., Buay M.G., Apares C.B., Dominoto L.R., Lipae H., Cabrera M.L.N., Torres M.A., Requieron E.A (2016) Species diversity and carbon stock assessments in selected mangrove forests of Malapatan and Glan, Saragani Province, Philippines Journal of Biodiversity and Environmental Scienc Vol.8, No.2, p.p 265-274 Boelman N., Stieglitz M., Rueth H., et al (2003) Response of NDVI, biomass, and ecosystem gas exchange to long-term warming and fertilization in wet sedge tundra Oecologia 135:414–421 doi:10.1007/s00442-003-1198-3 Bombelli A., Avitabile V., Balzter H., et al (2009) BIOMASS, assessment of the status of the development of the standards for the terrestrial essential climate variables, 10th edn Global Terrestrial Observing System, Rome Chave, J., Andalo, C., Brown, S., Cairns, M.A., Chambers, J.Q., Eamus, D., Fölster, H., Fromard, F., Higuchi, N., Kira, T et al (2005) Tree allometry and improved estimation of carbon stocks and balance in tropical forests Oecologia 145: 87-99 Christensen B (1978) Biomass and primary production of Rhizophora apiculata in mangrove in Southern Thailand Aquatic Botany 4, pp 43 – 52 10 Clough, B.F and Scott, K 1989 Allometric relationships for estimating above-ground biomass in six mangrove species Forest Ecology and Management 27: 117-127 11 Cuc, N.T.K., et al (2008) Mangrove forest restoration and management in northern provinces of Vietnam Mangrove restoration responds to climate change towards sustainable development Agriculture Publisher, Ha Noi 12 Dharmawan, I.W.S and Siregar, C.A 2008 Soil carbon and carbon estimation of Avicennia marina (Forsk) Vierh in Ciasem, Purwakarta J Penelitian Hutan dan Konservasi Alam Vol V(4): 317‐328 13 Donato D C, Kauffman J B., Murdiyarso D., Kurnianto S., Stidham M and Kanninen M (2011) Mangroves among the most carbon-rich forests in the tropics Nature Geoscience Vol.4, pp.293-294, doi: 10.1038/NGEO1123 14 Estrada G and Soares M (2016) Global patterns of aboveground carbon stock and sequestration in mangroves Annals of the Brazilian Academy of Sciences 89(2): 973-989 DOI: 10.1590/0001-3765201720160357 15 Foody G, Cutler M, McMorrow J et al (2001) Mapping the biomass of Bornean tropical rain forest from remotely sensed data Global Ecology and Biogeography 10:379– 387 doi:10.1046/j.1466-822x 2001.00248.x 16 Field C., Randerson J., Malmström C., Global net primary production: combining ecology and remote sensing Remote Sensing of Environment 51:74–88 doi:10.1016/0034- 4257(94)00066-v (1995) 17 Fromard, F., Puig, H., Mougin, E., Marty, G., Betoulle, J.L., Cadamuro, L 1998 Structure, above-ground biomass and dynamics of mangrove ecosystems: new data from French Guiana Oecologia 115: 39-53 18 Ghasemi N., Sahebi M., Mohammadzadeh A (2011) A review on biomass estimation methods using synthetic aperture radar International Journal of Geomatics and Geosciences 1:776–788 19 Giri C., Pengra B., Zhu Z., Singh A & Tieszen, L (2007) Monitoring mangrove forest dynamics of the sundarbans in Bangladesh and India using multi-temporal satellite data from 1973–2000 Esturine, Coastal and Shelf Science, 73, 1–2, 91 100 20 Giri C., Chieng O., Tieszen L L., Zhu Z., Singh A., Loveland T., Masek J., Duke N (2010) Status and distribution of the world using earth observation satellite data Global Ecology and Biogeography, A journal of macroecology, pp 1-2 doi: 10.1111/j.1466- 8238.2010.00584.x 21 Goward S, Tucker C, Dye D (1985) North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer Vegetatio 64:3–14 doi:10.1007/bf00033449 22 Hamdan O, Khairunnisa M, Ammar A et al (2013) Mangrove carbon stock assessment by optical satellite imagery Journal of Tropical Forest Science 25:554–56 23 Hai-Hoa N., (2016) Application of multispectral landsat data and GIS to monitor changes in coastal mangroves in Tien Yen district, Quang Ninh province during 1994 – 2015 Journal of Forestry Science and Technology p.p 4208 - 4217 24 Hoa N X., Lan P T., Truong X N (2018) Status of mangrove forest in the coastal waters of Vietnam Marine research collection, XVII: 167-177 25 Hanh N.T.H., Linh L.K., Tinh P.H., Truong L.D.,Thu B.T., Tam T.M (2018) Study on Quantification of Carbon Stock in Mangrove Forest along the Coast of Hai Lang Commune, Tien Yen District, Quang Ninh Province Scientific Journal of Vietnam National University, Hanoi Vol.34, No.3, p.p 1-3 26 Hanh N.T.H., (2014) study on quantifying carbon in the soil of mangrove plantation in Nam Hung community, Tien Hai district, Thai Binh province Journal of Biology.Vol.36, No.1, p.p: 51-57 27 Hong, N.P (1991) Ecology of Vietnam's mangrove vegetation Doctoral thesis in biological sciences Hanoi Pedagogical University I 28 Hung, V.M., Tien, D.D., Luong C.V (2015) Carbon sequestration potential in mangrove forests in Hai Phong coastal areas Vietnam Journal of Marine Science and Technology Vol.15, No.4, p.p 347-354, doi: 10.15625/1859-3097/15/4/7379 29 IPCC, Climate Change 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, S Solomon et al., Eds (Cambridge Univ Press, Cambridge, 2007) 30 IPCC, “Agriculture, Forestry and other Land Uses Prepared by the National Greenhouse Gas Inventories Programme”, Eggleston H.S.,Buendia I., Miwa K.,Ngara T and Tanabe K IPCC Guidelines for National Greenhouse Gas Inventories; Volume 4: IGES, Japan, 2006 31 Jaramillo, V.J., Ahedo-Hernandez, R., Kauffman, J.B 2003a Root biomass and carbon in a tropical evergreen forest of Mexico: changes with secondary succession and forest conversion to pasture Journal of Tropical Ecology 19: 457-464 32 Kathiresan K (2012) Importance of Mangrove Ecosystem, International Journal of Marine Science, Vol.2, No.10, pp 70-89 33 Kaufman, J.B & Donato, D.C reporting of structure, (2012) Protocols for the measurement, monitoring and biomass and carbon stocks in mangrove forests Working Paper 86 CIFOR, Bogor, Indonesia 34 Kasawani I., Norsaliza U., Hasamadi I.M (2010) Analysis of spectral vegetation indices related to soil-line for mapping mangrove forests using satellite imagery Applied Remote Sensing Journal 1:25–31 35 Kauffman, J.B and Cole T 2010 Micronesian mangrove forest structure and tree response to a severe typhoon Wetlands 30: 1077-1084 36 Kauffman, J.B., Heider, C., Cole, T., Dwire, K.A., Donato, D.C 2011 Ecosystem C pools of Micronesian mangrove forests: implications of land use and climate change Wetlands 31:343-352 37 Komiyama, A., Poungparn, S., Kato, S 2005 Common allometric equations for estimating the tree weight of mangroves Journal of Tropical Ecology 21: 471-477 38 Komiyama, A., Ong, J.E., Poungparn, S 2008 Allometry, biomass, and productivity of mangrove forests: A review Aquatic Botany 89: 128-137 39 Lillesand, T., Kiefer, R W., & Chipman, J (2015) Remote sensing and image interpretation New York: Wiley ISBN 978-1-118-34328-9 40 Louppe, D., Oteng-Amoako, A.A & Brink, M (Eds), (2008) Plant Resources of Tropical Africa 7(1) Timbers Prota Foundation, Wageningen, Netherlands 41 Lowe J A., et al., UK Climate Projections Science Report: Marine and Coastal Projections (Met Office Hadley Centre, Exeter, UK, 2009) 42 Luong C.V., Nga N.T (2017) Research on carbon storage capability of seagrass through biomass (in the case of Thi Nai lagoon, Binh Dinh province) Journal of Marine Science and Technology Vol.17, No.1, p.p: 63-71 43 Lyon J., Yuan D., Lunetta R., Elvidge C., (1998) A change detection experiment using vegetation indices Photogrammetric Engineering and Remote Sensing 64:143–150 44 Maeda Y., Fukushima A., Imai Y., Tanahashi Y., Nakama E., Ohta S., Kawazoe K., Akune N., Estimating carbon stock changes of mangrove forests using satellite imagery and airborne lidar data in the south sumatra state, indonesia, Volume XLI-B8 (2016) 45 Myeong S., Nowak D.J., Duggin M.J (2006) A temporal analysis of urban forest carbon storage using remote sensing Remote Sensing of Environment Vol 101, p.p 277 – 282 46 Murdiyarso, D., Donato, D.C., Kauffman, J.B., Stidham, M., Kurnianto, S., Kanninen, M 2010 Carbon storage in mangrove and peatland ecosystems in Indonesia – A preliminary account from plots in Indonesia Working paper 48 Center for International Forest Research Bogor, Indonesia 35p 47 Nicholls R J., et al., in Climate Change 2007: Impacts, Adaptation and Vulnerability Contribution of Working Group II to the Fourth Assessment Report of the Intergovermental Panel on Climate Change, M L Parry et al., Eds (Cambridge Univ Press, Cambridge, 2007) 48 Nicholls R J., Cazenave A., Sea-Level Rise and Its Impact on Coastal Zones., pp.1517– 1520 (2010) 49 Nichol C., Lloyd J., Shibistova O., et al (2002) Remote sensing of photosynthetic-light use efficiency of a Siberian boreal forest Tellus B doi:10.3402/tellusb.v54i5.16710 50 Patil, V., Singh, A., Naik, N., and Unnikrishnan, S (2013) Estimation of Carbon Stocks in Avicennia marina Stand Using Allometry, CHN Analysis, and GIS of Wetland Scientists, DOI 10.1007/s13157-013-0505-y Methods Society 51 Potter C., Randerson J., Field C., et al Terrestrial ecosystem production: a process model based on global satellite and surface data Global Biogeochemical Cycles 7:811–841 doi:10.1029/93gb02725 (1993) 52 Putz, F.E and Chan, H.T 1986 Tree growth, dynamics, and productivity in a mature mangrove forest in Malaysia Forest Ecology and Management 17: 211-230 53 Quang-Bao T and Sy-Hoa L., (2018) Using sentinel satellite image to estimate biomass of mangrove forest in Vinh Quang commune, Tien Lang district, Hai Phong city Journal of Forestry Science and Technology No.5, p.p 71-79 54 Quang-Son P., Duc-Anh N (2016) Evolution of the coastal zone in Hai Hau district (Nam Dinh province) and nearest region over the last 100 years based on analysis maps and multi-temporal remote sensing data Viet Nam Journal of topographic Earth Science Vol.38, No.1, p.p 118-130 55 Rignot E., Box J E., Burgess E., Hanna E (2008) Geophys Res Lett 35, L20502 56 Rogan, J., Miller, J., Stow, D., Franklin, J., Levien, L., & Fischer, C (2003) Land cover change mapping in California using classification trees with Landsat TM and ancillary data Photogrammetric Engineering and Remote Sensing, 69(7), 793–804 57 Siikamaki J., Sanchirico J N., Jardine S L (2012) Global economic potential for reducing carbon dioxide emissions from mangrove loss Gretchen C Daily, Stanford University, Stanford, CA 58 Sellers P (1987) Canopy reflectance, photosynthesis, and transpiration, II The role of biophysics in the linearity of their interdependence Remote Sensing of Environment 21:143–183 doi:10.1016/0034- 4257(87)90051-4 59 Seng O D (1951) Specific Gravity of Indonesian wood and it significant to Practical Use, FRDC, Forestry Department, Bongor, Indonesia 60 Smith T.J and Whelan, K.R.T 2006 Development of allometric relations for three mangrove species in south Florida for use in the Greater Everglades Ecosystem restoration Wetlands Ecology and Management 14: 409-419 61 Spalding, M.D., Blasco, F & Field, C.D (eds) (1997) World mangrove atlas The International Society for Mangrove Ecosystems, Okinawa 62 Spiers, A.G (1999) Review of international/ continental wetlan resources Global review of wetland resources and priorities for wetland inventory (ed by C.M and A.G Spiers), pp 63–104 Supervising Scientist Australia Report 144 Finlayson Canberra, 63 Tarlan, M.A 2008 Biomass estimation of nyirih (Xylocarpus granatum Koenig 1784) in primary mangrove forest in Batu Ampar, West Kalimantan Undergraduate thesis, Bogor Agricultural University, Indonesia 64 Tran Thi V., Tien Thi Xuan A., Phan Nguyen H., Dahdouh-Guebas, and Koedam N (2016) Application of remote sensing and GIS for detection of long-term mangrove shoreline changes in Mui Ca Mau, Vietnam Biogeosciences doi:10.5194/bg-11-37812014 65 Tomlinson P B (1986) The botany of mangrove Journal of Tropical Ecology 3(2), pp 188-189 doi:10.1017/ S0266467400002017 66 Tripathi S., Soni S., Maurya A., Soni P., (2010) Calculating carbon sequestration using remote sensing and GIS Society of wetland scientist, 67 Tucker C., (1979) Red and photographic infrared linear combinations for monitoring vegetation Remote Sensing of Environment 8:127–150 doi:10.1016/0034 4257(79)90013-0 68 UNEP (United Nations Environment Programme) (2004) Global environment outlook yearbook 2004 United Nations Environment Programme, Nairobi, Kenya 69 Velicogna I., Geophys Res Lett 36, L19503 (2009) 70 Viet-Dung L., Tai-Tue N., Trong-Nhuan M., Koji Omori (2016) Carbon storage in a restored mangrove forest in Can Gio Mangrove Forest Park, Mekong Delta, Vietnam Forest Ecology and Management 380 p.p 31-40 71 Pfeffer, W T., Harper, J T., O’Neel, S Science 321, 1340 (2008) 72 World Agroforestry Centre (2011) http://www.worldagroforestry.org/output/tree- functional-and-ecological-databases 73 World Bank Document (2019) Bill ENVIRONMENTAL AND SOCIAL MANAGEMENT FRAME, Emission reduction and emission right transfer scheme in the North Central Coast in the period of 2018 to 2025 74 Wu C., Munger J., Niu Z., Kuang D., Comparison of multiple models for estimating gross primary production using MODIS and eddy covariance data in Harvard Remote Sensing of Environment 114:2925–2939 (2010) Forest