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

VIETNAM NATIONAL UNIVERSITY OF FORESTRY FOREST RESOURCES & ENVIRONMENTAL MANAGEMENT FACULTY STUDENT THESIS BIOMASS AND CARBON STOCK ESTIMATION OF COASTAL MANGROVES USING DATA-BASED 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 INTRODUCTION CHAPTER II LITERATURE REVIEW 2.1 GIS and satellite image 2.1.1 The concept of GIS, remote sensing and GPS 2.1.2 Sentinel-2A satellite image 2.2 Overview of estimating of biomass and above carbon stock by using remote sensing 2.2.1 In the world 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 iii 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 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 iv 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 v 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 vi LIST OF TABLES Table 2.1: Spectral bands for the SENTINEL-2 sensors (S2A & S2B) 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 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 vii 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 viii 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 30 water 106.76453 20.70093 31 water 106.76409 20.70094 32 water 106.76371 20.69996 33 water 106.76365 20.70048 34 water 106.76424 20.69898 35 water 106.76476 20.69851 36 water 106.76457 20.69772 37 water 106.76351 20.69906 38 water 106.76229 20.69741 39 water 106.76246 20.69837 40 water 106.76288 20.69895 41 Water 106.76284 20.69646 42 Water 106.76326 20.69639 43 Water 106.76209 20.69629 44 Water 106.76129 20.69689 45 Water 106.76059 20.69698 46 Water 106.76032 20.69644 47 Water 106.76100 20.69543 48 Water 106.76017 20.69567 49 Water 106.76064 20.69488 50 Water 106.75868 20.69621 51 Water 106.75811 20.69720 52 Water 106.75924 20.69758 53 Water 106.75982 20.69798 54 Water 106.75928 20.69841 55 Water 106.75842 20.69841 56 Water 106.75937 20.69920 57 Water 106.75758 20.69880 58 Water 106.75691 20.69787 59 Water 106.75866 20.69485 60 Water 106.75946 20.69469 61 Water 106.75716 20.69658 62 Water 106.75632 20.69730 79 63 Water 106.75564 20.69851 64 Water 106.75519 20.69775 65 Water 106.75463 20.69920 66 Water 106.75421 20.70012 67 Water 106.75360 20.69963 68 Water 106.75385 20.70075 69 Water 106.75345 20.70157 70 Water 106.75311 20.70242 71 Water 106.75240 20.70232 72 Water 106.75265 20.70324 73 Water 106.75176 20.70348 74 Water 106.75199 20.70394 75 Water 106.75165 20.70436 76 Water 106.75114 20.70502 77 Water 106.75024 20.70586 78 Water 106.75005 20.70642 79 Water 106.75548 20.69569 80 Water 106.75691 20.69500 81 Water 106.75441 20.69478 82 Water 106.75217 20.69483 83 Water 106.75288 20.69652 84 Water 106.75079 20.69454 85 Water 106.74943 20.69492 86 Water 106.75322 20.69500 87 Water 106.75181 20.69376 88 Water 106.74996 20.69359 89 Water 106.74782 20.69371 90 Water 106.74708 20.69438 91 Water 106.74908 20.69271 92 Water 106.74686 20.69264 93 Water 106.74558 20.69283 94 Water 106.74544 20.69392 95 Water 106.74617 20.69040 80 96 Water 106.74513 20.69088 97 Water 106.74410 20.69145 98 Water 106.74470 20.68971 99 Water 106.74334 20.69064 100 Water 106.74353 20.68855 101 Water 106.73994 20.68579 102 Water 106.73842 20.68343 103 Water 106.73654 20.68243 104 Water 106.73535 20.68103 105 Water 106.73347 20.67970 106 Water 106.73216 20.67886 107 Water 106.73102 20.67832 108 Water 106.73002 20.67889 109 Water 106.72938 20.67965 110 Water 106.72990 20.67793 111 Water 106.72823 20.67865 112 Water 106.72754 20.67715 113 Water 106.72700 20.67798 114 Water 106.72616 20.67584 115 Water 106.72400 20.67534 116 Water 106.72236 20.67527 117 Water 106.72150 20.67644 118 Water 106.72050 20.67527 119 Water 106.71910 20.67634 120 Water 106.71848 20.67477 121 Water 106.71829 20.67567 122 Water 106.71719 20.67703 123 Water 106.71510 20.67610 124 Water 106.71605 20.67715 125 Water 106.71591 20.67515 126 Water 106.71491 20.67724 127 Water 106.71422 20.67546 128 Water 106.71393 20.67755 81 129 Water 106.71236 20.67655 130 Water 106.71308 20.67682 131 Water 106.71143 20.67874 132 Water 106.71153 20.67784 133 Water 106.71005 20.67989 134 Water 106.70827 20.67827 135 Water 106.70994 20.67753 136 Water 106.70891 20.67939 137 Water 106.70815 20.68046 138 Water 106.70729 20.68141 139 Water 106.70629 20.67934 140 Water 106.70741 20.68036 141 Water 106.70601 20.68177 142 Water 106.70513 20.68062 143 Water 106.70503 20.68257 144 Water 106.70363 20.68269 145 Water 106.70396 20.68167 146 Water 106.70370 20.68400 147 Water 106.70294 20.68269 148 Water 106.70294 20.68453 149 Water 106.70168 20.68481 150 Water 106.70135 20.68579 151 Water 106.76304 20.70000 152 Water 106.76203 20.69676 153 Water 106.74986 20.70654 154 Water 106.73941 20.69159 155 Water 106.73310 20.68500 156 Water 106.72603 20.68000 157 Water 106.71470 20.67775 158 Water 106.70885 20.68061 159 w 106.71098 20.67950 160 Water 106.72373 20.67643 161 Water 106.72494 20.67751 82 162 Water 106.74308 20.69204 163 Water 106.75286 20.70281 164 Water 106.75357 20.70045 165 Water 106.76840 20.70830 166 Water 106.76803 20.70989 167 Water 106.71931 20.68478 168 Water 106.71880 20.68546 169 Water 106.73320 20.69524 170 Water 106.71352 20.67604 Mangroves marked points in study area ID OBJECTID POINT_X POINT_Y Mangroves 106.7664566 20.71395294 Mangroves 106.7669563 20.71266803 Mangroves 106.7679319 20.71033616 Mangroves 106.7680984 20.70790911 Mangroves 106.7679081 20.70867054 Mangroves 106.7650289 20.71262044 Mangroves 106.763054 20.71138312 Mangroves 106.7630064 20.70814705 Mangroves 106.7662662 20.70714768 10 Mangroves 106.7666232 20.70610072 11 Mangroves 106.762126 20.70348331 12 Mangroves 106.7601034 20.70762357 13 Mangroves 106.7578905 20.71007442 14 Mangroves 106.7568674 20.70679076 15 Mangroves 106.7601748 20.70453027 16 Mangroves 106.7492602 20.70586515 17 Mangroves 106.7467023 20.70289083 18 Mangroves 106.7450367 20.70366415 19 Mangroves 106.7443823 20.70077905 20 Mangroves 106.7428059 20.7030098 21 M 106.7412593 20.69872677 22 M 106.7389988 20.70086828 83 23 M 106.7411998 20.70176058 24 M 106.7425085 20.69631756 25 M 106.7414378 20.69129095 26 M 106.7413485 20.69215351 27 M 106.738285 20.696615 28 M 106.7370358 20.69961907 29 M 106.7362327 20.69828062 30 M 106.7360542 20.69274837 31 M 106.7352809 20.69491963 32 M 106.7354296 20.69634731 33 M 106.7342994 20.69762627 34 M 106.733645 20.69628782 35 M 106.7341804 20.69197505 36 M 106.7326338 20.69444374 37 M 106.7312358 20.69554424 38 M 106.7301651 20.69334324 39 M 106.7296594 20.69167762 40 M 106.7275179 20.69268889 41 M 106.7311466 20.6855505 42 M 106.7262984 20.69078532 43 M 106.7251682 20.68983353 44 M 106.7260902 20.68787048 45 M 106.7278153 20.68647254 46 M 106.7271015 20.68331976 47 M 106.7262092 20.68471769 48 M 106.7226698 20.68920892 49 M 106.7202606 20.68709715 50 M 106.7239487 20.6842418 51 M 106.7252872 20.68230849 52 M 106.7229672 20.68629409 53 M 106.7236215 20.680048 54 M 106.722759 20.68189208 55 M 106.7165724 20.68599665 84 56 M 106.7211826 20.68031569 57 M 106.7198144 20.68215977 58 M 106.7213611 20.68480692 59 M 106.7149365 20.68593717 60 M 106.7142822 20.68442026 61 M 106.7155016 20.68337925 62 M 106.7182083 20.68162439 63 M 106.7160965 20.68174337 64 M 106.7179703 20.67909622 65 M 106.7197252 20.67951262 66 M 106.712676 20.68519358 67 M 106.7141334 20.68123773 68 M 106.7127355 20.67998851 69 M 106.7119027 20.68168388 70 M 106.7084525 20.68459872 71 M 106.7098504 20.68376591 72 M 106.7086904 20.68227874 73 M 106.7101776 20.68138645 74 M 106.7037233 20.68528281 75 M 106.7023849 20.68590742 76 M 106.704913 20.68373616 77 M 106.7298974 20.68941713 78 M 106.7327527 20.68855457 79 M 106.7522643 20.70015445 80 M 106.7481003 20.70173084 81 M 106.7498848 20.70196878 82 M 106.7542868 20.70532977 83 M 106.7542571 20.70140366 84 M 106.7553279 20.70042213 85 M 106.7583022 20.70348569 86 M 106.7587781 20.70179032 87 M 106.7637749 20.70565695 88 M 106.7607709 20.71133791 85 89 M 106.7627637 20.70958306 90 M 106.7651431 20.71074305 91 M 106.7684857 20.70771653 92 M 106.7644073 20.70498491 93 M 106.7635507 20.70323839 94 M 106.7618803 20.70235323 95 M 106.7599767 20.6997263 96 M 106.7583873 20.70094459 97 M 106.75651 20.70363 98 M 106.75377 20.70308 99 M 106.74689 20.69815 100 M 106.74552 20.69788 101 M 106.74531 20.69637 102 M 106.74170 20.69424 103 M 106.73417 20.68414 104 M 106.73163 20.68262 105 M 106.72876 20.68245 106 M 106.72835 20.68001 107 M 106.72222 20.68764 108 M 106.72459 20.68552 109 M 106.72587 20.68314 110 M 106.72056 20.68337 111 M 106.71930 20.68455 112 Ma 106.72079 20.68242 113 Ma 106.71268 20.68312 114 Ma 106.71127 20.68569 115 Ma 106.70933 20.68554 116 Ma 106.70516 20.68535 117 Ma 106.70457 20.68457 118 Ma 106.70657 20.68457 119 Ma 106.70855 20.68360 120 Ma 106.71167 20.68453 121 Ma 106.71709 20.68400 86 122 Ma 106.71746 20.68063 123 Ma 106.72250 20.68324 124 Ma 106.72873 20.68870 125 Ma 106.72760 20.68943 126 Ma 106.72951 20.68585 127 Ma 106.72825 20.68410 128 Ma 106.72684 20.68602 129 Ma 106.73154 20.68691 130 Ma 106.73031 20.68834 131 Ma 106.72739 20.69184 132 Ma 106.72517 20.69078 133 Ma 106.72376 20.69013 134 Ma 106.72871 20.69436 135 Ma 106.73179 20.69272 136 Ma 106.73451 20.69382 137 Ma 106.73689 20.69588 138 Ma 106.73946 20.69753 139 Ma 106.73881 20.69929 140 Ma 106.73312 20.69287 141 Ma 106.73194 20.68998 142 Ma 106.73847 20.69430 143 Ma 106.73689 20.69308 144 Ma 106.74382 20.69820 145 Ma 106.74058 20.70077 146 Ma 106.74369 20.70193 147 Ma 106.74611 20.69866 148 Ma 106.74690 20.70128 149 Ma 106.74622 20.70492 150 Ma 106.74856 20.70319 151 Ma 106.75065 20.70216 152 Ma 106.74887 20.70507 153 Ma 106.75294 20.70502 154 Ma 106.75032 20.70487 87 155 Ma 106.75076 20.70561 156 Ma 106.75110 20.70669 157 Ma 106.75178 20.70533 158 Ma 106.75390 20.70262 159 Ma 106.75468 20.70799 160 Ma 106.75327 20.70771 161 Ma 106.75565 20.70612 162 Ma 106.74694 20.69719 163 Ma 106.75798 20.70891 164 Ma 106.75598 20.70918 165 Ma 106.76029 20.71007 166 Ma 106.76217 20.70518 167 Ma 106.76428 20.70903 168 Ma 106.75882 20.70611 169 Ma 106.71911 20.68617 170 Ma 106.74828 20.70010 171 Ma 106.76833 20.71052 172 Ma 106.76814 20.71122 173 Ma 106.75619 20.69879 174 Ma 106.75599 20.69868 175 Ma 106.76730 20.71339 88 Others marked points in study area ID OBJECTID POINT_X POINT_Y O 106.76709 20.71027 O 106.76739 20.70954 O 106.76801 20.70919 O 106.76685 20.70996 O 106.76909 20.70734 O 106.76922 20.70665 O 106.76753 20.70702 O 106.76671 20.70795 O 106.76617 20.70494 10 O 106.76595 20.70540 11 O 106.76479 20.70503 12 O 106.76395 20.70401 13 O 106.76463 20.70385 14 O 106.76551 20.70371 15 O 106.76478 20.70285 16 O 106.76427 20.70233 17 O 106.76490 20.70111 18 O 106.76520 20.70085 19 O 106.76376 20.70128 20 O 106.76290 20.70040 21 O 106.76225 20.70191 22 O 106.76171 20.70287 23 O 106.76121 20.70118 24 O 106.76226 20.70006 25 O 106.76266 20.69938 26 O 106.76159 20.69934 27 O 106.76314 20.69850 28 O 106.76371 20.69805 29 O 106.76359 20.69733 30 O 106.76422 20.69661 31 O 106.76270 20.69700 89 32 O 106.76364 20.69599 33 OR 106.76231 20.69585 34 OR 106.76300 20.69541 35 OR 106.76183 20.69524 36 OR 106.76138 20.69836 37 OR 106.76062 20.69853 38 OR 106.75988 20.69894 39 OR 106.75977 20.69965 40 OR 106.76004 20.69734 41 OR 106.76118 20.69722 42 OR 106.75931 20.69690 43 OR 106.76222 20.69477 44 OR 106.75870 20.69936 45 OR 106.75762 20.69909 46 OR 106.75409 20.69835 47 OR 106.75367 20.69919 48 OR 106.75330 20.70059 49 OR 106.75237 20.70137 50 OR 106.75164 20.70094 51 OR 106.74959 20.69965 52 OR 106.74860 20.69950 53 OR 106.74859 20.69847 54 OR 106.75014 20.69693 55 OR 106.75305 20.69709 56 OR 106.75082 20.69578 57 OR 106.74741 20.69884 58 OR 106.74663 20.69978 59 OR 106.74782 20.69781 60 OR 106.74866 20.69696 61 OR 106.74927 20.69561 62 OR 106.74788 20.69583 63 OR 106.74778 20.69511 64 OR 106.74666 20.69549 65 OR 106.74650 20.69641 66 OR 106.74569 20.69797 90 67 OR 106.74534 20.69679 68 OR 106.74426 20.69628 69 OR 106.74362 20.69507 70 OR 106.74594 20.69459 71 Or 106.74481 20.69434 72 Or 106.74466 20.69346 73 Or 106.74234 20.69409 74 Or 106.74154 20.69486 75 Or 106.74095 20.69578 76 Or 106.74030 20.69669 77 Or 106.74318 20.69277 78 Or 106.74313 20.69113 79 Or 106.74252 20.69095 80 Or 106.74290 20.68924 81 Or 106.74025 20.69065 82 Or 106.73867 20.69278 83 Or 106.73742 20.69432 84 Or 106.73899 20.69142 85 Or 106.74065 20.68953 86 Or 106.74143 20.68891 87 Or 106.74099 20.68812 88 Or 106.74187 20.68653 89 Or 106.74092 20.68626 90 Or 106.73972 20.68692 91 Or 106.73927 20.68797 92 Or 106.73838 20.68859 93 Or 106.73761 20.68995 94 Or 106.73688 20.69040 95 Or 106.73593 20.69178 96 Or 106.73730 20.68803 97 Or 106.73537 20.68881 98 Or 106.73462 20.68827 99 Or 106.73562 20.68659 100 Or 106.73643 20.68554 101 Or 106.73695 20.68479 91 102 Or 106.73912 20.68655 103 Or 106.73887 20.68563 104 Or 106.73910 20.68502 105 Or 106.73824 20.68429 106 Or 106.73668 20.68328 107 Or 106.73518 20.68228 108 Or 106.73416 20.68340 109 Or 106.73338 20.68430 110 Or 106.73154 20.68311 111 Or 106.73321 20.68121 112 Or 106.73247 20.68050 113 Or 106.73140 20.67888 114 Or 106.73052 20.68002 115 Or 106.73005 20.68107 116 Or 106.72919 20.68207 117 Or 106.72843 20.68033 118 Or 106.72690 20.67900 119 Or 106.72414 20.67828 120 Or 106.72488 20.67705 121 Or 106.72467 20.67619 122 Or 106.71089 20.67976 123 Or 106.70939 20.68064 124 Or 106.71467 20.67807 125 Or 106.70678 20.68206 126 Or 106.70841 20.68092 127 Or 106.70106 20.68665 128 Or 106.71399 20.67825 129 Or 106.72036 20.67742 130 Or 106.73221 20.68226 131 Or 106.76212 20.69959 132 Or 106.76260 20.69869 133 Or 106.76237 20.69773 134 Or 106.76037 20.69880 135 Or 106.75047 20.69713 136 Or 106.74993 20.69777 92 137 Or 106.74588 20.69497 138 Or 106.73294 20.68503 139 Or 106.73165 20.68333 140 Or 106.73056 20.68074 141 Or 106.72590 20.68004 142 Or 106.72998 20.67978 143 Or 106.72457 20.67790 144 Or 106.72234 20.67845 145 Or 106.71519 20.67780 146 Or 106.71820 20.67686 147 Or 106.76687 20.70952 148 Or 106.76609 20.70936 149 Or 106.76483 20.70747 150 Or 106.75862 20.70274 151 Or 106.76020 20.70360 152 Or 106.75740 20.70301 153 Or 106.75462 20.70377 154 Or 106.75568 20.70340 155 Or 106.74679 20.69882 156 Or 106.74361 20.69741 157 Or 106.74359 20.70408 158 Or 106.75088 20.70429 159 Or 106.75482 20.70318 160 Or 106.76628 20.70847 161 Or 106.76806 20.71179 162 Or 106.74208 20.68942 163 Or 106.71032 20.67995 164 Or 106.70973 20.68023 165 Or 106.71934 20.67699 93 ... 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... distribution and structures of coastal mangroves in Kien Thuy and Do Son district, Hai Phong - Estimating biomass, carbon stock and SOC based on field survey - Estimating biomass, carbon stock and SOC based. .. 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