Mangrove Forest Landcover Changes in Coastal Vietnam: A Case Study from 1973 to 2020 in Thanh Hoa and Nghe An Provinces
08/11/2021Mangrove forests can ameliorate the impacts of typhoons and storms, but their extent is threatened by coastal development. The northern coast of Vietnam is especially vulnerable as typhoons frequently hit it during the monsoon season. However, temporal change information in mangrove cover distribution in this region is incomplete. Therefore, this study was undertaken to detect change in the spatial distribution of mangroves in Thanh Hoa and Nghe An provinces and identify reasons for the cover change. Landsat satellite images from 1973 to 2020 were analyzed using the NDVI method combined with visual interpretation to detect mangrove area change. Six LULC classes were categorized: mangrove forest, other forests, aquaculture, other land use, mudflat, and water. The mangrove cover in Nghe An province was estimated to be 66.5 ha in 1973 and increased to 323.0 ha in 2020. Mangrove cover in Thanh Hoa province was 366.1 ha in 1973, decreased to 61.7 ha in 1995, and rose to 791.1 ha in 2020. Aquaculture was the main reason for the loss of mangroves in both provinces. Overall, the percentage of mangrove loss from aquaculture was 42.5% for Nghe An province and 60.1% for Thanh Hoa province. Mangrove restoration efforts have contributed significantly to mangrove cover, with more than 1300 ha being planted by 2020. This study reveals that improving mangrove restoration success remains a challenge for these provinces, and further refinement of engineering techniques is needed to improve restoration outcomes.
1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Image Selection
2.3. Data Pre-Processing
2.4. Land Cover Classification
2.5. Accuracy Assessment
3. Results
3.1. Classification and Accuracy Assessment
3.2. LULC Changes in Thanh Hoa and Nghe An Provinces from 1973 to 2020
3.3. Change in Mangrove Cover at the District Level from 1973 to 2020
3.4. Drivers of Change in Mangrove Cover
4. Discussion
4.1. Mangrove Extent
4.2. Drivers of Change Over Time
5. Conclusions
References
1. Koch, E.W.; Barbier, E.B.; Silliman, B.R.; Reed, D.J.; Perillo, G.M.; Hacker, S.D.; Granek, E.F.; Primavera, J.H.; Muthiga, N.; Polasky, S.; et al. Non-linearity in ecosystem services: Temporal and spatial variability in coastal protection. Front. Ecol. Environ. 2009, 7, 29–37. [CrossRef]
2. Nagelkerken, I.; Blaber, S.; Bouillon, S.; Green, P.; Haywood, M.; Kirton, L.; Meynecke, J.-O.; Pawlik, J.; Penrose, H.M.; Sasekumar, A.; et al. The habitat function of mangroves for terrestrial and marine fauna: A review. Aquat. Bot. 2008, 89, 155–185. [CrossRef]
3. Ouyang, X.; Lee, S.Y.; Connolly, R.M.; Kainz, M.J. Spatially-explicit valuation of coastal wetlands for cyclone mitigation in Australia and China. Sci. Rep. 2018, 8, 3035. [CrossRef]
4. Hochard, J.P.; Hamilton, S.; Barbier, E.B. Mangroves shelter coastal economic activity from cyclones. Proc. Natl. Acad. Sci. USA 2019, 116, 12232–12237. [CrossRef] [PubMed]
5. Atwood, T.B.; Connolly, R.M.; Almahasheer, H.; Carnell, P.E.; Duarte, C.M.; Lewis, C.J.E.; Irigoien, X.; Kelleway, J.J.; Lavery, P.S.; Macreadie, P.I.; et al. Global patterns in mangrove soil carbon stocks and losses. Nat. Clim. Chang. 2017, 7, 523–528. [CrossRef]
6. Adame, M.F.; Brown, C.J.; Bejarano, M.; Herrera-Silveira, J.A.; Ezcurra, P.; Kauffman, J.B.; Birdsey, R.A. The undervalued contribution of mangrove protection in Mexico to carbon emission targets. Conserv. Lett. A J. Soc. Conversat. Biol. 2018, 11, e12445. [CrossRef]
7. Lovelock, C.E.; Cahoon, D.R.; Friess, D.A.; Guntenspergen, G.R.; Krauss, K.W.; Reef, R.; Rogers, K.; Saunders, M.L.; Sidik, F.; Swales, A.; et al. The vulnerability of Indo-Pacific mangrove forests to sea-level rise. Nature 2015, 526, 559–563. [CrossRef]
8. Schuerch, M.; Spencer, T.; Temmerman, S.; Kirwan, M.L.; Wolff, C.; Lincke, D.; McOwen, C.J.; Pickering, M.D.; Reef, R.; Vafeidis, A.T.; et al. Future response of global coastal wetlands to sea-level rise. Nature 2018, 561, 231–234. [CrossRef]
9. Valiela, I.; Bowen, J.L.; York, J.K. Mangrove Forests: One of the World’s Threatened Major Tropical Environments: At least 35% of the area of mangrove forests has been lost in the past two decades, losses that exceed those for tropical rain forests and coral reefs, two other well-known threatened environments. Bioscience 2001, 51, 807–815.
10. Hamilton, S.; Casey, D. Creation of a high spatio-temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC-21). Glob. Ecol. Biogeogr. 2016, 25, 729–738. [CrossRef]
11. Friess, D.A.; Rogers, K.; Lovelock, C.E.; Krauss, K.W.; Hamilton, S.E.; Lee, S.Y.; Lucas, R.; Primavera, J.; Rajkaran, A.; Shi, S. The state of the world’s mangrove forests: Past, present, and future. Annu. Rev. Environ. Resour. 2019, 44, 89–115. [CrossRef]
12. Mejía-Rentería, J.C.; Castellanos-Galindo, G.A.; Cantera-Kintz, J.R.; Hamilton, S.E. A comparison of Colombian Pacific mangrove extent estimations: Implications for the conservation of a unique Neotropical tidal forest. Estuar. Coast. Shelf Sci. 2018, 212, 233–240. [CrossRef]
13. Hong, P.N.; San, H.T. Mangroves of Vietnam; IUCN: Bangkok, Thailand, 1993.
14. Ratner, B.D. Wetlands Management in Vietnam: Issues and Perspectives; WorldFish: Penang, Malaysia, 2003.
15. Tuan, L.; Yukihiro, M.; Dao, P.; Tho, N.H.; Dao, Q. Environmental management in mangrove areas. Environ. Inform. Arch. 2003, 1,38–52.
16. FAO. Global Forest Resources Assessment 2015 Desk Reference; Food and Agriculture Organization of the United Nations: Rome, Italy, 2015.
17. Tuan, L.; Hong, P.N. Problems of coastal environment and restoration in Vietnam. In Proceedings of the Third International Workshop Yearbook of Vietnam, Hanoi, Vietnam, 5–7 December 2008. (In Vietnamese).
18. Forest Inventory and Planning Institute. Results of National Forest Survey Following Decision No 405/TTg-KTN of the Prime Minister, Dated 16 March 2007; Forest Inventory and Planning Institute (FIPI): Hanoi, Vietnam, 2007. (In Vietnamese)
19. MARD. Decision No. 3158/QD-BNN-TCLN “Announcing Forest Status in 2015–2016”; MARD: Hanoi, Vietnam, 2016.
20. MARD. Announcement of National Forest Status in 2019; MARD: Hanoi, Vietnam, 2020. (In Vietnamese)
21. Wang, X.; Mahul, O.; Stutley, C. Weathering the Storm: Options for Disaster Risk Financing in Vietnam; World Bank: Washington, DC, USA, 2010.
22. Takagi, H. Statistics on typhoon landfalls in Vietnam: Can recent increases in economic damage be attributed to storm trends? Urban. Clim. 2019, 30, 100506. [CrossRef]
23. Tinh, D.Q. Vietnam Country Report 1999; Asian Disaster Reducton Center (ADRC): Kobe, Japan, 1999.
24. Takagi, H.; Esteban, M.; Thao, N.D. Introduction: Coastal Disasters and Climate Change in Vietnam; Elsevier: Amsterdam, The Netherlands, 2014.
25. Hardisky, M.; Gross, M.; Klemas, V. Remote sensing of coastal wetlands. BioScience 1986, 36, 453–460. [CrossRef]
26. Green, E.; Clark, C.D.; Mumby, P.J.; Edwards, A.J.; Ellis, A.C. Remote sensing techniques for mangrove mapping. Int. J. Remote Sens. 1998, 19, 935–956. [CrossRef]
27. Blasco, F.; Gauquelin, T.; Rasolofoharinoro, M.; Denis, J.; Aizpuru, M.; Caldairou, V. Recent advances in mangrove studies using remote sensing data. Mar. Freshw. Res. 1998, 49, 287–296. [CrossRef]
28. Heumann, B.W. Satellite remote sensing of mangrove forests: Recent advances and future opportunities. Prog. Phys. Geogr. 2011, 35, 87–108. [CrossRef]
29. Held, A.; Ticehurst, C.; Lymburner, L.;Williams, N. High resolution mapping of tropical mangrove ecosystems using hyperspectral and radar remote sensing. Int. J. Remote Sens. 2003, 24, 2739–2759. [CrossRef]
30. Tuan, V.; Oppelt, N.; Leinenkugel, P.; Kuenzer, C. Remote sensing in mapping mangrove ecosystems—An object-based approach. Remote Sens. 2013, 5, 183–201.
31. Lucas, R.; Van De Kerchove, R.; Otero, V.; Lagomasino, D.; Fatoyinbo, L.; Omar, H.; Satyanarayana, B.; Dahdouh-Guebas, F. Structural characterisation of mangrove forests achieved through combining multiple sources of remote sensing data. Remote Sens. Environ. 2020, 237, 111543. [CrossRef]
32. Bindu, G.; Rajan, P.; Jishnu, E.; Joseph, K.A. Carbon stock assessment of mangroves using remote sensing and geographic information system. Egypt. J. Remote Sens. Space Sci. 2020, 23, 1–9. [CrossRef]
33. Thi, V.T.; Xuan, A.T.T.; Nguyen, H.P.; Dahdouh-Guebas, F.; Koedam, N. Application of remote sensing and GIS for detection of long-term mangrove shoreline changes in Mui Ca Mau, Vietnam. Biogeosciences 2014, 11, 3781.
34. Thu, P.M.; Populus, J. Status and changes of mangrove forest in Mekong Delta: Case study in Tra Vinh, Vietnam. Estuar. Coast. Shelf Sci. 2007, 71, 98–109. [CrossRef]
35. Luong, N.V.; Tateishi, R.; Hoan, N.T. Analysis of an impact of succession in mangrove forest association using remote sensing and GIS technology. J. Geogr. Geol. 2015, 7, 106. [CrossRef]
36. Lien, P.T.; Brabyn, L. Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms. ISPRS J. Photogramm. Remote Sens. 2017, 128, 86–97.
37. Tuan, V.; Kuenzer, C.; Oppelt, N. How remote sensing supports mangrove ecosystem service valuation: A case study in Ca Mau province, Vietnam. Ecosyst. Serv. 2015, 14, 67–75.
38. Son, N.T.; Chen, C.-F.; Chang, N.-B.; Chen, C.-R.; Chang, L.-Y.; Thanh, B.-X. Mangrove mapping and change detection in Ca Mau Peninsula, Vietnam, using Landsat data and object-based image analysis. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 8, 503–510. [CrossRef]
39. Tong, P.; Auda, Y.; Populus, J.; Aizpuru, M.; Habshi, A.A.; Blasco, F. Assessment from space of mangroves evolution in the Mekong Delta, in relation to extensive shrimp farming. Int. J. Remote Sens. 2004, 25, 4795–4812. [CrossRef]
40. Binh, T.; Vromant, N.; Hung, N.T.; Hens, L.; Boon, E. Land cover changes between 1968 and 2003 in Cai Nuoc, Ca Mau peninsula, Vietnam. Environ. Dev. Sustain. 2005, 7, 519–536. [CrossRef]
41. Van, T.;Wilson, N.; Thanh Tung, H.; Quisthoudt, K.; Quang Minh, V.; Tuan, L.; Dahdouh-Guebas, F.; Koedam, N. Changes in mangrove vegetation area and character in a war and land use change affected region of Vietnam (Mui Ca Mau) over six decades. Acta Oecologica 2015, 63, 71–81. [CrossRef]
42. Hauser, L.T.; Vu, G.N.; Nguyen, B.A.; Dade, E.; Nguyen, H.M.; Nguyen, T.T.Q.; Le, T.Q.; Vu, L.H.; Tong, A.T.H.; Pham, H.V. Uncovering the spatio-temporal dynamics of land cover change and fragmentation of mangroves in the Ca Mau peninsula, Vietnam using multi-temporal SPOT satellite imagery (2004–2013). Appl. Geogr. 2017, 86, 197–207. [CrossRef]
43. Tuan, V.; Kuenzer, C. Can Gio Mangrove Biosphere Reserve Evaluation 2012: Current status, Dynamics, and Ecosystem Services; International Union for the Conservation of Nature, IUCN: Hanoi, Vietnam, 2012.
44. Dat, P.T.; Le, N.N.; Ha, N.T.; Nguyen, L.V.; Xia, J.; Yokoya, N.; To, T.T.; Trinh, H.X.; Kieu, L.Q.; Takeuchi, W. Estimating Mangrove Above-Ground Biomass Using Extreme Gradient Boosting Decision Trees Algorithm with Fused Sentinel-2 and ALOS-2 PALSAR-2 Data in Can Gio Biosphere Reserve, Vietnam. Remote Sens. 2020, 12, 777.
45. Pham, M.H.; Do, T.H.; Pham, V.-M.; Bui, Q.-T. Mangrove forest classification and aboveground biomass estimation using an atom search algorithm and adaptive neuro-fuzzy inference system. PLoS ONE 2020, 15, e0233110. [CrossRef]
46. Hong, P.N. Ecology of Mangrove Vegetation in Vietnam; Hanoi Pedagogic University: Hanoi, Vietnam, 1991.
47. Van Thao, N.; Thanh, T.D.; Saito, Y.; Gouramanis, C. Monitoring coastline change in the Red River Delta using remotely sensed data. Vietnam J. Mar. Sci. Technol. 2013, 13, 151–160.
48. Dat, P.T.; Yokoya, N.; Xia, J.; Ha, N.T.; Le, N.N.; Nguyen, T.T.T.; Dao, T.H.; Vu, T.T.P.; Pham, T.D.; Takeuchi,W. Comparison of Machine Learning Methods for Estimating Mangrove Above-Ground Biomass Using Multiple Source Remote Sensing Data in the Red River Delta Biosphere Reserve, Vietnam. Remote Sens. 2020, 12, 1334.
49. Seto, K.C.; Fragkias, M. Mangrove conversion and aquaculture development in Vietnam: A remote sensing-based approach for evaluating the Ramsar Convention onWetlands. Glob. Environ. Chang. 2007, 17, 486–500. [CrossRef]
50. Ngoc, T.; Demaine, H. Potentials for different models for freshwater aquaculture development in the Red River Delta (Vietnam) using GIS analysis. Nagathe Iclarm Q. 1996, 19, 29–32.
51. Lan, P.T.; Son, T.S.; Gunasekara, K.; Nhan, N.T. Application of Remote Sensing and GIS technology for monitoring coastal changes in estuary area of the Red river system, Vietnam. J. Korean Soc. Surv. Geod. Photogramm. Cartogr. 2013, 31, 529–538. [CrossRef]
52. Hoa, N.H.; Ngoc, T.L.T.; Le An, T.; Nghia, N.H.; Khanh, D.L.V.; Thu, N.H.T.; Bohm, S.; Premnath, C.F.S. Monitoring changes in coastal mangrove extents using multi-temporal satellite data in selected communes, Hai Phong city, Vietnam. For. Soc. 2020, 4, 256–270.
53. Dat, P.T.; Yoshino, K.; Le, N.N.; Bui, D.T. Estimating aboveground biomass of a mangrove plantation on the Northern coast of Vietnam using machine learning techniques with an integration of ALOS-2 PALSAR-2 and Sentinel-2A data. Int. J. Remote Sens. 2018, 39, 7761–7788.
54. Dat, P.T.; Bui, D.T.; Yoshino, K.; Le, N.N. Optimized rule-based logistic model tree algorithm for mapping mangrove species using ALOS PALSAR imagery and GIS in the tropical region. Environ. Earth Sci. 2018, 77, 159.
55. Dat, P.T.; Yoshino, K. Aboveground biomass estimation of mangrove species using ALOS-2 PALSAR imagery in Hai Phong City, Vietnam. J. Appl. Remote Sens. 2017, 11, 026010.
56. Dat, P.T.; Yoshino, K.; Bui, D.T. Biomass estimation of Sonneratia caseolaris (l.) Engler at a coastal area of Hai Phong city (Vietnam) using ALOS-2 PALSAR imagery and GIS-based multi-layer perceptron neural networks. Giscience Remote Sens. 2017, 54, 329–353.
57. Hoa, N.H.; Nghia, N.H.; Nguyen, H.T.T.; Le, A.T.; Tran, L.T.N.; Duong, L.V.K.; Bohm, S.; Furniss, M.J. Classification methods for mapping mangrove extents and drivers of change in Thanh Hoa Province, Vietnam during 2005-2018. For. Soc. 2020, 4, 225–242.
58. Dat, P.T.; Yoshino, K. Monitoring mangrove forest using multi-temporal satellite data in the Northern Coast of Vietnam. In Proceedings of the 32nd Asian Conference on Remote Sensing, Taipei, Taiwan, 3–7 October 2011.
59. Dat, P.T.; Yoshino, K. Mangrove mapping and change detection using multi-temporal Landsat imagery in Hai Phong city, Vietnam. In Proceedings of the International Symposium on Cartography in Internet and Ubiquitous Environments, Tokyo, Japan, 17–19 March 2015.
60. Zhang, K.; Dong, X.; Liu, Z.; Gao, W.; Hu, Z.; Wu, G. Mapping tidal flats with Landsat 8 images and google earth engine: A case study of the China’s eastern coastal zone circa 2015. Remote Sens. 2019, 11, 924. [CrossRef]
61. Zhang, X.; Treitz, P.M.; Chen, D.; Quan, C.; Shi, L.; Li, X. Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure. Int. J. Appl. Earth Obs. Geoinf. 2017, 62, 201–214. [CrossRef]
62. Chen, B.; Xiao, X.; Li, X.; Pan, L.; Doughty, R.; Ma, J.; Dong, J.; Chen, G.; Yin, Z.; Pan, T.; et al. A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform. ISPRS J. Photogramm. Remote Sens. 2017, 131, 104–120. [CrossRef]
63. The Prime Minister of Vietnam. Decision No. 57/QÐ-TTg Dated 9th January 2012 “Aproving the Forest Protection and Development Plan for the 2011–2020 Period”; The Prime Ministrer of Vietnam: Hanoi, Vietnam, 2012. (In Vietnamese)
64. The Prime Minister of Vietnam. Decision No. 120/GÐ-TTg Dated 22nd January 2015 “Approving the Scheme on Protection and Development of Coastal Forest Respond to Climate Change, the Period 2015–2020”; Prime Minister of Vietnam: Hanoi, Vietnam, 2015. (In Vietnamese)
65. The Prime Minister of Vietnam. Decision No. 125/QD-TTg Dated 16th January 2020 “Aproving of the Investment Policy of the Project ‘Conservation and Management of Coastal Mangrove Ecosystems in the Red River Dealta Region–KfW11.’ Promotion loans of the German Contruction Bank (KfW) and EU Non-Refundable ODA”; The Prime Minister of Vietnam: Hanoi, Vietnam, 2020; p. 3. (In Vietnamese)
66. Chairwoman of the National Assembly. Vietnam Law on Forestry; Vietnam National Assembly: Hanoi, Vietnam, 2017.
67. MARD. Results of Forest Investigation in 25 Provinces in the Period 2014–2015 under the Project “Total National Forest Investigation in 2013–2016”; MARD: Hanoi, Vietnam, 2016.
68. MARD. Final Report of the Project “Total National Forest Investigation in the Period 2013–2016”; MARD: Hanoi, Vietnam, 2017. (In Vietnamese)
69. Joseph, G. Fundamentals of Remote Sensing; Universities Press: Hyderabad, India, 2018.
70. Bakr, N.; Weindorf, D.; Bahnassy, M.; Marei, S.; El-Badawi, M. Monitoring land cover changes in a newly reclaimed area of Egypt using multi-temporal Landsat data. Appl. Geogr. 2010, 30, 592–605. [CrossRef]
71. Pirotti, F.; Parraga, M.A.; Stuaro, E.; Dubbini, M.; Masiero, A.; Ramanzin, M. NDVI from Landsat 8 vegetation indices to study movement dynamics of Capra ibex in mountain areas. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2014, XL-7, 147–153. [CrossRef]
72. Singh, A. Digital change detection techniques using remotely-sensed data. Int. J. Remote Sens. 1989, 10, 989–1003. [CrossRef]
73. Tucker, C.J.; Pinzon, J.E.; Brown, M.E.; Slayback, D.A.; Pak, E.W.; Mahoney, R.; Vermote, E.F.; El Saleous, E. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sens. 2005, 26, 4485–4498. [CrossRef]
74. Bhandari, A.; Kumar, A.; Singh, G. Feature extraction using Normalized Difference Vegetation Index (NDVI): A case study of Jabalpur city. Procedia Technol. 2012, 6, 612–621. [CrossRef]
75. Ibrahim, N.; Mustapha, M.; Lihan, T.; Ghaffar, M. Determination of mangrove change in Matang Mangrove Forest using multi temporal satellite imageries. In Proceedings of the AIP Conference Proceedings, Rhodes, Greece, 21–27 September 2013; American Institute of Physics: College Park, MD, USA, 2013.
76. Taufik, A.; Ahmad, S.S.S.; Ahmad, A. Classification of landsat 8 satellite data using NDVI thresholds. J. Telecommun. Electron. Comput. Eng. 2016, 8, 37–40.
77. Hashim, H.; Abd Latif, Z.; Adnan, N.A. Urban vegetation classification with NDVI threshold value method with very high resolution (VHR) PLEIADES Imagery. In Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Kuala Lumpur, Malaysia, 1–3 October 2019; pp. 237–240.
78. Hu, Q.; Wu, W.; Xia, T.; Yu, Q.; Yang, P.; Li, Z.; Song, Q. Exploring the use of Google Earth imagery and object-based methods in land use/cover mapping. Remote Sens. 2013, 5, 6026–6042. [CrossRef]
79. Malarvizhi, K.; Kumar, S.V.; Porchelvan, P. Use of high resolution Google Earth satellite imagery in landuse map preparation for urban related applications. Procedia Technol. 2016, 24, 1835–1842. [CrossRef]
80. Dat, P.T.; Yoshino, K. Impacts of mangrove management systems on mangrove changes in the Northern Coast of Vietnam. Tropics 2016, 24, 141–151.
81. Anand, A. Unit-14 Accuracy Assessment. Processing and Classification of Remotely Sensed Images. Remote Sensing and Image Interpretaion; Indiara Gandhi National Open University: Delhi, India, 2017; pp. 59–78.
82. Conchedda, G.; Durieux, L.; Mayaux, P. An object-based method for mapping and change analysis in mangrove ecosystems. ISPRS J. Photogramm. Remote Sens. 2008, 63, 578–589. [CrossRef]
83. Maryantika, N.; Lin, C. Exploring changes of land use and mangrove distribution in the economic area of Sidoarjo District, East Java using multi-temporal Landsat images. Inf. Process. Agric. 2017, 4, 321–332. [CrossRef]
84. Thomas, N.; Bunting, P.; Lucas, R.; Hardy, A.; Rosenqvist, A.; Fatoyinbo, T. Mapping mangrove extent and change: A globally applicable approach. Remote Sens. 2018, 10, 1466. [CrossRef]
85. Institute of Ecology andWorks Protection. Profile of the Mangrove Planting Project in Nga Son District, Thanh Hoa Province. Unpublished. 2020. (In Vietnamese)
86. Tuominen, S.; Fish, S.; Poso, S. Combining remote sensing, data from earlier inventories, and geostatistical interpolation in multisource forest inventory. Can. J. For. Res. 2003, 33, 624–634. [CrossRef]
87. Ghosh, S.; Behera, M. Aboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data-The superiority of deep learning over semi-empirical model. Comput. Geosci. 2021, 150, 104737. [CrossRef]
88. Giri, C. Recent advancement in mangrove forests mapping and monitoring of the world using earth observation satellite data. Remote Sens. 2021, 13, 563. [CrossRef]
89. Hu, T.; Zhang, Y.; Su, Y.; Zheng, Y.; Lin, G.; Guo, Q. Mapping the global mangrove forest aboveground biomass using multisource remote sensing data. Remote Sens. 2020, 12, 1690. [CrossRef]
90. Wicaksono, P.; Danoedoro, P.; Hartono; Nehren, U. Mangrove biomass carbon stock mapping of the Karimunjawa Islands using multispectral remote sensing. Int. J. Remote Sens. 2016, 37, 26–52. [CrossRef]
91. Hamdan, O.; Khairunnisa, M.; Ammar, A.; Hasmadi, I.M.; Aziz, H.K. Mangrove carbon stock assessment by optical satellite imagery. J. Trop. For. Sci. 2013, 554–565.
92. Giri, S.; Mukhopadhyay, A.; Hazra, S.; Mukherjee, S.; Roy, D.; Ghosh, S.; Ghosh, T.; Mitra, D. A study on abundance and distribution of mangrove species in Indian Sundarban using remote sensing technique. J. Coast. Conserv. 2014, 18, 359–367. [CrossRef]
93. Wan, L.; Zhang, H.; Lin, G.; Lin, H. A small-patched convolutional neural network for mangrove mapping at species level using high-resolution remote-sensing image. Ann. GIS 2019, 25, 45–55. [CrossRef]
94. Valderrama-Landeros, L.; Flores-de-Santiago, F.; Kovacs, J.; Flores-Verdugo, F. An assessment of commonly employed satellitebased remote sensors for mapping mangrove species in Mexico using an NDVI-based classification scheme. Environ. Monit. Assess. 2018, 190, 23. [CrossRef]
95. Heenkenda, M.K.; Joyce, K.E.; Maier, S.W.; Bartolo, R. Mangrove species identification: Comparing WorldView-2 with aerial photographs. Remote Sens. 2014, 6, 6064–6088. [CrossRef]
96. Chellamani, P.; Singh, C.P.; Panigrahy, S. Assessment of the health status of Indian mangrove ecosystems using multi temporal remote sensing data. Trop. Ecol. 2014, 55, 245–253.
97. Nurhaliza, A.; Damayanti, A.; Dimyati, M. Monitoring Area and Health Changes of Mangrove Forest Using Multitemporal Landsat Imagery in Taman Hutan Raya Ngurah Rai, Bali Province. IOP Conf. Ser. Earth Environ. Sci. 2021, 673, 012050.
98. Vidhya, R.; Vijayasekaran, D.; Farook, M.A.; Jai, S.; Rohini, M.; Sinduja, A. Improved classification of mangroves health status using hyperspectral remote sensing data. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2014, 40, 667. [CrossRef]
99. Hamilton, S. Assessing the role of commercial aquaculture in displacing mangrove forest. Bull. Mar. Sci. 2013, 89, 585–601. [CrossRef]
100. Bryan-Brown, D.N.; Connolly, R.M.; Richards, D.R.; Adame, F.; Friess, D.A.; Brown, C.J. Global trends in mangrove forest fragmentation. Sci. Rep. 2020, 10, 7117. [CrossRef] [PubMed]
101. Primavera, J.H. Development and conservation of Philippine mangroves: Institutional issues. Ecol. Econ. 2000, 35, 91–106. [CrossRef]
102. Richards, D.R.; Friess, D.A. Rates and drivers of mangrove deforestation in Southeast Asia, 2000–2012. Proc. Natl. Acad. Sci. USA 2016, 113, 344–349. [CrossRef]
103. McNally, R.; McEwin, A.; Holland, T. The Potential for Mangrove Carbon Projects in Vietnam; SNV–Netherlands Development Organization REDD; Netherlands Development Organization: Hague, The Netherlands, 2011.
104. Webb, E.L.; Jachowski, N.R.; Phelps, J.; Friess, D.A.; Than, M.M.; Ziegler, A.D. Deforestation in the Ayeyarwady Delta and the conservation implications of an internationally-engaged Myanmar. Glob. Environ. Chang. 2014, 24, 321–333. [CrossRef]
105. Reed, S.; Nghi, N.; Minh, N.; Lien, H.; Hung, T.; Thien, N.; Anh, N.K. Building Coastal Resilience in Vietnam: An Integrated, Community-Based Approach to Mangrove Management, Disaster Risk Reduction, and Climate Change Adaptation; CARE international in Vietnam: Ha noi, Vietnam, 2015.
106. Ward, R.D.; Friess, D.A.; Day, R.H.; MacKenzie, R.A. Impacts of climate change on mangrove ecosystems: A region by region overview. Ecosyst. Health Sustain. 2016, 2, e01211. [CrossRef]
107. Chaudhuri, P.; Chaudhuri, S.; Ghosh, R. The Role of Mangroves in Coastal and Estuarine Sedimentary Accretion in Southeast. Asia; Sedimentary Processes-Examples from Asia, Turkey and Nigeria: London, UK, 2019; pp. 89–112.
108. Thakur, S.; Mondal, I.; Bar, S.; Nandi, S.; Ghosh, P.; Das, P.; De, T.K. Shoreline changes and its impact on the mangrove ecosystems of some islands of Indian Sundarbans, North-East coast of India. J. Clean. Prod. 2020, 124764. [CrossRef]
109. Thanh Hoa Department of Agriculture and Rural Development. Biên B£n Nghi»m Thu Ch§t L÷ñng Công Trình; Thanh Hoa Department of Agriculture and Rural Development: Thanh Hoa, Vietnam, 2016.
110. Kamthonkiat, D.; Rodfai, C.; Saiwanrungkul, A.; Koshimura, S.; Matsuoka, M.; Lasaponara, R. Geoinformatics in mangrove monitoring: Damage and recovery after the 2004 Indian Ocean tsunami in Phang Nga, Thailand. Nat. Hazards Earth Syst. Sci. 2011, 11, 1851–1862. [CrossRef]
111. Smith, T.J.; Anderson, G.H.; Balentine, K.; Tiling, G.; Ward, G.A.; Whelan, K.R. Cumulative impacts of hurricanes on Florida mangrove ecosystems: Sediment deposition, storm surges and vegetation. Wetlands 2009, 29, 24. [CrossRef]
112. People’s Committee of Thanh Hoa Province. Final Report of National Target Program Response to Climate Change, Period 2010–2015, Thanh Hoa Province; People’s Committee of Thanh Hoa province: Thanh Hoa, Vietnam, 2015. (In Vietnamese)
113. Thanh Hoa Department of Agriculture and Rural Development. Acceptance of the Construction Quality; Thanh Hoa Department of Agriculture and Rural Development: Thanh Hoa, Vietnam, 2015. (In Vietnamese)
114. Laengner, M.L.; Siteur, K.; van derWal, D. Trends in the seaward extent of saltmarshes across Europe from long-term satellite data. Remote Sens. 2019, 11, 1653. [CrossRef]
115. Dan, T.; Chen, C.; Chiang, S.; Ogawa, S. Mapping and change analysis in mangrove forest by using Landsat imagery. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2016, 3, 109. [CrossRef]
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► See detail: Mangrove Forest Landcover Changes in Coastal Vietnam: A Case Study from 1973 to 2020 in Thanh Hoa and Nghe An Provinces
Huong Thi Thuy Nguyen 1,2,*, Giles E. S. Hardy 1 , Tuat Van Le 3, Huy Quoc Nguyen 3, Hoang Huy Nguyen 2, Thinh Van Nguyen 2 and Bernard Dell 1,4
1 Agriculture and Forest Sciences, Murdoch University, Murdoch 6150, Australia; G.Hardy@murdoch.edu.au (G.E.S.H.); B.Dell@murdoch.edu.au (B.D.)
2 Silviculture Research Institute, Vietnamese Academy of Forest Sciences, Duc Thang, Bac Tu Liem, Ha Noi 11910, Vietnam; nguyenhuyhoangvfu@gmail.com (H.H.N.); nguyenthinhfsiv@gmail.com (T.V.N.)
3 Institute of Ecology and Works Protection, Vietnam Academy for Water Resources, 267 Chua Boc, Dong Da, Ha Noi 11910, Vietnam; tuatwip@gmail.com (T.V.L.); huy_ctcr@yahoo.com (H.Q.N.)
4 Forest Protection Research Centre, Vietnamese Academy of Forest Sciences, Duc Thang, Bac Tu Liem, Ha Noi 11910, Vietnam
* Correspondence: huong.nguyen@murdoch.edu.au; Tel.: +84-977-795-206
Forests 2021, 12, 637. https://doi.org/10.3390/f12050637 https://www.mdpi.com/journal/forests
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