OBJECTIVES
- Investigating spatial distribution and structure coastal mangrove in study area
- Quantification the land cover of coastal mangroves in Kien Thuy and Do Son Districts, Hai Phong City during the period from 2001 to 2016
- Identifying the key drivers of land cover change over 15 years and proposing possible solutions for sustainable management of coastal mangrove in study area.
STUDY AREA
The study area is the coastal mangrove in two communes, including Kien Thuy and Do Son Districts, coastal mangrove mainly distribution in Hai Phong City (figure 1.1)
Figure 1.1: : Study area: (a) Vietnam map, (b) Hai Phong province, (c) Kien Thuy and Do Son district and coastal mangrove distribution
Hai Phong, particularly the Kien Thuy and Do Son Districts along the Gulf of Tonkin, experiences a tropical monsoon climate characterized by warm, rainy weather The temperature typically ranges from 20 to 23 degrees Celsius, with extremes reaching up to 40 degrees Celsius and dropping as low as 5 degrees Celsius Humidity levels are high, averaging between 80% and 85%, peaking at 100% during July, August, and September, while the lowest levels occur in December and May The region receives an average annual rainfall of 1600 to 1800 mm and enjoys approximately 1700 hours of sunshine each year.
Dai Hop Commune, located in Kien Thuy District, spans an area of 1,097.78 hectares and is home to approximately 9,492 residents as of 2009, featuring a significant mangrove area of about 450 hectares The commune consists of 2,675 households, resulting in a population density of over 865 people per square kilometer In contrast, Bang La Commune in Do Son District covers around 4,237.29 hectares and had a population of 51,417 in 2010, with a per capita income of $1,100 (27 million VND) recorded in 2005.
METHODOLOGY
Data Sources
This study aimed to enhance efficiency in remote sensing and GIS technology applications for mangrove research by utilizing diverse data sources It focused on mapping land cover changes in coastal mangroves and detecting these changes through various vegetation indices, drawing from multiple legal documents, scientific studies, essays, and research projects both in Vietnam and internationally.
Thesis collected data with the following information:
- The study, texts, documents, data from the agency, the sectored level, books, dissertations, projects relating to the land cover change to study forest mangrove in Vietnam and the world
- The researching, reports, thesis, which related to distribution and structure, land cover change of coastal mangrove, dynamic coastal mangrove in Hai Phong Province, Vietnam and the world
- The natural, economic and social conditions of study area
Thesis used Landsat satellite images to monitor, investigate, and interpret the spatial distribution and structure and land cover change of coastal mangrove in study area
T ABLE 3.1: Landsat image collected in study
Year Landsat image code Date Resolution Path/Row
- Source: http://earthexplorer.usgs.gov
We downloaded Landsat image from Earth Explorer Image clustering channels were been collected including individual spectral channels due to needing combination and composition to easy conduct steps later
The study area encompasses not only coastal mangroves but also additional regions, necessitating the delineation of boundaries for effective analysis To conduct a thorough examination of the images, it is essential to isolate the coastal mangrove section from the surrounding areas, ensuring clarity and precision in the study.
Field survey method
To map the distribution and assess the structure of coastal mangroves, plots measuring 30x30 meters (900 m²) were established along contour lines These sample plots were carefully designed to facilitate accurate data collection and analysis.
Figure 3.1: Size of sample plot establishment
Standard plots perpendicular to the sea should be at least 100 meters long, with the first plot established 20 meters from the outer edge of the forest range.
To select the ideal plot locations, a comprehensive reconnaissance of the study area will be conducted, followed by the use of GPS technology to pinpoint the coordinates of each plot This approach ensures that the plots are evenly distributed across the area, accurately representing the typical conditions of the study site.
The sampling plots set up across the study area were crucial for assessing the health of mangrove subjects, aiding in the interpretation of the study's findings and ensuring the precision of training samples used for classifying coastal mangroves.
During the investigation, thesis established 47 represented typically plots, evenly distributed throughout the state for basic subjects in the study area
With 47 sample plots, we investigated the following table form on the health status of coastal mangrove study area
Table 3.3 Table form on the health status of coastal mangrove in the study area
Height under (m): The height will measured using calibrated meters, approximately, from the base to the lowest living branch and joined the tree canopy
Tree height (H total) (m): The height will be measured using calibrated ruler, approximately, from the base to the highest branches of the tree growth and joined the tree canopy
DBH (m): Diameter at breast height 1.3 m: Calculating from the base by tape to 1.3m in struck
Canopy diameter (m): The diameter will be measured using calibrated meters, by two directs W-E and N-S and the largest canopy of trees
Canopy Cover (%): Calculating by the tube had diameter 2cm and judged by the
This thesis aims to establish 47 sample plots to assess the status of mangroves using GPS for precise coordinate definition The collected survey data will be processed and integrated into ArcGIS to evaluate and map the health of coastal mangrove forests in the study area.
Study mapped the land cover of coastal mangrove forest in 2016 based on Landsat 8 and figure 3.2 will illustrate this process
Figure 3.2: Flow chart of methodology for image classification and change mapping
Supervised Classification in 2016 NDVI analysis in 2016
Using Supervised Classification and NDVI mapping land cover change in study area in
2016 In addition, we will take the change detection, which has the highest accuracy to monitor the land cover change over time
Assessment of accuracy refers to the extent to which a map or classification aligns with real-world conditions (Foody, 2002) Various methods exist for evaluating the accuracy of maps or classifications, but the confusion or error matrix is the most commonly used This method effectively describes both the accuracy of the classification and the characteristics of any errors present (Foody).
To evaluate the accuracy of classification classes when comparing various vegetation indices, this study utilized an error matrix table based on a comprehensive dataset from state forests and other locations A total of 335 reference points were surveyed in the field to provide validation samples for mapping purposes Subsequently, 102 points, representing over 30% of the total, were randomly selected for accuracy assessment using three distinct vegetation indices.
The study used Kappa coefficient, a discrete multivariate technique, representing the percentage of chance agreement (Congalton, R G; Foody Gile.M, 2002; and Foody Gile M,
1992) The Kappa coefficient is been calculated in following formula:
In a matrix, the variable r represents the number of rows, while x_ii denotes the observations in the ith row and ith column The marginal totals for row i and column i are indicated by x_i+ and x_+i, respectively, with N signifying the total number of observations (Bishop et al., 1975).
This study focuses on analyzing land cover change in the designated area through the examination of collected data and documents Following the assessment of mapping accuracy, the thesis presents a thematic analysis of fluctuations in land cover over time.
Monitoring the land cover change of coastal area of study area during the period
Satellite images are primarily utilized for two key purposes: mapping land cover through image classification and detecting land cover changes This study specifically examines these applications to analyze land cover changes in the coastal area of Hai Phong.
Supervised and unsupervised classification are two key techniques in remote sensing analysis (Bark et al., 2010; Singh, 1989) Supervised classification relies on ground truth points to create training sets that capture spectral signatures from different dates, while unsupervised classification operates solely on raw images without additional information (Bark et al., 2010) Despite its advantages, unsupervised classification faces significant challenges regarding the accuracy of ground truth points (Bruzzon and Prieto, 2002) This study highlights that supervised classification, particularly the maximum likelihood method, achieves higher accuracy and efficiency in detecting changes.
Vegetation indices are crucial for monitoring changes in plant life, as they interact with solar radiation differently from other materials By analyzing spectral variations, these indices provide valuable insights into plant health, water content, and environmental stress Commonly used vegetation indices, such as the Ratio Vegetation Index (RVI), Enhanced Vegetation Index (EVI), and Normalized Difference Vegetation Index (NDVI), enhance the vegetation signal in remote sensing data They serve various purposes, including phenological monitoring, vegetation classification, and assessing vegetation parameters, making them essential tools in ecological research and environmental management.
The NDVI (Normalized Difference Vegetation Index) is the most widely used vegetation index due to its ability to minimize noise from factors such as changing sun angles, topography, clouds, shadows, and atmospheric conditions (Brunkei, 2007) By utilizing two spectral channels, NDVI effectively distinguishes between areas with and without vegetation, leveraging the distinct light absorption characteristics of chlorophyll in leaves within the visible spectrum (0.4-0.7 µm) and the reflection of near-infrared light (0.7-1 µm) (Journal of Science and Technology of Nghe An, 5/2014) The NDVI is calculated using a specific formula that highlights these properties.
NDVI = (NIR - RED) / (NIR + RED)
Where, as before, NIR and RED (or VIS) are the response in the near-infrared and red (or visible) bands respectively
The Normalized Difference Vegetation Index (NDVI) is calculated by dividing the difference between near-infrared and red (or visible) reflectance by their sum This index ranges from -1 to +1, effectively illustrating vegetation cover distribution within the study area and categorizing various plant groups based on their NDVI values, typically into four distinct levels.
From negative value to zero is water;
From the value is less than 0.1 usually soil, rock, sand or snow;
From the value of approximately 0.2 to 0.5 are bushes, grass or dry fields;
From 0.6 to 0.9 ~ 1 as trees, plants
NDVI, or Normalized Difference Vegetation Index, is essential for analyzing vegetation, including estimating crop yields and assessing land suitability for agriculture It is closely linked to various parameters such as surface soil conditions, plant photosynthesis, water availability, and biomass estimation.
This thesis evaluates land use and land cover changes in a study area from 2010 to 2016 using two techniques: supervised image classification and NDVI (Normalized Difference Vegetation Index) The research aims to determine which method provides higher accuracy in mapping land cover changes over this period.
Sociological investigation method
In our study area, we conducted sociological surveys by interviewing local government officials and residents of Dai Hop and Bang La to gather accurate information The population of Dai Hop was 9,491 in April 2009, while Bang La had 8,765 residents in 2013 We surveyed 30 households and engaged 300 individuals from each commune, representing approximately 30% of the population, with most participants benefiting directly from the coastal mangroves The feedback and suggestions from local residents will aid in formulating strategies for local governments and forest managers to enhance the management, protection, and development of mangrove resources, ensuring sustainable exploitation of natural resources and fishing products.
Table 3.4: Sociological survey by interviewing
No Name Address History of mangrove (1997 to now)
Benefits Damages Policies Expected Other exploits
SPATIAL STRUCTURE AND DISTRIBUTION OF COASTAL MANGROVES 14
Spatial mangrove structure in Dai Hop commune, Kien Thuy district
The study area spans 12 km and features a mangrove area of 92.27 hectares, as measured by ArcMap Field surveys identified two mangrove species: Kandelia obovate and Sonneratia caseolaris This research combines field surveys, interviews, and existing data sources to enhance our understanding of the mangrove ecosystem.
2 areas have been planted since 1999 and regeneration layer has had a lot, mostly grown closely sea dikes but majority is Kandelia obovata species at that time of study (7/2014)
The average canopy diameter of the Sonneratia caseolaris species reaches up to 4.9 meters, with some individual trees exceeding 7 meters in diameter In contrast, the Kandelia obovata species has a significantly smaller average canopy diameter of 1.4 meters.
Canopy cover serves as a vital ecological indicator, aiding in the identification of various plant species and the estimation of functional variables It acts as an intermediary in differentiating signals reflected from both the forest canopy and the forest floor.
In 1999, the Kandelia obovate species was planted in Dai Hop commune, Kien Thuy district, contributing to a coastal mangrove canopy cover of nearly 87% This extensive coverage spans over 410 hectares, extending 650 to 720 meters toward the sea.
Spatial mangrove structure in Bang La commune, Do Son district
This study highlights the coastal mangrove species found in Bang La commune, Do Son district, including Sonneratia caseolaris, Kandelia obovata, Bruguiera gymnorrhiza, Avicennia marina, and Acanthus ebracteatus Notably, the two dominant species in the area are Sonneratia caseolaris and Kandelia obovata, which are primarily located along the dike and were part of a mangrove plantation initiative established in 1999.
The study revealed that the average canopy diameter of Sonneratia caseolaris in Dai Hop commune is the largest at 4.9 meters, with some trees exceeding 7 meters In comparison, Kandelia obovata and Bruguiera gymnorrhiza have average canopy diameters of 1.4 meters and 1.9 meters, respectively The smallest canopy diameters were observed in Avicennia marina and Acanthus ebracteatus, measuring 0.9 meters and 0.2 meters, respectively.
Canopy cover: Mangrove species at Bang La commune ranges from 90% to 95%
The Sonneratia caseolaris species, known for its impressive height, is strategically planted near the sea to mitigate flow rates, particularly during floods For additional information and ease of reference, we have included a table in the Appendix that details 335 points, complete with specific characteristics and coordinates.
In generally, spatial distribution and structure of coastal mangrove in the study is divided into three stable parts, the common tree is Kandelia obovata, Sonneratia caseolaris,
Bruguirea gymnorrhiza that has a significantly effect in protect the sea dikes, livelihood with local people, reducing the strong velocity, limiting the sediment transports, enhancing deposition and sedimentation.
MAPPING LAND COVER CHANGE
Monitoring land cover change
In 2016, the NDVI analysis revealed that mangrove areas exhibited values ranging from 0.215 to 0.385, while water bodies were identified with NDVI values between -0.78 and 0.0507 Wetland classifications showed NDVI values from 0.0508 to 0.131, and other classifications situated between wetland and mangrove areas ranged from 0.132 to 0.214 (refer to Table 4.2).
The analysis indicates that the mangrove area in the study sites has been estimated over a three-year period, as shown in Table 4.2 Since 1990, the total mangrove forest area in the city has been 293 hectares Significant growth occurred in 1997, thanks to support from the State and the 661 program for mangrove plantation by the Denmark Red Cross Over time, the coastal mangrove area has continued to expand, as detailed in Table 4.5.
Table 4.5: Coastal mangrove area at Hai Phong city over time
Year Mangrove (ha) Non-mangrove (ha)
According to ArcMap data, the mangrove area in 2001 was at its lowest, measuring approximately 106 hectares, while the non-mangrove area reached nearly 744 hectares By 2007, the mangrove area increased to around 126 hectares, with the non-mangrove area slightly decreasing to 724 hectares Notably, in 2016, both mangrove and non-mangrove areas saw significant growth, expanding to nearly 544 hectares and 306 hectares, respectively.
Figure 4.4.1: Land cover map coastal mangroves (a) Land cover in 2001, (b) Land cover in 2007, (c) Land cover in 2016 Table 4.6: Changing land cover area of coastal area in Hai Phong from 2001 to 2016
As the result of table 4.5.2 about change area of coastal mangrove in two communes of Hai Phong during period of 2001-2016, the proportion of mangrove area from 2001 to
Between 2001 and 2007, the area increased by approximately 19%, rising from 106.02 hectares to 126.0 hectares Conversely, the area designated for other uses, including grasses, dykes, roads, fields, and non-identified areas, decreased by about 15.1%, dropping from 143.28 hectares in 2001 to 121.70 hectares in 2007.
2016, the figure for mangrove area remarkable climbs, approximately 332% from 126.0ha in
Between 2007 and 2016, the area of mangroves increased significantly from 200ha to 379ha, marking a 332% growth In contrast, the percentage of wetland and water areas declined by approximately 43% and 46%, resulting in reductions of about 133ha and 179ha, respectively The other class saw a slight increase of around 53% Overall, the 15-year survey indicates a positive trend in mangrove area while highlighting the decline in wetland and water regions.
The following figures will show the dynamic of coastal mangrove in three periods: 2001-2007; 2007-2016 and 2001-2016
Figure 4.4.2: Mangrove change in Hai Phong during the period of 2001-2007
Figure 4.4.4: Mangrove change in Hai Phong during the period of 2001-2016
Figure 4.4.5: Chart of land cover changes during 15 year from 2001 to 2016
Figure 4.4.6: Land cover change in 2007 and 2016 compared to area in 2001
Between 2007 and 2016, the coastal mangrove area at the study site experienced a significant increase of nearly 400 hectares, rising from just over 106 hectares in 2001 In contrast, the wetland and water areas saw a gradual decline of approximately 133 hectares and 179 hectares, respectively, down from around 200 hectares and 400 hectares in 2001 This change can be attributed to government initiatives focused on mangrove protection and awareness campaigns, which have led to continuous improvements in both the area and quality of mangroves Additionally, the implementation of a mangrove allocation policy allows local communities to manage, protect, and benefit from these resources, contributing to the growth of coastal mangrove forests.
Between 2001 and 2007, significant changes in land cover were observed in the study area, as illustrated in Figures 4.4.5 and 4.4.6 During this period, mangrove coverage increased by approximately 19%, while other land types and water bodies saw increases of just over 15% and 1%, respectively This shift can be attributed to the initiation of mangrove plantation efforts supported by the central government through the 327 Program (now the 661 Program) and the Danish Red Cross project initiated in 1997 Additionally, local residents' growing awareness of the benefits of mangrove ecosystems contributed to these changes in land cover.
MAIN KEY DRIVERS OF LAND COVER CHANGE IN HAI PHONG DURING
From 2001 to 2016, the mangrove area in the research region exhibited a positive growth trend due to improved management policies, active restoration efforts, and conservation-linked exploitation Key initiatives include the community-based forest management model by the Vietnamese Academy of Forest Sciences, the completion of project 661, and Japanese investment in conservation and planning since 1997 Addressing challenges such as land use targets, population pressure, local livelihoods, global warming, sea level rise, and natural disasters is crucial Effective strategies involve raising local awareness, enhancing sea dykes, and coordinating conservation activities between the Youth Union and the ranger department.
Enhancing international cooperation is crucial for the sustainable management of coastal wetlands in Hai Phong city, particularly in mangrove areas Encouraging collaborative efforts in conservation and sustainable development is essential for promoting environmental protection and resource management.
A recent survey indicates that individuals seeking to utilize non-timber forest products, such as flowers, fruits, shrimp, and seafood, must obtain approval from local government authorities This requirement plays a crucial role in planning and ensures that the protective functions of the forest are minimally disrupted.
The integration of advanced technologies such as GIS and remote sensing in managing and monitoring forest resources significantly enhances the role of coastal mangrove ecosystems for local communities This advancement underscores the vital importance of local residents in the stewardship and protection of mangrove forests.
From the land cover changes mentioned above, we offered some solutions for sustainable development of coastal area in study site of Hai Phong city
In recent years, the Party and State have focused on developing a new business model for vegetable exports to countries like Japan However, this model is currently limited to small-scale, retail operations that lack scientific backing To enhance economic efficiency and make better use of land, it is essential to adopt a garden pond model for large-scale expansion, following a well-planned, long-term development strategy rather than the current spontaneous approach There is a pressing need to attract both domestic and foreign investment, particularly in forestry activities, to foster partnerships and support growth in this sector.
Increasing awareness through mass media—such as newspapers, radio, and television—about climate change and its effects is crucial Highlighting the importance of mangroves and coastal protection forests in mitigating coastal erosion and natural disaster damage is essential Educating the public, especially the younger generation, about the vital role of forests for humanity and the environment is necessary Organizing seminars focused on mangrove-related issues and providing specialized training for local staff can enhance community understanding Additionally, implementing training courses for local units on construction methods and sustainable coastal livelihoods will help improve the income of communities reliant on mangroves, ultimately contributing to their protection.
To enhance coastal forest lands, it is essential to continue applied research and technology transfer for forest tree seeds This includes promoting scientific breeding techniques and investigating various casuarina and mangrove species with robust branching and root systems, which can effectively safeguard against storms, stabilize soil, and bolster groundwater resources Additionally, integrating information technology into the management and monitoring of forest resource changes is crucial for the protection of coastal forests.
Agricultural and forestry encouraging activity
Advancing forestry science and technology is essential for enhancing skills in planting, tending, and protecting forests, as well as preventing fires and pests Developing replicable models that apply these scientific advancements can promote sustainable forest management By establishing guidelines and conventions, communities can actively participate in these initiatives, ensuring a collaborative approach to forestry conservation.
The study aimed to address four objectives, one of which focused on the spatial distribution and structure of mangroves in Hai Phong city, highlighting key species such as Kandelia obovata and Sonneratia caseolaris (L.) Engl The primary land cover types identified in the area include mangrove, wetland, water, and other categories like grasses, dykes, roads, and shrubs Land cover changes were mapped using two techniques: Supervised Classification and NDVI.
From 2001 to 2016, NDVI was selected for monitoring land cover changes due to its high accuracy of 87.6% Between 2007 and 2016, the coastal mangrove area in the study site increased significantly by nearly 400 hectares, while wetland and water areas experienced declines of approximately 133 hectares and 179 hectares, respectively In contrast, the period from 2001 to 2007 showed steady fluctuations, with mangrove coverage increasing by about 19%, while wetland and water areas saw modest increases of just over 15% and 1%, respectively.
Sociological research and documentation reveal that humans—comprising authorities, local residents, and visitors—are the primary contributors to land cover changes in the study area Additionally, policies and international collaborations, particularly Japan's mangrove plantation projects, significantly influence coastal mangrove land cover alterations in Hai Phong Consequently, this thesis emphasizes the need to enhance and promote policies aimed at protecting mangrove ecosystems.
Study has achieved some significant results, but there are still some exist as follows:
- The scope of the research is quite wide, so we could be missed some of mangroves area, especially where is near the sea
- The limited forest inventory parameters over the years and the documents should not assess overall health of mangrove forest
- Limited documentation, parameters estimated mangroves and lack, Landsat image is not clear, so we have not assessed status of mangroves in general
- When we surveyed also having some problems like bad weather, low satellites, interference, difficult terrain, health investigators are not good, so that research results are not high
In their 2003 study published in the IEEE Transactions on Geoscience and Remote Sensing, Peng Gong, Ruiliang Pu, Greg S Biging, and Mirta Rosa Larrieu explored the estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data Their research highlights the effectiveness of hyperspectral data in enhancing the accuracy of forest leaf area assessments, contributing valuable insights to remote sensing methodologies.
2 Huete, A.R.; Justice C MODIS vegetation index (MOD13) algorithm theoretical basis document Ver 3, 1999
In their 2007 study, Bunkei Matsushita, Wei Yang, Jin Chen, Yuyichi Onda, and Guoyu Qiu investigated the sensitivity of the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI) to topographic effects within a high-density cypress forest Their findings highlight the importance of considering topography when utilizing these vegetation indices for accurate environmental monitoring.
4 Korhonen, L., Korhonen, K.T., Rautiainen, M & Stenberg, P 2006 Estimation of forest canopy cover: a comparison of field measurement techniques Silva Fennica 40(4):
5 Giles M Foody 2002 Status of land cover classification accuracy assessment Department of Geography, University of Southampton, highfield, Southampton, SO17 1BJ, UK Remote Sensing of Environment 80 (2002) 185-201)
Aizpuru, M., Achard, F., and Blasco, F (2000) conducted a comprehensive global assessment of mangrove forest cover change utilizing medium to high-resolution satellite imagery This research was part of the EEC project no 15017-1999-05 FIED ISP FR, carried out by the Joint Research Center in Ispra.
7 Blasco, F., Aizpuru, M & Gers, C (2001) Depletion of the mangroves of Continental Asia
8 Aschbacher, A., Giri, C., Ofren, R., Tiangco, P.N., Suselo, T.B., Vibulsresth, S & sensing and GIS technology (main report) Asian Institute of Technology, National Research Council of Thailand, Royal Forest Department, and UNEP-GRID, Bangkok
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In 2008, Nguyen Quoc Khanh and colleagues conducted research on the application of remote sensing and GIS technology to create a comprehensive map of the current state of natural resources This initiative aims to support provincial environmental protection planning, as outlined by the Ministry of Natural Resources and Environment.
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20 Tran Dinh Lan, Do Thi Thu Huong, and Cao Thi Thu Trang (2014) Assessment of
Natural Resources Use for Sustainable Development - DPSIR Framework for Case Studies in Hai Phong and Nha Trang, Vietnam
21 Nguyen Xuan Trung Hieu (2013) Application remote sensing and GIS to establish mapping dynamic of vegetation land-cover in Hue city
22 Le Dai Ngoc (2013) Composite color to interpretation of Landsat 7 satellite image editing topographic maps at 1: 250,000
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Object-Based Approach Remote sensing 2013, 5 ,183-201; doi:10.3390/rs5010183, ISSN 2072-4292,
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Bàng La (Đồ Sơn, Hải Phòng), Tạp chí Khoa học Công nghệ biển, Đại học Hải Phòng
APPENDIX B COASTAL MANGROVE MANAGEMENT SURVEY
Part 1: Assessment and awareness of local people about coastal mangroves
1 When did the mangrove plantation project have?
2 Which was government or foreign organization proposes and fund that project?
3 Who were involved in plantation mangroves in this project?
4 How long did the project take to complete?
5 Since then, has the coastal mangrove protection managed closely?
6 After had mangroves, how do it influence to local people?