LITERATURE REVIEWS
Climate change
The unequivocal warming of the climate system is confirmed by observed increases in global air and ocean temperatures, widespread melting of snow and ice, and rising sea levels Since 1850, the Earth's average surface temperature has risen by 0.76°C, with projections indicating a further increase of 1.8 to 4.0°C this century, and potentially up to 6.4°C in the worst-case scenarios Human activities, such as the burning of fossil fuels, are significantly contributing to air pollution and excessive greenhouse gas emissions, which trap heat in the atmosphere and exacerbate global warming The rise in global temperatures is leading to the rapid melting of mountain glaciers, small ice caps, Greenland’s Ice Sheet, and the Antarctic Ice Sheet, causing sea levels worldwide to rise due to both melting ice and ocean water expansion Additionally, the proliferation of factories emitting harmful substances and vehicles producing damaging pollutants worsen air quality, posing serious health risks as clean air is essential for life.
Roles of Mangrove forest
Mangroves are vital ecosystems known for their rich biodiversity, offering shelter to a variety of species including fish, birds, frogs, snakes, insects, and endangered crocodiles, as well as mammals like swamp rats, monkeys, and tigers They serve as essential nursery areas for many fish species, supporting sustainable fisheries amid the global issue of overfishing In addition to promoting fish reproduction, mangroves protect coastlines from erosion and flooding by acting as natural barriers, while filtering pollutants from river runoff and preventing sediment buildup that can harm marine habitats such as coral reefs Furthermore, mangroves and coral reefs share a mutually beneficial relationship; reefs protect coastlines from erosion, while mangroves trap sediment, ensuring both ecosystems thrive Together, they provide critical breeding grounds and protection for marine life, supporting local communities' livelihoods and food security in coastal regions.
Remote sensing
Remote sensing has experienced significant growth over the past three decades, primarily through the use of satellite imagery since the 1960s The evolution of remote sensing techniques has closely paralleled advances in imaging technology, with notable milestones such as G.F Tournachon’s 1859 aerial photography using a hot air balloon at 80 meters, marking the birth of digital remote sensing In 1894, Aine Laussedat pioneered image-based topographic mapping, highlighting the early use of aerial photographs for terrain analysis The aviation industry's growth further enhanced remote sensing capabilities; Wilbur Wright took the first airplane-based photograph in 1910 over Centocatal, Italy The development of automatic, high-precision cameras gradually replaced manual shutter operation, improving image quality In 1929, the Soviet Union established the Institute of Aerial Photography in Leningrad to study landforms, vegetation, and soil A major breakthrough occurred on July 23, 1972, when the U.S launched the first Landsat satellite, enabling global environmental monitoring Since then, NASA has continued to expand remote sensing capabilities by launching six additional Landsat satellites, providing invaluable data for earth observation and environmental analysis.
2 (1975), Landsat 3 (1978), Landsat 4 (1982), Landsat 5 (1984), Landsat 6 (1993), Landsat
7 (1999) and Landsat 8 (2013) United States also launched meteorological satellite NOAA
3 rd generation after Trios NOAA, NOAA 7 NOAA 12; NOAA-1 (1992) and NOAA – J
In 1993, remote sensing technology advanced to provide updated photos with a spatial resolution of 1.1 km, enhancing data accuracy Today, remote sensing offers valuable aggregate information that supports various applications, including natural disaster management and monitoring resource recovery changes.
Remote sensing technology was first introduced in Vietnam in the 1980s and has since significantly enhanced various sectors of the national economy, including natural resource management, weather forecasting, pollution monitoring, and urban planning Between 1990 and 1995, the technology was successfully applied in meteorology, cartography, geology, and forest resource management, yielding notable results Combining remote sensing with geographic information systems (GIS), Vietnam has conducted scientific research on natural conditions, environmental monitoring, and disaster prevention, reducing the impact of natural calamities The country has equipped itself with advanced software such as ENVI, ERDAS, PCI, ER Mapper, and OCAPI for GIS development The National Remote Sensing Center, along with specialized divisions within the Ministry of Agriculture and Rural Development, has been crucial in advancing research and professional applications Despite these developments, the study of Vietnam's coastal mangrove forests using remote sensing has progressed slowly and on a smaller scale since early 1989.
Vietnam became the 50th member of the world and the first country in Southeast Asia signed the International Convention on Wetlands (Ramsar Convention)
Satellite imagery, remote sensing, and GIS technology have significantly advanced the study of environmental fluctuations, aiding in remote environmental and natural resource management In environmental research, remote sensing techniques are used to analyze soil variations, coatings, and desertification processes, while in forestry, they facilitate forest classification surveys and fire assessments However, low-resolution images and lack of expertise in interpretation can lead to misleading results High-resolution satellite images like Landsat are crucial for accurately assessing natural and forest resources Effective management, protection, and development of forest resources are vital for Vietnam’s economic and social progress Successful implementation depends on mechanisms that encourage local community participation, but challenges such as population pressure, poverty, low educational levels, underutilized indigenous knowledge, inefficient extension services, inadequate policies, and societal changes have contributed to rapid forest resource depletion.
OBJECTIVES AND METHODOLOGY
Objectives
- Research mangrove fluctuations over periods 2000 - 2005, 2005 - 2010 and 2010 - 2014 in Ha An district, Quang Yen town, Quang Ninh province
- Identify causes of mangrove fluctuation.
Scopes
- Mangrove forests in Ha An district, Quang Yen town, Quang Ninh province
- This thesis used images Landsat 5 (1993 &2003), Landsat 7 (2003 & 2014) and
Methods
This thesis used images Landsat 5 (1993 &2003), Landsat 7 (2003 & 2014) and Landsat 8 (2014) to detect fluctuations of mangrove forests
Table 2.1 Landsat Data used for data analysis
Year Image code Date captured Resolution Path/Row
Source: http://glovis.usgs.com
This thesis was conducted in 3 phases: (1) data collection, analysis and data processing and interpretation conduct, (2) established the current map status in 2000, 2005,
2010 and 2014 maps of mangroves fluctuation periods 2000-2005, 2005-2010, 2010-2014,
(3) summary statistics and evaluation results
Figure 2.1 Location of studied areas: Viet Nam, Quang Ninh province, Quang Yen town,
Forests are influenced by a variety of biological and physical processes and are significantly affected by societal activities Researchers conduct field surveys to gather essential data, which is then processed and analyzed using specialized equipment, tools, and software The overall study process, illustrated in Figure 2.2, involves systematic steps to ensure comprehensive understanding of forest dynamics.
Figure 2.2 Overview classification methods and image processing of Landsat remote sensing
The first, step was to survey the research area to identified a list of research objectives and scopes Then, combined with satellite images and map the research area,
Defining the suitable object, remote sensing
Statistics report Remote sensing data
GIS data Field survey data
Select and input the necessary data
Identifying the topic, areas of research
The study involved identifying the causes of mangrove fluctuations by surveying the area and interpreting satellite images, with efforts to distinguish relevant features amidst confusing objects A key was established for accurate satellite image interpretation of the study area To ensure precise mapping of the current forest resources, the classification accuracy was verified at sample points Additionally, data on natural characteristics and socioeconomic factors were collected through interviews with local residents to understand the underlying causes of mangrove fluctuations in the region.
This study employs high-resolution images from Google Maps and Google Earth to model and enhance the interpretation of low-resolution Landsat images Visual image interpretation yields accurate results when identifying distinct objects and spectral features; however, spectral similarities among some objects can lead to classification inaccuracies To address these challenges in mapping fluctuating mangrove forests, the approach combines visual interpretation with supporting documentation, including GPS verification to ensure object accuracy The methodology integrates ENVI 4.5 with ArcGIS 10.1 and other software applications, utilizing selected sample ranges to improve the precision of mangrove categorization and reduce classification errors.
2.3.4 Method mapping fluctuations mangrove forests:
After verifying the accuracy of thematic maps for each period, GIS layers were overlaid to analyze mangrove fluctuations over time Mangrove maps from 2000, 2005, 2010, and 2014 were created using ArcGIS 10.1 software Change detection was conducted by applying a change matrix to assess classification differences between the images, enabling precise analysis of mangrove cover dynamics across the studied periods.
2.3.5 Characteristics of Landsat images for the study area:
Documentary images, maps and specifications of Landsat image:
This thesis utilizes satellite images from Landsat 5, Landsat 7, and Landsat 8, all with a spatial resolution of 30 meters, capturing data from the years 2000, 2005, 2010, and 2014 The imagery was georeferenced to the VN-2000 coordinate system at Level 3, ensuring accurate spatial analysis The images were processed as composite natural color images to facilitate detailed visual interpretation and land cover change analysis.
Features and specifications of the Landsat image:
From 1972 to now, NASA has launched 8 satellites observing resources (Landsat);
The first three Landsat satellites launched between 1972 and 1978—Landsat 1, 2, and 3—were equipped with multispectral sensors using the MSS (Multispectral Scanner System) with an 80-meter resolution In 1982, NASA launched Landsat 4, followed by Landsat 5 in 1984, both featuring advanced sensors like the TM (Thematic Mapper), which captured images across seven bands with spatial resolutions ranging from 30 to 120 meters, including visible and infrared spectra Landsats 6 and 7, launched in 1993 and 1999 respectively, introduced the ETM (Enhanced Thematic Mapper) sensor, enhancing image quality and capabilities The latest in the series, Landsat 8, launched in 2013, is equipped with two sensors: the OLI (Operational Land Imager) for surface imagery and the TIRS (Thermal Infrared Sensor), designed to improve performance and reliability over previous models.
The specifications of the Landsat images are summarized in Table 3.1
Table 2.2 The specifications of the Landsat images
Maps used in this study:
Merge, crop the image to the boundary study (Figure 3.1)
Figure 2.3 Landsat 8 satellite image of study area in 2014
2.3.6 Fluctuations of mangrove forests in 2000 – 2014:
2.3.6.1 Processing studied area image from Landsat image
I used different Landsat image in each year, depending on image quality and satellites
Figure 2.4 Studied area in year 2000, 2005, 2010 and 2014
Thesis used Image Analysis Tool in ArcGIS 10.1 to calculate NDVI vegetation index for studied area in each year
The Near-Infrared Difference Vegetation Index (NDVI) is calculated using a standard difference formula that measures plant growth density on Earth This formula subtracts visible radiation from near-infrared radiation and divides the result by their sum, providing a reliable indicator of vegetation health Mathematically, NDVI is expressed as (NIR — VIS) / (NIR + VIS), making it a widely used tool in remote sensing for monitoring vegetation conditions from satellite imagery.
NDVI values for any pixel range from -1 to +1, with values near zero indicating no vegetation A NDVI close to +1 (0.8 - 0.9) signifies the highest density of green leaves, while a value near zero suggests the absence of vegetation Understanding these ranges is essential for accurately assessing vegetation health and density using NDVI.
Figure 2.5 NDVI of studied area in each year
2.3.6.3 Evaluating the accuracy of Landsat image interpretation methods:
Figure 3.6 Location of 20 points in the field to check the accuracy in 2014
This study assessed the accuracy of classification methods by comparing visual image interpretation with GPS validation Twenty random points were selected from Google Earth, and GPS was used to verify the actual location and classification of objects in the field, ensuring reliable accuracy assessment.
RESULTS
Processing mangrove forest area
Mangrove development and growth in coastal areas, salt water, outside dike so I used dike to make the boundary divide mangrove and mainland flora in Ha An
Figure 3.1 NDVI of mangrove in each year
Mapping mangrove forest in Ha An over time
Figure 3.2 Distribution of coastal mangroves overtime in Ha An district, Quang Yen
Research results of the analysis showed that mangrove distributed stretches in coastal area of Ha An
Table 3.1 Mangrove forest area each year in the study area
Evaluating the accuracy of Landsat image interpretation methods
Table 3.2 Evaluation accuracy table in 2014
Point Coordinate Landsat image Field Accuracy
Using high-resolution images for verifying low-resolution images and visual interpretation methods achieves approximately 75% accuracy, although these results may be lower than expected This is primarily due to the moderate resolution of Landsat images, spectral image perturbations, and the impact of photography angles Despite these limitations, the method remains valuable for image analysis and interpretation.
Fluctuations mangroves in 2000 – 2014
After I have finished mangrove classes in each year, I used the merge tool in Arc Toolbox to determine the fluctuation of mangroves
Figure 3.3 Maps of mangroves fluctuation periods 2000 - 2005, 2005 - 2010, 2010 - 2014
Figure 3.4 Fluctuations of mangroves period 2000 – 2014
Between 2000 and 2005, the distribution of mangroves was highly variable, with significant changes in their locations During this period, local authorities constructed dike systems to protect coastal areas, leading to a reduction of mangrove areas outside the dikes Conversely, they actively planted a large number of mangroves inside the dike boundaries to promote restoration and conservation efforts.
In the period 2005 – 2010: mangrove area was significantly reduced by a switch to other land cover types such as bare land, wetland, or converted to other plants
In the period 2010 – 2014: mangrove forest area has increased significantly after several mangrove planting project have been implemented in the study area.
Cause of fluctuations mangrove period 2000 – 2014
The mangrove forest area has experienced a significant decline, dropping from 298 hectares in 2000 to just 146 hectares in 2014, representing a reduction of approximately 152 hectares over 14 years Much of the lost mangrove land has been converted into shrimp ponds or left as bare land The primary reasons for this decline include habitat destruction due to agricultural expansion, aquaculture development, and deforestation Protecting remaining mangrove ecosystems is crucial for maintaining coastal biodiversity and preventing erosion.
Land conversion: Shrimp export demand while reducing the amount of artisanal fisheries, mangrove forests, and protection forests naturally be replaced by the aquaculture ponds or bare land
Overpopulation in coastal areas is a primary driver of mangrove loss, as growing populations increase the demand for housing and land development This surge in population puts significant pressure on land resources, leading to the destruction and reduction of mangrove ecosystems, which are vital for coastal protection and biodiversity.
Mangroves are highly resilient and capable of recovering from environmental stresses However, global warming leads to increased average temperatures, causing higher evaporation rates and elevated salinity levels in coastal alluvial lands This heightened salinity can be detrimental to mangroves, killing these vital ecosystems and resulting in a reduction of coastal biodiversity.
Mangroves play a crucial role in mitigating the impact of natural disasters by absorbing and reducing wave energy, thus protecting coastal areas However, climate change has led to an increase in the frequency and intensity of tropical storms, which swiftly damage and deform mangrove ecosystems As a result, the ability of mangroves to recover is compromised, highlighting the urgent need for conservation and climate action.
DISCUSSION
This is an under graduated student thesis, due to the limitation of time and other resources for implementing this research, the results still have some problems and concerns
This study only using visual image interpretation and a small sample of high- resolution imagery to verify low resolution images through visual interpretation method
There are some methods that I do not have the conditions for research, such as:
Unsupervised classification and supervised classification
The number of sample points to evaluate the accuracy of image classification methods are limited, just at 20 sample points
This study employed moderate-resolution Landsat images to map mangrove forest areas, which may limit classification accuracy To enhance the precision of image analysis, future research should utilize high-resolution satellite imagery.
Study area including mangroves along the coast is influenced by tide and can be very complex
Lesson learned by carrying out this study, included suggestion for further research
Need to increase the number of sample points to evaluate the accuracy of the image classification method in general and more reliable
Combine with other image processing methods to improve the accuracy
Using high-resolution photos and check the influence of tide to the study area
Land use and land-cover changes significantly impact environmental stability, driving numerous ecological fluctuations According to recent studies, global deforestation and urban expansion have led to a measurable increase in surface temperatures and biodiversity loss For instance, data indicates that land conversion for agriculture and infrastructure has contributed to approximately 30% of global greenhouse gas emissions These land-use changes alter natural habitats, disrupt climate patterns, and accelerate soil erosion, highlighting their critical role as key factors behind environmental fluctuations Understanding these dynamics is essential for developing sustainable land management strategies.
CONCLUSION
This study demonstrates the effective application of GIS and remote sensing technologies to accurately detect and analyze mangrove forest cover changes Between 2000 and 2014, significant modifications in mangrove coverage were observed in Ha An district, Quang Yen town, Quang Ninh province The research highlights how GIS and remote sensing provide reliable tools for monitoring environmental changes and supporting sustainable mangrove management The findings emphasize the importance of utilizing advanced spatial analysis techniques to assess long-term ecological transformations in coastal regions.
Landsat satellite images can used to evaluate fluctuation mangroves in Ha An district, Quang Yen town, Quang Ninh province in particular and Vietnam in general
Study area covers an area of nearly 500 hectares in Ha An district, Quang Yen Town, Quang Ninh Province Mangrove have been planted in Ha An coastal, mainly
Aegiceras Corniculatum, height about 70 – 100 cm
Not much has changed Mangrove forest area in the period 2000 – 2005 However mangrove area increased from 298 hectares in 2000 to 300 hectares in 2005 In the period
Between 2005 and 2010, the mangrove area experienced a sharp decline, decreasing from 300 hectares to just 60 hectares due to widespread conversion for shrimp pond development However, from 2010 to 2014, the mangrove forest area increased by 86 hectares, reaching 146 hectares, driven by numerous replanting initiatives and growing awareness of the ecological benefits of mangroves.
1 Tran Trong Duc Giam sat bien dong rung ngap man su dung ki thuat vien tham va GIS
Ho Chi Minh City University of Technology
2 Ha Van Hai (2002) Phuong phap vien tham Hanoi University of Mining and Geology
Nguyễn Huy Hoàng (2010) đã thực hiện nghiên cứu về ứng dụng ảnh vệ tinh độ phân giải cao trong việc xây dựng bản đồ tài nguyên rừng nhằm hỗ trợ công tác điều tra, kiểm kê rừng Báo cáo này nhấn mạnh tầm quan trọng của công nghệ ảnh vệ tinh trong quản lý và giám sát tài nguyên rừng, góp phần nâng cao hiệu quả công tác kiểm kê, điều tra rừng nhằm bảo vệ và phát triển bền vững rừng.
Phan Nguyen Hong (2007) nghiên cứu về vai trò của hệ sinh thái rừng ngập mặn và rạn san hô trong việc giảm nhẹ thiệt hại do thiên tai và cải thiện cuộc sống của cộng đồng ven biển Các hệ sinh thái này đóng vai trò quan trọng trong bảo vệ bờ biển, giảm thiểu tác động của bão lũ, đồng thời cung cấp nguồn lợi thủy sản và các dịch vụ sinh thái thiết yếu cho người dân khu vực ven biển Nghiên cứu nhấn mạnh tầm quan trọng của việc bảo tồn và phục hồi hệ sinh thái rừng ngập mặn, rạn san hô để đảm bảo an toàn và phát triển bền vững cho cộng đồng địa phương.
Le Thai Son's 2012 thesis investigates the application of SPOT-5 satellite imagery to analyze forest distribution and carbon absorption capacity in Cam My commune, Cam Xuyen district, Ha Tinh province The study highlights the effective use of remote sensing technology for forest resource assessment and carbon sequestration evaluation This research provides valuable insights into forest management and environmental monitoring using satellite data.
6 Nguyen Ngoc Thach (2005) Co so vien tham Agriculture Publisher
7 Vu Thi Thin, Pham Van Duan, Nguyen Van Thi, Nguyen Viet Hung, Nguyen Huu Van
(2014) Nghien cuu xay dung quy trinh xu li anh ve tinh Landsat8 trong ArcGIS
Institute for Forest Ecology & Environment
8 Nguyen Khac Thoi (2012) Giao trinh vien tham Hanoi University of Agriculture
9 Thomas M., Lillesand, Ralph W Kiefer (2000) Remote sensing and image interpretation
NDVI vegetation index through in each years
Mangrove Field Bare land Water