INTRODUCTION
Rationale of the study
Climate change is becoming the biggest challenge facing humanity on a global scale According to the Global Climate Risk Index 2021 (CRI) report, Vietnam ranked
On April 22, at the United Nations headquarters in New York, Vietnam joined 180 other countries in signing the Paris Agreement, marking a significant commitment to combat global climate change.
Over the past 50 years, Vietnam has experienced an average temperature increase of 0.5 to 0.7 °C and a sea level rise of approximately 20 cm Extreme weather events, such as storms, floods, and droughts, are becoming more severe According to the 2016 climate change and sea-level rise scenarios released by MONRE, Vietnam's average temperature could rise by up to 4 °C, and sea levels may increase by 1 meter by 2100 Vulnerable areas include coastal regions prone to storms and flooding, as well as mountainous areas susceptible to flash floods and landslides.
Vietnam boasts a coastline of 3,260 km, contributing to the robust development of its marine economy However, the challenges posed by climate change and rising sea levels heighten the risks of flooding, erosion, and salinity, which can adversely impact the economy, disrupt livelihoods, and threaten the integrity of coastal infrastructure.
Coastal erosion is a widespread issue affecting the entire coastline of Vietnam, with research by Pham Huy Tien et al (2002) identifying 397 eroded banks spanning a total length of 920.21 km The average erosion rate is estimated to be between 5-10 meters per year, but in certain areas, it can escalate dramatically to 50-100 meters or even 200-250 meters in short periods.
Binh Dinh, a coastal province in Central Vietnam, boasts a 134 km coastline along the East Sea, offering substantial natural advantages and immense potential for marine economic development.
Phu My district is identified as the most severe erosion hotspot in Binh Dinh Province, with four affected communes—My Duc, My Thang, My An, and My Thanh—experiencing a total landslide length of 3,900 meters, impacting 2,520 households Additionally, An Quang village in Cat Khanh commune of Phu Cat district has faced coastal erosion exceeding 520 meters, affecting 550 families Notably, coastal erosion in 2015 and 2017 uprooted numerous casuarina trees and caused waves to encroach inland by over 70 meters in certain areas In Cat Tien commune, the sea has advanced into Trung Luong village by over 500 meters, impacting 495 households.
Figure 1 1: Phu Cat coast was eroded in 2017
In June 2020, the Department of Science and Technology of Binh Dinh province recognized the potential risks posed by climate change and initiated a national science and technology mission This mission focuses on researching integrated technological solutions aimed at enhancing resilience and proactive response capabilities to erosion and accretion disasters affecting coastal villages and communes in Binh Dinh province, especially in the context of urbanization and climate change.
The research focuses on "Coastal Erosion in Phu Cat District, Binh Dinh Province," examining the effects of climate change on coastal erosion and identifying suitable adaptation measures that align with the local community's capacity to adapt.
Objectives of the research
This study has three main objectives: to analyze shoreline changes, to forecast shoreline changes in the context of climate change, and to propose adaptation measures
Objects and Scope of the research
- Research object: shoreline changes (focus on coastal erosion)
- Research scope: the entire coastal strip in Cat Khanh, Cat Thanh, Cat Hai, Cat Tien and Cat Chanh.
Research questions and hypotheses
Table 1 1: Research objectives, questions and hypotheses
[Q1]: How have the shorelines changed?
[H1]: Coastal erosion takes place both in the long term and seasonally
[H2]: Shoreline would continue to recede under CC
[Q3]: Which countermeasures could respond to coastal erosion?
[H3]: For the long-term preventing coastal erosion and responding to climate change, a combination of hard and soft measures is needed.
Significance of the research
Scientific significance: This study contributes to enriching research series on coastal erosion for coastal provinces and cities of Vietnam in the context of climate change
Practical significance: The study provides some analysis of shoreline change (focus on coastal erosion) in the Phu Cat district and some anti-erosion countermeasures based on existing local conditions.
The novelty of the research
This pioneering study focuses on the erosion phenomenon in Phu Cat district, marking the first examination of this issue in the area It uniquely integrates coastal erosion considerations into spatial planning and offers adaptive solutions tailored to the local context.
The structure of the thesis
The thesis, apart from the Conclusions, consists of four chapters as below:
Review related literature and studies
* Coastal zone: The coastal zone is “a zone of transition between the purely terrestrial and purely marine components on Earth‟s surface” [5]
(Source: Pearson Prentice Hall, Inc, 2009)
The coastal zone, as defined in the Ramsar handbooks for the use of wetlands, 4th edition, is a narrow area where land meets sea, characterized by complex and intensive ecological and functional processes driven by their interaction This zone encompasses both terrestrial and aquatic ecosystems that are intricately connected to socio-economic systems, creating multifaceted functional units.
In the book " Vietnam coastal zone - structure and natural resources", the author
Le Duc An described the coastal zone as comprising two distinct ribbon areas that encircle the shoreline: the coastal strip and the shallow coastal strip edge The inner limit of the coastal strip is defined by the administrative boundaries of coastal districts and cities.
5 the outer border of the external coastal strip is the edge of the continental shelf, usually up to a depth of 200m [7]
The Food and Agriculture Organization (FAO) defines coastal areas as the transition zones between land and sea, highlighting their significance in coastal management In this context, the term "coastal zone" specifically refers to these management areas, while "coastal area" is a broader term that encompasses the general geographical region along the coast.
The coastline is the contact line dividing the land from the coastal water bodies
[6] In general, the coastline is the boundary between land and sea This boundary is also not stationary but always moves under waves, tides, currents, etc
The coastline is the highest boundary of waves during interaction with the mainland This boundary is usually cliffs, dunes or terrestrial vegetation [9]
DOLAN et al., 1980 defined shoreline is ideally defined as the physical interface between soil and water [10]
Anders et al., 1991 defined the shoreline as an intersection separated by land, sea, and air [11]
Coastal erosion occurs when a specific area of the coast loses its material supply and material export [12]
Coastline retreat is the process by which the shoreline moves landward due to long-term erosion trends or due to sea level rise [12]
1.8.2 Overview of studies on coastal erosion
1.8.2.1 Overview of coastal erosion studies in the world
Historically, communities have congregated in coastal plains to exploit marine resources, leading to significant coastal erosion and shaping the trajectory of human development linked to the sea.
The IPCC report highlights significant coastal erosion trends, revealing that Louisiana's shoreline has retreated at an average rate of 0.61 meters per year from 1855 to 2002, escalating to 0.94 meters per year since 1988 Similarly, in China, coastal erosion affects nearly 50% of its coastline, with the Yellow Sea eroding by 49%, the East China Sea by 44%, and the coasts of Guangdong province and Hainan Island experiencing a 21% erosion rate.
Historical evidence of anti-erosion structures, such as ports and breakwaters built at the Nile river-mouth around 2500 BC, highlights the long-standing efforts to combat coastal erosion The interaction between land and sea within coastal zones encompasses both terrestrial and aquatic ecosystems that are intricately connected to socio-economic systems, creating complex ecological units Current research on coastal erosion incorporates geological, geomorphological, and hydrodynamic perspectives to better understand these dynamics.
Strahler (1952) and Hack (1960) conducted foundational studies on topographical evolution, focusing on the processes of erosion and accretion driven by morphological dynamics Zencovich (1962) further explored coastal evolution, emphasizing the impact of climate and coastal vegetation on geomorphologic changes Subsequent research by Eliot and Clark (1982), Thom and Hall (1991), and McLean and Shen (2006) examined coastal erosion through the analysis of beach profiles, highlighting the ongoing challenges posed by climate change on coastal landscapes.
The Bruun Rule, proposed by Bruun in 1962, suggests that a beach's horizontal profile achieves dynamic equilibrium when sea levels are stable, highlighting the relationship between sea-level rise and coastal changes In contrast, Zhang et al (2004) offer a differing perspective on this phenomenon.
7 the three possible causes of coastal erosion are: sea-level rise, storm regime changes, and human intervention [20]
Before 1990, coastal erosion studies primarily relied on fundamental theories and practical measurement techniques The launch of the first Landsat satellite in 1972 marked a significant turning point, as it enabled researchers to begin utilizing satellite imagery to study shoreline changes.
Since 1990, the study of coastal erosion has evolved through the integration of traditional geomorphological research, advanced geomorphic technologies, and modeling techniques Notable contributions include Kay's (1990) assessment of coastal erosion influenced by sea level rise (SLR) and global heating effects (GHE), and Corwell's (1991) analysis of shoreline change trends and the uncertainties in volatility assessment methods Cambers (1998) focused on coastal retreat and planning through satellite image interpretation, while Woodroffe (2002) evaluated coastal topography and sediment changes using both traditional investigative methods and satellite imagery.
Since the early twenty-first century, the Coastal Vulnerability Index (CVI) has gained popularity as a key tool for assessing coastal risks Notable contributors to this methodology include Gornitz et al (1994), Thieler and Hammar (1999), and Dwarakish et al (2009).
Currently, studies of coastal evolution have come a long way by combining many different methods such as satellite image interpretation, statistics, mathematical modelling, hydrodynamic modelling, etc
1.8.2.2 Overview of coastal erosion studies in Vietnam
In Vietnam, research on coastal erosion has only been popular since the 90s of the
20 th century until now Studies on coastal erosion in central Vietnam are also of interest to domestic scientists
The foundation research on coastal evolution in Vietnam is a State-level research project of author Nguyen Thanh Nga, with code KT-03-14, which assessed the current
8 state and the causes of coastal erosion in Vietnam and proposed technical solutions Following that, the studies of Cu et al (2001, 2003, 2005) [29], Nguyen Manh Hung
Recent studies by Pham Huy Tien et al (2005), Mimura (2008), and Le Phuoc Trinh et al (2011) have explored the dynamics of coastal erosion and accretion in Vietnam These researchers advocate for the use of remote sensing technology to develop detailed coastal maps Their findings highlight the current volatility of Vietnam's coastline, emphasizing the anticipated impact of global climate change, including rising sea levels and increased storm surges, on future coastal conditions.
Vietnamese scientists primarily employ geomorphology research methods, including morpho-dynamic and hydrodynamic analyses, along with techniques utilizing topographic maps, aerial photography, and remote sensing images These methods are essential for examining shoreline changes in their studies.
Binh Dinh province has seen significant research on coastal dynamics, particularly focusing on coastal erosion in the Central region Most studies, such as those by Do Minh Duc et al (2017) and Dinh Thi Quynh (2017), have primarily analyzed the accretion phenomena at key estuaries, including Tam Quan and De Gi estuaries.
A study by Vo Ngoc Duong et al (2019), published in the proceedings of the 10th International Conference on the Coasts of Asia and the Pacific (APAC 2019), investigated shoreline fluctuations in Binh Dinh province from 1975 to 2017 The research utilized remote sensing technology and the Digital Shoreline Analysis System (DSAS) application to analyze these changes.
1.8.3.1 Adaption measures in the world
Site descriptions
According to the website of Phu Cat district [41], Phu Cat is a coastal plain district of Binh Dinh province, located on 13 o 54'N- 14 o 32'N and 108 o 55'E- 109 o 05'E
- The North and the Northwest border Phu My district and Hoai An district
- The South borders An Nhon town
- The West and the Southwest border the districts of Vinh Thanh and Tay Son
- The East borders the East Sea with a length of 35 km
- The Southeast borders Tuy Phuoc district and Quy Nhon city
Figure 1 8: Location map of the study area
The Phu Cat coastal strip is about 30 kilometres in length, stretches through 5 communes Cat Khanh, Cat Thanh, Cat Hai, Cat Tien and Cat Chanh
Phu Cat district has a pretty favourable position for economic development associated with benefits from the sea
Phu Cat features a diverse topography that encompasses deltas, low mountainous regions, and coastal lagoons found in Cat Minh, Cat Khanh, and Cat Thanh Additionally, the Cat Chanh and Cat Tien communes are situated to the south of Ba Mountain.
Figure 1 9: Topography map of the study area
The region features a coastal plain with agricultural land along the Dai An River, while the communes of Cat Khanh, Cat Thanh, and Cat Hai are situated in the hilly coastal area of Phu Cat District.
Hills and mountains in this area account for more than half, but most are bare hills
South of De Gi estuary, the coastline is arc- shaped, concave to the West The intertidal topography in the study area is quite steep, with an average width of 30 - 50 m
With a total length of more than 20km, the beaches of Phu Cat district have much potential for tourism development but not yet fully exploited
Phu Cat experiences a tropical monsoon climate, characterized by hot, humid, and rainy conditions The year is divided into two distinct seasons: the dry season from December to August and the rainy season from September to November During the dry season, hot southwest winds prevail from March to August, while the northeast winds bring drizzling and cooler rains from September to February.
The region experiences its coldest air temperatures during winter months from November to March, while summer sees the highest temperatures from May to August Over the years, the average temperature hovers around 24.3 °C, with daily temperature variations ranging from 7 to 10 °C.
Average relative humidity for many years ranges from 85 to 90% Average evaporation for many years is in the range of 1,200 -1,300mm
Binh Dinh experiences highly irregular rainfall patterns throughout the year, with significant discrepancies between areas of maximum and minimum precipitation Historical data indicates that October and November consistently receive the highest levels of rainfall.
Table 1 4: Average rainfall in years at Phu Cat station
The coastal area is affected by both monsoon and land-sea breeze Therefore, the distribution of wave direction by month of the year also varies by region
From November to April, waves on the Binh Dinh continental shelf predominantly move in a Northeast direction May marks a transitional period characterized by unstable and weak waves From June to September, southwest waves become dominant, while other directional waves occur infrequently The maximum recorded wave height reaches 12 meters, with average summer wave heights ranging from 1.2 to 1.7 meters, and winter averages at 1 meter, with rogue waves measuring up to 2.2 meters In summer, average wave heights drop to 0.5 meters, with rogue waves reaching 2.3 meters.
Measuring waves is quite complicated According to the research by Duc et al
(2013) [42], the wave characteristics measured with an AWAC system Table 1.5 and Figure 1.10 showed that the Northeast-wave was dominant in September 2012, and the East-wave dominated in June 2013
Table 1 5: Wave and current characteristics at the De Gi estuary
Dominant wave direction 61.7° (NE) 111.2° (SE)
Figure 1 10: Wave rose at the De Gi estuary a) September 2012 and b) June 2013 b) Current
The characteristics of water flow in Binh Dinh vary with the seasons, primarily flowing west and southeast during the dry season, with occasional flows from the east, southwest, and northwest This flow is predominantly diurnal, influenced by tidal currents that affect the entire area from the surface to the bottom layer During tidal phases, the maximum surface flow rate reaches 10 cm/s, while during ebb tide, it increases to 13 cm/s at the surface and 17 cm/s at the bottom.
During the rainy and stormy season, the surface flow through Binh Dinh is mainly in the East and Southeast directions, the West flow accounts for a tiny proportion c) Tide
Binh Dinh experiences primarily irregular diurnal tides, with the number of diurnal days in a month varying between 18 to 26 While Thi Nai lagoon and the river mouth share a similar tidal regime with the Quy Nhon coastal area, the tidal amplitude in the lagoon is notably smaller than that of the coastal region.
The tidal amplitude in the lagoon ranges from 1.3m to 1.4m, while in the sea, it varies between 1.5m and 2.0m during the same period Additionally, the tidal peaks observed in the lagoon and at the Quy Nhon station remain relatively stable.
Phu Cat district, located in Binh Dinh province, covers a total area of 680.49 km² and has an average population of 193,262 residents The district is comprised of 18 administrative units, which include 17 communes—Cat Son, Cat Lam, Cat Hiep, Cat Hanh, Cat Tai, Cat Minh, Cat Khanh, Cat Thanh, Cat Hai, Cat Tien, Cat Chanh, Cat Thang, Cat Hung, Cat Nhon, Cat Tuong, Cat Trinh, and Cat Tan—as well as one town, Ngo May Town.
The study area includes five communes: Cat Khanh, Cat Thanh, Cat Hai, Cat Tien, and Cat Chanh, with the population shown in table 1.5
Table 1 6: Population of 5 coastal communes of Phu Cat district
No Commune Population in 2019 (people)
(Source: Binh Dinh Statistical Office)
Between 2011 and 2015, the average labor force represented 54.03% of the district's population, which rose to 58.36% by 2018 Additionally, the employment rate for individuals aged 15 and older was 70.2% during 2011-2015, decreasing slightly to 67.54% in 2018 Notably, there is no significant disparity in employment rates between male and female workers.
The Phu Cat district socio-economic report highlights a growth in the total value of production fields compared to previous years, with the Agriculture, Forestry, and Fishery sectors now representing 25.32% of the economic structure.
Industry and Construction accounted for 26.83%; Trade - Service accounts for 47.85%
The economic structure has shifted towards reducing agriculture, increasing the proportion of Trade and services
Figure 1 11: Economic structure of Phu Cat district Table 1 7: Land use status of 5 coastal communes of Phu Cat district in 2013
No Type of land Cat
Cat Thanh Cat Hai Cat Tien Cat
2 Non-agricultural land 568.04 462.74 190.61 255.57 195.11 2.1 Residential land 58.64 58.63 32.53 75.48 33.42 2.2 Specialized land 60.13 232.54 83.8 112.04 94.83
2.2.1 Land for offices, non-business works 0.73 0.66 0.65 0.91 0.38
2.2.3 Non-agricultural production and business land 9.06 165.78 22.05 15.43 0.5
2.5 River, stream land and specialized water surface 376.38 77.62 57.48 40.72 53.61
No Type of land Cat
Cat Thanh Cat Hai Cat Tien Cat
(Source: Binh Dinh Department of Statistic)
From table 1.6, it can be clearly seen that unused land still accounts for a large proportion
The coastal area of Phu Cat district, stretching from Cat Khanh to Cat Chanh communes, boasts stunning and unspoiled beaches largely untouched by human activity This pristine environment provides Phu Cat district with significant opportunities for economic development linked to the sea Key potentials include the diversification of agricultural production—encompassing cultivation, husbandry, fisheries, and afforestation—alongside the growth of tourism and services, all supported by a beautiful landscape and efficient transportation infrastructure, such as Phu Cat International Airport and National Highway 1A, as well as the development of human resources.
De Gi lagoon, a vital lagoon in our country, significantly contributes to the economic development of Phu Cat district's coastal area Currently, the aquaculture area spans 391 hectares, with total fishery and aquaculture production reaching 35,000 tons in 2012 Notably, 94% of this output comes from exploitation, while farming accounts for the remaining 6%.
In addition to the advantages of the beach, the infrastructure of Phu Cat District also has many potentials For example, according to the airport system planning, by
By 2030, Phu Cat Airport is set to become an international airport, which will significantly boost tourism and commerce in the region This development presents a valuable opportunity for the Phu Cat district to enhance its economic growth and tourism potential, while also advancing towards the establishment of an aviation logistics center.
METHODOLOGY
Approaches
Challenges of climate change in coastal areas need to be addressed through integrated and interdisciplinary perspectives
Rapid development activities utilizing marine resources are significantly altering both the quantity and quality of these resources, leading to detrimental effects on ecological conditions and environmental quality in coastal zones These impacts have heightened conflicts of interest and spatial disputes over coastal area exploitation, jeopardizing the livelihoods of local communities Furthermore, management of coastal sites varies considerably across different localities, and the coordination mechanisms among stakeholders—including local communities—remain ineffective in the long term.
To promote sustainable management and utilization of marine resources while reducing coastal erosion, the Government, along with the Ministry of Natural Resources and Environment (MONRE) and local authorities, has established and enforced laws and policies aimed at natural disaster prevention, climate change adaptation, and sea-level rise response in coastal regions.
The traditional "top-down" approach to assessing climate change impacts on coastal areas focuses on predicting shoreline changes by analyzing various causes and utilizing climate scenarios and coastal models However, this method is fraught with uncertainties stemming from datasets and models, often leading to theoretical solutions that lack local relevance In contrast, the "bottom-up" approach emphasizes understanding an area's historical and current conditions to inform future adaptation strategies By integrating both approaches, urban planners can gain a comprehensive perspective on coastal management, as illustrated in the shoreline change research framework.
Data collection
This study uses a variety of sources of documents and data, both primary data and secondary data
The use of Unmanned Aerial Vehicles (UAVs), commonly known as drones, has surged globally in recent years, particularly in the field of environmental surveying and monitoring Drones excel in applications such as erosion monitoring, coastal vegetation assessment, and analyzing land use changes, providing data with exceptional spatial accuracy and resolution This study focuses on utilizing UAVs to gather data on the shoreline conditions in the Phu Cat district.
The study utilized images captured by the DJI Phantom 4 Advance+, a multi-rotor UAV provided by the MCCD program, which boasts an impressive resolution of up to 20 megapixels Weighing 1,380 grams and featuring a diagonal size of 350 mm, the Phantom 4 Advance+ offers a flight duration of nearly 30 minutes, making it an efficient tool for aerial imaging.
The Phantom 4 Advance+ drone is equipped with advanced features such as GPS, an obstacle detection and auto-avoidance system, and a programmed automatic flight system Users can directly observe the drone's camera feed through the DJI GS PRO app, specifically designed for controlling and planning automatic flights for DJI aircraft Over two days, the drone captured 1,517 photos across six segments of coastline, covering approximately 12 kilometers, with its flight path set downwind and images taken at equal distances.
Figure 2 3: UAV photos of Phu Cat coastal area, captured by the DJI Phantom 4
Images captured by drones are processed using Agisoft Metashape version 1.7.2, a photogrammetry software developed by Agisoft LLC based in St Petersburg, Russia This versatile software is compatible with major operating systems, including Microsoft Windows, macOS, and Linux.
The author conducted a comprehensive study by collecting ten sand samples from various depths, specifically at 50cm underwater and an additional 15cm above the sand surface The sampling sites encompassed the top, middle, and end sections of Cat Khanh, Cat Hai, Vinh Hoi, and Trung Luong beaches.
This study involved interviews with three distinct groups: local residents, local officials, and experts Local residents were consulted on-site, while both experts and local officials participated in interviews conducted online and on-site For detailed information on the interview format, please refer to Appendix 2.
The research utilized three topographic maps of Hai Dong, Quy Nhon, and Phu My, all situated in UTM zone 49 and based on the Indian 1960 geographic coordinate system, with a scale of 1:50,000 These maps, identified as 6836-I, 6836-IV, and 6836-III, were originally published by the U.S Army Topographic Command and reprinted in the years 1965, 1969, and 1970.
Hai Dong (6836-I) Quy Nhon (6836-IV) Phu My (6836-III)
(Source: University of Texas Libraries)
Figure 2 4: Topographic maps of study area published by U S Army Topographic
In addition, this study also uses topographic maps at scale 1: 10,000, coordinate system VN2000, meridian 108, projection zone 3 (provided by the Institute of Geotechnical Engineering, VNU)
This study utilized Level-2 Surface reflectance data from Landsat 5 TM and Landsat 8 OLI/TIRS sensors to assess shoreline change rates between 1988 and 2016 The satellite imagery was made available at no cost by NASA and the US Geological Survey.
The Geological Survey (USGS) utilizes satellite images adjusted to the WGS-84 UTM frame of reference, specifically zone 49N, prioritizing those with less than 20% cloud cover In cases where cloud cover exceeds 20%, cloud removal is necessary The shoreline derived from satellite image processing is converted from raster to vector format for further calculations Landsat satellite images are sourced from the Earth Explorer website.
Information of bands of Landsat 8 OLI satellite images area presented in Table 2.1
Table 2 1: Landsat 8-9 OLI/TIRS (L2SP) Band Specifications
(Source: https://docs.sentinel-hub.com/) Band number Band Description Band Range
6 Short Wavelength Infrared (SWIR) (OLI) 1566-1651 30
10 Thermal Infrared Sensor (TIRS) 1 10600-11190 100 (30) (*) (*) TIRS bands are acquired at the 100-meter resolution but were resampled to 30- meter in the delivered data product
Information of bands of Landsat 5 TM satellite images area presented in Table 2.2
Table 2 2: Landsat 4-5 TM (L2SP) Band Specifications
(Source: https://docs.sentinel-hub.com/) Band number Band Description Band Range
(**) The thermal band is acquired at 120-meter resolution and then resampled to 30- meter in the delivered data product
Table 2.3 presents the Landsat images utilized in this study, collected consistently at a tidal level of 1.5 meters from 1989 to 2016, ensuring reliable comparisons of shoreline changes.
Table 2 3: List of satellite images used in the study
No Product Identifier Acquisition time
NE monsoon season LT05_L2SP_123050_19891001_20200916_02_T1 10/1/1989 28
LT05_L2SP_123050_19930214_20200914_02_T1 2/14/1993 26 LT05_L2SP_123050_19990319_20200908_02_T1 3/19/1999 0 LT05_L2SP_123050_20040401_20200903_02_T1 4/1/2004 1 LT05_L2SP_124050_20080318_20200829_02_T1 3/18/2008 5.45 LC08_L2SP_123050_20140208_20200912_02_T1 2/8/2014 0.16 LC08_L2SP_123050_20160418_20200907_02_T1 4/18/2016 22.96
SW monsoon season LT05_L2SP_123050_19890627_20200916_02_T1 6/27/1989 19
LT05_L2SP_123050_19930724_20200913_02_T1 7/24/1993 28 LT05_L2SP_123050_20000609_20200907_02_T1 6/9/2000 6 LT05_L2SP_123050_20050506_20200902_02_T1 5/6/2005 0 LT05_L2SP_123050_20080903_20200829_02_T1 9/3/2008 6 LC08_L2SP_123050_20140920_20200910_02_T1 9/20/2014 10.24
Meteorological and hydrological data were sourced from various agencies, including the General Statistics Office (GSO), Binh Dinh Statistical Office, Phu Cat Meteorological Station, and the Vietnam Oceanography Center Additionally, information was gathered from the Binh Dinh Provincial Commanding Committee for Natural Disaster Prevention and Control, as well as several domestic studies The collected data encompasses key elements such as temperature, precipitation, wave patterns, wind speeds, and tidal information.
Besides, this study also uses the Climate Change Scenario and sea-level rise published by MONRE in 2016
Planning data used in this study include:
- The reports on socio-economic development (approved),
- The reports on natural disaster prevention (approved),
- Construction plan of Binh Dinh province region to 2035 (approved),
- Construction planning of Phu Cat district until 2040, vision to 2050 (browsed),
- Action plan to respond to climate change in Binh Dinh province (approved)
Table 2 4: Summary of data used in the research
No Type of data Data sources
1.1 Maps of Hai Dong (6836-I), Phu My (6836-IV),
Quy Nhon (6836-III) published by U S Army
Topographic Command, the scale of 1:50,000
1.2 Maps of Binh Dinh province, scale 1:10,000 Department of Geology, VNU
2 Satellite images Level 2 Surface reflectance
2.1 Seven images generations of NE monsoon season
2.2 Six images generations of SW monsoon season
3.1 Monitoring data GSO, Binh Dinh Statistical
Meteorological Station, Binh Dinh CCNDPCSR
4.1 Phu Cat socio-economic development reports Phu Cat PC
4.2 Phu Cat natural disaster prevention reports Binh Dinh CCNDPCSR
4.3 Construction plan of Binh Dinh province region to 2035 Binh Dinh DC
4.4 Construction plan of Phu Cat district region to
4.5 Action plan to respond to climate change in
Binh Dinh province Binh Dinh DONRE
Methods
A type of research method (qualitative or quantitative) is insufficient to answer research questions and solve the research problems Therefore, the study orient uses mixed methods, which are listed below:
Table 2 5: List of research methods
M1 Interview Identify erosion hotspots, interview experts and local officials to find adaptive solutions
M2 Case study Compare, choose the suitable adaptive measures
M3 Survey Observation and measurement in some key areas
M4 Experiment Determination of sand particle size and distribution M5 Inheritance
Identify research methods, collect necessary databases (emission scenarios, climate models, development scenarios, etc.)
M6 Statistic Identify correlations among the historical shoreline changes and predict erosion
M7 Modelling Predict shoreline change under CC M8
Satellite image interpretation and map overlay
Analyze shoreline change, quantifying the shoreline change rate by spatial and temporal scale (using ArcGIS, QGIS, DSAS, etc.)
Interviewing is the most common way of data collection in qualitative research
[49] This study used a semi-structured format to collect data from three types of subjects: local people, local officials, and experts (For details, see appendix 2)
In March 2021, a series of questionnaire surveys were conducted to gather insights from local residents regarding their livelihoods, perceptions, and opinions on climate change and coastal issues.
35 erosion; the interview questions for local officials mainly related to coastal management, coastal erosion prevention solutions
The research used case studies as a tool to make decisions for appropriate adaptive solutions based on a complete analysis of investigated similar cases in actual conditions [50]
This study employed various field survey methods, including UAV surveys, field observations, sediment sampling, and beach descriptions, to analyze coastal morphology and vegetation cover While UAV technology is costly and susceptible to weather conditions, it proves invaluable for monitoring coastal erosion and accretion GPS measuring points are crucial for geometrical corrections of satellite images and topographic maps, enhancing the reliability of shoreline fluctuation assessments By integrating GPS data with UAV imagery, the study improves predictions of shoreline changes Essential tools for this research include a drone, smartphone, laptop or computer, and software such as Agisoft Photoscan Pro (or Agisoft Metashape), QGIS, and ArcGIS.
Figure 2 5: UAV photos processing flowchart
The images were processed using geolocation and camera information, which involved aligning photos, creating a dense point cloud, generating a Digital Elevation Model (DEM), and constructing orthomosaic images The field data collected is then integrated with satellite image interpretation techniques to validate the accuracy of the satellite imagery.
This study experiments on grain size classification The experimental procedure is as follows:
Figure 2 6: Sand samples after drying (Left) and sampling location (Right) of coastal sand in Phu Cat district
After being dried at 80 o C (Please see Figure
2.6), the sand samples were sized by manual sieving method in the Geotechnical Laboratory,
Department of Geology, University of Natural
Sciences The identification of sand color based on the Munsell color chart [51]
Particle composition analysis tools used are sieves ranging in size from 0.075mm to 5mm,
37 based on sediment classification of Wentworth grain size chart (Please see Figure 2.7) classification by manual sieving method
This study inherits some results from previous studies, combined with the use of climate change and sea-level rise scenarios published by MONRE in 2016 to forecast future shoreline change
This study employs the Digital Shoreline Analysis System (DSAS) version 5.0, utilizing statistical methods like End Point Rate (EPR) and Net Shoreline Movement (NSM) for shoreline analysis While DSAS can predict future shorelines, its reliability is limited.
This study employs Bruun's rule model to forecast shoreline retreat resulting from sea-level rise linked to climate change Introduced by Per Bruun in 1962, the Bruun Rule establishes a fundamental connection between rising sea levels and coastal degradation through the equilibrium platform theory This theory posits that beach profiles strive to maintain a balanced shape, causing them to retreat inland and upward in response to increasing sea levels to preserve their equilibrium.
Figure 2 8: Illustration of the Bruun Rule, by the Scientific Committee on Ocean
The equation of the Bruun Rule is:
- S: Sea level rise (unit: meter)
- L: The horizontal length of the bottom affected by the sea level rise (from the dune peak to the depth of closure) (unit: meter)
- h: the depth of closure (unit: meter)
- B: the dune height above sea level (unit: meter)
- β: the average slope of the active profile
Current models that effectively align with field and laboratory data for bay-shaped shorelines include the spiral model, tan-hyperbolic model, and parabolic model Among these, the parabolic model, which simulates the static equilibrium of headland bay-shaped shorelines, is widely utilized and was developed by Hsu and Evans in 1989.
Figure 2 9: Parabolic bay-shape model (after Hsu and Evans, 1989)
The empirical equation of this model as:
- R n : the distance between the control point and coastline
- β: the angle between the wave crest in the diffraction point and the control line R β
The correlation coefficients: C 0 , C 1 , C 2 are determined by curve fitting from the field and laboratory data as the wave angle β changes
MEPBAY software, developed by Vargas, Hsu, Klein, and Raabe, enables rapid estimation of shoreline equilibrium To utilize the MEPBAY model, users must first select a loading image and then identify the direction of the coast along with the movement of the waves.
→ determine upcoast control point, downcoast control point, and an endpoint along the tangent to the beach → change Rn and β to determine the equilibrium shoreline
RS analysis is an advanced technique designed to efficiently address macro-level spatial issues In this study, the author employed ArcGIS and QGIS for the interpretation of satellite imagery The primary objective of this method is to automatically define the land-sea boundary through segmentation algorithms that assess the surface reflectance of Landsat satellite images.
Step 1: Pre-processing and converting DNs to surface reflectance (using QGIS) Step 2: Calculate the MNDWI index
This study employs the automatic threshold classification method to delineate land and water boundaries using the Modified Normalized Difference Water Index (MNDWI) Developed by Xu in 2006, the MNDWI has gained widespread popularity for water body detection globally, as evidenced by its application in various studies (Singh et al., 2015; Sarp & Ozcelik, 2017; D Nandi et al., 2018).
The Modified Normalized Difference Water Index (MNDWI) is recognized as a superior technique for shoreline extraction from Landsat imagery, outperforming other indices such as NDWI and AWEI This method utilizes the green band and the short-wave infrared (SWIR) band; specifically, in Landsat 8 OLI, these correspond to band 3 and band 6, while in Landsat 5 TM, they are band 2 and band 5.
The MNDWI index is determined as follows:
For Landsat 5, MNDWI = (Band 2 – Band 5) / (Band 2 + Band 5) (4.4)
For Landsat 8, MNDWI = (Band 3 – Band 6) / (Band 3 + Band 6) (4.5)
Step 3: Binary land and water based on automatic thresholding algorithm (Otsu,
After calculating the MNDWI index, the reclassify tool in ArcGIS was utilized to differentiate between water and land, with positive values indicating water and negative values representing land It is essential to verify the results of automatic shoreline drawing to identify and manually correct any inaccuracies Subsequently, the raster data was transformed into vector data to facilitate the calculation of shoreline variations, resulting in line-shaped representations of the shorelines.
Step 4: Shoreline change analysis using DSAS
DSAS v5.0 is a powerful add-in utility for ArcGIS, created by the United States Geological Survey (USGS), designed to statistically assess shoreline change rates This tool is highly regarded for its capability to analyze geographic information series and effectively calculate shoreline changes over time, making it a popular choice among researchers and professionals in the field (Kaliraj et al., 2013; Oyedotun, 2014; Nassar et al., 2018).
The DSAS shoreline variation analysis process involves several key steps: first, shorelines are created, followed by the establishment of a baseline Next, transects are generated, and distances between the baseline and the shorelines are computed at each transect Finally, the shoreline change rate is calculated to assess variations over time.
In this study, the author analyzed shoreline changes by dividing the coastline into 50-meter transects and employing the Net Shoreline Movement (NSM) method to measure the distance between the oldest and newest shorelines over a specified time period Additionally, the End Point Rate (EPR) method was utilized to determine the rate of shoreline change by dividing the distance between the longest and most recent coastlines by the time elapsed EPR has emerged as the most widely used method for assessing shoreline fluctuations, calculated using a specific formula.
NSM time between oldest and most recent shoreline
(Source: DSAS Version 5.0 User Guide, USGS)
In summary, the logical framework of this study is presented in Figure 2.10.
RESULTS AND DISCUSSION
Results
Analysis results of grain composition of ten sand samples collected in the Phu Cat coastal area shown in Figure 3.1
Figure 3 1: Particle size distribution along Phu Cat coastline
Phu Cat district is characterized by its sandy beaches, with coastal sediments primarily consisting of fine, medium, and coarse sand Near De Gi jetty, the sediment is notably coarse and includes gravel, while Trung Luong beach also features coarse-grained sediments In other surveyed areas, sediment particle sizes vary from fine to medium and coarse.
Grain size sedimentation reflects the hydrodynamic conditions of coastal areas, with coarser sediments indicating stronger hydrodynamic regimes Consequently, deposition beaches tend to have finer sediment sizes, while eroding beaches are characterized by coarser sediments.
From figure 3.1, D 50 for ten sampling positions ranges from 0.32mm to 1.72mm Such particle size is suitable for sandy beach material characteristics, which satisfied
44 hydrodynamic regime to apply the Bruun model in section 3.1.5.3; and meet the necessary conditions to apply the parabolic equilibrium model to the headland bay beach presented in section 3.1.5.2
A total of 1,517 overlapping images were analyzed for five small shoreline segments, including Cat Khanh (north near De Gi estuary and south near Ong Lop headland), Cat Hai, Vinh Hoi, and Trung Luong, utilizing Agisoft Photoscan® for processing.
UAV image interpretation products, displayed in KMZ format and integrated with Google Earth, are illustrated in Figure 3.2 However, the UAV's survey duration was insufficient to encompass the entire coastal strip of Phu Cat, resulting in the processing of images from only select small segments, as shown in the figure.
Figure 3 2: UAV images interpretation results (overlap on Google Earth background) 3.1.3 Shoreline change analysis
The beach's landscape features an accumulation-erosion structure shaped by dominant wave action, resulting in noticeable seasonal variations in its topography.
This study analyzes the spatial and temporal variations of the shoreline in Phu Cat, focusing on long-term, short-term, and seasonal changes Shoreline alterations are evaluated across the entire coastline and in detail for specific sections during designated time periods As detailed in Chapter 4, the rate of shoreline change was calculated using the DSAS tool following the extraction of shoreline data.
This study categorizes the shoreline of Phu Cat into four segments—Cat Khanh, Cat Hai, Vinh Hoi, and Trung Luong—based on their location and morphological characteristics to evaluate erosion rates The specific positions and lengths of these segments are illustrated in Figure 3.3.
Cat Khanh segment: about 12.6 km in length; the largest width is 250m, the coast is gentle
Cat Hai segment: about 1.8 km in length; the largest width is 182m, the coast is gentle
Vinh Hoi segment: about 2.6 km in length; the largest width is 320m, the coast is gentle
Trung Luong segment: about 6.9 km in length; the largest width is 380m, the coast is gentle
The results of extracting the Phu Cat coastline through interpretation of Landsat 5
TM and Landsat 8 OLI satellite images from 1989 to 2016 are shown in Figure 3.4
Figure 3 4: Result of the shoreline extraction
Considering the EPR index, the shoreline change rate is classified into nine classes, as shown in Figure 3.5
Figure 3 5: Erosion - Accretion classification (EPR) 3.1.3.1 Long-term change
Long-term coastal evolution involves cumulative effects of storms, sea-level rise, changes in sediment supplies, and human activities (construction of shore protection, sand mining, etc.)
The analysis of long-term shoreline change in the Northeast monsoon season was performed using 7 Landsat level 2 Surface reflectance images, collected in 1989, 1993,
1999, 2004, 2008, 2014 and 2016 (with same tidal level) Below are the results of the shoreline change analysis for each period a) The period from 1989 to 2016
Table 3.1 shows the result of the analysis of erosion and accretion using the EPR index of DSAS software
From 1989 to 2016, Phu Cat's shoreline experienced both erosion and accretion processes, resulting in a generally stable coastline The average erosion rate was -3 meters per year, while the mean accretion rate was 0.7 meters per year across the entire shoreline.
Table 3 1: Shoreline change rate in NE monsoon season (1989-2016)
3 Mean shoreline change rate (m/yr) 0.27 0.24 -0.04 0.02
6 Total transects that record erosion 2 1 6 11
7 Total transects that record accretion 79 9 10 16
- The mean erosion rate in each segment was: Cat Khanh (-9.9m/yr), Cat Hai (- 0.59m/yr), Vinh Hoi (-0.97m/yr), Trung Luong (-0.95m/yr)
- The most vital erosion area: De Gi headland (erosion speed was -10m/yr)
- The segment of Cat Hai was stable during the whole period
Figure 3 6: Shoreline change map (EPR) in NE monsoon season (1989-2016) b) The period from 1989 to 1999
Table 3.2 shows the result of the analysis of erosion and accretion using the EPR index of DSAS software from 1989 to 1999
Table 3 2: Shoreline change rate in NE monsoon season (1989-1999)
3 Mean shoreline change rate (m/yr) 0.20 0.03 -1.02 -1.50
6 Total transects that record erosion 73 10 23 137
7 Total transects that record accretion 60 11 3 0
Between 1989 and 1999, shoreline change analysis in Phu Cat revealed a predominant trend of erosion, with an average erosion rate of -1.6m/yr compared to an accretion rate of 1.2m/yr While Cat Khanh beach experienced a net accretion of 2.31m/yr against an erosion rate of -0.94m/yr, Vinh Hoi beach faced significant erosion at a rate of -2.43m/yr, with a much lower accretion rate of 0.99m/yr Overall, the findings indicate that erosion was more prevalent than accretion along the coastline during this period.
During the specified period, the Trung Luong segment experienced significant coastal erosion, with an average erosion rate of 1.6 meters per year along its 7-kilometer shoreline.
The highest erosion rates, reaching approximately -5m per year, were observed in the northern regions of Trung Luong beach and Vinh Hoi beach In contrast, the De Gi area experienced significant accretion during the same period, with a maximum increase of 22.4m per year, surpassing other locations.
Figure 3 7: Shoreline change map (EPR) in NE monsoon season (1989-1999) c) The period from 1999 to 2008
Table 3 3: Shoreline change rate in NE monsoon season (1999-2008)
3 Mean shoreline change rate (m/yr) -0.15 0.27 0.59 0.05
6 Total transects that record erosion 76 3 6 32
7 Total transects that record accretion 105 9 27 41
Between 1999 and 2008, the shoreline exhibited both accretion and erosion, yet overall, the coastline remained stable The Cat Khanh and Trung Luong segments experienced more erosion than accretion, while the Cat Hai and Vinh Hoi segments showed a trend of predominant accretion Notably, Cat Khanh beach had several erosion points, with the most significant erosion occurring at De Gi jetty, measuring -60m per year.
Figure 3 8: Shoreline change map (EPR) in NE monsoon season (1999-2008) d) The period from 2008 to 2016
Table 3 4: Shoreline change rate in NE monsoon season (2008-2016)
3 Mean shoreline change rate (m/yr) 0.90 0.36 0.45 1.79
6 Total transects that record erosion 33 8 12 0
7 Total transects that record accretion 170 25 29 123
During the observed period, the overall trend along the coastline indicated slight accretion However, the Cat Hai, Cat Khanh, and Vinh Hoi segments experienced both erosion and accretion Specifically, the average erosion rates were recorded at -1.86 m/yr for Cat Khanh, -2.37 m/yr for Cat Hai, and -1.66 m/yr for Vinh Hoi, while the average accretion rates were 1.73 m/yr, 1.33 m/yr, and 1.50 m/yr for the respective segments.
The Trung Luong segment demonstrated a significant positive trend, with an impressive average accretion rate of 2.1 meters per year However, certain areas experienced notable erosion, including De Gi, which faced a loss of -9 meters per year, and the Ong Lop headland and Ganh hill near the Vu Nam eco-resort, both of which recorded erosion rates of approximately -4 meters per year Additionally, the Trung Luong headland at Vinh Hoi beach experienced erosion at a rate of about -3 meters per year.
Figure 3 9: Shoreline change map (EPR) in NE monsoon season (2008-2016)
The study of shoreline changes during the southwest monsoon season utilized six satellite images taken in 1989, 1993, 2000, 2005, 2008, and 2014, all reflecting the same tidal level This analysis spans a period of 25 years, from 1989 to 2014, providing insights into shoreline variation over time.
Discussion
In general, with the primary objective of analyzing shoreline change in Phu Cat district under CC and proposing solutions, the problems raised have been solved
Firstly, this study used Landsat level 2 Surface reflectance satellite images from
From 1989 to 2016, a consistent tidal level was utilized to analyze long-term, short-term, and seasonal shoreline changes, minimizing errors caused by tidal fluctuations and enhancing shoreline extraction accuracy The study employed the DSAS tool in ArcGIS to calculate erosion and accretion rates using the EPR and NSM indices Following USGS guidelines, the levels of erosion and accretion were classified based on these indices, leading to significant findings.
During the NE monsoon season from 1989 to 2016, the Phu Cat coastline experienced both erosion and accretion, maintaining overall stability Erosion was predominant from 1989 to 1999, with an average shoreline erosion rate of -1.6m/yr The period from 1999 to 2008 saw a generally stable shoreline, followed by slight accretion from 2008 to 2016 Notably, De Gi beach and the northern section of Trung Luong beach were the most severely affected by erosion, with De Gi beach recording a maximum erosion rate of 60m/yr.
From 1989 to 2014, the coastal area of Phu Cat experienced slight accretion during the SW monsoon season, with an average rate of 0.8m per year However, between 1989 and 2000, the region faced significant erosion, averaging -1.37m per year In contrast, from 2000 to 2008 and from 2008 to 2014, accretion prevailed over erosion Notably, the most severe erosion was observed at De Gi beach, Ong Lop headland, and the northern part of Trung Luong beach, with the maximum erosion occurring during the SW monsoon season.
De Gi in 1999-2008 (-9.5m/yr) and the North of Trung Luong in the period 2008-2014 (7.9m/yr)
Figure 3 40: Comparison of erosion rate (EPR index) in NEMS and SWMS
From Figure 3.39, it can be seen that the erosion rate in the northeast monsoon season is higher than in the southwest monsoon season
This study on seasonal shoreline change revealed that the shoreline of Phu Cat experienced fluctuations between erosion and accretion Overall, the shorelines remained stable in 1989 and 2008, while showing signs of accretion in 1993 and 2014.
Between 1989 and 2000, Phu Cat district experienced significant coastal erosion, largely due to the impact of storms During this period, Binh Dinh was affected by 15 storms, including the devastating typhoon Linda in 1997, which struck with winds of 102 kph, causing extensive damage to central provinces Additionally, three consecutive storms in November and December 1998 further highlighted the strong influence of hurricanes on shoreline changes in the region.
Among the analyzed erosion hotspots, De Gi is the one confirmed by most local people through interviews
This study employs the DSAS method to predict future shorelines by utilizing EPR and LPR indexes, along with statistical methods and historical shoreline data The findings indicate that the projected shorelines for 2025 and 2035 are expected to experience accretion.
The PBSE analysis reveals that the shorelines of Cat Hai, Vinh Hoi, Ong Lop headland, and Trung Luong are in a stable static equilibrium planform (SEP), indicating long-term stability without erosion or accretion In contrast, Cat Khanh beach is identified as being in a dynamic equilibrium planform (DEP), signifying a tendency towards erosion, which aligns with the findings of the shoreline change assessment.
A study assessing the effects of climate change and sea-level rise on the Phu Cat coastline utilized the Bruun model to calculate shoreline recession at four sandy-beach segments The findings indicate that by 2100, the shoreline is projected to retreat by approximately 5 meters under the RCP 4.5 scenario and by 7 meters under the RCP 8.5 scenario.
This study's findings on long-term shoreline change analysis and prediction align closely with those of previous research, which employed modeling methods to examine trends in accretion and erosion.
The Binh Dinh coastal safety corridor, approved by Decision 4383/QD-UBND from the People's Committee of Binh Dinh province, meets the requirements for the coastal setback outlined in this study.
Table 3 22: Compare the results of the study with related studies
Authors Result Compare to this study Review of the planning The shoreline from 1973 to 2017 showed Consistent
88 of coastal dike system from Quang Ngai to
Kien Giang (2017) [63] that the coast in this area was alternating erosion-accretion The beach of Cat Hai and Cat Tien communes is relatively stable
The southern coast of De Gi did not change significantly in the period 1965 -2012; the primary trend is erosion
Long et al., 2017 [67] Beach of Trung Luong is and will have a predominant accretion trend In Phu Cat district, accretion-erosion is alternating
Duong et al., 2019 [68] The period 1988-1997 recorded the most significant erosion in Binh Dinh Consistent
Limitation on UAV data: the author used UAV to investigate the beaches of Phu
The UAV flight route was insufficient to fully cover the Phu Cat shoreline, limiting the ability to compare UAV interpretation products with satellite image analysis Nevertheless, the findings can serve as a valuable database for small-scale shoreline management, organized into manageable segments.
Limitations on shoreline change analysis and prediction:
The remote sensing method effectively identifies current erosion and accretion phenomena across spatial and temporal scales; however, it overlooks the physical processes driving changes along the coastline.
The PBSE method or Bruun's model only qualitatively predicts shoreline change
The shoreline prediction method utilizing the Digital Shoreline Analysis System (DSAS) has notable limitations, including the necessity of at least four historical shorelines for analysis Additionally, it presumes that the linear regression of past shoreline positions will remain consistent for future predictions, an assumption that may not always hold true.
The analysis utilized 89 satellite images with a spatial resolution of 30 meters; however, the limited time series of these images introduced uncertainties in estimating future shoreline changes using the DSAS tool.
Therefore, it is necessary to apply some supplementary mathematical models to explain the causes of shoreline change and predict future shoreline changes
The assessment of adaptive capacity in Phu Cat district was primarily qualitative, relying on limited interview data and local documents To enhance the evaluation, it is essential to gather a larger sample of interview responses for a more comprehensive analysis.