Vulnerability Analysis of Coastal Zone Based on InVEST Model in Jiaozhou Bay, China

Ai, Bo; Tian, Ye; Wang, Peipei; Gan, Yuliang; Luo, Fa; Shi, Qingtong

doi:10.3390/su14116913

Cited by 19 publications

(16 citation statements)

References 29 publications

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“…This study uses a model segment of every 50 m which is the distance between points to each other. The results in the calculation process are divided into five classes using the Natural Break Points method [20]. The CEI category classes include very low, low, moderate, high, and very high with ranking from 1 to 5.…”

Section: Methodsmentioning

confidence: 99%

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Quantification of the Coastal Exposure Index (CEI) in the Kangean Islands using GIS Based Modelling

Rachman,

Hidayah,

As-syakur

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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Climate change has become an important issue in recent years, especially in the context of threats to small islands. This phenomenon causes a rise in sea surface and surface temperatures that endanger coastal ecosystems. Therefore, studies related to the calculation of coastal exposure levels are very important in an effort to mitigate and reduce these impacts. In this study, the calculation of Coastal Exposure Index (CEI) was carried out on the entire coastal area of the Kangean Islands, both large and small islands. We quantified the CEI using Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Modeling. Parameters used in this analysis include coastal topography, bathymetry, waves, surges, natural habitats (mangrove, coral, and seagrass), geomorphology, and sea level rise. The results of the analysis were then classified into fives categories using natural break point as very low, low, moderate, high, and very high. The results show that about 9.76% (58.03 km) of the coastline has a very high vulnerability level, 19.18% is categorized as high (114.04 km), and about 144.85 km or 24.37%, is a moderate category. Locations with very high and high vulnerability level dominate the northern part of Kangean Island. This area is more exposed to the open ocean and has relatively low natural habitat cover and high exposure of wave and surge potential. While the southern part of the island is dominated by mangrove cover, which is protected from exposure in the open ocean area. This condition is probably causing this area to dominantly have low and moderate CEI categories. In the Sepanjang Island area, areas with coral reef and seagrass cover have a lower CEI. The existence of natural habitats is one of the important components of coastal area exposure and reduces the level of vulnerability. The results of this study can be used as a reference in the Kangean Islands management programme as a response to climate change disasters.

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Section: Methodsmentioning

confidence: 99%

“…Therefore, a coastline containing attributes of each class was obtained. In determining the class range, the natural breakpoint method from the CEI calculation results is used [20]. The analysis results will be divided into classes such as very low, low, moderate, high, and very high.…”

Section: Methodsmentioning

confidence: 99%

Quantification of the Coastal Exposure Index (CEI) in the Kangean Islands using GIS Based Modelling

Rachman,

Hidayah,

As-syakur

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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“…Assessing coastal vulnerabilities and the potential for mangrove ecosystem to contribute towards the protection of coastal communities is essential for effective long‐term planning, sustainability and the management of coastal resilience (Al Ruheili & Boluwade, 2023 ). The Integrated Valuation of Ecosystem Service and Trade‐offs (InVEST) Coastal Vulnerability model has been applied elsewhere to assess coastal vulnerability and to explore the role of natural habitats such as mangroves to mitigate coastal hazards (Arkema et al, 2013 ; Aneseyee et al, 2022 ; Ai et al, 2022 ; Al Ruheili & Boluwade, 2023 ). Mangrove ecosystems must be accurately mapped and inventoried in order to determine how they are changing and to what pressures they are subjected to, before their potential role as a natural defence can be assessed.…”

Section: Introductionmentioning

confidence: 99%

Nonparametric assessment of mangrove ecosystem in the context of coastal resilience in Ghana

Aja,

Miyittah,

Angnuureng

2023

Ecology and Evolution

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Cloud cover effects make it difficult to evaluate the mangrove ecosystem in tropical locations using solely optical satellite data. Therefore, it is essential to conduct a more precise evaluation using data from several sources and appropriate models in order to manage the mangrove ecosystem as effectively as feasible. In this study, the status of the mangrove ecosystem and its potential contribution to coastal resilience were evaluated using the Google Earth Engine (GEE) and the InVEST model. The GEE was used to map changes in mangrove and other land cover types for the years 2009 and 2019 by integrating both optical and radar data. The quantity allocation disagreement index (QADI) was used to assess the classification accuracy. Mangrove height and aboveground biomass density were estimated using GEE by extracting their values from radar image clipped with a digital elevation model and mangrove vector file. A universal allometric equation that relates canopy height to aboveground biomass was applied. The InVEST model was used to calculate a hazard index of every 250 m of the shoreline with and without mangrove ecosystem. Our result showed that about 16.9% and 21% of mangrove and other vegetation cover were lost between 2009 and 2019. However, water body and bare land/built‐up areas increased by 7% and 45%, respectively. The overall accuracy of 2009 and 2019 classifications was 99.6% (QADI = 0.00794) and 99.1% (QADI = 0.00529), respectively. Mangrove height and aboveground biomass generally decreased from 12.7 to 6.3 m and from 105 to 88 Mg/ha on average. The vulnerability index showed that 23%, 51% and 26% of the coastal segment in the presence of mangrove fall under very low/low, moderate and high risks, respectively. Whereas in the absence of mangrove, 8%, 38%, 39% and 15% fall under low, moderate, high and very high‐risk zones, respectively. This study will among other things help the stakeholders in coastal management and marine spatial planning to identify the need to focus on conservation practices.

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“…Researchers have developed different approaches to estimate and identify vulnerable zones in coastal regions impacted by different coastal forces [22]. The InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) coastal vulnerability model has been applied to estimate the degree of coastal vulnerability based on several variables [23,24]. The Multi-Dimensional Coastal Vulnerability Index (MDim-CVI) combines a composite set of physical, environmental, and socio-economic indicators to deduce coastal vulnerabilities and changes in the environmental and socio-economic systems in the Italian coasts [25].…”

Section: Introductionmentioning

confidence: 99%

Characterization of the Coastal Vulnerability in Different Geological Settings: A Comparative Study on Kerala and Tamil Nadu Coasts Using FuzzyAHP

Dwivedi,

Pampattiwar,

Pandey

et al. 2023

Sustainability

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The acceleration of coastal processes is manifested in the form of coastal erosion, average sea level rise, drastic changes in coastlines, and more turbulent ocean waters. In this study, the coasts of Kerala and Tamil Nadu, India, were selected to identify the effects of increasing coastal processes. Therefore, it is necessary to identify and map vulnerable areas by taking into account the key parameters, such as topographical and socio-economic factors, to relate to coastal processes. The fuzzy Analytical Hierarchy Process (AHP) method was employed to identify the most vulnerable zones. The key findings revealed that about 14% and 2% of the coast of Tamil Nadu and Kerala, respectively, are classified under the physically highly vulnerable category. Similarly, ~17% and ~30% of coastal Tamil Nadu and Kerala, respectively, are highly socially vulnerable. The overall vulnerability assessment showed that 7–8% of both coastal areas were highly vulnerable. We concluded that the Thiruvallur, Chennai, Kanchipuram, Cuddalore, and Nagapattinam coasts on the east coast and the Malappuram, Thrissur, Ernakulam, Alappuzha, and Kollam coasts on the west coast were very highly vulnerable to coastal processes. Identifying vulnerable zones helps to better understand the impacts of rapid coastal processes and, with this prior estimation, the policymakers can take necessary actions to mitigate the irreversible impacts of coastal processes.

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Vulnerability Analysis of Coastal Zone Based on InVEST Model in Jiaozhou Bay, China

Cited by 19 publications

References 29 publications

Quantification of the Coastal Exposure Index (CEI) in the Kangean Islands using GIS Based Modelling

Quantification of the Coastal Exposure Index (CEI) in the Kangean Islands using GIS Based Modelling

Nonparametric assessment of mangrove ecosystem in the context of coastal resilience in Ghana

Characterization of the Coastal Vulnerability in Different Geological Settings: A Comparative Study on Kerala and Tamil Nadu Coasts Using FuzzyAHP

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