Grid-based Coastal Vulnerability Assessment in Small Island Communities Using Land-use and Habitats for Improved Adaptative Management and Governance

Grid-based Coastal Vulnerability Assessment in Small Island Communities Using Land-use and Habitats for Improved Adaptative Management and Governance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Grid-based Coastal Vulnerability Assessment in Small Island Communities Using Land-use and Habitats for Improved Adaptative Management and Governance Antonio Fabela Regis Jr This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9277512/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Small island communities face disproportionate climate risks, yet most coastal vulnerability assessments treat ecosystem services as static features, missing the dynamic habitat changes that fundamentally alter coastal protective capacity. We present an operational framework that integrates temporal land-use change and natural habitat dynamics directly into the Coastal Vulnerability Index (CVI) and quantify ecosystem service contributions to coastal risk outcomes. The study was benchmarked in the Siargao Island Protected Landscape and Seascape (SIPLAS), Philippines, where we applied across 418 coastal grids, individual parameters, habitat, and CVI risk ratings, treating coastal ecosystems as protective features, as opposed to vulnerability-amplifying built-up and barren land. Between 2015 and 2020, the built-up area expanded by 124%, while mangroves, corals, and seagrass increased by 34%, 121%, and 56%, respectively, generating an estimated 138–291 million USD per year in ecosystem services, but concentrated away from dense settlements. Mean CVI rose from 14.08 to 14.66, and the share of High and Very High grids increased from 41% to 47%, with 140 grids worsening, 77 improving, and Very High grids showing 99% persistence, indicating vulnerability lock-in. Change-based analysis shows that habitat risk transitions explain 87.4% of the variance in CVI change; each unit of habitat degradation increases CVI by about 2.25 points, making transitions stronger predictors of vulnerability than static habitat states. The framework provides a transparent, data-light tool for small-island governments to prioritize proactive habitat protection and nature-based solutions over costly grey infrastructure in coastal adaptation planning. Climate Analysis and Modeling Environmental Engineering Conservation Biology Geographic Information Systems Climatology Environmental Policy Coastal Vulnerability Climate Change Remote Sensing and Geographic Information System Coastal Habitats Land use Ecosystem services Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Globally, approximately 65 million people from small island communities face disproportionate climate-induced threats (Mimura et al. 2007 , Petzhold and Magnan 2019, Vousdoukas et al. 2023 ), including sea-level rise (Nicholls 2011 , Vousdoukas et al. 2018 ) and extreme weather events (Knutson et al. 2015 , Betts et al. 2018 ) that threaten their livelihoods, infrastructure, and sovereignty (IPCC 2022). The economic burden is staggering, as small island economies have experienced annual average losses of $ 153 billion between 1970 and 2020, representing approximately 2.1% of their combined annual GDP (WMO 2023, UNDP 2024). These aggregate statistics mask regional disparities in vulnerability and economic loss. In the Caribbean, average annual losses are projected to reach 5% of regional GDP by 2025 (> $ 22 billion annually by 2050), escalating to 20% by 2100 absent regional mitigation strategies (Bueno et al. 2008 ), with floods and storms alone projected to cause $ 56 billion in climate change-attributable loss and damage under a 2°C warming scenario by 2050 (UNDP 2024). In the Pacific, annual climate-related losses are projected at 0.75%–6.5% of GDP by 2030 (IPCC 2022, AFdD 2024), and over the past 50 years, Pacific Island countries have already sustained over $ 3 billion in natural disaster damage (Lee et al. 2018 , WMO 2023), accompanied by profound human displacement, migration, livelihood loss, land resource depletion, and food insecurity driven by sea-level rise, coastal erosion, and habitat and biodiversity loss. The same impacts have been observed in the Philippines, an archipelagic country considered the world's most disaster-prone (World Risk Index score of 46.82). The country faces compounded climate vulnerabilities from typhoons, floods, droughts, earthquakes, and tsunamis amplified by coastal development and ecosystem degradation (Bündnis Entwicklung Hilft e.V., 2022; Ravago et al., 2020 ). Super Typhoon Rai in 2021 unambiguously illustrated this risk, particularly in the Siargao Island Protected Landscape and Seascape (SIPLAS), a critical ecological and economic hub hosting diverse coastal ecosystems (coral reefs, mangroves, seagrass beds, wetlands) that provide essential services, including fisheries-based food security, coastal storm protection, carbon sequestration, and tourism revenue (Spalding et al., 2014 ; Guannel et al., 2016 ). This recurring issue prompted the need to identify high-risk and vulnerable areas within particular jurisdictions across different local government units (LGUs). However, due to the lack of technical capacity, technology, manpower, and financing, especially in low-income municipalities and small island communities, disaster risk reduction and climate adaptation have been less prioritized (Ravago et al. 2020 , Jamero et al. 2017 ); therefore, a need arises to develop a comprehensive coastal vulnerability assessment customized for small island communities. While most contemporary coastal vulnerability assessment studies employ the Coastal Vulnerability Index (CVI) or similar frameworks utilizing static parameters such as geomorphology, elevation, sea-level change, tidal range, and wave height (Md Noor & Maulud 2022 , Rocha et al. 2023 ), with limited integration of dynamic anthropogenic parameters, specifically, temporal land-use change and coastal habitat dynamics (Guannel et al. 2016 , Lu et al. 2018 , Jones et al. 2020 ). This omission creates a critical gap, often classifying the area as 'high risk' based on physical exposure (low elevation, high wave heights, rapid sea-level rise) without accounting for whether protective ecosystems - mangroves, coral reefs, seagrass beds, and coastal wetlands are present, degraded, or expanding (Spalding et al. 2014 , Rezaie et al. 2020 ). The practical consequence is that vulnerability hotspots identified through standard CVI assessments may not reflect actual coastal resilience driven by ecosystem service provision, leading to misallocation of adaptation resources (Barbosa et al. 2022). Studies from Sajjad et al. ( 2018 ) and Rezaie et al. ( 2020 ) suggest that habitat conservation and restoration can reduce coastal vulnerability by 30–50%, yet this evidence rarely translates into quantified projections within operational vulnerability frameworks accessible to local governments and resource-limited coastal communities (van der Meulen et al. 2023). Consequently, nature-based solutions remain aspirational policy concepts rather than evidence-based adaptation strategies amenable to cost-benefit analysis, investment prioritization, and adaptive management (Toimil et al. 2020 , Inacio et al. 2021). Despite growing recognition that nature-based solutions (NbS) that offer cost-effective alternatives to gray infrastructure (Spalding et al. 2014 , Fairchild et al. 2021 ), their integration into operational coastal vulnerability assessment remains limited. Most coastal vulnerability studies employ static parameters focused on geomorphology and physical hazards, with limited quantification of how ecosystem services dynamically influence vulnerability outcomes (Md Noor & Maulud 2022 , Rocha et al. 2023 ). This creates a critical methodological gap: coastal managers lack quantitative frameworks demonstrating whether habitat conservation/restoration reliably reduces measurable coastal vulnerability, a prerequisite for mainstreaming NbS into adaptation planning and resource allocation (van der Meulen et al. 2023). This study addresses this gap by quantifying the direct influence of ecosystem services on coastal vulnerability through an integrated land-use/habitat-dynamics approach, providing empirical evidence that dynamic habitat assessment can be operationalized for climate adaptation planning (Barbosa et al. 2022, van der Meulen et al. 2023). SIPLAS provides an ideal natural experiment to demonstrate how ecosystem service provision quantifiably influences coastal vulnerability: despite ongoing habitat conservation and rehabilitation programs, the area simultaneously experiences habitat loss driven by tourism expansion and coastal infrastructure development. By linking ecosystem dynamics to vulnerability outcomes in this contested landscape, SIPLAS offers a replicable evidence base for mainstreaming Nature-based Solutions into coastal adaptation planning across vulnerable small island and resource-limited coastal contexts, establishing natural habitats as measurable, economically comparable parameters for climate risk management and adaptation (Spalding et al., 2014 ; Rezaie et al., 2020 ; van der Meulen et al., 2023). Using geographic information systems (GIS) data, we developed a replicable 1×1 km grid-based vulnerability assessment for SIPLAS incorporating: (1) static physical parameters (elevation, shoreline change, sea-level change rate, tidal range, significant wave height), and (2) dynamic CLUH composition evaluated for both baseline (2015) and temporal (2020) periods (Pantusa et al. 2018 , Rezaie et al. 2020 ). This framework also operationalizes coastal habitats as quantifiable ecosystem service providers rather than merely qualitative conservation priorities by directly linking habitat presence, degradation, or restoration to measurable changes in grid-level vulnerability indices. The approach aims to enable local coastal managers to identify where and how ecosystem service loss has amplified vulnerability, and to quantify the potential reduction in vulnerability from specific habitat restoration or conservation scenarios (Anderson et al. 2022 , Esraz-Ul-Zannat et al. 2024 , Twomey et al. 2025 ). Providing baseline information on coastal vulnerability and incorporating ecosystem service provisions support evidence-based climate adaptation planning. This aids in facilitating the integration of habitat conservation/restoration targets into local disaster risk reduction and climate governance frameworks (Barbosa et al. 2025), without requiring costly proprietary datasets or external technical expertise (Bathi & Das 2016 , Kantamaneni et al. 2018 , Bukvic et al. 2020 ). Methodology Description of Study Area For this study, we applied the assessment method to the Siargao Protected Landscape and Seascape (SIPLAS), located in the province of Surigao del Norte, Philippines (9.8483° N, 126.0455° E). The island is approximately 627.88 square kilometers and is well known for its beautiful beaches and natural scenery. It is also renowned as a worldwide surfing and tourism destination. A base map of SIPLAS was acquired from the latest Philippine Administrative Map, available on geoportal.ph and maintained by the National Mapping and Resource Information Authority (NAMRIA), the central mapping agency of the government of the Philippines. The Shapefile contains the delineations of regions, municipalities/towns, and barangays (small territorial and administrative districts that form local levels of government in the Philippines). The Map has 41,931 data entries covering all administrative areas of the country. Digital grids covering coastal areas at 1 km by 1 km resolution were created to quantify the variables used in the study. A total of 418 grid cells were produced (Fig. 1 ) to represent the whole coastal area for SIPLAS. Data Sources and Acquisition Open-access and downloadable geographical maps and data were used in the study. Table 1 summarizes the downloaded data and their parameters. This was to ensure that the process can be applied in similar areas of interest and recommend that similar work can be done and replicated. Geographic base maps were taken from geoportal.gov.ph , an online database platform by NAMRIA. Specific maps were taken, including coastal resource maps for 2016 and 2020 and land use and land cover changes for 2015 and 2020. All post-processing of the maps and data was carried out in ArcGIS Pro. Table 1 Parameters used and their open-access sources, resolution, and time period considered. Parameter Data Source Resolution Time Period Coastal Land -Use And Habitat Land use data and Coastal Habitat Map by NAMRIA (National Mapping and Resource Information Authority Philippines) https://www.geoportal.gov.ph/ (accessed on October 2, 2023) Scale 1:25 k 2015, 2020 Elevation (m) AW3D30 DSM 1 arc second https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm (accessed October 2, 2023) 30 m 2014 Shoreline change rate (m/year) Sentinel—2A imagery https://earthexplorer.usgs.gov/ (accessed on October 2, 2023) 30 m 2015, 2020 Sea level change rate (mm/year) Sea Level Evaluation and Assessment Tool – NASA Sea Level Change Portal (accessed on October 4, 2023) 1993–2019 Tidal range (m) Global Tidal Range Classification (accessed on October 4, 2023) 2014–2023 Significant wave height (m) Marine Copernicus data Global Ocean Waves Analysis and Forecast https://data.marine.copernicus.eu/viewer/ (accessed on 04 October 2020) 1/10° 2021–2023 Coastal Land-Use and Habitat Coastal areas play an essential role in social and economic accessibility, including access to trade and livelihood, transportation, healthcare, goods and supplies, and services. What has not been accounted for is the ecosystem services of coastal habitats, the need for their conservation and protection, and the importance of monitoring and managing these changes for ecological and land management purposes. Land use impacts coastal habitats and their ability to provide ecosystem services (Aighewi et al. 2014 , Burden et al. 2020 , Zhang et al. 2020 , Wedding et al. 2020). As SIPLAS is a protected area, important coastal habitats were incorporated into the matrix scoring as ecosystem services. The coastal habitat map was consolidated with the land-use map to elaborate on coastal habitat use for an ecosystem-based approach to disaster risk reduction and climate adaptation. Coastal habitats can provide multiple ecosystem services, including provisions for food supply, medicine, green spaces, eco-tourism, and carbon storage (Guannel et al. 2016 , Mendoza-González at al. 2018, Masucci & Reimer 2019 , Liu et al. 2021 ) Available land-use and coastal habitat maps were used in the study to project current land-use for the area of interest and to complement the island's coastal development plan. Siargao's CLUH map was classified into primary land uses: built-up zones, annual and perennial crops, brush/shrubs, fishponds, grassland, inland water, marshland/swamp, open forest, and open/barren areas. The coastal habitat map, with three classifications: corals, seagrass/seaweed, and mangroves, was then overlaid on the land use map, bringing the total classification to thirteen classes. Only coastal areas within the identified cell grids were considered for the risk rating in this study. CLUH are used in coastal vulnerability assessments because they affect the coast's exposure and sensitivity to natural hazards such as sea level rise, storm surge, erosion, and flooding. This study emphasizes the importance of coastal habitats and existing land use in developing criteria for coastal vulnerability, given their dynamic nature and manageability (Rezaie et al. 2020 , Barbier 2020 , Fairchild et al. 2021 ). CLUH can influence coastal vulnerability in different ways, including an increase in the pressure and demand on the coastal resources due to the development of settlements and conversion of uses, leading to degradation and loss of coastal habitat, reducing the natural protection and resilience of the coast to hazards (Aitali et al. 2020 , Rezaie et al. 2020 , Hzami et al. 2021 ). Coastal land use can also alter the geomorphology and hydrology of the coast, affecting the sediment transport and erosion processes and changing the shape and stability of the coast, further increasing its vulnerability to hazards (Huijbers et al. 2013 , Hapke et al. 2013 , Melet et al. 2020 ). Elevation AW3D30 DSM was used to obtain elevation for SIPLAS. The ALOS World 3D − 30m (AW3D30) is a global digital surface model (DSM) dataset with a horizontal resolution of approximately 30 meters. Among other open-access Digital Elevation Models (DEM), the AW3D30 is considered the most accurate for inundation propagation (Talchabhadel et al. 2021 ). AOI was clipped in from the DSM, and the classification for elevation was done according to the following metrics, according to the study from Gornitz 1991 : Very high risk : 0–5 meters High risk: 6–10 meters Moderate risk: 11–20 meters Low risk: 21–30 meters Very low risk: > 30 meters The gridded cells were then applied to quantify coastal elevation, and coastal elevation data per grid were obtained. Shoreline change rate Sentinel-2A imagery data from 2015 and 2020 were used for the shoreline change analysis. The images were acquired from earthexplorer.usgs, with search parameters adjusted to the lowest cloud cover to emphasize the island's clear, concise shape. Due to the broad area of interest for the study and the complex geographic profile, higher-resolution digital shoreline analysis tools were not used. A simplified preprocessing method using the Landsat QA ArcGIS Toolbox was applied to the satellite image data to estimate wetness, NDVI, and brightness, in preparation for semi-automatic vectorization using the same toolbox to extract the shoreline. Semi-automatic vectorization is a technique that involves both automated and manual processes to extract shorelines from satellite imagery using the Landsat QA ArcGIS Toolbox (Ghorai et al. 2020, Domazetović et al. 2021 ). Deriving spectral indices from the wetness, NDVI, and brightness enhanced the shoreline's contrast and visibility and improved the accuracy of semi-automatic vectorization. Once shorelines from different years have been extracted, the detection feature changes under data management were run between the 2 data sets to determine the changes over time. The type of change detected is as follows: NC for no change, indicating a matched update feature with no change. N for new indicates an unmatched update feature new to the base data. D for deletion, indicating an unmatched base feature that might need to be deleted from base data. Where N implies accretion or build-up in the shoreline, D means coastal erosion, and NC indicates no change. Depending on the change values, designated scoring is applied per grid cell according to the vulnerability matrix. This method has practical applications in coastal vulnerability assessments by comparing datasets of the same coastal area across different periods. One dataset might serve as a baseline, depicting the coastal area's conditions before significant events, such as disasters, and before increased development driven by social and economic activities. Sea level change rate The sea level change rate considered and applied in the assessment is from NASA Sea Level Portal Data Analysis - Sea Level Evaluation and Assessment Tool for this study. While global mean sea level changes are a significant indicator of climate warming, the crucial factor for evaluating potential coastal impacts lies in the fluctuation of regional relative sea levels (Brown et al. 2016 , Wang et al. 2020 ), where variation in regional relative sea levels can be determined by physical parameters exhibiting spatial and temporal variability, often resulting in substantial deviations from the overall long-term trend of global mean sea level rise. In assigning the risk rating for the sea level change rate(mm/year), classification of vulnerability ranking for values less than or equal to − 1.1 mm/yr was considered very low, the values from − 1.0–0.99 mm/year as low, the values 1.0–2.0 mm/year as moderate, the values 2.1–4.0 mm/year as high and the values ≥ 4.1 mm/year as very high vulnerability. For this study, the ranking of scores is defined in accordance with those proposed by Gornitz ( 1991 ). Tidal range Evaluating coastal susceptibility to hazards such as erosion, inundation, and storm surges often involves using the mean tidal range as a standard. This method accounts for the influence of tidal patterns on a coast's exposure and vulnerability. The mean tidal range represents the discrepancy between the average high and low tide levels. Consequently, a greater tidal range indicates greater susceptibility of the coastal area to sea-level fluctuations, wave dynamics, and sediment transport (Mawdsley et al. 2015 , Jiang et al. 2020 , Truong et al. 2021 ). The mean tidal range can be determined through tide gauges or satellite altimetry data. For this study, the values from the open-access data of the Global Tidal Range Classification were calculated using the FES2014 (Finite Element Solution) model data obtained from AVISO+ Satelite Altimetry Data. The FES2014 global ocean tide atlas significantly improves de-aliasing performance in satellite altimetry. It provides accurate open-boundary tidal conditions for regional and coastal modeling (Lyard et al. 2021 , Ray et al, 2019 ). Data from FES were then plotted into the grids to project the tidal range risks. Significant wave height Significant wave height (SWH) was also used as a parameter to evaluate coastline susceptibility, representing the average of the highest one-third of waves over a specific time interval. It is a vital indicator in shoreline vulnerability assessment, often replacing wave energy and aiding in the study of coastal vulnerability (Pendleton et al. 2010 , Shi et al. 2019 ). The relationship between wave energy and wave height demonstrates that as wave height increases, wave energy amplifies erosion and coastal flooding risks, endangering settlements and coastal ecosystems (Guza and Feddersen 2013). Higher wave heights indicate greater energy and erosive potential, which vary with wind speed, direction, water depth, and tide. Consequently, significant wave height data is an invaluable tool for assessing the potential impact of waves on coastal regions, helping to anticipate shoreline erosion, flooding, and inundation. Regions experiencing higher wave heights face greater vulnerability than those exposed to lower wave heights (Rani et al. 2018 , Mavromatidi et al. 2018 , Serafim et al. 2019 , Ferreira et al. 2021 ), highlighting the critical role of SWH in understanding coastal hazards. The data used in the study were taken from Copernicus Marine Services - Global Ocean Waves Analysis and Forecast. Although different values for the entire AOI can be obtained, only the maximum daily average wave height from January 1, 2021, to December 31, 2022, incorporated into the current sea level, was used to describe the mean significant wave height (Mahapatra et al. 2015 ). The data were then projected onto the significant wave height risk map by interpolating the values onto the grids and assigning risk following Pendleton et al. ( 2010 ). Coastal Vulnerability Coastal vulnerability risk rating assignment for different parameters Based on the individual parameters obtained from the processed data, a scoring assignment was done using the risk factor criteria. Risk ratings were assigned based on the parameter-specific risk classifications (Table 2 ). For this study, CLUH were used to assess the risk and resilience of coastal areas to natural hazards such as storms, floods, erosion, and sea-level rise. Built-up, barren land and monocropping - perennial or annual, and other considered anthropological interventions in land use, were considered to impact the ratings negatively because of their human-induced alterations that compromise ecological integrity and potential adverse effects on natural ecosystems (Okpara et al. 2018 , Carter et al. 2019 ). On the other hand, open forests, grasslands, and natural habitat occurrences are designated as “habitats” which are considered to have a positive impact on land use as restoration and conservation of natural habitats can reduce coastal vulnerability (Ewing 2015 , Sajjad et al. 2018 , Wedding et al. 2020, Ryheili and Boluwade 2023), making them a crucial priority for coastal planning and development. The matrix can help assess and prioritize vulnerable coastal segments for adaptation and mitigation management. The rating can be used to compare vulnerabilities between coastal regions to evaluate the effectiveness of different management measures. Coastal vulnerability risk rating assignment is a useful tool for coastal planning and decision-making, especially in the context of climate change and its impact on coastal systems (Hamid et al. 2019 , Anfunso et al. 2021). Table 2 Coastal vulnerability risk rating matrix. Parameters Very Low Low Moderate High Very High 1 2 3 4 5 (a) CLUH Complete habitat, less to no built up Habitat present, No built up Intertidal zone with present habitat low to no built up Beach barren, intertidal zone, with built up Beach Barren and built up (b) Shoreline change rate (m/year) ≥ 2.1 Accretion 1.0 to 2.0 Accretion −1.0 to 1.0 Stable −1.1 to − 2.0 Erosion ≤−2.0 Erosion (c)Elevation (m) ≥ 30.1 20.1–30.0 10.1–20.0 5.1–10.0 0.0–5.0 (d)Sea level change rate (mm/year) ≤−1.1 Land rising −1.0 to 0.99 Land rising 1.0 to 2.0 within range of eustatic rise 2.1 to 4.0 Land sinking ≥ 4.1 Land sinking (e) Tidal range (m) ≤ 0.99 1.0–1.9 2.0–4.0 4.1–6.0 ≥ 6.1 (f) Significant wave height (m) 1.25 Coastal Vulnerability Index The Coastal Vulnerability Index (CVI) was then obtained based on the values from the risks rating matrix. CVI is calculated as the square root of the product of the ranked parameters divided by the total number of parameters (Pantusa et al. 2018 ) and represented as shown in Eq. (1). $$\:CVI=\sqrt{\frac{a\cdot\:b\cdot\:c\cdot\:d\cdot\:e\cdot\:f}{6}}$$ Equation 1. Coastal Vulnerability Index Where a = risk rating assigned to CLUH, b = risk rating assigned to shoreline change rate, c = risk rating assigned to coastal elevation, d = risk rating assigned to sea-level change rate, e = risk rating assigned to tidal range, f = risk rating assigned to significant wave height. The CVI incorporates CLUH as a critical ecosystem services parameter, recognizing its multiplicative influence on overall coastal vulnerability. We employed paired samples t-tests to assess whether the mean CVI changed significantly between 2015 and 2020. Given that Shapiro-Wilk tests indicated non-normal distributions in the CVI data (p < 0.001), we complemented the parametric analysis with the nonparametric Wilcoxon signed-rank test. Pearson correlation coefficients quantified the relationship between habitat risk ratings and CVI values, and linear regression (ordinary least squares) was used to model CVI as a function of habitat risk. Chi-square tests of independence assessed whether the distributions of vulnerability classifications differed significantly between years. All analyses used α = 0.05 as the significance threshold; effects were considered significant when p < 0.05. Coastal Habitat Risk Classification and Ecosystem Service Integration CLUH composition was integrated into the vulnerability assessment framework as dynamic ecosystem service indicators, recognizing that natural habitats provide critical protection services against coastal hazards while anthropogenic land uses compromise coastal resilience. To incorporate land-use and habitat classifications within the CVI framework, a habitat risk rating scale summarized in Table 3 was developed from the vulnerability assessments with reference to the works of Rezaie et al. ( 2020 ), Guannel et al. ( 2016 ) and Spalding et al. ( 2014 ). This metric used directly parallels the hazard-based risk ratings (elevation, shoreline change, sea-level rise) used for physical parameters, enabling integration of ecosystem service provision as a quantifiable coastal vulnerability determinant rather than a qualitative conservation priority. Table 3 Habitat Risk Rating Scale Risk Rating Risk Level Habitat Characteristics Built-up Development Ecosystem Service Provision 1 Very Low Complete natural habitat Minimal Maximum 2 Low Habitat presence None High (implied by presence) 3 Moderate Intertidal zones with partial habitat Low-to-moderate Moderate 4 High Beach barren and intertidal zones Substantial Limited 5 Very High Completely barren beach Extensive (built-up) Zero habitat-based protection To support evidence‑based adaptation planning, we estimated the relative cost‑effectiveness of nature‑based solutions versus gray infrastructure by scaling representative implementation costs to observed reductions in vulnerability. Using commonly reported implementation costs of $ 2,000– $ 3,500 per hectare for coastal restoration in the Philippines and lower‑end global estimates (Goto et al. 2025 , Bayraktarov et al. 2016 , Primavera and Esteban 2008 ), and $ 2,000– $ 8,000 per meter for coastal seawall and grey infrastructure construction (World Bank 2016 , Wong et al. 2014 , Ng and Mendelsohn 2005 ), we scale costs to our observed vulnerability reductions to match typical global cost ranges and our CVI‑based vulnerability reduction metric, rather than reflecting a single empirical cost‑per‑CVI study. The habitat risk classification reflects the principle that natural ecosystems provide measurable, economically comparable protective services: mangroves, coral reefs, and seagrass beds each provide wave attenuation reducing incoming wave energy by 30–70% coastal sediment stabilization, fisheries support ( $ 2,000–8,000 hectare⁻¹ year⁻¹), and carbon sequestration services, whereas built-up areas and barren zones contribute to vulnerability escalation through impervious surfaces that prevent water infiltration, reduce slope stability, and eliminate natural coastal barriers (Guannel et al. 2016 . Narayan et al. 2016 , Pendleton et al. 2012 , Mcleod et al. 2011 ). With this approach, we can quantify ecosystem service loss or gain at the grid level, enabling direct comparison with corresponding changes in the CVI for statistical validation of the habitat-vulnerability relationship. The thirteen-class habitat/land-use system was developed to capture ecological heterogeneity while remaining operationally simple for resource-limited coastal jurisdiction (Verutes et al. 2024 , Wedding et al. 2022 ). However, for subsequent statistical analysis, these classes were collapsed into five habitat risk rating categories (Table 3 ) to improve statistical robustness and interpretability. Across all 418 grid cells, habitat risk classifications for 2015 and 2020 were cross-tabulated to identify spatial patterns of habitat change and ecosystem service transitions, documenting transitions from high-functioning ecosystems (ratings 1–2) to degraded ecosystems (ratings 4–5) and vice versa. This enables grid-level documentation of habitat restoration and degradation scenarios and transforms qualitative statements about ecosystem service importance into measurable vulnerability parameters amenable to regression analysis, scenario testing, and cost-benefit comparison with gray infrastructure alternatives. To further quantify the influence of ecosystem services on coastal vulnerability, we employed five complementary statistical approaches. First, Pearson product-moment correlation coefficients evaluated the linear relationship between habitat risk changes (observed range: -4 to + 4 units) and CVI changes (observed range: -13.69 to + 13.69 points) across all 418 coastal grid cells between 2015 and 2020, with R² values quantifying variance explained and significance testing via two-tailed t-tests (α = 0.05). Second, ordinary least squares (OLS) linear regression modeled CVI as a function of habitat risk (CVI = β₀ + β₁×Habitat_Risk), enabling quantification of vulnerability reduction per unit habitat change and scenario-based projections. Lastly, we classified all 418 grids into five habitat-vulnerability scenarios (Habitat Restoration, Preservation, Contested, Degradation, Stable Systems), calculated as ΔRisk=Risk 2020 -Risk 2015 ; similarly, CVI change was calculated as ΔCVI=CVI 2020 -CVI 2015 (Table 4 .) Table 4 Classification rules used for habitat–vulnerability pathways scenarios based on joint changes in habitat risk and coastal vulnerability between 2015 and 2020. Scenario ΔRisk threshold (Habitat risk 2015–2020) ΔCVI behaviour (CVI 2015–2020) Definition Habitat Restoration ΔRisk ≤ − 1.5 High negative ΔCVI (strong reduction) Substantial improvement in habitat conditions (e.g., mangrove/coral/seagrass expansion) with marked CVI decrease Habitat Preservation ΔRisk ≈ 0 (no meaningful change, minimal new built‑up) low negative ΔCVI (moderate reduction) Habitats are largely maintained and development constrained, leading to reduced vulnerability via protection upkeep Contested Zones ΔRisk small (does not meet ± 1.5) with habitat gain and new built‑up High, lower positive ΔCVI (slight increase) Grids where habitat expansion and development co‑occur; development marginally outweighs ecosystem gains Habitat Degradation ΔRisk ≥ + 1.5 High positive ΔCVI (strong increase) Substantial habitat loss or conversion to built‑up/barren land, leading to marked vulnerability increase Stable Systems ΔRisk ≈ 0 and ΔCVI ≈ 0 No meaningful change in CVI Little to no change in both habitat risk and vulnerability; equilibrium trajectory Field validation Field validation of coastal vulnerability assessment is crucial for confirming the accuracy and reliability of remote-sensing-derived data and providing ground truth for comparison and calibration. Integrating field validation with remote sensing data enhances the understanding of coastal vulnerability by combining large-scale observations with local information. A field assessment was conducted to confirm the methodology's accuracy. 45 sampling points were visually assessed to compare with the model results. The validation was conducted from January 5 to 8, 2024, at SIPLAS in the Philippines. Validation was limited to accessible coastal roadsides (n = 45) due to resource constraints, potentially biasing accuracy assessment toward more developed areas. Remote high-risk zones remain unvalidated. No area within the municipality of Socorro was validated, as it requires a 2-hour boat trip from the closest municipality, Dapa. Coastal vulnerability was visually assessed according to land use and habitat, elevation, shoreline changes, and tidal range variability. Risk values were then assigned to each assessed grid point. Pearson's correlation showed a strong correlation between the model and observed values (r = 0.8384183636). Cohen’s kappa was also used to determine the agreement between the model values and the actual observations. This resulted in a κ value of 0.718749, meaning a substantial percent agreement of 88%. The classification transition analysis tracked how 418 grids moved across five vulnerability categories (Very Low to Very High) from 2015 to 2020, identifying persistence patterns that indicate irreversible risk thresholds. Field validation at 45 ground-truth sites confirmed model reliability by comparing modeled and observed vulnerability classifications. The linear regression model explained 87.4% of the variation in coastal vulnerability (R² = 0.874, p < 0.001), demonstrating that habitat dynamics are the primary determinant of vulnerability outcomes in SIPLAS. All calculations were performed in Microsoft Excel using standard spreadsheet functions, ensuring methods are transparent, reproducible, and accessible to resource-limited coastal jurisdictions. However, since the satellite data were obtained in 2015 and 2020, some actual physical parameters did not match, particularly for land use. Commercial areas, settlements, and wave barriers cum coastal roads have been constructed in most coastal areas, as well as coastal reclamation. These considerations were used to evaluate the risk ratings. Results and Discussions Coastal Land-Use and Habitat Notable changes in coastal land use were identified, including a slight decrease in annual crop (-1%) and a substantial decline in brush/shrubs (-19%), possibly due to urbanization. The built-up area increased significantly (124%), reflecting urban development. In contrast, coral and mangrove areas showed positive changes, with a 121% and 34% increase, respectively, indicating the effectiveness of habitat conservation and regeneration efforts. Table 5 summarizes land-use and habitat changes between 2015 and 2020, along with the corresponding percentage changes. Chi-square tests revealed no significant overall change in land-use distribution, though the analysis did not detect a statistically significant overall change; a notable increase in high-risk areas categorized as "beach barren and built-up" highlights localized intensification of coastal habitat degradation. Figure 2 (a) shows the risk classification for CLUHs. Marshland extent increased anomalously by 656% (5.77 to 43.64 ha), attributed to road construction and coastal barriers that enclosed coastal drainage pathways, locking in the water within low-lying inland areas. Table 5 Summary of CLUH change between 2015 and 2020. Land Use & Habitat Class 2015 (hectares) 2020 (hectares) Change (hectares) % Change Interpretation Annual Crop 1,796.07 1,780.95 -15.12 -1% Minimal change, slight decline Brush/Shrubs 16,199.31 13,057.18 -3,142.13 -19% Significant loss (urbanization) Built-up 699.72 1,565.00 + 865.28 + 124% Expansion (tourism/development) Corals 7,240.51 16,002.72 + 8,762.21 + 121% Recovery (conservation working) Fishpond 24.83 23.29 -1.54 -6% Minimal change, slight decline Grassland 2,341.72 4,188.48 + 1,846.76 + 79% Positive expansion Inland Water 674.55 1,164.97 + 490.42 + 73% Increase (road construction traps water) Mangroves 16,664.06 22,311.66 + 5,647.60 + 34% Expansion (conservation/regeneration) Marshland/Swamp 5.77 43.64 + 37.87 + 656% Increas (barriers trap water) Open Forest 184.52 196.99 + 12.47 + 7% Slight expansion Open/Barren 96.45 168.53 + 72.08 + 75% Expansion of degraded zones Perennial Crop 20,041.29 15,021.62 -5,019.67 -25% Significant loss (land conversion) Seagrass/Seaweeds 6,129.63 9,591.00 + 3,461.37 + 56% Expansion (seagrass recovery) TOTAL AREA 72,098.44 85,116.02 + 13,017.58 + 18% Overall coastal area increased Significant changes occurred in coastal land use patterns and their associated risk ratings. The land use risk distribution shifted substantially, with moderate-risk areas (category 3) increasing from 32.5% to 45.2% of grid points, while low-risk areas (categories 1–2) decreased from 45.5% to 30.4%. Coastal habitat composition analysis shows a transition from more diverse mixed-habitat systems in 2015 to more simplified coastal environments in 2020, with basic coral and seagrass systems becoming more prevalent. The maintenance of constant change type distributions (365 no-change points, 48 nourishment points, and five depletion points) across both periods suggests that the observed vulnerability increases are primarily driven by land use changes and habitat degradation rather than active coastal management interventions, highlighting the critical need for enhanced coastal protection strategies and sustainable land use planning. Areas classified under mono-crops, built-up infrastructure, and barren land are assigned high vulnerability scores due to their susceptibility to climate-related risks (Hopper and Meixler 2016 , Kantamaneni et al. 2020 ). Mono-crop areas, whether perennial or annual, are vulnerable to pests, diseases, and extreme weather events due to their dependence on specific conditions for growth (Winker et al. 2013, Meisner and Boer 2018, Desai et al. 2021). Built-up areas face challenges such as heat island effects, flooding, and infrastructure damage during extreme weather events (Pregnolat et al. 2016, Ferdowsi et al. 2024 ). Barren lands lack vegetation cover and stability, making them prone to erosion and desertification exacerbated by climate change (Nearing et al. 2005 , Sivakumar 2007 , Greipsson 2012 , Plangoen et al. 2013 ). In contrast, habitats such as forests and wetlands are rated higher due to their resilience, ecosystem services, and adaptive capacity to mitigate climate impacts (Bernhardt and Leslie 2013 , Spalding et al. 2014 , Seidl et al. 2016 , Filho et al. 2018 , Ferro-Azcona et al. 2019 ). In SIPLAS, most housing settlements and commercial areas are established near and within the coastal zone (mostly within 1 kilometer of the highest high-tide point). It is treated as a built-up area and was assigned a high-risk ranking on the vulnerability matrix. On the other hand, natural habitats in an area were considered contributors to risk deviation. This trend suggests growing anthropogenic pressures such as urbanization and land conversion into sensitive coastal zones, threatening vital ecosystems like coral reefs, seagrass beds, and mangroves that provide essential ecological services and coastal resilience. Shoreline Change Rate The study observed a moderate risk of Shoreline Change Rate in SIPLAS due to accretion or erosion, as shown in Fig. 2 (b). Shoreline change on the island can be attributed to many natural physical factors, including strong wave action, tidal variation, sea level rise (Dean and Houston 2016 ), and anthropogenic impacts such as coastal reclamation, land use, and erosion (Hapke et al. 2013 ). Many parts of the island's coastal areas showed little to no change from 2015 to 2020 in the feature change detection analysis. However, the isolated, smaller islands have noticeably changed primarily due to erosion. On the other hand, accretion on the coastal areas of the towns of Del Carmen, Pilar, Santa Monica, and Dapa was observed. This was attributed to the protected and rehabilitated mangrove forests as part of the locally implemented protected area management scheme. This was also similar to observations from the CLUH change assessment, which showed an increase in mangrove cover in areas that influenced coastal physical features (Lovelock et al. 2015 , Guo et al. 2020 , Pang et al. 2020). However, coastal reclamation and construction of ports and coastal barriers (considered gray infrastructures) in the last few years have also affected the sediment transport (sand budget) in these shorelines, which in the long run poses a threat to its integrity not only by changing the natural movement of the coastal sediments that are influenced by to seasons, tide and wave actions but also because of its impacts on natural habitats and ecological pathways (Tian et al. 2016 , Chee et al. 2017 ). Elevation Coastal elevation data indicated significant variation across the island's coastal area, from below sea level to relatively high points (minimum of -1 m to a maximum of 255 m), as shown in Fig. 2 (c). The elevation risk assessment covers a total area of 418 sq. km of coastal area (418 grids x 1 sq.km.), which includes the coastal part of the coral reefs, intertidal zone, and mangrove forests. Based on the elevation risk analysis using the Digital Surface Model, 32.4% of the coastal area, or approximately 135 square kilometers, is at Very High Risk, as shown in Table 6 . In this study, coastal areas with elevations below 5 meters were considered to have “Very High” risk due to their high exposure to climate impacts such as storm surges, sea-level rise, and coastal flooding (Twumasi et al. 2020 , Kron 2013 , Weiss et al. 2011 ). Furthermore, most settlements and urban areas on the island are located 1–2 kilometers from the coast, potentially increasing socio-economic and population exposure to these climate-associated risks (Kulp & Strauss 2019 , Vousdoukaset al. 2018 , Neuman et al. 2015). Table 6 Summary table of coastal elevation Elevation Risk Classification Percent Risk (%) Area Covered (sq.km) Very High 32.4 135.42 High 12.98 54.27 Moderate 16.33 68.28 Low 9.7 40.56 Very Low 28.58 119.47 Total Area Covered 418 Sea-Level Change Rate Sea level change risk directly relates to coastal inundation, increasing the susceptibility of coastal areas to climate-related hazards such as flooding, saltwater intrusion, and habitat alteration (Nicholls 2011 , Hague et al. 2020 ). The impacts of the sea level change rate on coastal vulnerability assessment can be associated with elevation and land use within the grids. Sea level change rate risk is projected to be higher in lower-elevated regions than in those with higher elevations (Magnan et al. 2022 , Vousdoukas et al. 2023 , Martyr-Koller et al. 2021 ) Using data from the NASA Sea Level Portal Data Analysis Sea Level Evaluation and Assessment Tool, the study assessed the risks according to the recorded sea level change rate for SIPLAS in the Philippines, 6.34 mm/year, rated as Very High in the vulnerability matrix. Accordingly, the World Bank and Asian Development Bank Climate Risk Country Profile 2021 also considered the Philippines one of the countries with the highest risk of sea level rise. Tidal Range The average value of Tidal Range Risk for SIPLAS was obtained from the Global Tidal Range Classification, calculated using the FES2014 (Finite Element Solution) model and AVISO+ Satellite Altimetry Data. According to the risk matrix applied in this study, the coastal vulnerability for the whole island was Moderate (2.11 m), which partly influences sediment transport and the establishment of coastal habitats. However, due to the low elevation of coastal areas, tidal range poses a significant risk to coastal settlements and establishments (Anthony E.J. 2019). Figure 3 shows how locals have adapted their dwellings in the intertidal zones by elevating their houses, eventually exposing them to more risks. Natural and anthropogenic changes to estuaries and tidal rivers, such as wetland reclamation and channel dredging, significantly affect the tidal range and its impact on coastal communities and ecosystems (Talke and Jay, 2020 ). Tidal processes also significantly affect sediment transport and the distribution of biodiversity in coastal areas, influencing shoreline change and vulnerability over time (Dashtgard et al. 2012 ). Higher tidal ranges are linked to intense coastal erosion and increased sediment buildup due to the greater energy and water movement they bring (Couperthwaite et al. 1999 , Rossi et al. 2016 ). This energy can generate high shear stresses, leading to substantial sediment transport and erosion, particularly during storm events or when tidal forces peak. The lack of protective barriers and the variability in sediment sources can exacerbate these effects, making coastlines with macro-tidal ranges more vulnerable to changes in morphology and sediment dynamics. Significant Wave Height (SWH) SIPLAS is known as the surfing capital of the Philippines, hosting various local and international surfing competitions throughout the year. This is because the eastern parts of the island face the Pacific Ocean, where more than seasonal winds blow regularly, creating waves. The Significant Wave Height was taken from Copernicus Marine Services - Global Ocean Waves Analysis and Forecast for this study. The value for SIPLAS was approximated at 1.8 m and is classified as Very High according to the vulnerability matrix. The risk associated with Significant Wave Height (SWH) is compounded by factors such as sea level rise and tidal range (Gornitz 1991 , Suh and Kim 2020 , Chaigneau et al. 2023 ). Rising sea levels due to climate change increase the extent of coastal inundation and erosion, amplifying the impact of significant wave events by allowing waves to penetrate further inland and causing more severe flooding (Zhang et al. 2004 , Hassan and Hassaan 2020 ). Similarly, high tidal ranges contribute to greater water level fluctuations, enhancing the effects of waves on coastal flooding and erosion dynamics. Coastal Vulnerability Index (CVI) The Coastal Vulnerability Index (CVI) data analysis from 2015 to 2020 shows significant temporal changes in vulnerability across the 418 coastal grid points examined (Table 7 ). The mean CVI increased from 14.08 in 2015 to 14.66 in 2020. This indicated an upward trend given that 140 grid points (33.5%) experienced increased vulnerability, while only 77 points (18.4%) showed decreased vulnerability, and 201 points (48.1%) remained unchanged. Table 7 Summary table of coastal vulnerability and percent coverage for 418 sq.km. Coasts of SIPLAS for 2015 and 2020. CVI Classification 2015 2020 Grid Count (%) Grid Count (%) Very Low 4 1 5 1 Low 75 18 63 15 Moderate 167 40 154 37 High 104 25 113 27 Very High 68 16 83 20 The temporal dynamics of vulnerability transitions reveal a predominantly downward trajectory: 140 grids (33.5%) transitioned to higher vulnerability categories, while only 77 grids (18.4%) improved. Figure 4 illustrates a critical threshold effect: Very High vulnerability grids demonstrated 99% persistence (67 of 68 grids), indicating an essentially irreversible vulnerability state once they reach this critical classification. In contrast, the Low-risk habitat category shows substantial erosion, with 35 of 80 grids (43.8%) transitioning to higher vulnerability categories, predominantly to Moderate (32 grids) but with concerning direct jumps to High (3 grids), indicating episodic vulnerability-amplifying events. These transitions directly correlate with documented coastal land-use changes (built-up expansion + 124%, habitat loss − 66 Low-risk grids) and ecosystem service degradation (mangroves + 34% but offset by habitat loss elsewhere). The distribution of vulnerability categories was observed to have shifted notably. A comparison of CVI for 2015 and 2020 (Fig. 5 ) shows a shift toward higher risk over time: with the "Very Low" and "Low" risk categories decreasing from 18% to 15%, while "High" and "Very High" categories notably increased by 3–4%. The "Moderate" category remained the most populated in both years but slightly decreased (3%) from 2015 to 2020. This overall trend indicates that coastal areas are facing increasing vulnerability, moving out of lower risk categories and into higher ones, suggesting escalating risks for CLUHs over this five-year period. Coastal land-use and habitat classification analysis reveals a fundamental contradiction in SIPLAS's coastal development trajectory. Habitat areas expanded substantially between 2015 and 2020, with mangrove forests increasing by 34% (+ 5,648 hectares), coral reefs by 121% (+ 8,762 hectares), and seagrass beds by 56% (+ 3,461 hectares), reflecting effective conservation and regeneration programs. These habitat gains generate estimated annual ecosystem service values of $ 138–291 million, including coastal protection (wave dissipation 30–70%), fisheries support (USD $ 2,000–8,000/hectare/year), and carbon sequestration. However, this positive trajectory is offset by rapid built-up expansion of 124% (+ 865 hectares), concentrated in tourism development corridors, combined with loss of perennial crops (-25%, -5,020 hectares) and brush/shrubs (-19%, -3,142 hectares). Changes in the CVI from 2015 to 2020 show areas with high degradation transitioning to high vulnerability, rather than low recovery, as shown in Fig. 6 . Spatially, habitat recovery occurs in remote areas, while development pressure concentrates in areas of highest human population density, creating a spatial mismatch between ecosystem service gains and reduced vulnerability. Grid-level analysis reveals that 140 grids (33.5%) experienced increased vulnerability despite habitat recovery elsewhere, while only 77 grids (18.4%) improved, demonstrating that development outpaces restoration at 1.82:1 ratio. The field validation observed that urban/rural development is prominent within areas of Very High and High vulnerability, increasing their exposure to climate-related risks and other potential natural hazards (see Fig. 7 ). It was also observed during the field validation that various adaptation measures, mostly gray infrastructure, were being constructed in these areas to mitigate potential damage and losses from climate change impacts. Gray infrastructures like breakwaters, dikes, and sea walls have a long-term impact on coastal ecosystems (Powell et al. 2018 , Waryszak et al. 2021 ), are expensive to build and require constant maintenance, making them inefficient for coastal flooding mitigation (Powell et al. 2018 , Livingstonn et al. 2018, Waryszak et al. 2021 , Inacio et al. 2021). Quantifying Ecosystem Service Influence on Coastal Vulnerability The changes in habitat risk rating and CVI showed a strong positive correlation (r = 0.935, 95% CI [0.910, 0.956], p < 0.001), with risk changes explaining 87.4% of variance in CVI transitions (R² = 0.85). This opposing trajectory, with a high correlation, demonstrates that ecosystem service provision (operationalized as habitat risk rating) is a major determinant of coastal vulnerability dynamics. Linear regression analysis (Fig. 8 ) showed: \(\:CVI=8.229+2.25\times\:Habitat\_Risk\) , where each unit increase in habitat risk rating corresponds to a 2.25 CVI increase. The slope coefficient is highly significant (p < 0.001), as indicated by the Model R² = 0.8519, suggesting that vulnerability variance is due to habitat changes. These habitat gains correspond to the estimated annual ecosystem service values of $ 138–291 million (including coastal protection, fisheries support, and carbon sequestration valued at $ 1,500–8,000 hectare⁻¹ year⁻¹). Table 8 summarizes ecosystem valuation between 2015–2020 based on changes in habitat risk and CVI. However, a 124% (+ 865 hectares) expansion of built-up areas in high-population-density areas systematically offset habitat gains by permanently losing coastal protection services. Table 8 Estimated Quantified Ecosystem Service Values by Habitat (2015–2020). Habitat Type Area 2015 (ha) Area 2020 (ha) Change (ha) Ecosystem Service Value Total Annual Benefit (2020) Mangroves 16,664 22,312 + 5,648 $ 4,000–8,000/ha/year $ 89–178 million Corals 7,241 16,003 + 8,762 $ 2,000–5,000/ha/year $ 32–80 million Seagrass 6,130 9,591 + 3,461 $ 1,500-3,000/ha/year $ 14–29 million Grassland 2,342 4,188 + 1,846 $ 500-1,000/ha/year $ 2–4 million Forest 185 197 + 12 $ 1,000–2,000/ha/year $ 0.2–0.4 million Built-up/Barren 696 1,565 + 869 $ 0 (liability) $ 0 Subtotal 33,162 ha 52,291 ha + 19,129 — $ 138–291 million Quantified Ecosystem Service Values by Habitat (2015–2020): The + 19,129 hectares of habitat gain generate $138–291 million in annual ecosystem services, yet this is concentrated in remote areas, while + 869 hectares of built-up in high-density zones eliminate coastal protection, creating a net vulnerability increase. As summarized in Table 9 , it was noted that Very Low risk grids (rating 1, complete habitat) increased modestly from 18 to 22 grids (+ 22%), while High risk grids (rating 4, limited habitat) increased substantially from 98 to 118 grids (+ 20%), and Very High risk grids (rating 5, no habitat) decreased from 49 to 38 grids (− 22%). The net distribution shift indicated concentration of vulnerability in mid-to-high risk categories (High + Very High grids: 147 in 2015 vs. 156 in 2020, + 6%), with 140 grids deteriorating versus only 77 grids improving, yielding a 1.82:1 ratio of vulnerability escalation to reduction. Table 9 Habitat Risk Classification, Land-Use Changes, and Ecosystem Service Valuation (2015–2020) Risk Rating Description Primary Land Uses Ecosystem Services 2015 Grids 2020 Grids Change Service Value 1 Very Low Complete habitat (mangrove/coral/seagrass) Maximum protection: Wave dissipation 70%, Storm surge reduction, Fisheries support, Carbon storage 18 22 + 4 $ 8,000–12,000/ha/year 2 Low Habitat present, no built-up (mix of forest, grassland, partial habitat) Good protection: Wave dissipation 30–50%, Coastal stabilization, Biodiversity support 97 98 + 1 $ 3,000–5,000/ha/year 3 Moderate Mixed: Intertidal+habitat or grassland+limited built-up Adequate protection: Partial wave dissipation, Some stabilization 156 142 -14 $ 1,000–2,000/ha/year 4 High Beach barren, intertidal, with built-up development Limited protection: Minimal wave dissipation, Rapid erosion 98 118 + 20 $ 0-500/ha/year 5 Very High Beach barren AND built-up (no vegetation) No protection: Zero ecosystem services, Maximum erosion vulnerability 49 38 -11 $ 0/ha/year (Negative: cleanup costs) TOTAL AREA — — 72,098 ha 85,116 ha + 13,018 ha — The findings of the scenario-based analysis of habitat risk (classification in Fig. 9 ), quantifies vulnerability outcomes for specific habitat management pathways. Grids undergoing habitat restoration (Δ Risk <- 1.5) showed a mean decrease in CVI of − 7.71 units (n = 12, 95% CI [− 9.29, − 6.13], SD = 3.05), representing a 54.8% reduction in vulnerability relative to baseline CVI of 14.08. Conversely, grids experiencing habitat degradation (Δ Risk ≥ + 1.5) showed mean CVI increases of + 6.99 units (n = 19, 95% CI [+ 5.65, + 8.33], SD = 2.82), representing 49.6% vulnerability increase. The 14.7-unit differential between restoration and degradation scenarios (− 7.71 to + 6.99) quantifies the ecosystem service value in terms of reduced coastal vulnerability. These findings demonstrate that ecosystem service modifications, as measured by changes in habitat risk ratings, directly affect coastal vulnerability, though the absolute level of vulnerability remains dominated by geophysical hazard exposure. Summary and Conclusion The assessment revealed multiple high-risk coastal hazards for SIPLAS. Elevation analysis identifies 32.4% of the coastal area (135 square kilometers) as very high risk, with most coastal settlements concentrated within 1–2 kilometers of shorelines at elevations below 5 meters. The island experiences a very high sea level change rate of 6.34 mm/year, significantly above global averages and consistent with the Philippines' classification as one of the world's most vulnerable countries to sea level rise. Additional risk factors include a significant wave height of 1.8 meters from Pacific Ocean exposure and a moderate tidal range of 2.11 meters that affects sediment transport. These combined factors create a complex risk environment where multiple climate-related hazards interact to amplify coastal vulnerability. It is also currently being influenced by the construction of coastal barriers cum coastal roads, new ports, and other gray infrastructure on the coast. The integration of CLUH into the CVI framework also assessed how ecosystem services and land-use dynamics have influenced coastal vulnerability between 2015 and 2020. During this period, built-up area increased by 124%, while brush/shrub and perennial crop areas declined by 19% and 25%, respectively, indicating strong pressures from urbanization and land conversion in the coastal zone. In contrast, mangrove cover increased by 34% and coral reef area by 121%, reflecting ongoing habitat conservation and regeneration efforts within SIPLAS. At the island scale, the CVI results show a net shift towards higher vulnerability classes, with the mean CVI increasing from 14.08 in 2015 to 14.66 in 2020, and the proportion of grids in the High and Very High categories rising from 41% to 47%. This indicates that, despite positive trends in key protective habitats such as mangroves and corals, the rate and spatial distribution of built-up expansion and other high-risk land uses are currently outpacing the protective effect of ecosystem services. Most critically, the vulnerability lock-in effect demonstrated by 99% Very High category persistence suggests a narrowing window for meaningful adaptation before irreversible coastal risk states are entrenched. Using a habitat risk classification analysis to quantify the impact of land use and coastal habitat on coastal vulnerability, the study revealed strong relationships between ecosystem service provision and coastal vulnerability. Change-based analysis revealed a strong positive relationship between habitat risk change and CVI (r = 0.935, 95% CI [0.91, 0.96], p < 0.001), with habitat dynamics explaining 87.4% of vulnerability variance (R²=0.874). Linear regression of vulnerability changes as a function of habitat risk changes yielded: ΔCvi = 2.25 × ΔHabitat_Risk + constant, quantifying that each unit of habitat risk degradation produces 2.25 CVI points of vulnerability increase. In contrast, cross-sectional analysis of static habitat risk ratings with CVI values showed moderate correlations (2015: r = 0.375, p < 0.001; 2020: r = 0.389, p < 0.001), indicating that habitat status explains only 15.1% of total CVI variance in any given year (R²=0.151), with physical hazard parameters (elevation, wave height, shoreline change, sea-level rise) explaining the remaining 84.9%. This disparity between change-based (r = 0.935) and static (r = 0.38) correlations reveals a critical finding: habitat transitions are stronger predictors of coastal vulnerability than habitat state. This indicates that ecosystem service loss or recovery at the grid level drives changes in vulnerability more strongly than the absolute level of habitat provision determines baseline vulnerability. This irreversibility has profound implications for climate adaptation governance. Coastal vulnerability reduction requires immediate habitat protection in currently low-risk zones (preventing escalation to High-Very High categories) rather than deferring adaptation until vulnerability becomes critical, at which point remediation costs become economically prohibitive and physically infeasible. SIPLAS presents a natural experiment demonstrating this principle: the 77 grids successfully preserved in the Preservation scenario required only habitat retention and development restriction, incurring minimal financial cost, whereas the 19 grids in the Degradation scenario now require full ecosystem restoration to reverse + 6.99 CVI point increases. This asymmetry in prevention versus restoration costs, combined with the irreversibility persistence rate of 69.1%, provides quantitative justification for precautionary coastal governance: protecting Low-Moderate habitat zones through restrictive zoning, property acquisition, and marine spatial planning yields equivalent or superior vulnerability reduction per dollar invested compared to restoration-focused strategies in degraded zones, while potentially avoiding irreversible risk thresholds that foreclose future adaptation options. This cost-effective evidence directly informs resource allocation decisions in resource-constrained small island communities. For identical coastal protection outcomes, habitat-based adaptation delivers greater cost-effectiveness than gray infrastructure while also supporting fisheries, carbon sequestration, recreational ecosystem services, and cultural heritage. The habitat risk classification framework thus transforms coastal vulnerability assessment from a physical hazard assessment into an evidence-based decision support system for coastal adaptation planning. This enables policymakers to quantify which habitat conservation and restoration investments yield the greatest vulnerability reduction per dollar invested, facilitating mainstreaming of nature-based solutions into operational adaptation planning. The vulnerability assessment findings highlight critical challenges for sustainable coastal management, not only for SIPLAS but also for small island communities worldwide. The concentration of settlements and infrastructure in very high- and high-vulnerability zones necessitates urgent adaptation strategies that balance economic development with environmental protection. Current reliance on gray infrastructure solutions, while providing short-term protection, may prove insufficient and environmentally counterproductive given their high costs, maintenance requirements, and potential negative impacts on coastal ecosystems. The study emphasizes the need for integrated coastal zone management that leverages the observed positive trends in coastal habitat conservation while addressing the underlying drivers of habitat conversion and urban expansion. Declarations Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper. Acknowledgments Mr. Antonio Fabela Regis Jr. would like to thank the DIA- Doctoral Fellowship in India for ASEAN and the Government of India for their financial support in undertaking this study. 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Ocean Coastal Manage 90:50–57. https://doi.org/10.1016/J.OCECOAMAN.2013.09.007 Suh S, Kim H (2020) Extreme Surge Heights Incorporating Long-term Tidal Variation and Sea-level Rise. J Coastal Res 95:1542–1546. https://doi.org/10.2112/SI95-297.1 Talchabhadel R, Nakagawa H, Kawaike K, Yamanoi K, Thapa BR (2021) Assessment of vertical accuracy of open source 30m resolution space-borne digital elevation models. Geomatics Nat Hazards Risk 12(1):939–960. https://doi.org/10.1080/19475705.2021.1910575 Talke S, Jay D (2020) Changing Tides: The Role of Natural and Anthropogenic Factors. Annual review of marine science. https://doi.org/10.1146/annurev-marine-010419-010727 The World Bank Group and the Asian Development Bank (2021) Climate Risk Country Profile: Philippines Tian B, Wu W, Yang Z, Zhou Y (2016) Drivers, trends, and potential impacts of long-term coastal reclamation in China from 1985 to 2010. Estuar Coastal Shelf Sci 170:83–90. https://doi.org/10.1016/J.ECSS.2016.01.006 Toimil A, Losada I, Nicholls R, Dalrymple R, Stive M (2020) Addressing the challenges of climate change risks and adaptation in coastal areas: A review. Coast Eng. https://doi.org/10.1016/j.coastaleng.2019.103611 Truong D, Tri D, Don N (2021) The Impact of Waves and Tidal Currents on the Sediment Transport at the Sea Port. Civil Eng J. https://doi.org/10.28991/cej-2021-03091749 Twomey AJ, Erickson K, Bishop MJ, Boody K, Callaghan DP, Cannard T, Lovelock CE, Mayer-Pinto M, Morris RL, Pomeroy AW, Saunders MI, Steven A, Waltham NJ, Bugnot A (2025) Interdisciplinary solutions enable nature-based coastal protection to achieve ecological and engineering outcomes. Environ Sci Policy 171:104157. https://doi.org/10.1016/j.envsci.2025.104157 Twumasi Y, Merem E, Namwamba J, Ayala-Silva T, Okwemba R, Mwakimi O, Abdollahi K, Lukongo O, LaCour-Conant K, Tate J, Akinrinwoye C (2020) Modeling the Risks of Climate Change and Global Warming to Humans Settled in Low Elevation Coastal Zones in Louisiana, USA. Atmospheric and Climate Sciences. https://doi.org/10.4236/acs.2020.103017 United Nations Development Programme (2024), November 5 Snapshot of loss and damage in SIDS under the Climate Promise. UNDP Climate Promise. https://www.undp.org/publications/snapshot-loss-and-damage-sids-under-climate-promise Verutes GM, Yang PF, Eastman SF, Doughty CL, Adgie TE, Dietz K, Dix NG, North A, Guannel G, Chapman SK (2024) Using vulnerability assessment to characterize coastal protection benefits provided by estuarine habitats of a dynamic intracoastal waterway. PeerJ 12:e16738. https://doi.org/10.7717/peerj.16738 Vousdoukas MI, Mentaschi L, Voukouvalas E, Verlaan M, Jevrejeva S, Jackson LP, Feyen L (2018) Global probabilistic projections of extreme sea levels show intensification of coastal flood hazard. Nat Commun 9(1):2360. https://doi.org/10.1038/s41467-018-04692-w Vousdoukas M, Athanasiou P, Giardino A, Mentaschi L, Stocchino A, Kopp R, Menéndez P, Beck M, Ranasinghe R, Feyen L (2023) Small Island Developing States under threat by rising seas even in a 1.5°C warming world. Nat Sustain 6:1552–1564. https://doi.org/10.1038/s41893-023-01230-5 Wang W, Wang J, Choi F, Ding P, Li X, Han G, Ding M, Guo M, Huang X, Duan W, Cheng Z, Chen Z, Hawkins S, Jiang Y, Helmuth B, Dong Y (2020) Global warming and artificial shorelines reshape seashore biogeography. Glob Ecol Biogeogr. https://doi.org/10.1111/geb.13019 Waryszak P, Gavoille A, Whitt A, Kelvin J, Macreadie P (2021) Combining gray and green infrastructure to improve coastal resilience: lessons learnt from hybrid flood defenses. Coastal Eng J 63:335–350. https://doi.org/10.1080/21664250.2021.1920278 Wedding L, Reiter S, Moritsch M, Hartge E, Reiblich J, Gourlie D, Guerry A (2022) Embedding the value of coastal ecosystem services into climate change adaptation planning. PeerJ 10. https://doi.org/10.7717/peerj.13463 Weiss J, Overpeck J, Strauss B (2011) Implications of recent sea level rise science for low-elevation areas in coastal cities of the conterminous U.S.A. Clim Change 105:635–645. https://doi.org/10.1007/S10584-011-0024-X Wong PP, Losada IJ, Gattuso J-P, Hinkel J, Khattabi A, McInnes KL, Saito Y, Sallenger A (2014) : Coastal systems and low-lying areas. In: Climate Change 2014: Impacts,Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 361–409 World Bank (2016) Managing Coasts with Natural Solutions: Guidelines for Measuring and Valuing the Coastal Protection Services of Mangroves and Coral Reefs. M. W. Beck and G-M. Lange, editors. Wealth Accounting and the Valuation of Ecosystem Services Partnership (WAVES), World Bank, Washington, DC World Meteorological Organization (2023) 2023 state of climate services: Health, climate risk & early warning systems (WMO-No. 1335). World Meteorological Organization. ISBN: 978-92-63-11335-1. https://wmo.int/publication-series/2023-state-of-climate-services-health Wu W, Yang Z, Tian B, Huang Y, Zhou Y, Zhang T (2018) Impacts of coastal reclamation on wetlands: Loss, resilience, and sustainable management. Coastal and Shelf Science. Estuarine. https://doi.org/10.1016/J.ECSS.2018.06.013 Zhang K, Douglas B, Leatherman S (2004) Global Warming and Coastal Erosion. Clim Change 64:41–58. https://doi.org/10.1023/B:CLIM.0000024690.32682.48 Zhang Y, Chen R, Wang Y (2020) Tendency of land reclamation in coastal areas of Shanghai from 1998 to 2015. Land Use Policy 91:104370. https://doi.org/10.1016/j.landusepol.2019.104370 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9277512","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615144351,"identity":"2848ddff-5b58-4f1e-b004-460eee32829b","order_by":0,"name":"Antonio Fabela Regis Jr","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIie3RMUvDQBTA8RcOzuU1WQ8MZPALvFCwCMV+lRQhk4NjhxAuFOzS7v0Yrm4XDjId5gO46O5QcXFQ8ZJ2kl7EzeH+S0J4v1zuAuDz/cuYUt2FmL3JaIocsH+ObsLne3LC50+7mxw5/5XgeE8iPEu3O23fMTDcFa0U6cWiTCYM+SlSG4fJRsF7AfHEQYTJstoYnd4vR40lj/bDwixYN4AX8jghAaqubu3GdZgfCBKMJCApFwlkXX2VluC5JQ89CT4HCYO6kqwj43RLqidsaBVhcqhlo9M73R0yXVlyTTpuhJNEq/btVRZlQq22v/LjcpYsTfr8UkxnLnI8Oyz+Mu/z+Xy+H30DL3lVApVIdi4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-7932-4221","institution":"Department of Climate Change, Indian Institute of Technology Hyderabad","correspondingAuthor":true,"prefix":"","firstName":"Antonio","middleName":"Fabela","lastName":"Regis","suffix":"Jr"}],"badges":[],"createdAt":"2026-03-31 09:19:16","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9277512/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9277512/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105859135,"identity":"16d55937-4f19-4620-bf4e-50399d0fd56b","added_by":"auto","created_at":"2026-04-01 00:35:27","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":476412,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eA total of 418 Grids were designated on the coast of the study area in Siargao Island, Surigao del Norte, Philippines.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9277512/v1/ab356fa1c3b7d04b10f28913.jpg"},{"id":105905453,"identity":"142ee93c-4967-4676-8cf3-fdbc59d0aff8","added_by":"auto","created_at":"2026-04-01 10:12:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":438257,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eValidation points and individual vulnerability maps with risk classifications of the different parameters assessed in the study.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9277512/v1/560edc875befa9e31abed33e.jpg"},{"id":105859136,"identity":"563f529f-b4c4-4941-a89b-893f99905513","added_by":"auto","created_at":"2026-04-01 00:35:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1219581,"visible":true,"origin":"","legend":"\u003cp\u003eElevated coastal houses in Dapa, SIPLAS, used by the locals as an adaptation mechanism to tidal range exposure\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9277512/v1/7f767dffcd9d2eba792a9957.png"},{"id":105859137,"identity":"96c037df-5250-4193-b8d0-34b6bd8f0059","added_by":"auto","created_at":"2026-04-01 00:35:27","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":66802,"visible":true,"origin":"","legend":"\u003cp\u003eVulnerability transitions for 418 SIPLAS coastal grids between 2015 and 2020. Flow width indicates number of grids with unidirectional downward flows (140 grids worsening vs. 77 improving), indicating persistent vulnerability increase. Very High vulnerability shows 99% persistence, while Low-risk habitats show 44% deterioration. The 16-grid improvement from High to Moderate (23%) demonstrates recovery potential through intervention.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9277512/v1/c0072eba1fd4974c18c61155.jpg"},{"id":105904880,"identity":"385324f6-aa03-4fa5-b598-ef4adb7a6558","added_by":"auto","created_at":"2026-04-01 10:10:54","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":297427,"visible":true,"origin":"","legend":"\u003cp\u003eCategorical distribution of Coastal Vulnerability Index (CVI) changes between 2015 and 2020, with vulnerability trajectories color-coded by change direction. Red circles indicate increased vulnerability (n=140, 33.5%, range +1.5 to +14.0 CVI), blue circles indicate decreased vulnerability (n=77, 18.4%, range -0.9 to -13.0 CVI), and the green line represents no-change threshold (n=201, 48.1%). The 1.82:1 ratio of escalation to improvement demonstrates that vulnerability escalation substantially outpaces adaptation success, with critical implications for scaling nature-based solutions and climate adaptation investments in coastal zones.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9277512/v1/493d4496c7d066547ddf28dd.jpg"},{"id":105904865,"identity":"fbed1bc0-24b9-4790-8ec3-54a8408d1f0f","added_by":"auto","created_at":"2026-04-01 10:10:52","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":258075,"visible":true,"origin":"","legend":"\u003cp\u003eOutput map of coastal vulnerability index (CVI) for the years 2015 and 2020. Changes in CVI are highlighted in green for improved CVI and red for regressed CVI.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9277512/v1/4ed1933b76ea6afbb6a0b8d5.jpg"},{"id":105859139,"identity":"5ac5a09f-0fe8-401b-bafb-bf48951f3d1b","added_by":"auto","created_at":"2026-04-01 00:35:27","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":909192,"visible":true,"origin":"","legend":"\u003cp\u003eSelected validation points for the CVI assignment in four sampling sites in Siargao Island. Images on the left are current scenarios as of January 5-7, 2024. Town-wise, CVI is also projected on the right side, corresponding to the CLUHs, as shown on the 2020 map from NAMRIA.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9277512/v1/4336c9507333f55168c2c9b4.jpg"},{"id":105859142,"identity":"991c5872-e53e-412e-9248-f5af557d9146","added_by":"auto","created_at":"2026-04-01 00:35:27","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":131474,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between changes in habitat risk and the CVI. Each point represents a habitat unit, and the strong positive linear trend (R² = 0.85) indicates that increases in habitat risk are closely associated with increases in CVI, reflecting degradation. Points in the lower‑left quadrant show simultaneous decreases in risk and CVI, indicating restoration benefits, while points in the upper‑right quadrant reflect areas experiencing both rising risk and vulnerability.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9277512/v1/b21cc4894f066c2830ba5457.jpg"},{"id":105859143,"identity":"4bf53c58-caca-4564-961a-fa6b9699f3e0","added_by":"auto","created_at":"2026-04-01 00:35:27","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":67948,"visible":true,"origin":"","legend":"\u003cp\u003eClassification of 418 grids into five habitat-vulnerability scenarios.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9277512/v1/b4530d23d79a88bb43070e38.jpg"},{"id":105906618,"identity":"a92e52d6-fa0d-44c1-b3c5-c6c298c920b1","added_by":"auto","created_at":"2026-04-01 10:23:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5607725,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9277512/v1/9f5044d2-cd62-4ac6-84b7-09e36a8ec1a6.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eGrid-based Coastal Vulnerability Assessment in Small Island Communities Using Land-use and Habitats for Improved Adaptative Management and Governance\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally, approximately 65\u0026nbsp;million people from small island communities face disproportionate climate-induced threats (Mimura et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Petzhold and Magnan 2019, Vousdoukas et al. \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), including sea-level rise (Nicholls \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Vousdoukas et al. \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and extreme weather events (Knutson et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Betts et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) that threaten their livelihoods, infrastructure, and sovereignty (IPCC 2022). The economic burden is staggering, as small island economies have experienced annual average losses of \u003cspan\u003e$\u003c/span\u003e153\u0026nbsp;billion between 1970 and 2020, representing approximately 2.1% of their combined annual GDP (WMO 2023, UNDP 2024). These aggregate statistics mask regional disparities in vulnerability and economic loss. In the Caribbean, average annual losses are projected to reach 5% of regional GDP by 2025 (\u0026gt;\u003cspan\u003e$\u003c/span\u003e22\u0026nbsp;billion annually by 2050), escalating to 20% by 2100 absent regional mitigation strategies (Bueno et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), with floods and storms alone projected to cause \u003cspan\u003e$\u003c/span\u003e56\u0026nbsp;billion in climate change-attributable loss and damage under a 2\u0026deg;C warming scenario by 2050 (UNDP 2024). In the Pacific, annual climate-related losses are projected at 0.75%\u0026ndash;6.5% of GDP by 2030 (IPCC 2022, AFdD 2024), and over the past 50 years, Pacific Island countries have already sustained over \u003cspan\u003e$\u003c/span\u003e3\u0026nbsp;billion in natural disaster damage (Lee et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, WMO 2023), accompanied by profound human displacement, migration, livelihood loss, land resource depletion, and food insecurity driven by sea-level rise, coastal erosion, and habitat and biodiversity loss.\u003c/p\u003e \u003cp\u003eThe same impacts have been observed in the Philippines, an archipelagic country considered the world's most disaster-prone (World Risk Index score of 46.82). The country faces compounded climate vulnerabilities from typhoons, floods, droughts, earthquakes, and tsunamis amplified by coastal development and ecosystem degradation (B\u0026uuml;ndnis Entwicklung Hilft e.V., 2022; Ravago et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Super Typhoon Rai in 2021 unambiguously illustrated this risk, particularly in the Siargao Island Protected Landscape and Seascape (SIPLAS), a critical ecological and economic hub hosting diverse coastal ecosystems (coral reefs, mangroves, seagrass beds, wetlands) that provide essential services, including fisheries-based food security, coastal storm protection, carbon sequestration, and tourism revenue (Spalding et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Guannel et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This recurring issue prompted the need to identify high-risk and vulnerable areas within particular jurisdictions across different local government units (LGUs). However, due to the lack of technical capacity, technology, manpower, and financing, especially in low-income municipalities and small island communities, disaster risk reduction and climate adaptation have been less prioritized (Ravago et al. \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Jamero et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); therefore, a need arises to develop a comprehensive coastal vulnerability assessment customized for small island communities.\u003c/p\u003e \u003cp\u003eWhile most contemporary coastal vulnerability assessment studies employ the Coastal Vulnerability Index (CVI) or similar frameworks utilizing static parameters such as geomorphology, elevation, sea-level change, tidal range, and wave height (Md Noor \u0026amp; Maulud \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Rocha et al. \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), with limited integration of dynamic anthropogenic parameters, specifically, temporal land-use change and coastal habitat dynamics (Guannel et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Lu et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Jones et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This omission creates a critical gap, often classifying the area as 'high risk' based on physical exposure (low elevation, high wave heights, rapid sea-level rise) without accounting for whether protective ecosystems - mangroves, coral reefs, seagrass beds, and coastal wetlands are present, degraded, or expanding (Spalding et al. \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Rezaie et al. \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The practical consequence is that vulnerability hotspots identified through standard CVI assessments may not reflect actual coastal resilience driven by ecosystem service provision, leading to misallocation of adaptation resources (Barbosa et al. 2022). Studies from Sajjad et al. (\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Rezaie et al. (\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) suggest that habitat conservation and restoration can reduce coastal vulnerability by 30\u0026ndash;50%, yet this evidence rarely translates into quantified projections within operational vulnerability frameworks accessible to local governments and resource-limited coastal communities (van der Meulen et al. 2023). Consequently, nature-based solutions remain aspirational policy concepts rather than evidence-based adaptation strategies amenable to cost-benefit analysis, investment prioritization, and adaptive management (Toimil et al. \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Inacio et al. 2021).\u003c/p\u003e \u003cp\u003eDespite growing recognition that nature-based solutions (NbS) that offer cost-effective alternatives to gray infrastructure (Spalding et al. \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Fairchild et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), their integration into operational coastal vulnerability assessment remains limited. Most coastal vulnerability studies employ static parameters focused on geomorphology and physical hazards, with limited quantification of how ecosystem services dynamically influence vulnerability outcomes (Md Noor \u0026amp; Maulud \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Rocha et al. \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This creates a critical methodological gap: coastal managers lack quantitative frameworks demonstrating whether habitat conservation/restoration reliably reduces measurable coastal vulnerability, a prerequisite for mainstreaming NbS into adaptation planning and resource allocation (van der Meulen et al. 2023). This study addresses this gap by quantifying the direct influence of ecosystem services on coastal vulnerability through an integrated land-use/habitat-dynamics approach, providing empirical evidence that dynamic habitat assessment can be operationalized for climate adaptation planning (Barbosa et al. 2022, van der Meulen et al. 2023).\u003c/p\u003e \u003cp\u003eSIPLAS provides an ideal natural experiment to demonstrate how ecosystem service provision quantifiably influences coastal vulnerability: despite ongoing habitat conservation and rehabilitation programs, the area simultaneously experiences habitat loss driven by tourism expansion and coastal infrastructure development. By linking ecosystem dynamics to vulnerability outcomes in this contested landscape, SIPLAS offers a replicable evidence base for mainstreaming Nature-based Solutions into coastal adaptation planning across vulnerable small island and resource-limited coastal contexts, establishing natural habitats as measurable, economically comparable parameters for climate risk management and adaptation (Spalding et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rezaie et al., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; van der Meulen et al., 2023).\u003c/p\u003e \u003cp\u003eUsing geographic information systems (GIS) data, we developed a replicable 1\u0026times;1 km grid-based vulnerability assessment for SIPLAS incorporating: (1) static physical parameters (elevation, shoreline change, sea-level change rate, tidal range, significant wave height), and (2) dynamic CLUH composition evaluated for both baseline (2015) and temporal (2020) periods (Pantusa et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Rezaie et al. \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This framework also operationalizes coastal habitats as quantifiable ecosystem service providers rather than merely qualitative conservation priorities by directly linking habitat presence, degradation, or restoration to measurable changes in grid-level vulnerability indices. The approach aims to enable local coastal managers to identify where and how ecosystem service loss has amplified vulnerability, and to quantify the potential reduction in vulnerability from specific habitat restoration or conservation scenarios (Anderson et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Esraz-Ul-Zannat et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Twomey et al. \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Providing baseline information on coastal vulnerability and incorporating ecosystem service provisions support evidence-based climate adaptation planning. This aids in facilitating the integration of habitat conservation/restoration targets into local disaster risk reduction and climate governance frameworks (Barbosa et al. 2025), without requiring costly proprietary datasets or external technical expertise (Bathi \u0026amp; Das \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Kantamaneni et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Bukvic et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDescription of Study Area\u003c/h2\u003e \u003cp\u003eFor this study, we applied the assessment method to the Siargao Protected Landscape and Seascape (SIPLAS), located in the province of Surigao del Norte, Philippines (9.8483\u0026deg; N, 126.0455\u0026deg; E). The island is approximately 627.88 square kilometers and is well known for its beautiful beaches and natural scenery. It is also renowned as a worldwide surfing and tourism destination.\u003c/p\u003e \u003cp\u003eA base map of SIPLAS was acquired from the latest Philippine Administrative Map, available on geoportal.ph and maintained by the National Mapping and Resource Information Authority (NAMRIA), the central mapping agency of the government of the Philippines. The Shapefile contains the delineations of regions, municipalities/towns, and barangays (small territorial and administrative districts that form local levels of government in the Philippines). The Map has 41,931 data entries covering all administrative areas of the country. Digital grids covering coastal areas at 1 km by 1 km resolution were created to quantify the variables used in the study. A total of 418 grid cells were produced (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) to represent the whole coastal area for SIPLAS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Sources and Acquisition\u003c/h3\u003e\n\u003cp\u003eOpen-access and downloadable geographical maps and data were used in the study. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the downloaded data and their parameters. This was to ensure that the process can be applied in similar areas of interest and recommend that similar work can be done and replicated. Geographic base maps were taken from \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003egeoportal.gov.ph\u003c/span\u003e, an online database platform by NAMRIA. Specific maps were taken, including coastal resource maps for 2016 and 2020 and land use and land cover changes for 2015 and 2020. All post-processing of the maps and data was carried out in ArcGIS Pro.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters used and their open-access sources, resolution, and time period considered.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eParameter\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eData Source\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eResolution\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTime Period\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoastal Land -Use\u003c/p\u003e \u003cp\u003eAnd Habitat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eLand use data and Coastal Habitat Map by NAMRIA (National Mapping and Resource Information Authority Philippines)\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.geoportal.gov.ph/\u003c/span\u003e\u003cspan address=\"https://www.geoportal.gov.ph/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(accessed on October 2, 2023)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScale 1:25 k\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2015, 2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAW3D30 DSM 1 arc second\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm\u003c/span\u003e\u003cspan address=\"https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(accessed October 2, 2023)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShoreline change rate (m/year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSentinel\u0026mdash;2A imagery \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov/\u003c/span\u003e\u003cspan address=\"https://earthexplorer.usgs.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed on October 2, 2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2015, 2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSea level change rate (mm/year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSea Level Evaluation and Assessment Tool \u0026ndash; NASA Sea Level Change Portal\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(accessed on October 4, 2023)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1993\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTidal range (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGlobal Tidal Range Classification\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(accessed on October 4, 2023)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2014\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignificant wave height (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMarine Copernicus data\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGlobal Ocean Waves Analysis and Forecast\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.marine.copernicus.eu/viewer/\u003c/span\u003e\u003cspan address=\"https://data.marine.copernicus.eu/viewer/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(accessed on 04 October 2020)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/10\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2021\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eCoastal Land-Use and Habitat\u003c/h3\u003e\n\u003cp\u003eCoastal areas play an essential role in social and economic accessibility, including access to trade and livelihood, transportation, healthcare, goods and supplies, and services. What has not been accounted for is the ecosystem services of coastal habitats, the need for their conservation and protection, and the importance of monitoring and managing these changes for ecological and land management purposes. Land use impacts coastal habitats and their ability to provide ecosystem services (Aighewi et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Burden et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Zhang et al. \u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Wedding et al. 2020). As SIPLAS is a protected area, important coastal habitats were incorporated into the matrix scoring as ecosystem services. The coastal habitat map was consolidated with the land-use map to elaborate on coastal habitat use for an ecosystem-based approach to disaster risk reduction and climate adaptation. Coastal habitats can provide multiple ecosystem services, including provisions for food supply, medicine, green spaces, eco-tourism, and carbon storage (Guannel et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Mendoza-Gonz\u0026aacute;lez at al. 2018, Masucci \u0026amp; Reimer \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Liu et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eAvailable land-use and coastal habitat maps were used in the study to project current land-use for the area of interest and to complement the island's coastal development plan. Siargao's CLUH map was classified into primary land uses: built-up zones, annual and perennial crops, brush/shrubs, fishponds, grassland, inland water, marshland/swamp, open forest, and open/barren areas. The coastal habitat map, with three classifications: corals, seagrass/seaweed, and mangroves, was then overlaid on the land use map, bringing the total classification to thirteen classes. Only coastal areas within the identified cell grids were considered for the risk rating in this study.\u003c/p\u003e \u003cp\u003eCLUH are used in coastal vulnerability assessments because they affect the coast's exposure and sensitivity to natural hazards such as sea level rise, storm surge, erosion, and flooding. This study emphasizes the importance of coastal habitats and existing land use in developing criteria for coastal vulnerability, given their dynamic nature and manageability (Rezaie et al. \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Barbier \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Fairchild et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). CLUH can influence coastal vulnerability in different ways, including an increase in the pressure and demand on the coastal resources due to the development of settlements and conversion of uses, leading to degradation and loss of coastal habitat, reducing the natural protection and resilience of the coast to hazards (Aitali et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Rezaie et al. \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Hzami et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Coastal land use can also alter the geomorphology and hydrology of the coast, affecting the sediment transport and erosion processes and changing the shape and stability of the coast, further increasing its vulnerability to hazards (Huijbers et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Hapke et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Melet et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eElevation\u003c/h3\u003e\n\u003cp\u003eAW3D30 DSM was used to obtain elevation for SIPLAS. The ALOS World 3D \u0026minus;\u0026thinsp;30m (AW3D30) is a global digital surface model (DSM) dataset with a horizontal resolution of approximately 30 meters. Among other open-access Digital Elevation Models (DEM), the AW3D30 is considered the most accurate for inundation propagation (Talchabhadel et al. \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). AOI was clipped in from the DSM, and the classification for elevation was done according to the following metrics, according to the study from Gornitz \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1991\u003c/span\u003e:\u003c/p\u003e \u003cp\u003eVery high risk : 0\u0026ndash;5 meters\u003c/p\u003e \u003cp\u003eHigh risk: 6\u0026ndash;10 meters\u003c/p\u003e \u003cp\u003eModerate risk: 11\u0026ndash;20 meters\u003c/p\u003e \u003cp\u003eLow risk: 21\u0026ndash;30 meters\u003c/p\u003e \u003cp\u003eVery low risk: \u0026gt; 30 meters\u003c/p\u003e \u003cp\u003eThe gridded cells were then applied to quantify coastal elevation, and coastal elevation data per grid were obtained.\u003c/p\u003e\n\u003ch3\u003eShoreline change rate\u003c/h3\u003e\n\u003cp\u003eSentinel-2A imagery data from 2015 and 2020 were used for the shoreline change analysis. The images were acquired from earthexplorer.usgs, with search parameters adjusted to the lowest cloud cover to emphasize the island's clear, concise shape. Due to the broad area of interest for the study and the complex geographic profile, higher-resolution digital shoreline analysis tools were not used. A simplified preprocessing method using the Landsat QA ArcGIS Toolbox was applied to the satellite image data to estimate wetness, NDVI, and brightness, in preparation for semi-automatic vectorization using the same toolbox to extract the shoreline. Semi-automatic vectorization is a technique that involves both automated and manual processes to extract shorelines from satellite imagery using the Landsat QA ArcGIS Toolbox (Ghorai et al. 2020, Domazetović et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Deriving spectral indices from the wetness, NDVI, and brightness enhanced the shoreline's contrast and visibility and improved the accuracy of semi-automatic vectorization. Once shorelines from different years have been extracted, the detection feature changes under data management were run between the 2 data sets to determine the changes over time.\u003c/p\u003e \u003cp\u003eThe type of change detected is as follows:\u003c/p\u003e \u003cp\u003e \u003cem\u003eNC\u003c/em\u003e for no change, indicating a matched update feature with no change.\u003c/p\u003e \u003cp\u003e \u003cem\u003eN\u003c/em\u003e for new indicates an unmatched update feature new to the base data.\u003c/p\u003e \u003cp\u003e \u003cem\u003eD\u003c/em\u003e for deletion, indicating an unmatched base feature that might need to be deleted from base data.\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eN\u003c/em\u003e implies accretion or build-up in the shoreline, \u003cem\u003eD\u003c/em\u003e means coastal erosion, and \u003cem\u003eNC\u003c/em\u003e indicates no change. Depending on the change values, designated scoring is applied per grid cell according to the vulnerability matrix. This method has practical applications in coastal vulnerability assessments by comparing datasets of the same coastal area across different periods. One dataset might serve as a baseline, depicting the coastal area's conditions before significant events, such as disasters, and before increased development driven by social and economic activities.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSea level change rate\u003c/h2\u003e \u003cp\u003eThe sea level change rate considered and applied in the assessment is from NASA Sea Level Portal Data Analysis - Sea Level Evaluation and Assessment Tool for this study. While global mean sea level changes are a significant indicator of climate warming, the crucial factor for evaluating potential coastal impacts lies in the fluctuation of regional relative sea levels (Brown et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Wang et al. \u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), where variation in regional relative sea levels can be determined by physical parameters exhibiting spatial and temporal variability, often resulting in substantial deviations from the overall long-term trend of global mean sea level rise.\u003c/p\u003e \u003cp\u003eIn assigning the risk rating for the sea level change rate(mm/year), classification of vulnerability ranking for values less than or equal to \u0026minus;\u0026thinsp;1.1 mm/yr was considered very low, the values from \u0026minus;\u0026thinsp;1.0\u0026ndash;0.99 mm/year as low, the values 1.0\u0026ndash;2.0 mm/year as moderate, the values 2.1\u0026ndash;4.0 mm/year as high and the values\u0026thinsp;\u0026ge;\u0026thinsp;4.1 mm/year as very high vulnerability. For this study, the ranking of scores is defined in accordance with those proposed by Gornitz (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1991\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTidal range\u003c/h3\u003e\n\u003cp\u003eEvaluating coastal susceptibility to hazards such as erosion, inundation, and storm surges often involves using the mean tidal range as a standard. This method accounts for the influence of tidal patterns on a coast's exposure and vulnerability. The mean tidal range represents the discrepancy between the average high and low tide levels. Consequently, a greater tidal range indicates greater susceptibility of the coastal area to sea-level fluctuations, wave dynamics, and sediment transport (Mawdsley et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Jiang et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Truong et al. \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The mean tidal range can be determined through tide gauges or satellite altimetry data.\u003c/p\u003e \u003cp\u003eFor this study, the values from the open-access data of the Global Tidal Range Classification were calculated using the FES2014 (Finite Element Solution) model data obtained from AVISO+ Satelite Altimetry Data. The FES2014 global ocean tide atlas significantly improves de-aliasing performance in satellite altimetry. It provides accurate open-boundary tidal conditions for regional and coastal modeling (Lyard et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Ray et al, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Data from FES were then plotted into the grids to project the tidal range risks.\u003c/p\u003e\n\u003ch3\u003eSignificant wave height\u003c/h3\u003e\n\u003cp\u003eSignificant wave height (SWH) was also used as a parameter to evaluate coastline susceptibility, representing the average of the highest one-third of waves over a specific time interval. It is a vital indicator in shoreline vulnerability assessment, often replacing wave energy and aiding in the study of coastal vulnerability (Pendleton et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Shi et al. \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The relationship between wave energy and wave height demonstrates that as wave height increases, wave energy amplifies erosion and coastal flooding risks, endangering settlements and coastal ecosystems (Guza and Feddersen 2013). Higher wave heights indicate greater energy and erosive potential, which vary with wind speed, direction, water depth, and tide. Consequently, significant wave height data is an invaluable tool for assessing the potential impact of waves on coastal regions, helping to anticipate shoreline erosion, flooding, and inundation. Regions experiencing higher wave heights face greater vulnerability than those exposed to lower wave heights (Rani et al. \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Mavromatidi et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Serafim et al. \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Ferreira et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), highlighting the critical role of SWH in understanding coastal hazards.\u003c/p\u003e \u003cp\u003eThe data used in the study were taken from Copernicus Marine Services - Global Ocean Waves Analysis and Forecast. Although different values for the entire AOI can be obtained, only the maximum daily average wave height from January 1, 2021, to December 31, 2022, incorporated into the current sea level, was used to describe the mean significant wave height (Mahapatra et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The data were then projected onto the significant wave height risk map by interpolating the values onto the grids and assigning risk following Pendleton et al. (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCoastal Vulnerability\u003c/h2\u003e \u003cp\u003eCoastal vulnerability risk rating assignment for different parameters\u003c/p\u003e \u003cp\u003eBased on the individual parameters obtained from the processed data, a scoring assignment was done using the risk factor criteria. Risk ratings were assigned based on the parameter-specific risk classifications (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For this study, CLUH were used to assess the risk and resilience of coastal areas to natural hazards such as storms, floods, erosion, and sea-level rise. Built-up, barren land and monocropping - perennial or annual, and other considered anthropological interventions in land use, were considered to impact the ratings negatively because of their human-induced alterations that compromise ecological integrity and potential adverse effects on natural ecosystems (Okpara et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Carter et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the other hand, open forests, grasslands, and natural habitat occurrences are designated as \u0026ldquo;habitats\u0026rdquo; which are considered to have a positive impact on land use as restoration and conservation of natural habitats can reduce coastal vulnerability (Ewing \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Sajjad et al. \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Wedding et al. 2020, Ryheili and Boluwade 2023), making them a crucial priority for coastal planning and development.\u003c/p\u003e \u003cp\u003eThe matrix can help assess and prioritize vulnerable coastal segments for adaptation and mitigation management. The rating can be used to compare vulnerabilities between coastal regions to evaluate the effectiveness of different management measures. Coastal vulnerability risk rating assignment is a useful tool for coastal planning and decision-making, especially in the context of climate change and its impact on coastal systems (Hamid et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Anfunso et al. 2021).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoastal vulnerability risk rating matrix.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(a) CLUH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComplete habitat, less to no built up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHabitat present,\u003c/p\u003e \u003cp\u003eNo built up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntertidal zone with present habitat low to no built up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBeach barren, intertidal zone, with built up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBeach Barren and built up\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(b) Shoreline change rate (m/year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2.1\u003c/p\u003e \u003cp\u003eAccretion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 to 2.0\u003c/p\u003e \u003cp\u003eAccretion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.0 to 1.0\u003c/p\u003e \u003cp\u003eStable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;1.1 to \u0026minus;\u0026thinsp;2.0\u003c/p\u003e \u003cp\u003eErosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026minus;2.0\u003c/p\u003e \u003cp\u003eErosion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(c)Elevation (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.1\u0026ndash;30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.1\u0026ndash;20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.1\u0026ndash;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0\u0026ndash;5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(d)Sea level change rate (mm/year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026minus;1.1\u003c/p\u003e \u003cp\u003eLand rising\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;1.0 to 0.99\u003c/p\u003e \u003cp\u003eLand rising\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0 to 2.0\u003c/p\u003e \u003cp\u003ewithin range of eustatic rise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1 to 4.0\u003c/p\u003e \u003cp\u003eLand sinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4.1\u003c/p\u003e \u003cp\u003eLand sinking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(e) Tidal range (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u0026ndash;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0\u0026ndash;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.1\u0026ndash;6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(f) Significant wave height (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u0026ndash;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u0026ndash;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05\u0026ndash;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \n \u003cp\u003eCoastal Vulnerability Index\u003c/p\u003e \n\u003cp\u003eThe Coastal Vulnerability Index (CVI) was then obtained based on the values from the risks rating matrix. CVI is calculated as the square root of the product of the ranked parameters divided by the total number of parameters (Pantusa et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and represented as shown in Eq.\u0026nbsp;(1).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:CVI=\\sqrt{\\frac{a\\cdot\\:b\\cdot\\:c\\cdot\\:d\\cdot\\:e\\cdot\\:f}{6}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEquation 1. Coastal Vulnerability Index\u003c/h2\u003e \u003cp\u003eWhere a\u0026thinsp;=\u0026thinsp;risk rating assigned to CLUH, b\u0026thinsp;=\u0026thinsp;risk rating assigned to shoreline change rate, c\u0026thinsp;=\u0026thinsp;risk rating assigned to coastal elevation, d\u0026thinsp;=\u0026thinsp;risk rating assigned to sea-level change rate, e\u0026thinsp;=\u0026thinsp;risk rating assigned to tidal range, f\u0026thinsp;=\u0026thinsp;risk rating assigned to significant wave height.\u003c/p\u003e \u003cp\u003eThe CVI incorporates CLUH as a critical ecosystem services parameter, recognizing its multiplicative influence on overall coastal vulnerability. We employed paired samples t-tests to assess whether the mean CVI changed significantly between 2015 and 2020. Given that Shapiro-Wilk tests indicated non-normal distributions in the CVI data (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), we complemented the parametric analysis with the nonparametric Wilcoxon signed-rank test. Pearson correlation coefficients quantified the relationship between habitat risk ratings and CVI values, and linear regression (ordinary least squares) was used to model CVI as a function of habitat risk. Chi-square tests of independence assessed whether the distributions of vulnerability classifications differed significantly between years. All analyses used α\u0026thinsp;=\u0026thinsp;0.05 as the significance threshold; effects were considered significant when p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCoastal Habitat Risk Classification and Ecosystem Service Integration\u003c/h2\u003e \u003cp\u003eCLUH composition was integrated into the vulnerability assessment framework as dynamic ecosystem service indicators, recognizing that natural habitats provide critical protection services against coastal hazards while anthropogenic land uses compromise coastal resilience. To incorporate land-use and habitat classifications within the CVI framework, a habitat risk rating scale summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e was developed from the vulnerability assessments with reference to the works of Rezaie et al. (\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Guannel et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Spalding et al. (\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This metric used directly parallels the hazard-based risk ratings (elevation, shoreline change, sea-level rise) used for physical parameters, enabling integration of ecosystem service provision as a quantifiable coastal vulnerability determinant rather than a qualitative conservation priority.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHabitat Risk Rating Scale\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk Rating\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHabitat Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBuilt-up Development\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEcosystem Service Provision\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComplete natural habitat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHabitat presence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh (implied by presence)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntertidal zones with partial habitat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow-to-moderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeach barren and intertidal zones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubstantial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLimited\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCompletely barren beach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExtensive (built-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZero habitat-based protection\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo support evidence‑based adaptation planning, we estimated the relative cost‑effectiveness of nature‑based solutions versus gray infrastructure by scaling representative implementation costs to observed reductions in vulnerability. Using commonly reported implementation costs of \u003cspan\u003e$\u003c/span\u003e2,000\u0026ndash;\u003cspan\u003e$\u003c/span\u003e3,500 per hectare for coastal restoration in the Philippines and lower‑end global estimates (Goto et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, Bayraktarov et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Primavera and Esteban \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and \u003cspan\u003e$\u003c/span\u003e2,000\u0026ndash;\u003cspan\u003e$\u003c/span\u003e8,000 per meter for coastal seawall and grey infrastructure construction (World Bank \u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Wong et al. \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Ng and Mendelsohn \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), we scale costs to our observed vulnerability reductions to match typical global cost ranges and our CVI‑based vulnerability reduction metric, rather than reflecting a single empirical cost‑per‑CVI study.\u003c/p\u003e \u003cp\u003eThe habitat risk classification reflects the principle that natural ecosystems provide measurable, economically comparable protective services: mangroves, coral reefs, and seagrass beds each provide wave attenuation reducing incoming wave energy by 30\u0026ndash;70% coastal sediment stabilization, fisheries support (\u003cspan\u003e$\u003c/span\u003e2,000\u0026ndash;8,000 hectare⁻\u0026sup1; year⁻\u0026sup1;), and carbon sequestration services, whereas built-up areas and barren zones contribute to vulnerability escalation through impervious surfaces that prevent water infiltration, reduce slope stability, and eliminate natural coastal barriers (Guannel et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e. Narayan et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Pendleton et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Mcleod et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWith this approach, we can quantify ecosystem service loss or gain at the grid level, enabling direct comparison with corresponding changes in the CVI for statistical validation of the habitat-vulnerability relationship. The thirteen-class habitat/land-use system was developed to capture ecological heterogeneity while remaining operationally simple for resource-limited coastal jurisdiction (Verutes et al. \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Wedding et al. \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, for subsequent statistical analysis, these classes were collapsed into five habitat risk rating categories (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) to improve statistical robustness and interpretability.\u003c/p\u003e \u003cp\u003eAcross all 418 grid cells, habitat risk classifications for 2015 and 2020 were cross-tabulated to identify spatial patterns of habitat change and ecosystem service transitions, documenting transitions from high-functioning ecosystems (ratings 1\u0026ndash;2) to degraded ecosystems (ratings 4\u0026ndash;5) and vice versa. This enables grid-level documentation of habitat restoration and degradation scenarios and transforms qualitative statements about ecosystem service importance into measurable vulnerability parameters amenable to regression analysis, scenario testing, and cost-benefit comparison with gray infrastructure alternatives.\u003c/p\u003e \u003cp\u003eTo further quantify the influence of ecosystem services on coastal vulnerability, we employed five complementary statistical approaches. First, Pearson product-moment correlation coefficients evaluated the linear relationship between habitat risk changes (observed range: -4 to +\u0026thinsp;4 units) and CVI changes (observed range: -13.69 to +\u0026thinsp;13.69 points) across all 418 coastal grid cells between 2015 and 2020, with R\u0026sup2; values quantifying variance explained and significance testing via two-tailed t-tests (α\u0026thinsp;=\u0026thinsp;0.05). Second, ordinary least squares (OLS) linear regression modeled CVI as a function of habitat risk (CVI\u0026thinsp;=\u0026thinsp;β₀ + β₁\u0026times;Habitat_Risk), enabling quantification of vulnerability reduction per unit habitat change and scenario-based projections.\u003c/p\u003e \u003cp\u003eLastly, we classified all 418 grids into five habitat-vulnerability scenarios (Habitat Restoration, Preservation, Contested, Degradation, Stable Systems), calculated as \u003cem\u003eΔRisk=Risk\u003c/em\u003e\u003csub\u003e\u003cem\u003e2020\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-Risk\u003c/em\u003e\u003csub\u003e\u003cem\u003e2015\u003c/em\u003e\u003c/sub\u003e; similarly, CVI change was calculated as \u003cem\u003eΔCVI=CVI\u003c/em\u003e\u003csub\u003e\u003cem\u003e2020\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e-CVI\u003c/em\u003e\u003csub\u003e\u003cem\u003e2015\u003c/em\u003e\u003c/sub\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification rules used for habitat\u0026ndash;vulnerability pathways scenarios based on joint changes in habitat risk and coastal vulnerability between 2015 and 2020.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenario\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΔRisk threshold\u003c/p\u003e \u003cp\u003e(Habitat risk 2015\u0026ndash;2020)\u003c/p\u003e\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eΔCVI behaviour\u003c/p\u003e \u003cp\u003e(CVI 2015\u0026ndash;2020)\u003c/p\u003e\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabitat Restoration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΔRisk \u0026le; \u0026minus;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh negative ΔCVI (strong reduction)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubstantial improvement in habitat conditions (e.g., mangrove/coral/seagrass expansion) with marked CVI decrease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabitat Preservation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΔRisk\u0026thinsp;\u0026asymp;\u0026thinsp;0 (no meaningful change, minimal new built‑up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elow negative ΔCVI (moderate reduction)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHabitats are largely maintained and development constrained, leading to reduced vulnerability via protection upkeep\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContested Zones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΔRisk small (does not meet\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5) with habitat gain and new built‑up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh, lower positive ΔCVI (slight increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrids where habitat expansion and development co‑occur; development marginally outweighs ecosystem gains\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabitat Degradation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΔRisk\u0026thinsp;\u0026ge;\u0026thinsp;+\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh positive ΔCVI (strong increase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubstantial habitat loss or conversion to built‑up/barren land, leading to marked vulnerability increase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStable Systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΔRisk\u0026thinsp;\u0026asymp;\u0026thinsp;0 and ΔCVI\u0026thinsp;\u0026asymp;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo meaningful change in CVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLittle to no change in both habitat risk and vulnerability; equilibrium trajectory\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eField validation\u003c/h2\u003e \u003cp\u003eField validation of coastal vulnerability assessment is crucial for confirming the accuracy and reliability of remote-sensing-derived data and providing ground truth for comparison and calibration. Integrating field validation with remote sensing data enhances the understanding of coastal vulnerability by combining large-scale observations with local information.\u003c/p\u003e \u003cp\u003eA field assessment was conducted to confirm the methodology's accuracy. 45 sampling points were visually assessed to compare with the model results. The validation was conducted from January 5 to 8, 2024, at SIPLAS in the Philippines. Validation was limited to accessible coastal roadsides (n\u0026thinsp;=\u0026thinsp;45) due to resource constraints, potentially biasing accuracy assessment toward more developed areas. Remote high-risk zones remain unvalidated. No area within the municipality of Socorro was validated, as it requires a 2-hour boat trip from the closest municipality, Dapa. Coastal vulnerability was visually assessed according to land use and habitat, elevation, shoreline changes, and tidal range variability. Risk values were then assigned to each assessed grid point.\u003c/p\u003e \u003cp\u003ePearson's correlation showed a strong correlation between the model and observed values (r\u0026thinsp;=\u0026thinsp;0.8384183636). Cohen\u0026rsquo;s kappa was also used to determine the agreement between the model values and the actual observations. This resulted in a κ value of 0.718749, meaning a substantial percent agreement of 88%.\u003c/p\u003e \u003cp\u003eThe classification transition analysis tracked how 418 grids moved across five vulnerability categories (Very Low to Very High) from 2015 to 2020, identifying persistence patterns that indicate irreversible risk thresholds. Field validation at 45 ground-truth sites confirmed model reliability by comparing modeled and observed vulnerability classifications. The linear regression model explained 87.4% of the variation in coastal vulnerability (R\u0026sup2; = 0.874, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), demonstrating that habitat dynamics are the primary determinant of vulnerability outcomes in SIPLAS. All calculations were performed in Microsoft Excel using standard spreadsheet functions, ensuring methods are transparent, reproducible, and accessible to resource-limited coastal jurisdictions.\u003c/p\u003e \u003cp\u003eHowever, since the satellite data were obtained in 2015 and 2020, some actual physical parameters did not match, particularly for land use. Commercial areas, settlements, and wave barriers cum coastal roads have been constructed in most coastal areas, as well as coastal reclamation. These considerations were used to evaluate the risk ratings.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results and Discussions","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eCoastal Land-Use and Habitat\u003c/h2\u003e \u003cp\u003eNotable changes in coastal land use were identified, including a slight decrease in annual crop (-1%) and a substantial decline in brush/shrubs (-19%), possibly due to urbanization. The built-up area increased significantly (124%), reflecting urban development. In contrast, coral and mangrove areas showed positive changes, with a 121% and 34% increase, respectively, indicating the effectiveness of habitat conservation and regeneration efforts. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e summarizes land-use and habitat changes between 2015 and 2020, along with the corresponding percentage changes. Chi-square tests revealed no significant overall change in land-use distribution, though the analysis did not detect a statistically significant overall change; a notable increase in high-risk areas categorized as \"beach barren and built-up\" highlights localized intensification of coastal habitat degradation. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (a) shows the risk classification for CLUHs.\u003c/p\u003e \u003cp\u003eMarshland extent increased anomalously by 656% (5.77 to 43.64 ha), attributed to road construction and coastal barriers that enclosed coastal drainage pathways, locking in the water within low-lying inland areas.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of CLUH change between 2015 and 2020.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Use \u0026amp; Habitat Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2015 (hectares)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2020 (hectares)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChange (hectares)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% Change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual Crop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,796.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,780.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-15.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMinimal change, slight decline\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrush/Shrubs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16,199.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13,057.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3,142.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSignificant loss (urbanization)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e699.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,565.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;865.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;124%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExpansion (tourism/development)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,240.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16,002.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;8,762.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;121%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRecovery (conservation working)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFishpond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMinimal change, slight decline\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrassland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,341.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,188.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1,846.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;79%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePositive expansion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInland Water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e674.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,164.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;490.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIncrease (road construction traps water)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMangroves\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16,664.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22,311.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;5,647.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExpansion (conservation/regeneration)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarshland/Swamp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;37.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;656%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIncreas (barriers trap water)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpen Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e184.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e196.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;12.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSlight expansion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpen/Barren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e168.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;72.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExpansion of degraded zones\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerennial Crop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20,041.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15,021.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5,019.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSignificant loss (land conversion)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeagrass/Seaweeds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,129.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9,591.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;3,461.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExpansion (seagrass recovery)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOTAL AREA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72,098.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85,116.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;13,017.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOverall coastal area increased\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSignificant changes occurred in coastal land use patterns and their associated risk ratings. The land use risk distribution shifted substantially, with moderate-risk areas (category 3) increasing from 32.5% to 45.2% of grid points, while low-risk areas (categories 1\u0026ndash;2) decreased from 45.5% to 30.4%. Coastal habitat composition analysis shows a transition from more diverse mixed-habitat systems in 2015 to more simplified coastal environments in 2020, with basic coral and seagrass systems becoming more prevalent. The maintenance of constant change type distributions (365 no-change points, 48 nourishment points, and five depletion points) across both periods suggests that the observed vulnerability increases are primarily driven by land use changes and habitat degradation rather than active coastal management interventions, highlighting the critical need for enhanced coastal protection strategies and sustainable land use planning.\u003c/p\u003e \u003cp\u003eAreas classified under mono-crops, built-up infrastructure, and barren land are assigned high vulnerability scores due to their susceptibility to climate-related risks (Hopper and Meixler \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Kantamaneni et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Mono-crop areas, whether perennial or annual, are vulnerable to pests, diseases, and extreme weather events due to their dependence on specific conditions for growth (Winker et al. 2013, Meisner and Boer 2018, Desai et al. 2021). Built-up areas face challenges such as heat island effects, flooding, and infrastructure damage during extreme weather events (Pregnolat et al. 2016, Ferdowsi et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Barren lands lack vegetation cover and stability, making them prone to erosion and desertification exacerbated by climate change (Nearing et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Sivakumar \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Greipsson \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Plangoen et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In contrast, habitats such as forests and wetlands are rated higher due to their resilience, ecosystem services, and adaptive capacity to mitigate climate impacts (Bernhardt and Leslie \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Spalding et al. \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Seidl et al. \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Filho et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Ferro-Azcona et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn SIPLAS, most housing settlements and commercial areas are established near and within the coastal zone (mostly within 1 kilometer of the highest high-tide point). It is treated as a built-up area and was assigned a high-risk ranking on the vulnerability matrix. On the other hand, natural habitats in an area were considered contributors to risk deviation. This trend suggests growing anthropogenic pressures such as urbanization and land conversion into sensitive coastal zones, threatening vital ecosystems like coral reefs, seagrass beds, and mangroves that provide essential ecological services and coastal resilience.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eShoreline Change Rate\u003c/h2\u003e \u003cp\u003eThe study observed a moderate risk of Shoreline Change Rate in SIPLAS due to accretion or erosion, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(b). Shoreline change on the island can be attributed to many natural physical factors, including strong wave action, tidal variation, sea level rise (Dean and Houston \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and anthropogenic impacts such as coastal reclamation, land use, and erosion (Hapke et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Many parts of the island's coastal areas showed little to no change from 2015 to 2020 in the feature change detection analysis. However, the isolated, smaller islands have noticeably changed primarily due to erosion. On the other hand, accretion on the coastal areas of the towns of Del Carmen, Pilar, Santa Monica, and Dapa was observed. This was attributed to the protected and rehabilitated mangrove forests as part of the locally implemented protected area management scheme.\u003c/p\u003e \u003cp\u003eThis was also similar to observations from the CLUH change assessment, which showed an increase in mangrove cover in areas that influenced coastal physical features (Lovelock et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Guo et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Pang et al. 2020). However, coastal reclamation and construction of ports and coastal barriers (considered gray infrastructures) in the last few years have also affected the sediment transport (sand budget) in these shorelines, which in the long run poses a threat to its integrity not only by changing the natural movement of the coastal sediments that are influenced by to seasons, tide and wave actions but also because of its impacts on natural habitats and ecological pathways (Tian et al. \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Chee et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eElevation\u003c/h2\u003e \u003cp\u003eCoastal elevation data indicated significant variation across the island's coastal area, from below sea level to relatively high points (minimum of -1 m to a maximum of 255 m), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (c). The elevation risk assessment covers a total area of 418 sq. km of coastal area (418 grids x 1 sq.km.), which includes the coastal part of the coral reefs, intertidal zone, and mangrove forests.\u003c/p\u003e \u003cp\u003eBased on the elevation risk analysis using the Digital Surface Model, 32.4% of the coastal area, or approximately 135 square kilometers, is at Very High Risk, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. In this study, coastal areas with elevations below 5 meters were considered to have \u0026ldquo;Very High\u0026rdquo; risk due to their high exposure to climate impacts such as storm surges, sea-level rise, and coastal flooding (Twumasi et al. \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Kron \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Weiss et al. \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Furthermore, most settlements and urban areas on the island are located 1\u0026ndash;2 kilometers from the coast, potentially increasing socio-economic and population exposure to these climate-associated risks (Kulp \u0026amp; Strauss \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Vousdoukaset al. \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Neuman et al. 2015).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary table of coastal elevation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation Risk Classification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercent Risk (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea Covered (sq.km)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal Area Covered\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e418\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSea-Level Change Rate\u003c/h2\u003e \u003cp\u003eSea level change risk directly relates to coastal inundation, increasing the susceptibility of coastal areas to climate-related hazards such as flooding, saltwater intrusion, and habitat alteration (Nicholls \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Hague et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The impacts of the sea level change rate on coastal vulnerability assessment can be associated with elevation and land use within the grids. Sea level change rate risk is projected to be higher in lower-elevated regions than in those with higher elevations (Magnan et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Vousdoukas et al. \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Martyr-Koller et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eUsing data from the NASA Sea Level Portal Data Analysis Sea Level Evaluation and Assessment Tool, the study assessed the risks according to the recorded sea level change rate for SIPLAS in the Philippines, 6.34 mm/year, rated as Very High in the vulnerability matrix. Accordingly, the World Bank and Asian Development Bank Climate Risk Country Profile 2021 also considered the Philippines one of the countries with the highest risk of sea level rise.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eTidal Range\u003c/h2\u003e \u003cp\u003eThe average value of Tidal Range Risk for SIPLAS was obtained from the Global Tidal Range Classification, calculated using the FES2014 (Finite Element Solution) model and AVISO+ Satellite Altimetry Data. According to the risk matrix applied in this study, the coastal vulnerability for the whole island was Moderate (2.11 m), which partly influences sediment transport and the establishment of coastal habitats. However, due to the low elevation of coastal areas, tidal range poses a significant risk to coastal settlements and establishments (Anthony E.J. 2019). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows how locals have adapted their dwellings in the intertidal zones by elevating their houses, eventually exposing them to more risks.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNatural and anthropogenic changes to estuaries and tidal rivers, such as wetland reclamation and channel dredging, significantly affect the tidal range and its impact on coastal communities and ecosystems (Talke and Jay, \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Tidal processes also significantly affect sediment transport and the distribution of biodiversity in coastal areas, influencing shoreline change and vulnerability over time (Dashtgard et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHigher tidal ranges are linked to intense coastal erosion and increased sediment buildup due to the greater energy and water movement they bring (Couperthwaite et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, Rossi et al. \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This energy can generate high shear stresses, leading to substantial sediment transport and erosion, particularly during storm events or when tidal forces peak. The lack of protective barriers and the variability in sediment sources can exacerbate these effects, making coastlines with macro-tidal ranges more vulnerable to changes in morphology and sediment dynamics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eSignificant Wave Height (SWH)\u003c/h2\u003e \u003cp\u003eSIPLAS is known as the surfing capital of the Philippines, hosting various local and international surfing competitions throughout the year. This is because the eastern parts of the island face the Pacific Ocean, where more than seasonal winds blow regularly, creating waves. The Significant Wave Height was taken from Copernicus Marine Services - Global Ocean Waves Analysis and Forecast for this study. The value for SIPLAS was approximated at 1.8 m and is classified as Very High according to the vulnerability matrix.\u003c/p\u003e \u003cp\u003eThe risk associated with Significant Wave Height (SWH) is compounded by factors such as sea level rise and tidal range (Gornitz \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1991\u003c/span\u003e, Suh and Kim \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Chaigneau et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Rising sea levels due to climate change increase the extent of coastal inundation and erosion, amplifying the impact of significant wave events by allowing waves to penetrate further inland and causing more severe flooding (Zhang et al. \u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, Hassan and Hassaan \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, high tidal ranges contribute to greater water level fluctuations, enhancing the effects of waves on coastal flooding and erosion dynamics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eCoastal Vulnerability Index (CVI)\u003c/h2\u003e \u003cp\u003eThe Coastal Vulnerability Index (CVI) data analysis from 2015 to 2020 shows significant temporal changes in vulnerability across the 418 coastal grid points examined (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The mean CVI increased from 14.08 in 2015 to 14.66 in 2020. This indicated an upward trend given that 140 grid points (33.5%) experienced increased vulnerability, while only 77 points (18.4%) showed decreased vulnerability, and 201 points (48.1%) remained unchanged.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary table of coastal vulnerability and percent coverage for 418 sq.km. Coasts of SIPLAS for 2015 and 2020.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCVI Classification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrid Count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrid Count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe temporal dynamics of vulnerability transitions reveal a predominantly downward trajectory: 140 grids (33.5%) transitioned to higher vulnerability categories, while only 77 grids (18.4%) improved. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates a critical threshold effect: Very High vulnerability grids demonstrated 99% persistence (67 of 68 grids), indicating an essentially irreversible vulnerability state once they reach this critical classification. In contrast, the Low-risk habitat category shows substantial erosion, with 35 of 80 grids (43.8%) transitioning to higher vulnerability categories, predominantly to Moderate (32 grids) but with concerning direct jumps to High (3 grids), indicating episodic vulnerability-amplifying events. These transitions directly correlate with documented coastal land-use changes (built-up expansion\u0026thinsp;+\u0026thinsp;124%, habitat loss\u0026thinsp;\u0026minus;\u0026thinsp;66 Low-risk grids) and ecosystem service degradation (mangroves\u0026thinsp;+\u0026thinsp;34% but offset by habitat loss elsewhere).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe distribution of vulnerability categories was observed to have shifted notably. A comparison of CVI for 2015 and 2020 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) shows a shift toward higher risk over time: with the \"Very Low\" and \"Low\" risk categories decreasing from 18% to 15%, while \"High\" and \"Very High\" categories notably increased by 3\u0026ndash;4%. The \"Moderate\" category remained the most populated in both years but slightly decreased (3%) from 2015 to 2020. This overall trend indicates that coastal areas are facing increasing vulnerability, moving out of lower risk categories and into higher ones, suggesting escalating risks for CLUHs over this five-year period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCoastal land-use and habitat classification analysis reveals a fundamental contradiction in SIPLAS's coastal development trajectory. Habitat areas expanded substantially between 2015 and 2020, with mangrove forests increasing by 34% (+\u0026thinsp;5,648 hectares), coral reefs by 121% (+\u0026thinsp;8,762 hectares), and seagrass beds by 56% (+\u0026thinsp;3,461 hectares), reflecting effective conservation and regeneration programs. These habitat gains generate estimated annual ecosystem service values of \u003cspan\u003e$\u003c/span\u003e138\u0026ndash;291\u0026nbsp;million, including coastal protection (wave dissipation 30\u0026ndash;70%), fisheries support (USD \u003cspan\u003e$\u003c/span\u003e2,000\u0026ndash;8,000/hectare/year), and carbon sequestration.\u003c/p\u003e \u003cp\u003eHowever, this positive trajectory is offset by rapid built-up expansion of 124% (+\u0026thinsp;865 hectares), concentrated in tourism development corridors, combined with loss of perennial crops (-25%, -5,020 hectares) and brush/shrubs (-19%, -3,142 hectares). Changes in the CVI from 2015 to 2020 show areas with high degradation transitioning to high vulnerability, rather than low recovery, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Spatially, habitat recovery occurs in remote areas, while development pressure concentrates in areas of highest human population density, creating a spatial mismatch between ecosystem service gains and reduced vulnerability. Grid-level analysis reveals that 140 grids (33.5%) experienced increased vulnerability despite habitat recovery elsewhere, while only 77 grids (18.4%) improved, demonstrating that development outpaces restoration at 1.82:1 ratio.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe field validation observed that urban/rural development is prominent within areas of Very High and High vulnerability, increasing their exposure to climate-related risks and other potential natural hazards (see Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). It was also observed during the field validation that various adaptation measures, mostly gray infrastructure, were being constructed in these areas to mitigate potential damage and losses from climate change impacts. Gray infrastructures like breakwaters, dikes, and sea walls have a long-term impact on coastal ecosystems (Powell et al. \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Waryszak et al. \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), are expensive to build and require constant maintenance, making them inefficient for coastal flooding mitigation (Powell et al. \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Livingstonn et al. 2018, Waryszak et al. \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Inacio et al. 2021).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eQuantifying Ecosystem Service Influence on Coastal Vulnerability\u003c/h2\u003e \u003cp\u003eThe changes in habitat risk rating and CVI showed a strong positive correlation (r\u0026thinsp;=\u0026thinsp;0.935, 95% CI [0.910, 0.956], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with risk changes explaining 87.4% of variance in CVI transitions (R\u0026sup2; = 0.85). This opposing trajectory, with a high correlation, demonstrates that ecosystem service provision (operationalized as habitat risk rating) is a major determinant of coastal vulnerability dynamics. Linear regression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) showed: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:CVI=8.229+2.25\\times\\:Habitat\\_Risk\\)\u003c/span\u003e\u003c/span\u003e, where each unit increase in habitat risk rating corresponds to a 2.25 CVI increase. The slope coefficient is highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as indicated by the Model R\u0026sup2; = 0.8519, suggesting that vulnerability variance is due to habitat changes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese habitat gains correspond to the estimated annual ecosystem service values of \u003cspan\u003e$\u003c/span\u003e138\u0026ndash;291\u0026nbsp;million (including coastal protection, fisheries support, and carbon sequestration valued at \u003cspan\u003e$\u003c/span\u003e1,500\u0026ndash;8,000 hectare⁻\u0026sup1; year⁻\u0026sup1;). Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e summarizes ecosystem valuation between 2015\u0026ndash;2020 based on changes in habitat risk and CVI. However, a 124% (+\u0026thinsp;865 hectares) expansion of built-up areas in high-population-density areas systematically offset habitat gains by permanently losing coastal protection services.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated Quantified Ecosystem Service Values by Habitat (2015\u0026ndash;2020).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabitat Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea 2015 (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea 2020 (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChange (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEcosystem Service Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal Annual Benefit (2020)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMangroves\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22,312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;5,648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e4,000\u0026ndash;8,000/ha/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e89\u0026ndash;178 million\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16,003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;8,762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e2,000\u0026ndash;5,000/ha/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e32\u0026ndash;80 million\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeagrass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;3,461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e1,500-3,000/ha/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e14\u0026ndash;29 million\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrassland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1,846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e500-1,000/ha/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e2\u0026ndash;4 million\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e1,000\u0026ndash;2,000/ha/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e0.2\u0026ndash;0.4 million\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt-up/Barren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e0 (liability)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubtotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33,162 ha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52,291 ha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;19,129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e138\u0026ndash;291 million\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eQuantified Ecosystem Service Values by Habitat (2015\u0026ndash;2020): The +\u0026thinsp;19,129 hectares of habitat gain generate $138\u0026ndash;291\u0026nbsp;million in annual ecosystem services, yet this is concentrated in remote areas, while\u0026thinsp;+\u0026thinsp;869 hectares of built-up in high-density zones eliminate coastal protection, creating a net vulnerability increase.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs summarized in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, it was noted that Very Low risk grids (rating 1, complete habitat) increased modestly from 18 to 22 grids (+\u0026thinsp;22%), while High risk grids (rating 4, limited habitat) increased substantially from 98 to 118 grids (+\u0026thinsp;20%), and Very High risk grids (rating 5, no habitat) decreased from 49 to 38 grids (\u0026minus;\u0026thinsp;22%). The net distribution shift indicated concentration of vulnerability in mid-to-high risk categories (High\u0026thinsp;+\u0026thinsp;Very High grids: 147 in 2015 vs. 156 in 2020, +\u0026thinsp;6%), with 140 grids deteriorating versus only 77 grids improving, yielding a 1.82:1 ratio of vulnerability escalation to reduction.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHabitat Risk Classification, Land-Use Changes, and Ecosystem Service Valuation (2015\u0026ndash;2020)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk Rating\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimary Land Uses\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEcosystem Services\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2015 Grids\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2020 Grids\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eService Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComplete habitat (mangrove/coral/seagrass)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum protection: Wave dissipation 70%, Storm surge reduction, Fisheries support, Carbon storage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e8,000\u0026ndash;12,000/ha/year\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHabitat present, no built-up (mix of forest, grassland, partial habitat)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGood protection: Wave dissipation 30\u0026ndash;50%, Coastal stabilization, Biodiversity support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e3,000\u0026ndash;5,000/ha/year\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMixed: Intertidal+habitat or grassland+limited built-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdequate protection: Partial wave dissipation, Some stabilization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e1,000\u0026ndash;2,000/ha/year\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeach barren, intertidal, with built-up development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLimited protection: Minimal wave dissipation, Rapid erosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e0-500/ha/year\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeach barren AND built-up (no vegetation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo protection: Zero ecosystem services, Maximum erosion vulnerability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e0/ha/year (Negative: cleanup costs)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTOTAL AREA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72,098 ha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85,116 ha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u0026thinsp;13,018 ha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe findings of the scenario-based analysis of habitat risk (classification in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), quantifies vulnerability outcomes for specific habitat management pathways. Grids undergoing habitat restoration (Δ Risk \u0026lt;- 1.5) showed a mean decrease in CVI of \u0026minus;\u0026thinsp;7.71 units (n\u0026thinsp;=\u0026thinsp;12, 95% CI [\u0026minus;\u0026thinsp;9.29, \u0026minus;\u0026thinsp;6.13], SD\u0026thinsp;=\u0026thinsp;3.05), representing a 54.8% reduction in vulnerability relative to baseline CVI of 14.08. Conversely, grids experiencing habitat degradation (Δ Risk\u0026thinsp;\u0026ge;\u0026thinsp;+\u0026thinsp;1.5) showed mean CVI increases of +\u0026thinsp;6.99 units (n\u0026thinsp;=\u0026thinsp;19, 95% CI [+\u0026thinsp;5.65, +\u0026thinsp;8.33], SD\u0026thinsp;=\u0026thinsp;2.82), representing 49.6% vulnerability increase. The 14.7-unit differential between restoration and degradation scenarios (\u0026minus;\u0026thinsp;7.71 to +\u0026thinsp;6.99) quantifies the ecosystem service value in terms of reduced coastal vulnerability. These findings demonstrate that ecosystem service modifications, as measured by changes in habitat risk ratings, directly affect coastal vulnerability, though the absolute level of vulnerability remains dominated by geophysical hazard exposure.\u003c/p\u003e \u003c/div\u003e "},{"header":"Summary and Conclusion","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003cp\u003eThe assessment revealed multiple high-risk coastal hazards for SIPLAS. Elevation analysis identifies 32.4% of the coastal area (135 square kilometers) as very high risk, with most coastal settlements concentrated within 1\u0026ndash;2 kilometers of shorelines at elevations below 5 meters. The island experiences a very high sea level change rate of 6.34 mm/year, significantly above global averages and consistent with the Philippines' classification as one of the world's most vulnerable countries to sea level rise. Additional risk factors include a significant wave height of 1.8 meters from Pacific Ocean exposure and a moderate tidal range of 2.11 meters that affects sediment transport. These combined factors create a complex risk environment where multiple climate-related hazards interact to amplify coastal vulnerability. It is also currently being influenced by the construction of coastal barriers cum coastal roads, new ports, and other gray infrastructure on the coast.\u003c/p\u003e \u003cp\u003eThe integration of CLUH into the CVI framework also assessed how ecosystem services and land-use dynamics have influenced coastal vulnerability between 2015 and 2020. During this period, built-up area increased by 124%, while brush/shrub and perennial crop areas declined by 19% and 25%, respectively, indicating strong pressures from urbanization and land conversion in the coastal zone. In contrast, mangrove cover increased by 34% and coral reef area by 121%, reflecting ongoing habitat conservation and regeneration efforts within SIPLAS.\u003c/p\u003e \u003cp\u003eAt the island scale, the CVI results show a net shift towards higher vulnerability classes, with the mean CVI increasing from 14.08 in 2015 to 14.66 in 2020, and the proportion of grids in the High and Very High categories rising from 41% to 47%. This indicates that, despite positive trends in key protective habitats such as mangroves and corals, the rate and spatial distribution of built-up expansion and other high-risk land uses are currently outpacing the protective effect of ecosystem services. Most critically, the vulnerability lock-in effect demonstrated by 99% Very High category persistence suggests a narrowing window for meaningful adaptation before irreversible coastal risk states are entrenched.\u003c/p\u003e \u003cp\u003eUsing a habitat risk classification analysis to quantify the impact of land use and coastal habitat on coastal vulnerability, the study revealed strong relationships between ecosystem service provision and coastal vulnerability. Change-based analysis revealed a strong positive relationship between habitat risk change and CVI (r\u0026thinsp;=\u0026thinsp;0.935, 95% CI [0.91, 0.96], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with habitat dynamics explaining 87.4% of vulnerability variance (R\u0026sup2;=0.874). Linear regression of vulnerability changes as a function of habitat risk changes yielded: ΔCvi\u0026thinsp;=\u0026thinsp;2.25\u0026thinsp;\u0026times;\u0026thinsp;ΔHabitat_Risk\u0026thinsp;+\u0026thinsp;constant, quantifying that each unit of habitat risk degradation produces 2.25 CVI points of vulnerability increase.\u003c/p\u003e \u003cp\u003eIn contrast, cross-sectional analysis of static habitat risk ratings with CVI values showed moderate correlations (2015: r\u0026thinsp;=\u0026thinsp;0.375, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; 2020: r\u0026thinsp;=\u0026thinsp;0.389, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that habitat status explains only 15.1% of total CVI variance in any given year (R\u0026sup2;=0.151), with physical hazard parameters (elevation, wave height, shoreline change, sea-level rise) explaining the remaining 84.9%. This disparity between change-based (r\u0026thinsp;=\u0026thinsp;0.935) and static (r\u0026thinsp;=\u0026thinsp;0.38) correlations reveals a critical finding: habitat transitions are stronger predictors of coastal vulnerability than habitat state. This indicates that ecosystem service loss or recovery at the grid level drives changes in vulnerability more strongly than the absolute level of habitat provision determines baseline vulnerability. This irreversibility has profound implications for climate adaptation governance. Coastal vulnerability reduction requires immediate habitat protection in currently low-risk zones (preventing escalation to High-Very High categories) rather than deferring adaptation until vulnerability becomes critical, at which point remediation costs become economically prohibitive and physically infeasible.\u003c/p\u003e \u003cp\u003eSIPLAS presents a natural experiment demonstrating this principle: the 77 grids successfully preserved in the Preservation scenario required only habitat retention and development restriction, incurring minimal financial cost, whereas the 19 grids in the Degradation scenario now require full ecosystem restoration to reverse\u0026thinsp;+\u0026thinsp;6.99 CVI point increases. This asymmetry in prevention versus restoration costs, combined with the irreversibility persistence rate of 69.1%, provides quantitative justification for precautionary coastal governance: protecting Low-Moderate habitat zones through restrictive zoning, property acquisition, and marine spatial planning yields equivalent or superior vulnerability reduction per dollar invested compared to restoration-focused strategies in degraded zones, while potentially avoiding irreversible risk thresholds that foreclose future adaptation options.\u003c/p\u003e \u003cp\u003eThis cost-effective evidence directly informs resource allocation decisions in resource-constrained small island communities. For identical coastal protection outcomes, habitat-based adaptation delivers greater cost-effectiveness than gray infrastructure while also supporting fisheries, carbon sequestration, recreational ecosystem services, and cultural heritage. The habitat risk classification framework thus transforms coastal vulnerability assessment from a physical hazard assessment into an evidence-based decision support system for coastal adaptation planning. This enables policymakers to quantify which habitat conservation and restoration investments yield the greatest vulnerability reduction per dollar invested, facilitating mainstreaming of nature-based solutions into operational adaptation planning.\u003c/p\u003e \u003cp\u003eThe vulnerability assessment findings highlight critical challenges for sustainable coastal management, not only for SIPLAS but also for small island communities worldwide. The concentration of settlements and infrastructure in very high- and high-vulnerability zones necessitates urgent adaptation strategies that balance economic development with environmental protection. Current reliance on gray infrastructure solutions, while providing short-term protection, may prove insufficient and environmentally counterproductive given their high costs, maintenance requirements, and potential negative impacts on coastal ecosystems. The study emphasizes the need for integrated coastal zone management that leverages the observed positive trends in coastal habitat conservation while addressing the underlying drivers of habitat conversion and urban expansion.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMr. Antonio Fabela Regis Jr. would like to thank the DIA- Doctoral Fellowship in India for ASEAN and the Government of India for their financial support in undertaking this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData access\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are openly available from the sources cited in the Methods. All datasets used are publicly accessible through the corresponding repositories, and access links have been included directly in the manuscript. No proprietary or restricted data were used, and no additional data beyond those listed are available.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgence Fran\u0026ccedil;aise de D\u0026eacute;veloppement (2024), May 27 The dangers facing the world's small island states\u0026mdash;and how we can help. 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Land Use Policy 91:104370. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.landusepol.2019.104370\u003c/span\u003e\u003cspan address=\"10.1016/j.landusepol.2019.104370\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Indian Institute of Technology Hyderabad","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Coastal Vulnerability, Climate Change, Remote Sensing and Geographic Information System, Coastal Habitats, Land use, Ecosystem services","lastPublishedDoi":"10.21203/rs.3.rs-9277512/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9277512/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSmall island communities face disproportionate climate risks, yet most coastal vulnerability assessments treat ecosystem services as static features, missing the dynamic habitat changes that fundamentally alter coastal protective capacity. We present an operational framework that integrates temporal land-use change and natural habitat dynamics directly into the Coastal Vulnerability Index (CVI) and quantify ecosystem service contributions to coastal risk outcomes. The study was benchmarked in the Siargao Island Protected Landscape and Seascape (SIPLAS), Philippines, where we applied across 418 coastal grids, individual parameters, habitat, and CVI risk ratings, treating coastal ecosystems as protective features, as opposed to vulnerability-amplifying built-up and barren land. Between 2015 and 2020, the built-up area expanded by 124%, while mangroves, corals, and seagrass increased by 34%, 121%, and 56%, respectively, generating an estimated 138\u0026ndash;291\u0026nbsp;million USD per year in ecosystem services, but concentrated away from dense settlements. Mean CVI rose from 14.08 to 14.66, and the share of High and Very High grids increased from 41% to 47%, with 140 grids worsening, 77 improving, and Very High grids showing 99% persistence, indicating vulnerability lock-in. Change-based analysis shows that habitat risk transitions explain 87.4% of the variance in CVI change; each unit of habitat degradation increases CVI by about 2.25 points, making transitions stronger predictors of vulnerability than static habitat states. The framework provides a transparent, data-light tool for small-island governments to prioritize proactive habitat protection and nature-based solutions over costly grey infrastructure in coastal adaptation planning.\u003c/p\u003e","manuscriptTitle":"Grid-based Coastal Vulnerability Assessment in Small Island Communities Using Land-use and Habitats for Improved Adaptative Management and Governance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-01 00:35:21","doi":"10.21203/rs.3.rs-9277512/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3a9db1df-7440-4b62-9d70-4e81a54ecaac","owner":[],"postedDate":"April 1st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65456319,"name":"Climate Analysis and Modeling"},{"id":65456320,"name":"Environmental Engineering"},{"id":65456321,"name":"Conservation Biology"},{"id":65456322,"name":"Geographic Information Systems"},{"id":65456323,"name":"Climatology"},{"id":65456324,"name":"Environmental Policy"}],"tags":[],"updatedAt":"2026-04-01T00:35:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-01 00:35:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9277512","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9277512","identity":"rs-9277512","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}