Coastal Change Research Articles

Assessing the impact of sea level rise on the Indus delta in Pakistan: A comprehensive analysis of flooded areas and future vulnerabilities

This research investigates the impact of sea level rise (SLR) on the Indus Delta, a vital ecosystem increasingly vulnerable to climate change repercussions. The objective of this study is to comprehensively assess the flooded areas under various shared socioeconomic pathway (SSP) scenarios based on the Intergovernmental Panel on Climate Change's (IPCC) 6th Assessment Report. The study employs a GIS-based bathtub model, utilizing historical (1995–2014) and IPCC-projected (2020–2150) tide gauge data from Karachi, Kandla, and Okha stations to identify potential inundated areas threatened by coastal flooding. Additionally, it analyzes LANDSAT-derived multispectral images to identify coastal erosion hotspots and changes in the landscape. A supervised random forest classifier is used to classify major landforms and understand alterations in land cover. Furthermore, neural network-based cellular automata simulations are applied to predict future land cover for 2050, 2100, and 2150 at risk of inundation. The results indicate that under different SSP scenarios, the estimated inundated land area varies from 307.36 km2 (5 % confidence on SSP1-1.9) to 7150.8 km2 (95 % confidence on SSP5-8.5). By 2150, the region will lose over 550 km2 of agricultural land and 535 km2 of mangroves (mean SLR projection). This work emphasizes identifying sensitive land cover for SLR-induced coastal flooding. It might fuel future policy and modeling endeavors to reduce SLR uncertainty and build effective coastal inundation mitigation methods.

Quantitative Monitoring of Coastal Erosion and Changes Using Remote Sensing in a Mediterranean Delta

Quantitative Monitoring of Coastal Erosion and Changes Using Remote Sensing in a Mediterranean Delta

Landmark Base Point Approach to Positional and Coastal Accuracy Analysis for Historical Map Before WWI: A Case Study 1836 Moltke and 1914 German Blue about in Historical Peninsula of İstanbul

AbstractCartography, unquestionably one of the world’s oldest disciplines of scientific study, is among the fields that have been most influenced by technological improvements. These improvements have enhanced interest in historical maps while also paving the way for modern mapping. All of this process has created new topics of study for cartographers. It is possible to examine old maps using software such as MapAnalyst which can enable the investigation of time-dependent changes. In this study, two historical maps, namely, Moltke and German Blue, were analyzed in the study area including the Historical Peninsula, Golden Horn, some part of Bosphorus, and the Marmara Sea in İstanbul, Türkiye, employing Helmert, Affine, and Huber loss robust estimation methods. It was revealed that the region with the highest deterioration on both maps is between Beşiktaş and Üsküdar. The coastline change analysis was also performed. According to the results, it was observed that the regions with the most coastal change are located on the southern and eastern coasts of the Historical Peninsula.

Landscape evolution during Holocene transgression of a mid‐latitude low‐relief coastal plain: The southern North Sea

AbstractLow‐relief coastal landscapes are at major risk of rising sea levels, as vertical changes in relative sea level have far‐reaching lateral effects. Integration of a dense 2D grid of seismic reflection data with sedimentological and geotechnical data obtained in two offshore wind farm zones allows detailed documentation of postglacial landforms and environmental change over a 1,021 km2 area in the western sector of the southern North Sea. Following the retreat of Last Glacial Maximum ice sheets from the southern North Sea, the resulting postglacial terrestrial landscape provided a surface for peatland formation as climate started to warm and the water table rose in response to relative sea‐level rise. Southward‐draining fluvial networks formed contemporaneously with the peatlands, and remnants of this terrestrial wetland landscape are buried beneath Holocene marine sediments. Distinctive isolated incisional features and discrete widening of fluvial channels that cut through the peats are interpreted as either tidal ponds or relict tidal channels. These features record the evolution of this landscape through the Early Holocene as marine transgression inundated a low‐relief coastal plain. The erosion of the peatlands observed in the cores, the patchy preservation of the organic wetland landscape, and the lack of preserved barrier systems recorded by the seismic reflection data suggest that the rate of relative sea‐level rise outpaced sediment supply during the Late Postglacial and Early Holocene in this area of the southern North Sea. In a regional context, the southward draining river channels contrast to northward fluvial drainage to the North Sea, pointing to a subtle drainage divide in the palaeolandscape and the presence of a low‐relief land bridge separating the North Sea and the English‐Channel/La Manche during the Early Holocene. The documented scenario of rising sea levels combined with decreasing sediment supply in a low‐relief setting is a situation faced by many global deltas and coastlines, which makes the southern North Sea a crucial archive of coastal landscape change.

VedgeSat: An automated, open‐source toolkit for coastal change monitoring using satellite‐derived vegetation edges

AbstractPublic satellite platforms offer regular observations for global coastal monitoring and climate change risk management strategies. Unfortunately, shoreline positions derived from satellite imagery, representing changes in intertidal topography, are noisy and subject to tidal bias that requires correction. The seaward‐most vegetation boundary reflects a change indicator which shifts on event–decadal timescales, and informs coastal practitioners of storm damage, sediment availability and coastal landform health. We present and validate a new open‐source tool VedgeSat for identifying vegetation edges (VEs) from high (3 m) and moderate (10–30 m) resolution satellite imagery. The methodology is based on the CoastSat toolkit, with streamlined image processing using cloud‐based data management via Google Earth Engine. Images are classified using a newly trained vegetation‐specific neural network, and VEs are extracted at subpixel level using dynamic Weighted Peaks thresholding. We performed validation against ground surveys and manual digitisation of aerial imagery across eroding and accreting open coasts and estuarine environments at a site in Scotland. Smaller‐than‐pixel vegetation boundary detection was achieved across 83% of Sentinel‐2 imagery (Root Mean Square Error of 9.3 m). An overall RMSE of 19.0 m was achieved across Landsat 5 & 8, Sentinel‐2 and PlanetScope images. Performance varied by coastal geomorphology, with highest accuracies across sandy open coasts owing to high spectral contrast and less false positives from intertidal vegetation. The VedgeSat tool can be readily applied in tandem with waterlines near‐globally, to support adaptation decisions with historic coastal trends across the whole shoreface, even in normally data‐scarce areas.

Shoreline position trends in the Niger Delta: analyzing spatial and temporal changes through Sentinel-1 SAR imagery

Various natural and human-induced processes operating across different temporal scales contribute to the spatial and temporal evolution of shoreline alterations. Deltaic coasts, particularly dynamic geomorphic systems, experience continuous changes at various spatial and temporal dimensions. This investigation underscores the effectiveness of Sentinel-1 Synthetic Aperture Radar (SAR) data in assessing long-term shifts in shoreline positions, focusing on the Niger Delta region. Out of 255 images from 2015 to 2020, 36 were selected based on high tide conditions for each year. This study has pioneered a straightforward and systematic approach rooted in Geographic Information Systems (GIS). This approach offers an accessible pathway for evaluating coastal changes in regions with limited data availability. To better comprehend the heterogeneity of change processes along the shoreline and to pinpoint their causes and mechanisms, we divided the shoreline into distinct sediment cells. This division adheres to a convenient and well-established methodology. This study identifies prevailing erosion trends outweighing accretion, with specific areas showing distinct erosion (cells II, IV, VI, VII, and VIII) and accretion (cells I, III, and V) patterns. Furthermore, coastal areas near river mouths exhibit a higher susceptibility to change. This approach offers a pathway for evaluating coastal changes in data-limited regions and can establish an effective monitoring system benefiting coastal modellers, scientists, and government agencies. It lays the groundwork for comprehensive coastal zone frameworks, facilitating improved planning and management strategies.

Landcover-based detection of rapid impacts of extreme storm on coastal landscape

On September 24, 2022, Post-Tropical Hurricane Fiona made landfall in Atlantic Canada and caused unprecedented damages to the coastal communities and ecosystems therein. The aftermath triggered local government and communities in Prince Edward Island (PEI), Canada to rethink current policies and practices for coastal protection in the context of climate change. This historic hazard represents the escalating frequency and intensity of extreme weather events that globally threaten coastal regions, accelerating coastal erosion and endangering communities. This study employs landcover-based detection to assess rapid storm impact of Fiona on coastline of PEI using Sentinel-2 satellite images, to gauge the efficacy of landcover-based detection and quantify storm-induced coastal environmental changes. Our results indicate that, following Fiona, over 51 km2 coastal land loss due to the erosion at beach foreshore and inundation at tidal flat, and over 11 km2 sand dune loss mainly on the PEI north shore. This constitutes a 3.5 % loss of coastal land resources within the 1798 km2 PEI coastal zone. Fiona also caused over 194 km2 area in coastal buffer zone showed temporal fluid-mud from the eroded sediments of sand dunes, cliffs, and tidal flats, suggesting the significant sediment loss from vertical structures in addition to the direct retreat. The landcover-based method can be regarded as a valuable tool for the storm impacts on coastal environments. Based on the coastal change pattern, more sustainable coastal protection and adaptation measures should be developed, focusing on reducing hydrodynamic intensity and improving erosion capacity, with consideration of the increasing likelihood of more intense and frequent storm events in a warming climate.

Holocene relative sea-level variation and coastal changes in the Bay of Cádiz: New insights on the influence of local subsidence and glacial hydro-isostatic adjustments

The Bay of Cádiz is located in South-western Spain (Andalusia region) and constitutes an example of a typical estuarine salt marsh environment. In this study we reconstruct its Holocene morpho-evolution and relative sea-level change history by assembling a geodatabase of geological sea-level markers derived from boreholes and bibliographic data, standardized to the most recent international guidelines for RSL studies. The identified high-precision sea-level index points were compared to a number of new site-specific glacio-hydro-isostatic adjustment (GIA) models in order to disentangle potential components which influenced the sea-level evolution and finally obtain the vertical displacement (VD) trends that affect this coastal area by using a Bayesian statistical approach including Monte Carlo simulations. In general, the whole area was affected by overall subsidence related to the local sediment compaction, which had an impact on the morpho-evolution of the different zones with a variable entity and completely outclassed the GIA-driven component. Between 6.7 and 3.0 ka BP, the northern sector of the bay was characterized by subsidence rates of about - 0.65 mm/yr while, during the last 3.0 ka, the general trend appears to be homogeneous for both the main sectors of the bay with an average subsidence rate of - 1.6 mm/yr. The Holocene RSL curve from the Gulf of Cádiz aligns with past reconstructions, revealing subtle differential trends in subsidence rates in the northern and southern sectors of the Bay of Cádiz due to varying substratum.

Studying development trend of coastline change in China using CiteSpace

The investigation of coastline alterations holds significant scientific and societal importance, particularly in guiding offshore engineering planning and design, as well as the economic development of coastal regions. In this study, we used CiteSpace software to examine relevant literature within the China National Knowledge Infrastructure database to explore the current status, hotspots, and frontiers of research on coastline change in China. Our findings revealed a consistent increase in the number of publications within this domain, with 1996 representing the first growth point in the number of publications on coastal changes. Since 2002, the number of publications has significantly increased owing to scientific and technological advancements and urban development. Research on coastal changes in China is primarily conducted in universities and research institutes, particularly at the Ocean University of China. However, research institutions and authors are widely dispersed, and the consistency between them is low. An apparent, balanced distribution phenomenon exists in this research field. Content related to keywords such as “coastline,” “coastal zone,” and “remote sensing” is the focus of research in this field. Remote sensing monitoring, big data analysis, and deep learning techniques are closely related to the study of coastline changes, representing the latest advancements in this field.

Evaluating and predicting coastal shoreline changes in the Doi Duong - Phan Thiet beach area by remote sensing and GIS technology

Assessing the shoreline variability is paramount in understanding the erosion and coastal accretion processes. The potential impact of erosion on coastal communities, particularly in coastal regions, amplifies the vulnerability to coastal disasters. In this study, Landsat satellite imagery captured from 1990 to 2022 was utilized, in conjunction with Geographic Information System (GIS), to scrutinize alterations in the shoreline of Doi Duong Beach - Phan Thiet. The satellite images were independently processed using aspect-ratio methods and thresholding techniques to determine the prevailing condition of the coastline. The rate of shoreline change was quantified using the DSAS tool. The investigation’s findings reveal a dynamic interplay between erosion and accretion, with accretion dominance explicitly evident within the areas surrounding the Ca Ty - Phu Hai river mouth. The average annual shoreline change rate is 27.1 m/yr for the Ca Ty River mouth and 16.5 m/yr for the Phu Hai River mouth. The most significant disparity between the recorded coastline in 1990 and 2022 measures 868 m. Additionally, the projections for the coastline until 2032 indicate that accretion remains the dominant process. This research shows that combining geospatial techniques and numerical models is a reliable way to study shoreline change trends.

Level of Climate Change-Related Coastline Change Case Study: East Coast Tourism Area of Surabaya, Indonesia

The effects of climate change are most readily apparent in coastal regions, where rising sea levels have changed the coasts. Since several of the Surabaya Coastal Areas serve as tourist hotspots, this influence could lead to fewer visitors as a result of a decline in environmental quality. This study attempts to quantify the extent of coastal modification brought on by climate change in Surabaya’s popular tourism region along the east coast, Indonesia. The Digital Shoreline Analysis System (DSAS) extension tool in ArcGis is the technique employed. The information used is satellite picture data collected over a ten-year period. This study’s findings show that the shoreline has changed by 55.08 meters, which is the biggest amount. The subdistricts of Keputih Village, Wonorejo Village, and Medokan Ayu Village have the fastest rate of coastal change.

THE IMPACT SHORELINE MODIFICATIONS ON LEKANG TURTLE (Lepidochelys olivacea) CONSERVATION ALONG COASTAL OF KULONPROGRO, INDONESIA

Changes in oceanographic circumstances that continue to occur will generate challenges for coastal ecology, one of which is a change in the sea-land boundary. It is possible to have an impact on the olive ridley turtles that rise to lay their eggs by changing the region of this coastal area, particularly at Trisik Beach, Kulonprogo, and Yogyakarta Special Region. The goal of this study was to use a Geographic Information System to estimate the effect of changes in the coastline on the distribution of olive ridley turtles on Trisik Beach, Kulon Progo, and Yogyakarta Special Region in 2020-2022. Digitizing the shoreline reveals changes in the coastline, which is then analyzed using Landsat 8 satellite data retrieved with ArcGIS software. The Landsat 8 photos span the years 2020-2022. Using the Digital Shoreline Analysis System (DSAS) toolbox, each image will go through area cropping or image cutting, geometric and radiometric correction, demarcation or development of a land-sea boundary line, on-screen digitalization, and automatic calculation of coastal changes. Trisik Beach's coastline alterations from 2020 to 2022 tended to create abrasion due to the shrinking beach area. The greatest average distance to a coastline alteration was -29.61 m, the smallest was -15.03 m, and the average speed of the greatest abrasion is -8.26 m/year, the smallest is -4.19 m/year, resulting in a 111,967.03 m2 reduction in coastal area. The association between shoreline alterations and the distribution of olive ridley turtle nesting on Trisik Beach is quite significant, with the beach area affecting 91.3% of turtle nests and 88.5% of turtle eggs.

Assessment and spatialization of vulnerability of Benin coast to sea level rise using composite/blended approach

Climate change, particularly sea level rise, poses significant challenges to global coastal regions, necessitating a profound understanding of their vulnerability for effective mitigation and adaptation strategies. This study focuses on enhancing scientific insight into West African coastal vulnerability, aiming to assist policymakers and coastal managers in developing sustainable coastal zone management strategies. The methodology employed combines Geographic Information System (GIS) and remote sensing techniques with the Coastal Vulnerability Index (CVI) formula. Parameters such as slope, topography, distance to shoreline, population density, accessibility, land cover, and soil and vegetation types are assessed for their contribution to physical and socio-economic changes induced by sea level rise. These parameters undergo weighting using the Analytic Hierarchy Process (AHP). The socio-economic vulnerability is quantified using the Community-Based Risk Screening Tool-Adaptation and Livelihoods (CRiSTAL). The assessment of the Benin coast reveals significant vulnerability, with very high vulnerability (36.69–70.4, indicated by red cells) and high vulnerability (30.84–36.69, indicated by orange cells) observed in all coastal municipalities (Grand-Popo, Ouidah, Abomey-Calavi, Cotonou, Sèmè-Kpodji), encompassing 40 % and 21 % of the study area, respectively. Moderate (25.69–30.84) and low (10.87–25.69) vulnerability areas are situated at higher elevations inland, covering 35 % and 4 % of the total study area, respectively. No areas of very low vulnerability have been identified. This research contributes significantly to coastal management decisions, adaptation strategies, and climate change policies in Benin by advancing scientific understanding of coastal vulnerability and facilitating the development of sustainable strategies.

Quantifying the scale of erosion along major coastal aquifers of Pakistan using geospatial and machine learning approaches.

Insufficient freshwater recharge and climate change resulted in seawater intrusion in most of the coastal aquifers in Pakistan. Coastal aquifers represent diverse landcover types with varying spectral properties, making it challenging to extract information about their state hence, such investigation requires a combination of geospatial tools. This study aims to monitor erosion along the major coastal aquifers of Pakistan and propose an approach that combines data fusion into the machine and deep learning image segmentation architectures for the erosion and accretion assessment in seascapes. The analysis demonstrated the image segmentation U-Net with EfficientNet backbone achieved the highest F1 score of 0.93, while ResNet101 achieved the lowest F1 score of 0.77. Resultant erosion maps indicated that Sandspit experiencing erosion at 3.14 km2 area. Indus delta is showing erosion, approximately 143 km2 of land over the past 30years. Sonmiani has undergone substantial erosion with 52.2 km2 land. Miani Hor has experienced erosion up to 298 km2, Bhuri creek has eroded over 4.11 km2, east Phitii creek over 3.30 km2, and Waddi creek over 3.082 km2 land. Tummi creek demonstrates erosion, at 7.12 km2 of land, and East Khalri creek near Keti Bandar has undergone a measured loss of 5.2 km2 landlinked with quantified reduction in the vertical sediment flow from 50 (billion cubic meters) to 10 BCM. Our analysis suggests that intense erosions are primarily a result of reduced sediment flow and climate change. Addressing this issue needs to be prioritized coastal management and climate change mitigation framework in Pakistan to safeguard communities. Leveraging emerging solutions, such as loss and damage financing and the integration of nature-based solutions (NbS), should be prioritized for the revival of the coastal aquifers.

Benthos as a key driver of morphological change in coastal regions

Abstract. Benthos has long been recognized as an important factor influencing local sediment stability, deposition, and erosion rates. However, its role in long-term (annual to decadal scale) and large-scale coastal morphological change remains largely speculative. This study aims to derive a quantitative understanding of the importance of benthos in the morphological development of a tidal embayment (Jade Bay) as representative of tidal coastal regions. To achieve this, we first applied a machine-learning-aided species abundance model to derive a complete map of benthos (functional groups, abundance, and biomass) in the study area, based on abundance and biomass measurements. The derived data were used to parameterize the benthos effect on sediment stability, erosion rates and deposition rates, erosion and hydrodynamics in a 3-dimensional hydro-eco-morphodynamic model, which was then applied to Jade Bay to hindcast the morphological and sediment change for 2000–2009. Simulation results indicate significantly improved performance with the benthos effect included. Simulations including benthos show consistency with measurements regarding morphological and sediment changes, while abiotic drivers (tides, storm surges) alone result in a reversed pattern in terms of erosion and deposition contrary to measurement. Based on comparisons among scenarios with various combinations of abiotic and biotic factors, we further investigated the level of complexity of the hydro-eco-morphodynamic models that is needed to capture long-term and large-scale coastal morphological development. The accuracy in the parameterization data was crucial for increasing model complexity. When the parameterization uncertainties were high, the increased model complexity decreased the model performance.

Evaluating Sea Level Rise Impacts on the Southeastern Türkiye Coastline: a Coastal Vulnerability Perspective

Coastal areas are inherently sensitive and dynamic, susceptible to natural forces like waves, winds, currents, and tides. Human activities further accelerate coastal changes, while climate change and global sea level rise add to the challenges. Recognizing and safeguarding these coasts, vital for both socioeconomic and environmental reasons, becomes imperative. The objective of this study is to categorize the coasts of the Mersin and İskenderun bays along the southeastern coast of Türkiye based on their vulnerability to natural forces and human-induced factors using the coastal vulnerability index (CVI) method. The study area encompasses approximately 520 km of coastline. The coastal vulnerability analysis reveals that the coastal zone comprises various levels of vulnerability along the total coastline: 24.7% (128 km) is categorized as very high vulnerability, 27.4% (142 km) as high vulnerability, 23.7% (123 km) as moderate vulnerability, and 24.3% (126 km) as low vulnerability. Key parameters influencing vulnerability include coastal slope, land use, and population density. High and very high vulnerability are particularly prominent in coastal plains characterized by gentle slopes, weak geological and geomorphological features, and significant socioeconomic value.

Do numerical probabilities promote informed stated preference responses under inherent outcome uncertainty? Insight from a coastal adaptation choice experiment

Stated preference (SP) valuation provides insight into individuals’ preferences for environmental goods and assigns a monetary value to these preferences. It employs survey questions that elicit information on choices or behaviors in response to hypothetical future scenarios involving changes to these goods. Outcome uncertainty (OU) within SP studies refers to uncertainty concerning whether the effects communicated in these hypothetical scenarios would actually occur, were the scenario to be implemented as indicated in the questionnaire. Inherent OU may be further defined as OU that is invariant across scenarios. For example, the effect of installing coastal flood defenses may depend on a probability of severe storms that is fixed in the study area over the relevant time horizon. Inherent OU has received little attention in the environmental SP literature. For example, it is unknown whether numerical, percentage probabilities—a ubiquitous means of communicating uncertainty in SP questionnaires—are an effective risk communication tool for inherent OU in environmental valuation scenarios. This article evaluates two treatments in a discrete choice experiment survey related to coastal climate change adaptation in Connecticut, USA: one provides only raw frequencies as a risk communication tool, while the other provides implied percentage probabilities in addition to the same raw frequencies. Results show that the use of percentage probabilities to communicate inherent OU has no additional effect on average welfare estimates or the choice behavior of respondents. These findings suggest that percentage probabilities may not provide useful information to SP respondents, beyond that conveyed by past event frequencies.

Coastal management and climate change on the island and the sea of Chiloé (Chile): An evaluation of policies, regulations, and instruments

The objective of the research described here was to examine the policies, regulations, and instruments that directly or indirectly affect the coastal zones of the island and the sea of Chiloé (CZISC) and to evaluate the adequacy of the current political-administrative system concerning climate change. For this purpose, the Decalogue for Integrated Coastal Zone Management (ICZM) has been used as a methodological basis. Likewise, all policies, regulations, and instruments that have an impact on coastal management in Chiloé have been identified and classified based on their adaptation to problems that result from climate change. The documents have been grouped into seven numerical categories (1−7) that range from tools that omit the discourse on climate change (1) to those that focus exclusively on this subject and provide detailed information on the required actions and provisions (7). The lowest level (1) of the classification is predominant in the policies, regulations, and instruments studied, especially those that deal with subnational planning. Based on the strategic diagnosis presented, it was concluded that the lack of a holistic approach and the absence of discourse on climate change observed in Chiloé prevent the transition towards an ICZM model capable of improving human welfare and protecting socio-ecological systems in the long term.

Nation-scale multidecadal shoreline extraction and coastal spatio-temporal change monitoring using cross-mission remote sensing data

Mapping shorelines is critical for coastal management and conservation, as shorelines are essential indicators for understanding coastal geomorphology and dynamics. Nonetheless, as shorelines are often lengthy and rugged, in-situ surveys are labor-intensive and costly. Oppositely, spaceborne remote sensing data provide cost-efficient alternatives, yet, most earlier studies rely on manual digitization or thresholding band ratio-based water indices to delineate instantaneous shorelines, which locations are affected by varying water color and tides. Consequently, cross-temporal assessments of environmental and anthropogenic impacts on coastal stability remain challenging. The present study develops an edge detection-based, dual-temporal, nationwide, tide-coordinated, and sediment cell-based shoreline dataset in Japan, i.e., the TSSD-JP. Both 1980s and 2020s shorelines along the Japanese coasts are extracted using cross-mission multispectral data and an automated shoreline extraction method. The results are validated with national coast surveys and global shoreline databases, confirming a positional accuracy better than 80% at 50 m. Moreover, founded on the extracted dual-temporal shorelines, 30+ year coastal changes are measured. An increased land area of 293.21 km2 at a rate of 8.50 km2/yr is estimated. Evident accretions in bay regions of southern Honshu due to extensive land reclamation and engineering constructions are observed. Since the analysis is conducted on natural management units with a physical basis, potential biases caused by jurisdictional boundaries are overcome. Practically, because TSSD-JP is derived from freely accessible data and tools, its universal applicability is guaranteed. Thence, the proposed approach can be applied to different nations' intertidal zones to assess the coastal vulnerability and resilience as well as support local governments’ shoreline management plans (SMPs).

Evolutionary trends and analysis of the driving factors of Ulva prolifera green tides: A study based on the random forest algorithm and multisource remote sensing images

Understanding the prolonged spatiotemporal evolution and identifying the underlying causes of Ulva prolifera green tides play pivotal roles in managing such occurrences, restoring water ecology, and fostering sustainable development in marine ecosystems. Satellite remote sensing represents the primary choice for monitoring Ulva prolifera green tides due to its capability for extensive, long-term ocean monitoring. Based on multi-source remote sensing images, ecological and environmental datasets, and machine learning algorithms, therefore, this study focused on "remote sensing modelling - evolution history - change trends - mechanism analysis" to elucidate both the remote sensing monitoring models and the underlying driving factors governing the spatiotemporal evolution of Ulva prolifera green tides in the highly impacted South Yellow Sea of China. With the use of GOCI Ⅰ/Ⅱ images, an hybrid remote sensing extraction model merging the robustness of the random forest (RF) model and the optical algae cloud index (ACI) was established to map Ulva prolifera distribution patterns. The ACI-RF method exhibited exceptional performance, with an F1 score surpassing 0.95, outperforming alternative methods such as the support vector machine (SVM) and K-nearest neighbour (KNN) methods. On the basis, we analysed the evolutionary trends and the driving factors determining these distribution patterns using meteorological data, runoff data, and data on various water quality parameters (SST, ocean current speed, wind speed, precipitation, DO, PAR, Si, NO3-, PO43-and N/P). Over the period from 2011 to 2022, excluding 2021, there was a notable decline in the area of Ulva prolifera green tides, varying between 397 and 2689.9 km2, with an average annual reduction rate of 3%. The maximum annual biomass varied between 0.12 and 15.9 kt. Notably, more than 75% of the area of Ulva prolifera green tides exhibited northward drift, which was significantly influenced by northern currents and wind fields. The analysis of driving factors indicates that factors such as average sea surface temperature, eastward wind speed, northward wind speed, precipitation, PO43- and N/P/Si significantly influence the biological growth rate of Ulva prolifera. Furthermore, coastal land use change and surface runoff, particularly surface runoff in June, significantly impacted the growth rate of Ulva prolifera, with Pearson correlation coefficients of 0.74 and 0.67, respectively. Against the background of global warming and severe deterioration in the marine environment, Ulva prolifera blooms persist. Consequently, two distinct management strategies were proposed based on the distribution patterns and cause analysis results for addressing Ulva prolifera green tides: establishing a continuous protection framework for rivers, lakes, and nearshore areas to mitigate pollutant inputs and implementing precise environmental monitoring measures in urban expansion areas and farmlands to combat overgrowth-induced green tides. This methodology could be applied in other regions affected by marine ecological disasters, and the criteria for selecting influencing factors offer a valuable reference for designing tailored and proactive measures aimed at controlling Ulva prolifera green tides.