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Papers
61,005 resultsShowing papers similar to Coastal Dynamics Analysis Based on Orbital Remote Sensing Big Data and Multivariate Statistical Models
ClearMicroplastic deposits prediction on Urban Sandy Beaches: Integrating Remote Sensing, GNSS Positioning, µ-Raman Spectroscopy, and Machine Learning Models
Researchers integrated remote sensing, GNSS altimetric surveys, micro-Raman spectroscopy, and machine learning models to predict microplastic deposition patterns on urban sandy beaches along the central Sao Paulo coastline, finding MP concentrations ranging from 6 to 35 MPs/m2.
Assessing Shoreline Changes in Fringing Salt Marshes from Satellite Remote Sensing Data
This paper is not about microplastics; it uses satellite remote sensing (Landsat and Sentinel-2) to track historical shoreline changes in narrow salt marshes of the Aveiro lagoon in Portugal, documenting significant retreat since 2000.
Recognizing microplastic deposits on sandy beaches by altimetric positioning, μ-Raman spectroscopy and multivariate statistical models
Researchers combined satellite positioning, Raman spectroscopy, and statistical modeling to map and characterize microplastic deposits on sandy beaches along the Sao Paulo coast in Brazil. They found that microplastic distribution was linked to beach elevation, tidal patterns, and proximity to industrial and port activities. The study introduces a replicable methodology for systematically monitoring plastic pollution on coastlines.
Microplastic Deposit Predictions on Sandy Beaches by Geotechnologies and Machine Learning Models
Researchers used satellite imagery and machine learning to predict where microplastics accumulate on sandy beaches along Brazil's northern coast. They found that beach shape, slope, and proximity to urban areas were strong predictors of microplastic deposits. The study demonstrates that geotechnology tools can help identify pollution hotspots without costly field sampling at every location.
Coastal Marine Debris Detection and Density Mapping With Very High Resolution Satellite Imagery
Researchers used high-resolution satellite imagery combined with machine learning to detect and map coastal marine debris density in southern Japan, finding that satellite-based methods can estimate debris amounts and types on beaches with reasonable accuracy.
Evaluating Microplastic Pollution Along the Dubai Coast: An Empirical Model Combining On-Site Sampling and Sentinel-2 Remote Sensing Data
Researchers collected coastal water samples from Dubai and combined laboratory spectral measurements with Sentinel-2 satellite imagery to build a model that estimates microplastic concentrations from space. The model achieved an R² of 87% and was used to map microplastic pollution trends along the Dubai coast from 2018 to 2021. This remote-sensing approach demonstrates a scalable method for monitoring coastal microplastic pollution over large areas without intensive fieldwork.
Microplastic Deposits Prediction on Urban Sandy Beaches: Integrating Remote Sensing, GNSS Positioning, µ-Raman Spectroscopy, and Machine Learning Models
Researchers used remote sensing, GNSS positioning, Raman spectroscopy, and machine learning to predict microplastic deposition on urban beaches along the Sao Paulo coastline in Brazil. Microplastic concentrations ranged from 6 to 35 particles per square meter, with the highest densities near the Port of Santos linked to industrial activities. The predominant types were foams, fragments, and pellets, and machine learning models showed high predictive accuracy for mapping their distribution.
Microplastics distribution on the beach sediment based on satellite remote sensing: A case study in Bali, Indonesia
Researchers examined how seasonal ocean currents and tourism activity influence microplastic distribution across three beaches in Bali, Indonesia, between January and July 2024. The study integrated polymer-level characterization with site-specific hydrodynamic data and satellite remote sensing to map microplastic accumulation patterns in beach sediments.
Microplastic deposit predictions on sandy beaches by geotechnologies and machine learning models
Researchers used geotechnologies and machine learning models to predict microplastic deposition hotspots on sandy beaches, identifying environmental and anthropogenic variables that drive spatial variation in beach microplastic accumulation.
Study on the Impact of the Coastline Changes on Hydrodynamics in Xiangshan Bay
Not relevant to microplastics — this Chinese hydrodynamics study uses ocean modeling to examine how decades of coastline modification in Xiangshan Bay have altered tidal currents and water flow patterns.
Quantifying the Geomorphic Effect of Floods Using Satellite Observations of River Mobility
This paper is not about microplastics; it uses satellite imagery and machine learning to study how flood magnitude, duration, and hydrograph shape determine lateral erosion and channel change in rivers.
Microplastic pollution in Brazil's coastal marine surface waters: The first macroregional baseline from the global south
Researchers conducted the largest microplastic survey in the Global South, sampling 4,134 surface water sites across 7,500 km of Brazilian coastline, finding the highest concentrations in the Eastern Coastline (16.87 MPs/L) and lowest in the Amazonian Equatorial region (1.29 MPs/L), with spatial patterns driven by hydrodynamic conditions, salinity, proximity to sewage, and anthropogenic inputs.
Microplastics on Santos Beach: Sources of Pollution, Waste Characteristics and Possible Collection Solutions
This Brazilian study mapped and characterized microplastic contamination on Santos beach near submarine sewage outfalls and storm drains, finding plastic pollution hotspots linked to coastal discharge infrastructure. The authors estimated that ~60 tons of solid waste enter the sea daily in the region and identified possible collection solutions.
Meso- and microplastic composition, distribution patterns and drivers: A snapshot of plastic pollution on Brazilian beaches
A standardized survey of plastic pollution across 22 sandy beaches spanning over 4600 km of Brazilian coast found widespread contamination in coastal sediments, with polymer type, size, and distribution patterns reflecting diverse sources including fishing activity and urban runoff.
Coastal dynamism in Southern Thailand: An application of the CoastSat toolkit
Researchers applied the CoastSat satellite-derived shoreline mapping toolkit to analyze coastal dynamics in southern Thailand, quantifying shoreline change rates in a region where 11 million people face threats from coastal erosion, sea level rise, and land subsidence.
Mapping the plastic legacy: Geospatial predictions of a microplastic inventory in a complex estuarine system using machine learning
Researchers applied machine learning techniques to develop geospatial predictions of microplastic inventory in a complex estuarine system, overcoming the limitations of coarse ocean basin models by accounting for the intricate geomorphological and hydrodynamic conditions that govern sediment-associated microplastic distribution.
Macroplastic fate and transport in an Amazonian Estuarine System: A Lagrangian Modelling Approach
Scientists used computer models to track how large plastic waste travels from the city of Belém, Brazil through rivers and waterways to the ocean. They found that plastic pollution gets stuck in certain areas during low water periods but moves quickly to the ocean during high water periods, creating pollution hotspots near the city. This research helps identify where plastic waste accumulates so communities can better target cleanup efforts and prevent this pollution from reaching the ocean and potentially entering our food chain.
Microplastic dynamics and risk projections in West African coastal areas: Developing a vulnerability index, adverse ecological pathways, and mitigation framework using remote-sensed oceanographic profiles
Researchers analyzed microplastic dynamics along West African coastal areas using remote-sensed oceanographic data from 2019 to 2024. They developed a vulnerability index to assess ecological risk and identified key environmental factors driving microplastic transport in the region. The study proposes a mitigation framework to help coastal communities and policymakers address this growing pollution challenge.
Predicting microplastic accumulation zones and shoreline changes along the Kelantan coast, Malaysia, using integrated GIS and ANN models
Researchers combined GIS with an artificial neural network to predict microplastic accumulation zones along Malaysia's Kelantan coast, achieving R=0.972 predictive accuracy and identifying shoreline erosion-prone areas as the primary deposition hotspots for microplastic pollution.
Litter assessment on sandy beaches along the Brazilian coast: a large-scale analysis of macrolitter and microplastics
Researchers conducted a large-scale assessment of macrolitter and microplastic contamination on sandy beaches along the Brazilian coast, characterizing pollution patterns, dominant polymer types, and potential anthropogenic sources across multiple sites.
Application of Remote Sensing for the Detection and Monitoring of Microplastics in the Coastal Zone of the Colombian Caribbean
Researchers explored using remote sensing technology, including Sentinel-2 satellite imagery and machine learning algorithms, to detect and monitor microplastic pollution along the Colombian Caribbean coast. The study found that combining multispectral satellite data with computational models shows promise for systematic, large-scale monitoring of coastal microplastic contamination in regions where ground-level surveillance remains limited.
Microplastics on Santos Beach: Sources of Pollution, Waste Characteristics and Possible Collection Solutions
Researchers characterized microplastics collected from three zones of Santos beach in Brazil, finding contamination dominated by fragments and films near sewage outfalls. The study highlights inadequate waste management as the primary driver of beach microplastic accumulation and assessed feasibility of mechanical collection interventions.
Predicting the Dispersal and Accumulation of Microplastic Pellets Within the Estuarine and Coastal Waters of South-Eastern Brazil Using Integrated Rainfall Data and Lagrangian Particle Tracking Models
This study used particle tracking models combined with rainfall data to predict how plastic pellets and microplastics move and accumulate in estuarine and coastal waters of southeastern Brazil after entering from industrial and river sources. The modeling approach revealed that storm events pulse high concentrations of microplastics into coastal areas, creating temporary hotspots of contamination.
Beach morphodynamics and its relationship with the deposition of plastic particles: A preliminary study in southeastern Brazil
Researchers found that beach morphodynamic characteristics influence the deposition of plastic particles on beaches in São Paulo, Brazil, with 745 particles recovered — mostly styrofoam — and accumulation patterns correlating with beach profile dynamics.