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Papers
61,005 resultsShowing papers similar to Extraction the Spatial Distribution of Mangroves in the Same Month Based on Images Reconstructed with the FSDAF Model
ClearSpatiotemporal Analysis of the Impacts of Climate Change on UAE Mangroves
Researchers analyzed spatiotemporal changes in UAE mangrove ecosystems using remote sensing, finding that climate variables such as land surface temperature and salinity significantly influenced mangrove growth and distribution patterns over time.
Classification of Eddy Sea Surface Temperature Signatures Under Cloud Coverage
Researchers developed a deep learning approach to classify mesoscale oceanic eddy signatures in sea surface temperature satellite images, overcoming the challenges posed by complex eddy structures and cloud coverage that corrupts large fractions of imagery.
Distribution and Structure of China–ASEAN’s Intertidal Ecosystems: Insights from High-Precision, Satellite-Based Mapping
Researchers used multi-source satellite data to create high-precision maps of intertidal ecosystems across the China-ASEAN region, distinguishing between mangroves, salt marshes, and tidal flats. They developed an improved classification framework to address inconsistencies in previous mapping efforts. The study provides a valuable baseline for monitoring how climate change and human activities are affecting these ecologically important coastal zones.
Luasan dan Kerapatan ekosistem mangrove di Kecamatan Cilamaya Wetan, Kabupaten Karawang
This study mapped the area and density of mangrove ecosystems in Cilamaya Wetan District, West Java, Indonesia, using remote sensing and field surveys. The findings document mangrove coverage and health status in an area that has been developed as a tourism site. Baseline mangrove data is essential for conservation planning and sustainable coastal management.
Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model
Researchers used Landsat satellite imagery and machine learning to estimate forest carbon storage in a region of China over two decades. The study demonstrates remote sensing as a practical tool for tracking carbon stocks and the effects of land-use change.
Continuous Monitoring of Forests in Wetland Ecosystems with Remote Sensing and Probability Sampling
This paper is not about microplastics; it develops a remote-sensing statistical method for monitoring above-ground biomass in wetland forest areas to improve carbon accounting.
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.
Seasonal and annual tropical river pattern change detection using machine learning
Researchers applied machine learning to Sentinel-2 satellite imagery to detect seasonal and annual changes in tropical river channel patterns in a region with strongly seasonal rainfall, successfully classifying active channel landforms including water, bare sediment, and vegetated bars. The approach provides a scalable method for monitoring dynamic tropical river systems.
Spatio-Temporal Analysis of Oil Spill Impact and Recovery Pattern of Coastal Vegetation and Wetland Using Multispectral Satellite Landsat 8-OLI Imagery and Machine Learning Models
Researchers used Landsat 8 satellite imagery and machine learning to assess the spatial extent and recovery trajectory of oil spill damage to coastal vegetation and wetlands in Nigeria, demonstrating that remote sensing combined with AI models can track long-term ecosystem recovery.
Enhanced spatiotemporal mapping of urban wetland microplastics: An interpretable CNN-GRU approach using satellite imagery and limited samples
Researchers built an interpretable CNN-GRU deep learning model combining satellite remote sensing with limited in-situ measurements to map microplastic distribution in urban wetlands with enhanced spatiotemporal resolution, enabling more comprehensive monitoring with less field sampling.
Enhancing the Detection of Coastal Marine Debris in Very High-Resolution Satellite Imagery via Unsupervised Domain Adaptation
Researchers proposed a satellite-based marine debris detection model using unsupervised domain adaptation to overcome limitations of applying high-resolution trained models to lower-resolution imagery. The approach improves practical applicability for monitoring coastal debris distributions across diverse satellite data sources.
Automatic Detection and Identification of Floating Marine Debris Using Multispectral Satellite Imagery
Researchers developed a machine learning approach using Sentinel-2 satellite imagery and extreme gradient boosting to automatically detect and distinguish floating plastic debris from other marine materials like driftwood and seaweed.
Flux to Flow: a Clearer View of Earth’s Water Cycle Via Neural Networks and Satellite Data
This dissertation developed neural network methods to enhance the spatial resolution of satellite measurements of Earth's water cycle, enabling finer-scale monitoring of hydrological processes such as precipitation, evaporation, and runoff across diverse environments.
Exploring the Influencing Factors in Identifying Soil Texture Classes Using Multitemporal Landsat-8 and Sentinel-2 Data
This remote sensing study tested whether multitemporal Landsat and Sentinel satellite data could help map soil texture across large areas, finding that time-series imagery improved predictions compared to single-date observations.
Assessment of surface water dynamics through satellite mapping with Google Earth Engine and Sentinel-2 data in Manipur, India
Researchers used Google Earth Engine and Sentinel-2 satellite imagery to map seasonal surface water dynamics in Manipur, India, accurately tracking the extent and timing of water body changes across the region to support watershed planning.
Unveiling coastal ecosystem dependencies: multifactorial analysis of climate change influencing mangrove system in the United Arab Emirates over the period 1990–2020
This study used remote sensing and machine learning to analyze how climate factors have affected mangrove forests in the United Arab Emirates over 30 years. Researchers found that land surface temperature had the strongest relationship with mangrove growth, with optimal conditions between 30 and 35 degrees Celsius, while rising sea levels appeared to negatively affect mangrove biomass. The findings emphasize that multiple climate change factors interact in complex ways to influence the health of these vital coastal ecosystems.
The Utilization of Satellite Data and Machine Learning for Predicting the Inundation Height in the Majalaya Watershed
This paper is not about microplastics; it uses satellite rainfall data, HEC-RAS flood modeling, and artificial neural networks to predict flood inundation heights in the Majalaya Watershed of Indonesia.
Suitability of Mangrove Tourism Areas in Cilamaya Wetan District, Karawang Regency
This study used satellite imagery and field surveys to assess the suitability of mangrove forests in Cilamaya Wetan District, Indonesia for ecotourism development, evaluating tree density, water quality, and coastal accessibility to identify areas with highest potential for sustainable marine tourism.
Enhancing discharge estimation from SWOT satellite data in a tropical tidal river environment
Researchers developed a methodology to improve river discharge estimates from the SWOT satellite mission in tidally influenced river environments in southern Vietnam. They found that measurement errors from the satellite could be reduced by optimizing the size of river segments analyzed. While not directly related to microplastics, the study advances remote sensing tools that can help monitor coastal water dynamics relevant to understanding pollutant transport in estuarine systems.
Scient metrics analysis of mangrove studies from 1980 to 2020
This scientometric review analyzed 40 years of published research on mangrove ecosystems, tracking how scientific focus has shifted from basic ecology to conservation and human impact assessment. Mangroves are important coastal filters that trap plastic debris and microplastics before they reach the open ocean.
Spatial distribution of microplastics in the superficial sediment of a mangrove in Southeast Brazil: A comparison between fringe and basin
Microplastic distribution was compared between fringe and basin mangrove zones in southeastern Brazil, finding that basin forests trapped significantly more microplastics than fringe areas due to lower hydrodynamic energy and greater sediment retention.
Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review
This review discusses the application of artificial intelligence as a predictive modelling tool for analysing environmental pollution impacts on mangrove ecosystems, examining how AI can support law enforcement and conservation decision-making in the face of data scarcity and complex pollution interactions.
Extraction of Surface Water Extent: Automated Thresholding Approaches
This paper is not relevant to microplastics — it evaluates automated thresholding algorithms applied to satellite remote sensing data for mapping surface water extent and monitoring floods and droughts.
Innovative software for analysing satellite data and methane emissions using radiative transfer model
Researchers developed software integrating a radiative transfer model with satellite data analysis and GIS to improve methane emissions monitoring, demonstrating that accounting for atmospheric factors such as cloud cover and aerosols significantly reduces errors in methane concentration calculations.