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Papers
20 resultsShowing papers similar to Estimating Forest Aboveground Carbon Storage in Hang-Jia-Hu Using Landsat TM/OLI Data and Random Forest Model
ClearContinuous 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.
Soil Organic Carbon Estimation via Remote Sensing and Machine Learning Techniques: Global Topic Modeling and Research Trend Exploration
Researchers used advanced topic modeling and bibliometric analysis to map global research trends in estimating soil organic carbon using remote sensing and machine learning. They identified key research clusters including satellite imagery analysis, deep learning methods, and regional carbon mapping efforts. The study provides a roadmap for future research priorities in monitoring soil carbon stocks, which is critical for understanding climate change.
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.
Analysis of Potential Supply of Ecosystem Services in Forest Remnants through Neural Networks
Researchers applied an artificial neural network to geospatial indicators to assess the potential supply of regulating ecosystem services from forest remnants in Campinas, Brazil. The study analyzed landscape configuration factors and evaluated how both the supply of and societal demand for ecosystem services influence the actual benefits provided by fragmented forest patches.
Extraction the Spatial Distribution of Mangroves in the Same Month Based on Images Reconstructed with the FSDAF Model
Researchers applied the FSDAF spatiotemporal fusion model to reconstruct cloud-free satellite images for the same target month, enabling accurate extraction of mangrove spatial distributions in coastal wetlands despite the persistent cloud cover that limits image availability in mangrove-growing regions. The approach demonstrated improved accuracy in mapping mangrove extent compared to methods relying on mosaicked images spanning several months.
Analysis of Land Use Evolution of Suzhou Wetlands Based on RS and GIS
Researchers used satellite remote sensing and GIS to track changes in land use and wetland coverage in Suzhou, China over time. Understanding how wetland ecosystems change is important for assessing their capacity to filter pollutants, including microplastics carried by stormwater and runoff.
Water Quality Grade Identification for Lakes in Middle Reaches of Yangtze River Using Landsat-8 Data with Deep Neural Networks (DNN) Model
Researchers developed a deep neural network model applied to Landsat-8 satellite data to automatically identify water quality grades for lakes in the middle Yangtze River reaches, demonstrating that machine learning and remote sensing can provide cost-effective large-scale monitoring as an alternative to labor-intensive in situ measurements.
Sustainable Management of Land Resources: The Case of China’s Forestry Carbon Sink Mechanism
Researchers used a difference-in-differences model on 30 Chinese provincial-level regions from 2005 to 2020 to evaluate which forestry carbon sink mechanism (FCSM) policies most effectively generated socioeconomic value, finding that China's forest carbon policies have underutilized potential for meeting carbon neutrality goals.
Evaluation of the Spatiotemporal Change of Ecological Quality under the Context of Urban Expansion—A Case Study of Typical Urban Agglomerations in China
Researchers tracked changes in ecological quality across three major urban areas in China over two decades of rapid urbanization. They found that urban expansion significantly reduced ecological quality in surrounding areas, with the most severe impacts occurring in newly developed zones. The study provides a framework for monitoring how urbanization affects local ecosystems using remote sensing data.
Large Scale Agricultural Plastic Mulch Detecting and Monitoring with Multi-Source Remote Sensing Data: A Case Study in Xinjiang, China
Satellite imagery was used to monitor plastic mulch film coverage across large agricultural areas in China, mapping both spatial extent and temporal changes. Accurately tracking plastic mulch use is important because agricultural film residues are a major source of microplastic contamination in farmland soils.
Soil Nitrogen Content Detection Based on Near-Infrared Spectroscopy
Researchers developed a near-infrared spectroscopy method combined with random forest regression to rapidly measure soil nitrogen content from 143 soil samples collected near a river in Hubei, China. The model achieved high accuracy and provided a faster, non-destructive alternative to conventional soil nitrogen analysis.
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.
Estimation of Pb and Cd Content in Soil Using Sentinel-2A Multispectral Images Based on Ensemble Learning
This paper is not relevant to microplastics research — it develops machine learning models using Sentinel-2 satellite imagery to estimate lead and cadmium concentrations in soil near a mining area in China.
Machine learning-assisted assessment of key meteorological and crop factors affecting historical mulch pollution in China
Researchers used machine learning models (Elastic Net and Random Forest) to assess how meteorological and crop factors influenced plastic mulch contamination levels across China from 1993 to 2012, estimating mulch-derived microplastics and phthalic acid esters during the rapid expansion period of mulch use. The study identified key drivers of mulch pollution to inform more targeted management strategies for agricultural soils.
Remote sensing detection of plastic-mulched farmland using a temporal approach in machine learning: case study in tomato crops
Researchers tested machine learning classifiers on Sentinel-2 satellite time-series images to map plastic-mulched farmlands, achieving 99.7% accuracy using a multilayer perceptron model and demonstrating that a 3-image composite series reduces confusion with background vegetation — producing the first plastic mulch map for Latin America.
Response Time of Vegetation to Drought in Weihe River Basin, China
This is a hydrology study analyzing how vegetation in China's Weihe River Basin responds to drought using satellite vegetation indices; it is not a microplastics research paper.
Application of Remote Sensing Technology in Ecological Engineering—A Case Study of Phase I Tao River Water Diversion Project
Researchers used remote sensing satellite imagery to monitor ecological restoration and erosion control during a large Chinese water diversion project. While focused on construction impacts, remote sensing methods are also being developed to detect and map microplastic pollution from satellite data.
Analysis of the spatio-temporal evolution of sustainable land use in China under the carbon emission trading scheme: A measurement idea based on the DID model
Researchers applied a difference-in-differences estimation model to assess the effect of China's carbon emission trading scheme on sustainable land use across provinces from a spatio-temporal perspective. The scheme improved sustainable land use in pilot areas from both economic and environmental dimensions, with effects concentrated in eastern regions and urban agglomerations including the Pearl River Delta.
Assessment of Uncertainties in Ecological Risk Based on the Prediction of Land Use Change and Ecosystem Service Evolution
Using the PLUS land use change model, researchers simulated future land use scenarios in southern China and evaluated how projected changes would alter ecosystem services and associated ecological risk under uncertainty.
Improving the Accuracy of Random Forest Classifier for Identifying Burned Areas in the Tangier-Tetouan-Al Hoceima Region Using Google Earth Engine
This study used remote sensing and machine learning to detect burned areas from forest fires in Morocco's Tangier-Tetouan-Al Hoceima region using Google Earth Engine. Researchers found that combining multiple spectral indices with a random forest classifier improved the accuracy of identifying fire-damaged areas compared to using individual indices alone.