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Papers
61,005 resultsShowing papers similar to Determine stormwater pond geometrics and hydraulics using remote sensing technologies: A comparison between airborne-LiDAR and UAV-photogrammetry field validation against RTK-GNSS
ClearRecent Issues and Challenges in the Study of Inland Waters
This paper reviews emerging challenges and methods in inland water monitoring, including use of UAVs for remote sensing, threats to freshwater biodiversity, and detection of contaminants such as microplastics and algal blooms.
Advancing hydrological monitoring using image-based techniques: challenges and opportunities
This paper is not about microplastics — it reviews image-based techniques (including remote sensing and computer vision) for hydrological monitoring of water bodies, discussing challenges and opportunities in measuring water flow, flood events, and water quality.
AqUavplant Dataset: A High-Resolution Aquatic Plant Classification and Segmentation Image Dataset Using UAV
Researchers created a high-resolution image dataset of 31 aquatic plant species in Bangladesh using drone photography, designed to help train machine learning models for automated plant mapping. The dataset includes detailed segmentation masks that can help identify individual species, track plant growth, and monitor the spread of invasive species. This tool could support conservation efforts by making it easier to monitor aquatic plant biodiversity across large areas.
UAV Approach for Detecting Plastic Marine Debris on the Beach: A Case Study in the Po River Delta (Italy)
UAV imaging was used to detect and map anthropogenic marine debris on beaches in the Po River Delta, Italy, testing different image processing strategies and demonstrating that centimeter-scale spatial resolution UAV surveys can efficiently locate macroplastics before they degrade into harder-to-remove microplastics.
AI-Prepared Autonomous Freshwater Monitoring and Sea Ground Detection by an Autonomous Surface Vehicle
Researchers developed an AI-guided autonomous surface vehicle capable of monitoring freshwater quality, mapping lake bathymetry, and detecting underwater objects, offering a new tool for intensive climate-change-driven water body surveillance.
Estimating Reed Bed Cover in Hungarian Fish Ponds Using NDVI-Based Remote Sensing Technique
Researchers demonstrated that NDVI-based remote sensing using freely available Sentinel-2 satellite imagery can accurately estimate reed bed cover in Hungarian fish ponds, providing a cost-effective tool for monitoring aquaculture pond ecosystems.
Designing Unmanned Aerial Survey Monitoring Program to Assess Floating Litter Contamination
Researchers tested drone-based aerial surveys with high-resolution cameras as a cost-effective method for monitoring floating litter contamination in coastal waters, comparing manual counting, automated detection, and modeling approaches to optimize survey design.
Mini Uav-based Litter Detection on River Banks
Researchers developed a drone-based litter detection system combining high-resolution mapping, deep learning object detection, and vision-based localization that locates riverbank litter with decimeter-level accuracy, enabling automated monitoring of plastic pollution in urban waterway areas.
Urban Water Quality Assessment Based on Remote Sensing Reflectance Optical Classification
Researchers developed an urban water quality assessment method combining remote sensing reflectance optical classification with traditional water quality grading principles, enabling spatially and temporally continuous monitoring of urban water bodies.
New Radiometric Approaches to Compute Underwater Irradiances: Potential Applications for High-Resolution and Citizen Science-Based Water Quality Monitoring Programs
This paper presents new radiometric methods for calculating underwater light attenuation, a measure used to assess water quality and clarity. Accurate water quality monitoring tools are important for tracking pollution levels, including the optical effects of microplastics suspended in water.
Use of Mobile Autonomous Systems for Pollution Control of Inland Water Bodies
Researchers examined the use of mobile autonomous aerial and floating systems for monitoring and controlling pollution in inland water bodies, including detection of illegally dumped construction and household waste that contributes to microplastic and groundwater contamination. The study analyzes existing practices and proposes improvements for using drones and autonomous surface vehicles to enable early detection of unregulated dumping with minimal resources.
Численное моделирование изменения рельефа дна водоема при наличии гравитационных волн
This study develops mathematical models to simulate changes in underwater terrain caused by wave processes, integrating remote sensing and survey data to account for incomplete environmental information. The resulting algorithms can help predict how riverbeds and lake floors change under varying climatic and geographic conditions.
UAV imaging and deep learning based method for predicting residual film in cotton field plough layer
Researchers developed a method combining UAV imaging with three deep learning frameworks (LinkNet, FCN, and DeepLabv3) to segment and predict residual plastic film content in the plough layer of cotton fields, offering a lower-cost and higher-efficiency alternative to traditional manual sampling for agricultural plastic pollution monitoring.
Monitoring Water Diversity and Water Quality with Remote Sensing and Traits
This study defines five characteristics of water diversity and quality that can be monitored using remote sensing technology, from local waterbodies to continental scales. Researchers demonstrate how satellite and aerial sensing methods can track changes in water traits, structure, and biological communities more efficiently than traditional in-person sampling. The approach is particularly relevant for detecting pollution impacts, including emerging contaminants, across large and dynamic aquatic ecosystems.
Citizen science approaches for water quality measurements
Researchers reviewed 72 studies that used citizen science — data collection by trained volunteers rather than professional scientists — to monitor surface water quality, evaluating the methods used and the strengths and weaknesses of each approach. The review highlights citizen science as a valuable complement to traditional water monitoring, particularly for expanding geographic and temporal coverage of data collection.
Object Detection of Macroplastic Waste Using Unmanned Aerial Vehicles in Urban Canal
Researchers developed and tested an unmanned aerial vehicle-based system for detecting macroplastic waste along riverbanks and beaches using object detection algorithms. The system achieved reliable detection performance and offers a scalable tool for large-area plastic litter surveys.
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.
UAV-Based Hyperspectral Ultraviolet-Visible Interpolated Reflectance Images for Remote Sensing of Leaf Area Index
This paper is not relevant to microplastics research; it investigates using UV-visible reflectance imaging from drones (UAVs) to estimate leaf area index for vegetation monitoring.
Conceptual Design of a Novel Autonomous Water Sampling Wing-in-Ground-Effect (WIGE) UAV and Trajectory Tracking Performance Optimization for Obstacle Avoidance
This paper is not relevant to microplastics research; it presents the conceptual design of a wing-in-ground-effect drone for autonomous water sampling, focusing on aerodynamics and trajectory optimization rather than microplastic detection or pollution.
Chlorophyll-a Detection Algorithms at Different Depths Using In Situ, Meteorological, and Remote Sensing Data in a Chilean Lake
Researchers used a combination of field measurements, weather data, and satellite imagery to estimate chlorophyll-a concentrations at different depths in a Chilean lake. They compared deep learning and statistical models and found all three approaches performed well for predicting algal levels in the freshwater ecosystem. The study advances water quality monitoring techniques that can help track environmental changes, including those potentially linked to pollution.
An innovative approach for microplastic sampling in all surface water bodies using an aquatic drone
Researchers adapted an aquatic drone to sample microplastics in surface water, finding it produced results comparable to the standard Manta net while offering better reproducibility and improved capture of smaller, lighter particles in both river and coastal environments.
Robust and Fair Undersea Target Detection with Automated Underwater Vehicles for Biodiversity Data Collection
Researchers developed a robust and fair underwater target detection system for automated underwater vehicles (AUVs) to support marine biodiversity data collection, addressing unique challenges of subsea imaging compared to above-ground remote sensing.
GNSS-R Observations of Marine Plastic Litter in a Water Flume: An Experimental Study
Researchers conducted controlled water flume experiments to test GNSS-Reflectometry for detecting marine plastic litter, finding that while reflected power changes were unreliable, statistical analysis of short-integration-time reflectivity measurements could detect large accumulations of wave-dampening debris like nets and bags.
The reliability of evaporation ponds as a final basin for industrial effluent: Demonstration of an environmental risk management methodology
Researchers developed a new environmental risk assessment method for industrial evaporation ponds, which are used to store wastewater from power plants and factories. Their analysis found that contaminants in these ponds become more concentrated over time due to evaporation, creating measurable ecological risks that industries need to manage more carefully.