• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.835-850
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• 2022

Microplastics have arisen as a worldwide environmental concern, becoming ubiquitous in all marine compartments, and various researches on monitoring marine microplastics are being actively conducted worldwide. Recently, application of a remote detection technology that enables large-scale real-time observation to marine plastic monitoring has been conducted overseas. However, in South Korea, there is little information linking remote detection to marine microplastics and some field studies have demonstrated remote detection of medium- and large-sized marine plastics. This study introduces research cases with remote detection of marine plastics in South Korea and overseas, investigates potential feasibility of using the remote detection technology to marine microplastic monitoring, and suggests some future works to monitor marine microplastics with the remote detection.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2020.10a
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• pp.47-47
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• 2020

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2020.10a
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• pp.89-89
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• 2020

• Water for future
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• v.54 no.11
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• pp.34-46
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• 2021

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.859-873
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• 2023

Wildfires cause a lot of environmental and economic damage to the Earth over time. Various experiments have examined the harmful effects of wildfires. Also, studies for detecting wildfires and pollutant emissions using satellite remote sensing have been conducted for many years. The wildfire product for the Geostationary Environmental Monitoring Spectrometer (GEMS), Korea's first environmental satellite sensor, has not been provided yet. In this study, a false-color composite for better expression of wildfire smoke was created from GEMS and used in a U-Net model for wildfire detection. Then, a classification model was constructed to distinguish yellow dust from the wildfire smoke candidate pixels. The proposed method can contribute to disaster monitoring using GEMS images.

• Water for future
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• v.52 no.10
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• pp.37-44
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• 2019

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2018.06a
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• pp.88-88
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• 2018

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1209-1216
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• 2020

In this study, we propose a method for detecting coastal surface wastes using an UAV(Unmanned Aerial Vehicle) remote sensing method and an object detection algorithm based on deep learning. An object detection algorithm based on deep neural networks was proposed to detect coastal debris in aerial images. A deep neural network model was trained with image datasets of three classes: PET, Styrofoam, and plastics. And the detection accuracy of each class was compared with Darknet-53. Through this, it was possible to monitor the wastes landing on the shore by type through unmanned aerial vehicles. In the future, if the method proposed in this study is applied, a complete enumeration of the whole beach will be possible. It is believed that it can contribute to increase the efficiency of the marine environment monitoring field.

• Atmosphere
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• v.31 no.1
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• pp.113-141
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• 2021

The impacts of ice clouds on the energy budget of the Earth and their representation in climate models have been identified as important and unsolved problems. Ice clouds consist almost exclusively of non-spherical ice crystals with various shapes and sizes. To determine the influences of ice clouds on solar and infrared radiation as required for remote sensing retrievals and numerical models, knowledge of scattering and microphysical properties of ice crystals is required. A conventional method for representing the radiative properties of ice clouds in satellite retrieval algorithms and numerical models is to combine measured microphysical properties of ice crystals from field campaigns and pre-calculated single-scattering libraries of different shapes and sizes of ice crystals, which depend heavily on microphysical and scattering properties of ice crystals. However, large discrepancies between theoretical calculations and observations of the radiative properties of ice clouds have been reported. Electron microscopy images of ice crystals grown in laboratories and captured by balloons show varying degrees of complex morphologies in sub-micron (e.g., surface roughness) and super-micron (e.g., inhomogeneous internal and external structures) scales that may cause these discrepancies. In this study, the current idealized models representing morphologies of ice crystals and the corresponding numerical methods (e.g., geometric optics, discrete dipole approximation, T-matrix, etc.) to calculate the single-scattering properties of ice crystals are reviewed. Current problems and difficulties in the calculations of the single-scattering properties of atmospheric ice crystals are addressed in terms of cloud microphysics. Future directions to develop physically consistent ice-crystal models are also discussed.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.811-818
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• 2022

Recently, the number of natural and environmental disasters is rapidly increasing due to extreme weather caused by climate change, and the scale of economic losses and damage to human life is increasing accordingly. In addition, with urbanization and industrialization, the characteristics and scale of extreme weather appearance are becoming more complex and large in different ways from the past, and need for remote sensing and artificial intelligence technology for responding and managing global environmental disasters. This special issue investigates environmental disaster observation and management research using remote sensing and artificial intelligence technology, and introduces the results of disaster-related studies such as drought, flood, air pollution, and marine pollution, etc. in South Korea performed by the i-SEED (School of Integrated Science for Sustainable Earth and Environmental Disaster at Pukyong National University). In this special issue, we expect that the results can contribute to the development of monitoring and management technologies that may prevent environmental disasters and reduce damage in advance.