• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1691-1707
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• 2022

Cheonji Lake, the caldera of Baekdu Mountain, located on the border of the Korean Peninsula and China, alternates between melting and freezing seasonally. There is a magma chamber beneath Cheonji, and variations in the magma chamber cause volcanic antecedents such as changes in the temperature and water pressure of hot spring water. Consequently, there is an abnormal region in Cheonji where ice melts quicker than in other areas, freezes late even during the freezing period, and has a high-temperature water surface. The abnormal area is a discharge region for hot spring water, and its ice gradient may be used to monitor volcanic activity. However, due to geographical, political and spatial issues, periodic observation of abnormal regions of Cheonji is limited. In this study, the degree of ice change in the optimal region was quantified using a Landsat -5/-7/-8 optical satellite image and a Modified U-Net regression model. From January 22, 1985 to December 8, 2020, the Visible and Near Infrared (VNIR) band of 83 Landsat images including anomalous regions was utilized. Using the relative spectral reflectance of water and ice in the VNIR band, unique data were generated for quantitative ice variability monitoring. To preserve as much information as possible from the visible and near-infrared bands, ice gradient was noticed by applying it to U-Net with two encoders, achieving good prediction accuracy with a Root Mean Square Error (RMSE) of 140 and a correlation value of 0.9968. Since the ice change value can be seen with high precision from Landsat images using Modified U-Net in the future may be utilized as one of the methods to monitor Baekdu Mountain's volcanic activity, and a more specific volcano monitoring system can be built.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.933-948
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• 2023

Atmospheric aerosols not only have adverse effects on human health but also exert direct and indirect impacts on the climate system. Consequently, it is imperative to comprehend the characteristics and spatiotemporal distribution of aerosols. Numerous research endeavors have been undertaken to monitor aerosols, predominantly through the retrieval of aerosol optical depth (AOD) via satellite-based observations. Nonetheless, this approach primarily relies on a look-up table-based inversion algorithm, characterized by computationally intensive operations and associated uncertainties. In this study, a novel high-resolution AOD direct retrieval algorithm, leveraging machine learning, was developed using top-of-atmosphere reflectance data derived from the Geostationary Ocean Color Imager-II (GOCI-II), in conjunction with their differences from the past 30-day minimum reflectance, and meteorological variables from numerical models. The Light Gradient Boosting Machine (LGBM) technique was harnessed, and the resultant estimates underwent rigorous validation encompassing random, temporal, and spatial N-fold cross-validation (CV) using ground-based observation data from Aerosol Robotic Network (AERONET) AOD. The three CV results consistently demonstrated robust performance, yielding R²=0.70-0.80, RMSE=0.08-0.09, and within the expected error (EE) of 75.2-85.1%. The Shapley Additive exPlanations(SHAP) analysis confirmed the substantial influence of reflectance-related variables on AOD estimation. A comprehensive examination of the spatiotemporal distribution of AOD in Seoul and Ulsan revealed that the developed LGBM model yielded results that are in close concordance with AERONET AOD over time, thereby confirming its suitability for AOD retrieval at high spatiotemporal resolution (i.e., hourly, 250 m). Furthermore, upon comparing data coverage, it was ascertained that the LGBM model enhanced data retrieval frequency by approximately 8.8% in comparison to the GOCI-II L2 AOD products, ameliorating issues associated with excessive masking over very illuminated surfaces that are often encountered in physics-based AOD retrieval processes.

• Journal of the Korean earth science society
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• v.30 no.2
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• pp.196-209
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• 2009

Occurrences of sandstorms in the deserts and loess of Mongolia and northern China and associated dustfall episodes in the Korean Peninsula were monitored during the period January through December, 2005. False colour images were made by directly receiving the NOAA Advanced Very High Resolution Radiometer (AVHRR) data, and the distribution and transport of sandstorms were analyzed. The ground concentrations for PM10, PM2.5 and visibility of the dustfall episodes (PM10 concentration over $190{\mu}g\;m^{-3}$) were analyzed at Cheongwon, located midway in South Korea, and in the leeward direction of the place of origin of the sandstorms. Variations in the concentrations of $O_3,\;NO_2$, CO and $SO_2$ were also compared with dust concentrations in the dustfall episodes. Fewer occurrences of strong sandstorms in the place of origin were observed in 2005, due largely to the accumulation of snow and mild fluctuations of high and low pressure systems in the place of origin, thereby accounting for a low frequency of dustfall episodes in Korea, compared with those during the period 1997-2005. A total of 7 dustfall episodes were monitored in Korea in 2005 that lasted 11 days. In summer, sandstorms occurred less frequently in the source region in 2005 due to high humidity and milder winds, thereby causing no dustfall episodes in Korea. In case the sandstorms occurring at the place of source head directly to Korea without passing through large cities and industrial areas of China, the PM2.5 concentrations were measured at 20% or lower than the PM10 concentrations. However, when the sandstorms headed to Korea via the industrial areas of eastern China, where they pick up anthropogenic air pollutants, the PM2.5 concentrations were at least 25% higher of the PM10 concentrations. On the other hand, over 5 cases were observed and analyzed in 2005 where the PM10 concentrations of sand dust originating from the deserts were measured at $190{\mu}g\;m^{-3}$ or lower, falling short of the level of a dustfall episode.