• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.447-460
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• 2022

It is well known that satellite synthetic aperture radar interferometry (InSAR) has been widely used for the observation of surface displacement owing to earthquakes, volcanoes, and subsidence very precisely. In wetlands where vegetation exists on the surface of the water, it is possible to create a water level change map with high spatial resolution over a wide area using the InSAR technique. Currently, a number of imaging radar satellites are in operation, and most of them support a ScanSAR mode observation to gather information over a large area at once. The Cienaga Grande de Santa Marta (CGSM) wetland, located in northern Colombia, is a vast wetland developed along the Caribbean coast. The CGSM wetlands face serious environmental threats from human activities such as reclamation for agricultural uses and residential purposes as well as natural causes such as sea level rise owing to climate change. Various restoration and protection plans have been conducted to conserve these invaluable environments in recognition of the ecological importance of the CGSM wetlands. Monitoring of water level changes in wetland is very important resources to understand the hydrologic characteristics and the in-situ water level gauge stations are usually utilized to measure the water level. Although it can provide very good temporal resolution of water level information, it is limited to fully understand flow pattern owing to its very coarse spatial resolution. In this study, we evaluate the L-band ALOS-2 PALSAR-2 ScanSAR mode to observe the water level change over the wide wetland area using the radar interferometric technique. In order to assess the quality of the interferometric product in the aspect of spatial resolution and coherence, we also utilized ALOS-2 PALSAR-2 stripmap high-resolution mode observations.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.247-253
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• 2023

As climate change problem has recently become serious, studies are being conducted to identify carbon dioxide (CO₂) emission dynamics based on satellite data to reduce emissions. It is also very important to analyze spatial patterns by estimating and mapping CO₂ emissions dynamic. Therefore, in this study, CO₂ emissions in the Korean Peninsula from 2013 to 2020 were estimated and mapped. To spatially estimate and map emissions, we use the enhanced vegetation index adjusted nighttime light index, an index that combines nighttime light (NTL) and vegetation index, to map both areas where NTL is observed and areas where NTL is not observed. In order to spatially estimate and map CO₂ emissions, the total annual emissions of the Korean Peninsula were calculated, resulting in an increase of 11% from 2013 to 2017 and a decrease of 13% from 2017 to 2020. As a result of the mapping, it was confirmed that the spatial pattern of CO₂ emissions in the Korean Peninsula were concentrated in urban areas. After being divided into 17 regions, which included the downtown area, the metropolitan area accounted for roughly 40% of CO₂ emissions in the Korean Peninsula. The region that exhibited the most significant change from 2013 to 2020 was Sejong City, showing a 96% increase.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.19-31
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• 2024

Ports are vital social infrastructures that significantly influence both people's lives and a country's economy. In South Korea, the aging of port infrastructure combined with the increased frequency of various natural disasters underscores the necessity of displacement monitoring for safety management of the port. In this study, the time-series displacements of Yeongilman Port and surrounding areas in Pohang, South Korea, were measured by applying Permanent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) to Sentinel-1 SAR images collected from the satellite's ascending (February 2017-July 2023) and descending (February 2017-December 2021) nodes, and the displacement associated with the 2017 Pohang earthquake in the port was analyzed. The southern (except the southernmost) and central parts of Yeongilman Port showed large displacements attributed to construction activities for about 10 months at the beginning of the observation period, and the coseismic displacement caused by the Pohang earthquake was up to 1.6 cm of the westward horizontal motion and 0.5 cm of subsidence. However, little coseismic displacement was observed in the southernmost part of the port, where reclamation was completed last, and in the northern part of the oldest port. This represents that the weaker the consolidation of the reclaimed soil in the port, the more vulnerable it is to earthquakes, and that if the soil is very weakly consolidated due to ongoing reclamation, it would not be significantly affected by earthquakes. Summer subsidence and winter uplift of about 1 cm have been repeatedly observed every year in the entire area of Yeongilman Port, which is attributed to volume changes in the reclaimed soil due to temperature changes. The ground of the 1st and 2nd General Industrial Complexes adjacent to Yeongilman Port subsided during the observation period, and the rate of subsidence was faster in the 1st Industrial Complex. The 1st Industrial Complex was observed to have a westward horizontal displacement of 3 mm and a subsidence of 6 mm as the coseismic displacement of the Pohang earthquake, while the 2nd Industrial Complex was analyzed to have been little affected by the earthquake. The results of this study allowed us to identify the time-series displacement characteristics of Yeongilman Port and understand the impact of earthquakes on the stability of a port built by coastal reclamation.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1341-1352
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• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.

• 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 Korean Society of Forest Science
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• v.88 no.3
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• pp.352-363
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• 1999

It has been rare to obtain reliable information related to the size of forest land in North Korea. Several sources of forest statistics, ranging from the first map of forest distribution in Korean Peninsula produced in 1910 to official data reported by the North Korea Government in 1997, were gathered and analyzed to define the characteristics of forest cover changes over years. In addition, Landsat satellite data obtained from 1973 to 1993 were processed for the two study areas of the provinces of Pyungyang and Heasan, where the topography and land use pattern are significantly different each other. Using three sets of multitemporal Landsat imagery, land cover ma-ps were produced by computer classification. Although forest statistics reported before 1990 are somewhat inconsistent, they mere gradually decreasing over years. The estimates of 1991 satellite data and the recent statistics reported in 1998 shows very steep decline in forest lands as compared to the ones before 1990. The abrupt decrease of forest lands after 1990 was also found on the detailed analysis of Landsat data for the two study areas of Pyungyang and Heasan. The rapid decline of forest lands may have something to do with the poor economic situation of the country and the continuing natural disasters of severe flooding and drought. Unstocked forest, which was not classified into forest land, was a very distinct and pervasive land cover type that can be easily observed on satellite imagery. Since unstocked forest land in North Korea may be a critical factor for degrading environmental quality as well as for the continuing natural disasters, further analysis is necessary to define the exact extent and the physical characteristics of the cover type.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2002.04a
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• pp.3-10
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• 2002

요즘 측량분야에서 Geomatics 또는 Geoinformatics라는 새로운 용어가 대두되고 있다. 해외의 유수 기관 및 대학교에서도 측량이라는 학과명 또는 기관명을 Geomatics라는 용어로 교체하고 있으며, 이는 측량분야의 다양화와 학제간 연구수행의 필요성을 반영하는 것이다. 최근 45로 일컬어지는 GPS, 원격탐사(RS), GIS, ITS의 경우 각기 독자적인 영역을 구축하기보다는 상호 보완적인 역할을 하며 통합되어 가는 추세이다. 1950년대에 기본적인 위성관측기술 및 계산 기술의 개발을 시작으로 1980년대에 이르러서는 위성 기술은 측지학 및 측량 분야에 적용되기 시작했다. 그 대표적인 것이 GPS(Global Positioning System)로 기존의 천체측정학 방식을 대체하는 유용한 위치결정 수단으로 사용되기 시작했다. DNSS로부터 시작된 GPS는 측지 측량분야, 지구물리학분야, 항법 및 교통, GIS Mapping, 기상 및 해양, 재난 및 레저, 인공위성 궤도결정 등 다양한 분야에 적용되었으며, 특히 2000년 5월 1일 SA가 해제되면서 그 활용도는 더욱 증가하고 있다. 위성원격탐사의 경우 초창기에 중ㆍ저해상도의 다중분광영상에서 시작하여 그 해상도가 꾸준히 향상되어 오늘날 0.61 미터급 고해상도 위성인 QuickBird 위성이 발사되어 운용 중에 있다. 위성영상의 공간해상도, 분광해상도, 방사해상도는 향후 계속 향상될 것이며 이에 따른 방대한 데이터의 처리 문제 및 하드웨어/소프트웨어 지원에 대한 연구가 수반되어야 할 것이다. GPS 및 원격탐사(RS)는 더 이상 독자적인 영역으로서가 아니라 Geomatics를 이루는 중요한 분야로서 타 시스템과의 보완적인 관계로서 통합되어 나갈 것이다. 이를 위해서는 공간정보에 대한 표준화 및 데이터 처리, 통합, 시스템 구축을 위한 기술적 연구가 계속 진행되어야 할 것이다.분 공부상면적보다 늘어났다. 2. 좌표의 이동량이 일률적이지 못하므로 기초점에 대한 문제라고 생각되며, 따라서 도해지적을 수치지적으로 전환함에 있어서 가장 우선되어야 할 사항이 기초점 정비라 하겠다.승이 뚜렷하였다. 따라서 비파 착즙액 첨가 기능성 yoghurt 제조시 비파 착즙액을 10% 첨가하여 혼합 starter로 Str. thermophilus와 Lac. acidophilus 혼합균주를 사용하는 것이 이들 유산균의 증식에 가장 적합한 것으로 나타났다.타났다..297, 0.293, 0.205에서 50일 경과 후 0.612, 0.472, 0.898로 비살균에서 높은 값을 보였다. 따라서 비살균의 경우 저장말기에 TBA값이 높아지는 경향을 보였다. 5. L값은 살균처리의 경우 저장 30일 이후 약간 어두워지는 경향을 보였고, 121$^{\circ}C$ 살균처리에서 높은 값을 보였다. 대체로 저장온도가 높고 저장기간이 길어질수록 약간 밝아지는 경향을 보였다. 적색도는 인삼 첨가구의 경우 상온 및 냉장저장에서 10$0^{\circ}C$ 살균이 121$^{\circ}C$ 처리구 보다 약간 높은 값을 보였다. 저장기간에 따른 적색도의 변화는 인삼과 송이 첨가구에서 비교적 안정적이었다. 황색도는 상온 및 냉장저장에서 저장기간에 따라 약간 감소하는 경향을 보인 후 상온저장 50일 째, 냉장 60일 째 가장 높게 나타났다. 121$^{\circ}C$ 살균처리구가 10$0^{\circ}C$ 처리구보다 약간 높은 경향을 보였다.^{\circ}C$$,에서는 20시간 가열시 0.706$\mu\textrm{g}$/kg으로 가장 높게 생성된 후 서서히 감소하였다. 그러나 산값과 공액이중산값은 계속 증가하는 양상을 나타냈다. 즉 B(a)

• Korean Journal of Environment and Ecology
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• v.19 no.4
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• pp.375-384
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• 2005

This study was conducted selecting 44 places with a block unit subject to urban area in Gangnam-gu, to analyze a temperature change according to land use and green structure. In this study, it was used the broad-wide urban temperature, supported by Landset TM and ETM+ satellite image 6scene(1999${\~}$2002). The result of the research, the land use pattern has slightly influence on a temperature change of urban area. The result from correlation analysis between temperature and the factors affected by land cover type, such as building-to-land ratio(A correlation coefficient is 0.368${\~}$0.709) have positive correlation and green area ratio(a correlation coefficient is -0.551${\~}$-0.860) have negative correlation. The result from correlation analysis between temperature and green capacity of the land, crown projection area ratio, each factor have negative correlation with temperature, as showing that a correlation coefficient of green capacity of the land is -0.577(June 2006)${\~}$-0.882(June 1999) and crown projection area ratio's is -0.549(June 2001)${\~}$-0.817(June 1999). The result of the regression analysis for establishing urban area temperature change prediction model showed that green capacity of the land of the explanation variable was accepted.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.1
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• pp.84-93
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• 2011

Levee line mapping is critical to the protection of environments in river zones, the prevention of river flood and the development of river zones. Use of the remote sensing data such as LiDAR and aerial orthoimage is efficient for river mapping due to their accessibility and higher accuracy in horizontal and vertical direction. Airborne laser scanning (LiDAR) has been used for river zone mapping due to its ability to penetrate shallow water and its high vertical accuracy. Use of image source is also efficient for extraction of features by analysis of its image source. Therefore, aerial orthoimage also have been used for river zone mapping tasks due to its image source and its higher accuracy in horizontal direction. Due to these advantages, in this paper, research on three dimensional levee line mapping is implemented using LiDAR and aerial orthoimage separately. Accuracy measurement is implemented for both extracted lines generated by each data using the ground truths and statistical comparison is implemented between two measurement results. Statistical results show that the generated 3D levee line using LiDAR data has higher accuracy than the generated 3D levee line using aerial orthoimage in horizontal direction and vertical direction.