• Korean Journal of Remote Sensing
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• v.34 no.6_3
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• pp.1369-1382
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• 2018

As the number of multispectral satellites increases, it is expected that it will be possible to acquire and use images for periodically. However, there is a problem of data discrepancy due to different overpass time, period and spatial resolution. In particular, the difference in band bandwidths became different reflectance even for images taken at the same time and affect uncertainty in the analysis of vegetation activity such as vegetation index. The purpose of this study is to estimate the band adjustment factor according to the difference of bandwidth with other multispectral satellites for the application of KOMPSAT-3 satellite in agriculture field. The Spectral band adjustment factor (SBAF) were calculated using the hyperspectral satellite images acquired in the desert area. As a result of applying SBAF to the main crop area, the vegetation index showed a high agreement rate of relative percentage difference within 3% except for the Hapcheon area where the zenith angle was 25. For the estimation of SBAF, this study used only one set of images, which did not consider season and solar zenith angle of SBAF variation. Therefore, long-term analysis is necessary to solve SBAF uncertainty in the future.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.203-215
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• 2019

As hyperspectral sensors that can be mounted on drones are developed, it is possible to acquire hyperspectral imagery with high spatial and spectral resolution. Although the importance of atmospheric correction has been reduced since imagery of drones were acquired at a low altitude,studies on the conversion process from raw data to spectral reflectance should be done for studies such as estimating the concentration of surface materials using hyperspectral imagery. In this study, a vicarious radiometric calibration and an atmospheric correction algorithm based on atmospheric radiation transfer model were applied to hyperspectral data of drone and the results were compared and analyzed. The vicarious calibration method was applied to an empirical line calibration using the spectral reflectance of a tarp made of uniform material. The atmospheric correction algorithm used ATCOR-4 based Modran-5 that was widely used for the atmospheric correction of aerial hyperspectral imagery. As a result of analyzing the RMSE of the difference between the reference reflectance and the correction, the vicarious calibration using the tarp in a single period of hyperspectral image was the most accurate, but the atmospheric correction was possible according to the application purpose of using hyperspectral imagery. If the correction process of normalized spectral reflectance is carried out through the additional vicarious calibration for imagery from multiple periods in the future, accurate analysis using hyperspectral drone imagery will be possible.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.1
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• pp.49-61
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• 2021

The repeated shipboard measurements that have been conducted by the National Institute of Fisheries Science (NIFS) for more than a half century, provide the valuable long-term hydrographic data with high spatial-temporal resolution. However, this unprecedent dataset has been rarely used for oceanic climate sciences because of its reliability issue. In this study, temporal variability of salinity error in the NIFS data was quantified by means of extremely small variability of salinity in the deep layer of the south-western East Sea, in order to contribute to studies on long-term variability of the East Sea. The NIFS salinity errors estimated on the isothermal surfaces of 1℃ have a remarkable temporal variation, such as ~0.160 g/kg in the year of 1961~1980, ~0.060 g/kg in 1981~1994,~0.020 g/kg in 1995~2002, and ~0.010 g/kg in 2003~2014 on average, which basically represent bias error. In the recent years, even though the quality of salinity has been improved, there still remain relatively large bias errors in salinity data presumably due to failure of salinity sensor managements, especially in 2011, 2013, and 2014. On the contrary, the salinity in the year of 2012 was very accurate and stable, whose error was estimated as about 0.001 g/kg comparable to the salinity sensor accuracy. Thus, as long as developing proper data quality control procedures and sensor management systems, I expect that the NIFS shipboard hydrographic data could have good enough quality to support various studies on ocean response to climate variabilities. Additionally, a few points to improve the current NIFS shipboard measurements were suggested in the discussion section.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.68-81
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• 2021

The agricultural ecosystem is one of the major sources of greenhouse gas (GHG) emissions. In order to search for climate change adaptation options which mitigate GHG emissions while maintaining crop yield, it is advantageous to integrate multiple models at a high spatial resolution. The objective of this study was to develop a tool to support integrated assessment of climate change impact b y coupling the DSSAT model and the DNDC model. DNDC Regional Input File Tool(DRIFT) was developed to prepare input data for the regional mode of DNDC model using input data and output data of the DSSAT model. In a case study, GHG emissions under the climate change conditions were simulated using the input data prepared b y the DRIFT. The time to prepare the input data was increased b y increasing the number of grid points. Most of the process took a relatively short time, while it took most of the time to convert the daily flood depth data of the DSSAT model to the flood period of the DNDC model. Still, processing a large amount of data would require a long time, which could be reduced by parallelizing some calculation processes. Expanding the DRIFT to other models would help reduce the time required to prepare input data for the models.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.424-433
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• 2021

Recent climate change has caused abnormal weather phenomena all over the world and a lot of damage in many fields of society. Particularly, a lot of recent damages were due to extreme precipitation, such as torrential downpour or drought. The objective of this study was to analyze the temporal and spatial changes in the precipitation pattern in South Korea. To achieve this objective, this study selected some of the precipitation indices suggested in previous studies to compare the temporal characteristics of precipitation induced by climate change. This study selected ten ASOS observatories of the Korea Meteorological Administration to understand the change over time for each location with considering regional distribution. This study also collected daily cumulative precipitation from 1951 to 2020 for each point. Additionally, this study generated high-resolution national daily precipitation distribution maps using an orographic precipitation model from 1981 to 2020 and analyzed them. Temporal analysis showed that although annual cumulative precipitation revealed an increasing trend from the past to the present. The number of precipitation days showed a decreasing trend at most observation points, but the number of torrential downpour days revealed an increasing trend. Spatially, the number of precipitation days and the number of torrential downpour days decreased in many areas over time, and this pattern was prominent in the central region. The precipitation pattern of South Korea can be summarized as the fewer precipitation days and larger daily precipitation over time.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.268-279
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• 2021

The geospatial schemes based on topo-climatology have been developed to produce digital climate maps at a site-specific scale. Their development processes are reviewed here to derive the needs for new schemes in the future. Agricultural and forestry villages in Korea are characterized by complexity and diversity in topography, which results in considerably large spatial variations in weather and climate over a small area. Hence, the data collected at a mesoscale through the Automated Synoptic Observing System (ASOS) operated by the Korea Meteorological Administration (KMA) are of limited use. The geospatial schemes have been developed to estimate climate conditions at a local scale, e.g., 30 m, lowering the barriers to deal with the processes associated with production in agricultural and forestry industries. Rapid enhancement of computing technologies allows for near real-time production of climate information at a high-resolution even in small catchment areas and the application to future climate change scenarios. Recent establishment of the early warning service for agricultural weather disasters can provide growth progress and disaster forecasts for cultivated crops on a farm basis. The early warning system is being expanded worldwide, requiring further advancement in geospatial schemes and digital climate mapping.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.991-1005
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• 2022

Satellite imaging is an effective supplementary data source for detecting and verifying nuclear activity. It is also highly beneficial in regions with limited access and information, such as nuclear installations. Time series analysis, in particular, can identify the process of preparing for the conduction of a nuclear experiment, such as relocating equipment or changing facilities. Differences in the semantic segmentation findings of time series photos were employed in this work to detect changes in meaningful items connected to nuclear activity. Building, road, and small object datasets made of KOMPSAT 3/3A photos given by AIHub were used to train deep learning models such as U-Net, PSPNet, and Attention U-Net. To pick relevant models for targets, many model parameters were adjusted. The final change detection was carried out by including object information into the first change detection, which was obtained as the difference in semantic segmentation findings. The experiment findings demonstrated that the suggested approach could effectively identify altered pixels. Although the suggested approach is dependent on the accuracy of semantic segmentation findings, it is envisaged that as the dataset for the region of interest grows in the future, so will the relevant scope of the proposed method.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.535-543
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• 2022

A drone is used to diagnose crop growth and to provide information through images in the agriculture field. In the case of using high spatial resolution drone images, growth information for each object can be produced. However, accurate object detection is required and adjacent objects should be efficiently classified. The purpose of this study is to develop a Chinese cabbage object detection algorithm using multispectral reflectance images observed from drone and computer vision techniques. Drone images were captured between 7 and 15 days after planting a Chinese cabbage from 2018 to 2020 years. The thresholds of object detection algorithm were set based on 2019 year, and the algorithm was evaluated based on images in 2018 and 2019 years. The vegetation area was classified using the characteristics of spectral reflectance. Then, morphology techniques such as dilatation, erosion, and image segmentation by considering the size of the object were applied to improve the object detection accuracy in the vegetation area. The precision of the developed object detection algorithm was over 95.19%, and the recall and accuracy were over 95.4% and 93.68%, respectively. The F1-Score of the algorithm was over 0.967 for 2 years. The location information about the center of the Chinese cabbage object extracted using the developed algorithm will be used as data to provide decision-making information during the growing season of crops.

• Journal of Space Technology and Applications
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• v.2 no.2
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• pp.104-120
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• 2022

The Small Scale magNetospheric and Ionospheric Plasma Experiment (SNIPE)'s scientific goal is to observe spatial and temporal variations of the micro-scale plasma structures on the topside ionosphere. The four 6U CubeSats (~10 kg) will be launched into a polar orbit at ~500 km. The distances of each satellite will be controlled from 10 km to more than ~1,000 km by the formation flying algorithm. The SNIPE mission is equipped with identical scientific instruments, Solid-State Telescopes(SST), Magnetometers(Mag), and Langmuir Probes(LP). All the payloads have a high temporal resolution (sampling rates of about 10 Hz). Iridium communication modules provide an opportunity to upload emergency commands to change operational modes when geomagnetic storms occur. SNIPE's observations of the dimensions, occurrence rates, amplitudes, and spatiotemporal evolution of polar cap patches, field-aligned currents (FAC), radiation belt microbursts, and equatorial and mid-latitude plasma blobs and bubbles will determine their significance to the solar wind-magnetosphere-ionosphere interaction and quantify their impact on space weather. The formation flying CubeSat constellation, the SNIPE mission, will be launched by Soyuz-2 at Baikonur Cosmodrome in 2023.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3D
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• pp.445-450
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• 2008

Recently, environmental and ecological approaches is in progress in urban stream, especially the guarantee of instream flow becomes very important. In this paper, it is suggested that water volume estimation method utilizing the topographical shape data obtained by field investigation and satellite image to manage the urban stream efficiently. The data obtained at Gap River is the study area are analysed and those results are as belows. First, surveying to investigate topographic shape characteristics of urban stream is carried out. In details, the gradient characteristics from water surface to bottom in case of sand area and in case of grass area are 0.013 and 0.065 respectively. In conclusion, the gradient characteristic of grass area is five times bigger than that of sand area. Besides, IKONOS image is classified by spectrum analysis and Minimum Distance Method and the sand area extraction method by the generalization method as Median filter is suggested to calculate water volume. Finally, mapping process on the sand area extracted from the topographical shape field data in river and satellite images is carried out by the GIS spatial analysis. And on the assumption that the water level was 1m at that time when satellite image was taken, the water volume was $225,258m^3$. It is clarified that the effect of water volume improvement was about 10.5% in comparison with water volume that had no consideration on the gradient characteristics of sand-bar.