• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.54-59
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• 1999

The KITSAT-3, launched on May 26th of 1999, is equiped with a high-resolution earth-watch sensor that has spectral bands similar to that of the SPOT. In this paper, the primary discussion is on Investigation of possible application of images acquired from this sensor The secondary discussion is on the comparison of the images with those of Landsat TM and SPOT.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.579-581
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• 2003

Recently, the environment of the Ariake Sea, Japan has changed drastically. In this study, the result of sea survey, synchronizing the passage time of the Landsat in August 2002 was collated with the satellite data to develop the evaluating equation for transparency and sea surface temperature (SST). By Applying these equations to 5 satellite images of the same season, the transparency and SST in summer of 1985, 1991, 1996 and 2000 is estimated. Consequently, the transparency had increased until 2000 and then decreased in 2002. The SST, on the other hand, shows no remarkable trend.

• Proceedings of the KSRS Conference
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• 2000.04a
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• pp.178-182
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• 2000

본 연구의 목적은 실험실에서 퇴적물의 다분광반사 특성과 물성을 측정하고 이를 위성 영상에 적용하여 영상에서 나타나는 퇴적물의 분광차이의 원인을 해석하고, 위성영상에서의 퇴적물의 분류 가능성을 연구하는데 있다. 연구에서는 Landsat TM위성 영상과 350~2500nm 파장대역에 대한 퇴적물 시료의 분광측정 자료를 사용하였으며, 기존의 조사 자료를 토대로 TM 영상에서 퇴적물을 분류한 후, 현장에서 시료를 채취하여 입도 분류를 실시하였다. 퇴적물의 입도와 함수비에 따른 분광특성변화를 검증하였으며, 입도와 함수비에 대한 회귀식을 구하여 이를 영상에 적용 분류하였다. 분석 결과 다분광자료 측정시 퇴적물입도에 따른 분광차이는 미약하였으며 이를 TM 자료로 재구성하였을 때는 분광특성을 구분할 수 없었다. 퇴적물의 분류는 TM Band 4, 5, 7을 이용한 회귀식을 적용할 때 비교적 정확하게 나타났으나, 영상에서 퇴적물의 분광 차이는 입도 크기가 직접적인 요인이 아니라 입도에 의한 함수비 및 유기물 함량의 차이에 기인한 것으로 해석된다.

• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.587-598
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• 2017

Volcanic ash is a fine particle smaller than 2 mm in diameters. It falls after the volcanic eruption and causes various damages to transportation, manufacturing industry and respiration of living things. Therefore diffusion information of volcanic ash is highly significant for preventing the damages from it. It is advantageous to utilize satellites for observing the widely diffusing volcanic ash. In this study volcanic ash diffusion information about two eruptions of Mt. Sakurajima were calculated using the geostationary satellite, Communication, Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) and polar-orbiting satellite, Landsat-8 Operational Land Imager (OLI) and the Thermal InfraRed Sensor (TIRS). The direction and velocity of volcanic ash diffusion were analyzed by extracting the volcanic ash pixels from COMS-MI images and the height was retrieved by adjusting the shadow method to Landsat-8 images. In comparison between the results of this study and those of Volcanic Ash Advisories center (VAAC), the volcanic ash tend to diffuse the same direction in both case. However, the diffusion velocity was about four times slower than VAAC information. Moreover, VAAC only provide an ash height while our study produced a variety of height information with respect to ash diffusion. The reason for different results is measured location. In case of VAAC, they produced approximate ash information around volcano crater to rapid response, while we conducted an analysis of the ash diffusion whole area using ash observed images. It is important to measure ash diffusion when large-scale eruption occurs around the Korean peninsula. In this study, it can be used to produce various ash information about the ash diffusion area using different characteristics satellite images.

• Journal of Korea Water Resources Association
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• v.34 no.6
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• pp.597-604
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• 2001

The objective of the paper was to estimate the changes in acreages of tidal flats after the seawall construction at the Asan Bay and the Chunsu Bay from topographic maps, hydrographic charts, and Landsat TM images. The tidal floats from topographic maps published in one year differ significantly from that in the other, which appears to be attributed to the tide levels at the time of photographing. The hydrographic charts showed that tidal flats increase at rates of 22.3 ha/yr at the Asan Bay and 56.6 ha/yr at the Chunsu Bay after the dike construction. Applying the ISODATA method of unsupervised classifications for the Landsat TM images, the tidal flats were identified, and the resulting acreages for each image estimated. The resulting tidal flats increased at the rates of 21.3 ha/yr at the Asan Bay and 47.3 ha/yr at the Chunsu Bay during twelve years after the dike construction. It was found that the rates of the annual increases from the two data are very close and the differences result from the coastal lines at the charts and the TM images.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.251-263
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• 2019

Sentinel-2 Multi Spectral Instrument(MSI) launched by the European Space Agency (ESA) offered high spatial resolution optical products, enhanced temporal revisit of five days, and 13 spectral bands in the visible, near infrared and shortwave infrared wavelengths similar to Landsat mission. Landsat satellite imagery has been applied to various previous studies, but Sentinel-2 optical satellite imagery has not been widely used. Currently, for global coverage, Sentinel-2 products are systematically processed and distributed to Level-1C (L1C) products which contain the Top-of-Atmosphere (TOA) reflectance. Furthermore, ESA plans a systematic global production of Level-2A(L2A) product including the atmospheric corrected Bottom-of-Atmosphere (BOA) reflectance considered the aerosol optical thickness and the water vapor content. Therefore, the Sentinel-2 L2A products are expected to enhance the reliability of image quality for overall coverage in the Sentinel-2 mission with enhanced spatial,spectral, and temporal resolution. The purpose of this work is a quantitative comparison Sentinel-2 L2A products and fully simulated image to evaluate the applicability of the Sentinel-2 dataset in cultivated land growing various kinds of crops in Korea. Reference image of Sentinel-2 L2A data was simulated by airborne hyperspectral data acquired from AISA Fenix sensor. The simulation imagery was compared with the reflectance of L1C TOA and that of L2A BOA data. The result of quantitative comparison shows that, for the atmospherically corrected L2A reflectance, the decrease in RMSE and the increase in correlation coefficient were found at the visible band and vegetation indices to be significant.

• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.931-946
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• 2017

The purpose of this study is to observe and analyze soil moisture conditions with high resolution and to evaluate its application feasibility to agriculture. For this purpose, we used three Landsat-8 OLI (Operational Land Imager)/TIRS (Thermal Infrared Sensor) optical and thermal infrared satellite images taken from May to June 2015, 2016, and 2017, including the rural areas of Jeollabuk-do, where 46% of agricultural areas are located. The soil moisture conditions at each date in the study area can be effectively obtained through the SPI (Standardized Precipitation Index)3 drought index, and each image has near normal, moderately wet, and moderately dry soil moisture conditions. The temperature vegetation dryness index (TVDI) was calculated to observe the soil moisture status from the Landsat-8 OLI/TIRS images with different soil moisture conditions and to compare and analyze the soil moisture conditions obtained from the SPI3 drought index. TVDI is estimated from the relationship between LST (Land Surface Temperature) and NDVI (Normalized Difference Vegetation Index) calculated from Landsat-8 OLI/TIRS satellite images. The maximum/minimum values of LST according to NDVI are extracted from the distribution of pixels in the feature space of LST-NDVI, and the Dry/Wet edges of LST according to NDVI can be determined by linear regression analysis. The TVDI value is obtained by calculating the ratio of the LST value between the two edges. We classified the relative soil moisture conditions from the TVDI values into five stages: very wet, wet, normal, dry, and very dry and compared to the soil moisture conditions obtained from SPI3. Due to the rice-planing season from May to June, 62% of the whole images were classified as wet and very wet due to paddy field areas which are the largest proportions in the image. Also, the pixels classified as normal were analyzed because of the influence of the field area in the image. The TVDI classification results for the whole image roughly corresponded to the SPI3 soil moisture condition, but they did not correspond to the subdivision results which are very dry, wet, and very wet. In addition, after extracting and classifying agricultural areas of paddy field and field, the paddy field area did not correspond to the SPI3 drought index in the very dry, normal and very wet classification results, and the field area did not correspond to the SPI3 drought index in the normal classification. This is considered to be a problem in Dry/Wet edge estimation due to outlier such as extremely dry bare soil and very wet paddy field area, water, cloud and mountain topography effects (shadow). However, in the agricultural area, especially the field area, in May to June, it was possible to effectively observe the soil moisture conditions as a subdivision. It is expected that the application of this method will be possible by observing the temporal and spatial changes of the soil moisture status in the agricultural area using the optical satellite with high spatial resolution and forecasting the agricultural production.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.1
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• pp.107-118
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• 2003

The purpose of this study is to assess the hydrological impact on a watershed from long-term land cover changes. Gyeongan-cheon watershed($558.2km^2$) was selected and WMS(watershed modeling system) HEC-1 model was adopted as an evaluation tool. To identify land cover changes, five Landsat images(1980/2/15, 1986/4/15, 1990/4/26, 1996/4/26, 2000/5/17) were selected and analyzed using maximum likelihood method. As a result, urban areas have increased by 5.6% and forest areas have decreased by 6.1% between 1980 and 2000. SCS curve number increased by 9.8. To determine model parameters and evaluate HEC-1 model, five storm events(1998/5/2, 1998/8/23, 1998/9/30, 1999/5/3, 2000/7/29) were used. The simulated stream flow agreed well with the observed one with relative errors ranging from 9% to 36%. For 254 mm daily rainfall of 30 years frequency, due to the increase of urban areas peak flow increased by $455m^3/sec$ and the time of peak flow reduced about four hours for 20 years land cover changes.

• Korean Journal of Remote Sensing
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• v.25 no.4
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• pp.339-357
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• 2009

The Saemangeum Reclamation Project was launched as a national project in 1991 to reclaim a large coastal area of 401 km$^2$ by constructing a 33-km long dyke. The final dyke enclosure in April 2006 has transformed the tidal flat into lake and land. The dyke construction has abruptly changed not only the estuarine tidal system inside the dyke, but also the coastal marine environment outside the dyke. In this study, we investigated the spatial change of SST distribution using the Landsat-5/7 and NOAA data before and after the dyke completion in the Saemangeum area. Satellite-induced SST was verified by compared with the various in situ measurements such as tower, buoy, and water sample. The correlation coefficient resulted in above 0.96 and RMSE was about 1$^{\circ}C$ in all data. 38 Landsat satellite images from 1985 to 2007 were analyzed to estimate the temporal and spatial change of SST distribution from the beginning to the completion of the Samangeum dyke's construction. The seasonal change in detailed spatial distribution of SST was measured, however, the estimation of change during the Saemangeum dyke's construction was hard to figure out owing to the various environmental conditions. Monthly averaged SST induced from NOAA data from 1998 to 2007 has been analyzed for a complement of Landsat's temporal resolution. At the inside of the dyke, the change of SST from summer to winter was large due to the relatively high temperature in summer. In this study, multi-sensor thermal remote sensing is an efficient tool for monitoring the temporal and spatial distribution of SST in coastal area.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.