• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1343-1356
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• 2022

Clouds or shadows are the most problematic when monitoring crops using optical satellite images. To reduce this effect, a composite algorithm was used to select the maximum Normalized Difference Vegetation Index (NDVI) for a certain period. This Maximum NDVI Composite (MNC) method reduces the influence of clouds, but since only the maximum NDVI value is used for a certain period, it is difficult to show the phenomenon immediately when the NDVI decreases. As a way to maintain the spectral information of crop as much as possible while minimizing the influence of clouds, a Score-Based Composite (SBC) algorithm was proposed, which is a method of selecting the most suitable pixels by defining various environmental factors and assigning scores to them when compositing. In this study, the Sentinel-2A/B Level 2A reflectance image and cloud, shadow, Aerosol Optical Thickness(AOT), obtainging date, sensor zenith angle provided as additional information were used for the SBC algorithm. As a result of applying the SBC algorithm with a 15-day and a monthly period for Dangjin rice fields and Taebaek highland cabbage fields in 2021, the 15-day period composited data showed faster detailed changes in NDVI than the monthly composited results, except for the rainy season affected by clouds. In certain images, a spatially heterogeneous part is seen due to partial date-by-date differences in the composited NDVI image, which is considered to be due to the inaccuracy of the cloud and shadow information used. In the future, we plan to improve the accuracy of input information and perform quantitative comparison with MNC-based composite algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.359-364
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• 2022

The change detection in remote sensing results in errors due to the camera's optical factors, seasonal factors, and land cover characteristics. The inclination of the building in the image was simulated according to the camera angle using the Cycle Generative Adversarial Network method, and the simulated image was used to contribute to the improvement of change detection accuracy. Based on CycleGAN, the inclination of the building was similarly simulated to the building in the other image based on the image of one of the two periods, and the error of the original image and the inclination of the building was compared and analyzed. The experimental data were taken at different times at different angles, and Kompsat-3A high-resolution satellite images including urban areas with dense buildings were used. As a result of the experiment, the number of incorrect detection pixels per building in the two images for the building area in the image was shown to be reduced by approximately 7 times from 12,632 in the original image and 1,730 in the CycleGAN-based simulation image. Therefore, it was confirmed that the proposed method can reduce detection errors due to the inclination of the building.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.77-82
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• 2023

The Ministry of Agriculture, Food and Rural Affairs established the FarmMap, an digital map of agricultural land. In this study, using deep learning, we suggest the application of farm map reading to farmland such as paddy fields, fields, ginseng, fruit trees, facilities, and uncultivated land. The farm map is used as spatial information for planting status and drone operation by digitizing agricultural land in the real world using aerial and satellite images. A reading manual has been prepared and updated every year by demarcating the boundaries of agricultural land and reading the attributes. Human reading of agricultural land differs depending on reading ability and experience, and reading errors are difficult to verify in reality because of budget limitations. The farmmap has location information and class information of the corresponding object in the image of 5 types of farmland properties, so the suitable AI technique was tested with ResNet50, an instance segmentation model. The results of attribute reading of agricultural land using deep learning and attribute reading by humans were compared. If technology is developed by focusing on attribute reading that shows different results in the future, it is expected that it will play a big role in reducing attribute errors and improving the accuracy of digital map of agricultural land.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.111-111
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• 2021

메콩지역은 최근 연 7%에 육박하는 경제성장률을 달성하며 아세안의 고성장을 지속 견인하고 있으나, 기후변화 및 급속한 도시화로 매년 가뭄·홍수 등 물 관련 재해 발생 빈도 및 강도 증가와 이에 따른 상·하류 국가간 물 분쟁 등으로 인해 메콩지역 지속가능 발전에 지장이 초래되고 있다. 이에 한국과 미국은 메콩우호국(Friends of the Lower Mekong, FLM) "메콩지역 수자원 데이터 관리 및 정보공유 강화에 관한 공동성명(2018년 8월)"을 계기로 메콩유역의 실시간 수자원 변동 모니터링 및 분석과 수자원 데이터 공동활용 역량을 강화하여 효율적이고 과학적인 수자원관리 지원과 함께 한국의 신남방정책과 미국의 인도-태평양 전략 시너지효과를 극대화하고자 메콩 주변국 재해경감 및 수자원 데이터 활용 역량강화를 위한 글로벌 위성기반 수문자료의 생산·활용 및 홍수·가뭄 등의 수재해 분석기술을 개발하고 있다. 여기에는 한국 K-water의 물관리 기술과 미국 NASA, USACE의 위성활용 및 수자원분석 기술을 접목하여 메콩지역의 체계적인 물관리 및 재해로부터 안전성 확보 기여에 목표를 두고 연구를 진행 중에 있다. 본 연구에서는 전 세계적으로 광범위하게 활용되고 있는 미공병단(USACE, U.S. Army Corps of Engineers)의 HEC software 프로그램을 메콩 시범지역(pilot area)에 적용하여 수리/수문모델 구축을 진행코자 한다. 구축되는 모형은 유역 상류 댐의 연계 모의운영 및 하류 홍수분석이 동시 가능한 HEC-RTS(Real-Time Simulation)로 이는 HEC-HMS, -ResSim, -RAS와 -FIA 모형이 순차적으로 결합된 수리/수문 모델링 시스템이다. 모형의 시범적용 지역은 현지 메콩위원회(MRC, Mekong River Comission)의 의견 등을 반영, 메콩강 하류지역(Lower Mekong) 본류 유역에 위성 자료 활용 및 준실시간(near real-time)으로 댐 모의운영 등을 고려할 수 있는 JingHong댐(중국 란창강 최하류)에서 라오스 Xayaburi댐(메콩강 최상류)까지의 구간을 선정하였다. 한편, 금번 연구에서는 HEC-RTS 중 HMS 모형 적용을 중심으로 가용한 위성자료(GPM IMERG)와 K-LIS 지표 모형 생산 자료를 활용하여 과거 홍수사상에 대한 모의를 고려하였다. 아울러, 연구에서 구축된 HMS 모형은 HEC-RTS에 포함되어 메콩 시범지역의 종합적 수리/수문분석에 적용될 예정이다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.187-197
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• 2008

This study is to assess the impact of future land use change on hydrology and water quality in Gyungan-cheon watershed ($255.44km^2$) using SWAT (Soil and Water Assessment Tool) model. Using the 5 past Landsat TM (1987, 1991, 1996, 2004) and $ETM^+$ (2001) satellite images, time series of land use map were prepared, and the future land uses (2030, 2060, 2090) were predicted using CA-Markov technique. The 4 years streamflow and water quality data (SS, T-N, T-P) and DEM (Digital Elevation Model), stream network, and soil information (1:25,000) were prepared. The model was calibrated for 2 years (1999 and 2000), and verified for 2 years (2001 and 2002) with averaged Nash and Sutcliffe model efficiency of 0.59 for streamflow and determination coefficient of 0.88, 0.72, 0.68 for Sediment, T-N (Total Nitrogen), T-P (Total Phosphorous) respectively. The 2030, 2060 and 2090 future prediction based on 2004 values showed that the total runoff increased 1.4%, 2.0% and 2.7% for 0.6, 0.8 and 1.1 increase of watershed averaged CN value. For the future Sediment, T-N and T-P based on 2004 values, 51.4%, 5.0% and 11.7% increase in 2030, 70.5%, 8.5% and 16.7% increase in 2060, and 74.9%, 10.9% and 19.9% increase in 2090.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6B
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• pp.665-673
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• 2008

This study is to assess the future potential climate and land use change impact on streamflow and stream water quality of the study watershed using the established model parameters (I). The CCCma (Canadian Centre for Climate Modelling and Analysis) CGCM2 (Canadian Global Coupled Model) based on IPCC SRES (Special Report Emission Scenarios) A2 and B2 scenarios were adopted for future climate condition, and the data were downscaled by Stochastic Spatio-Temporal Random Cascade Model technique. The future land use condition was predicted by using modified CA-Markov (Cellular Automata-Markov chain) technique with the past time series of Landsat satellite images. The model was applied for the future extreme precipitation cases of around 2030, 2060 and 2090. The predicted results showed that the runoff ratio increased 8% based on the 2005 precipitation (1160.1 mm) and runoff ratio (65%). Accordingly the Sediment, T-N and T-P also increased 120%, 16% and 10% respectively for the case of 50% precipitation increase. This research has the meaning in providing the methodological procedures for the evaluation of future potential climate and land use changes on watershed hydrology and stream water quality. This model result are expected to plan in advance for healthy and sustainable watershed management and countermeasures of climate change.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.121-121
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• 2022

한국과 미국은 2018년 8월에 발표한 메콩우호국(Friends of the Lower Mekong, FLM) "메콩지역 수자원 데이터 관리 및 정보공유 강화에 관한 공동성명"을 계기로 메콩유역의 실시간 수자원 변동 모니터링 및 분석과 수자원 데이터 공동활용 역량을 강화하여 효율적이고 과학적인 수자원관리 지원과 함께 한국의 신남방정책과 미국의 인도-태평양 전략 시너지효과를 극대화하고자 메콩 주변국 재해경감 및 수자원 데이터 활용 역량강화를 위한 글로벌 위성기반 수문자료의 생산·활용 및 홍수·가뭄 등의 수재해 분석기술을 개발하고 있다. 여기에는 한국 K-water의 물관리 기술과 미국 NASA, USACE의 위성활용 및 수자원분석 기술을 접목하여 메콩지역의 체계적인 물관리 및 재해로부터 안전성 확보 기여에 목표를 두고 연구를 진행 중에 있다. 본 연구에서는 전 세계적으로 광범위하게 활용되고 있는 미공병단(USACE, U.S. Army Corps of Engineers)의 HEC software 프로그램을 메콩 시범지역(pilot area)에 적용하여 수리/수문모델 구축을 진행하고 있다. 구축되는 모형은 유역 상류 댐의 연계 모의운영 및 하류 홍수분석이 동시 가능한 HEC-RTS(Real-Time Simulation)로 이는 HEC-HMS, -ResSim, -RAS와 -FIA 모형이 순차적으로 결합된 수리/수문 모델링 시스템이다. 모형의 시범적용 지역은 현지 메콩위원회(MRC, Mekong River Comission)의 의견 등을 반영, 메콩강 하류지역(Lower Mekong) 본류 유역에 위성자료 활용 및 준실시간(near real-time)으로 댐 모의운영 등을 고려할 수 있는 JingHong댐(중국 란창강 최하류)에서 라오스 Xayaburi댐(메콩강 최상류)까지의 구간을 선정하였으며, 전년도에는HEC-RTS 중 HMS(Hydrologic Modeling System) 모형 적용을 중심으로 가용한 위성자료(GPM IMERG)를 활용하여 과거 홍수사상에 대한 모의를 고려한 강우-유출모형의 구축을 완료하였다. 이에 연속하여 금년도에는 동일유역 내 하천 단면 등이 확보된 Chiang Saen 지점에서 Xayaburi 댐까지의 구간에 대해 RAS(River Analysis System)을 구축할 예정으로 구축된 RAS 모형은 HEC-RTS에 포함되어 메콩 시범지역의 종합적 수리/수문분석에 적용될 예정이다.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1235-1243
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• 2023

This study proposes the detection algorithm for the cold water mass (CWM) along the eastern coast of the Korean Peninsula using sea surface temperature (SST) data provided by the Korea Institute of Ocean Science and Technology (KIOST). Considering the occurrence and distribution of the CWM, the eastern coast of the Korean Peninsula is classified into 3 regions("Goseong-Uljin", "Samcheok-Guryongpo", "Pohang-Gijang"), and the K-means clustering is first applied to SST field of each region. Three groups, K-means clusters are used to determine CWM through applying a double threshold filter predetermined using the standard deviation and the difference of average SST for the 3 groups. The estimated sea area is judged by the CWM if the standard deviation in the sea area is 0.6℃ or higher and the average water temperature difference is 2℃ or higher. As a result of the CWM detection in 2022, the number of CWM occurrences in "Pohang-Gijang" was the most frequent on 77 days and performance indicators of the confusion matrix were calculated for quantitative evaluation. The accuracy of the three regions was 0.83 or higher, and the F1 score recorded a maximum of 0.95 in "Pohang-Gijang". The detection algorithm proposed in this study has been applied to the KIOST SST system providing a CWM map by email.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1467-1475
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• 2023

The public-interest direct payment program involves providing direct payments to agricultural producers and rural residents through public funds, premised on performing public functions such as environmental conservation, stable food supply, and maintaining rural communities via agricultural activities. Scientific estimation of crop cultivation areas and production levels is crucial for formulating agricultural policies linked to regulating food supply, which increasingly impacts the national economy. Conducting comprehensive on-site inspections for compliance monitoring of direct payment programs has shown very low efficiency in relation to budget and time. The expansion of areas subject to compliance monitoring and various challenges in on-site inspections necessitate streamlining current monitoring methods and devising effective strategies. As a solution, the application of Remote Sensing technology and spatial information utilization, allowing swift acquisition of necessary information for policies without overall on-site visits, is being discussed as an efficient compliance monitoring method. Therefore, this study evaluated the potential use of remote sensing for improving operational efficiency in monitoring compliance with public-interest direct payment programs. Using satellite images during farming seasons in Gimje and Hapcheon, vegetation indices and spatial variations were utilized to identify cultivated areas, presence of mixed crops, validated against on-site inspection data.

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.517-525
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• 2013

Time-series data of Normal Difference Vegetation Index (NDVI) obtained by the Moderate-resolution Imaging Spectroradiometer(MODIS) satellite imagery gives a waveform that reveals the characteristics of the phenology. The waveform can be decomposed into harmonics of various periods by the Fourier transformation. The resulting $n^{th}$ harmonics represent the amount of NDVI change in a period of a year divided by n. The values of each harmonics or their relative relation have been used to classify the vegetation species and to build a vegetation map. Here, we propose a method to estimate the annual amount of carbon absorbed on the forest from the $1^{st}$ harmonic NDVI value. The $1^{st}$ harmonic value represents the amount of growth of the leaves. By the allometric equation of trees, the growth of leaves can be considered to be proportional to the total amount of carbon absorption. We compared the $1^{st}$ harmonic NDVI values of the 6220 sample points with the reference data of the carbon absorption obtained by the field survey in the forest of South Korea. The $1^{st}$ harmonic values were roughly proportional to the amount of carbon absorption irrespective of the species and ages of the vegetation. The resulting proportionality constant between the carbon absorption and the $1^{st}$ harmonic value was 236 tCO2/5.29ha/year. The total amount of carbon dioxide absorption in the forest of South Korea over the last ten years has been estimated to be about 56 million ton, and this coincides with the previous reports obtained by other methods. Considering that the amount of the carbon absorption becomes a kind of currency like carbon credit, our method is very useful due to its generality.