AMSR2 위성영상 기반 토양수분을 이용한우리나라 월별 FDSI 산정 및 공간 분포 특성 분석

천범석,신용철,이태화,정광준 한국농공학회 2022 한국농공학회논문집 Vol.64 No.4

In this study, we estimated the monthly FDSI (Flash Drought Stress Index) for assessing flash drought on South Korea using AMSR2(AdvancedMicrowave Scanning Radiometer 2) satellite-based soil moisture footprints. We collected the AMSR2 soil moisture and climate-land surface data fromApril to November 2018 for analyzing the monthly FDSI values. We confirmed that the FDSI values were high at the regions with the hightemperature/evapotranspiration while the precipitation is relatively low. Especially, the regions which satisfied an onset of flash drought (FDSI≧0.71)were increased from June. Then, the most of regions suffered by flash drought during the periods (July to August) with the high temperature andevapotranspiration. Additionally, the impacts of landuse and slope degree were evaluated on the monthly FDSI changes. The forest regions that havethe steep slope degree showed the relatively higher FDSI values than the others. Thus, our results indicated that the the slope degree has the relativelyhigher impact on the onset and increasing of flash drought compared to the others.

CMIP5 기반 하천유량 예측을 위한 딥러닝 LSTM 모형의 최적 학습기간 산정

천범석 ( Chun Beom-Seok ),이태화 ( Lee Tae-hwa ),김상우 ( Kim Sang-woo ),임경재 ( Lim Kyoung-jae ),정영훈 ( Jung Young-hun ),도종원 ( Do Jong-won ),신용철 ( Shin Yong-chul ) 한국농공학회 2022 한국농공학회논문집 Vol.64 No.1

In this study, we suggested the optimal training period for predicting the streamflow using the LSTM (Long Short-Term Memory) model based on the deep learning and CMIP5 (The fifth phase of the Couple Model Intercomparison Project) future climate scenarios. To validate the model performance of LSTM, the Jinan-gun (Seongsan-ri) site was selected in this study. We comfirmed that the LSTM-based streamflow was highly comparable to the measurements during the calibration (2000 to 2002/2014 to 2015) and validation (2003 to 2005/2016 to 2017) periods. Additionally, we compared the LSTM-based streamflow to the SWAT-based output during the calibration (2000∼2015) and validation (2016∼2019) periods. The results supported that the LSTM model also performed well in simulating streamflow during the long-term period, although small uncertainties exist. Then the SWAT-based daily streamflow was forecasted using the CMIP5 climate scenario forcing data in 2011∼2100. We tested and determined the optimal training period for the LSTM model by comparing the LSTM-/SWAT-based streamflow with various scenarios. Note that the SWAT-based streamflow values were assumed as the observation because of no measurements in future (2011∼2100). Our results showed that the LSTM-based streamflow was similar to the SWAT-based streamflow when the training data over the 30 years were used. These findings indicated that training periods more than 30 years were required to obtain LSTM-based reliable streamflow forecasts using climate change scenarios.

Sentinel-1 위성을 이용한 북한 지역의 고해상도 토양수분 산정

천범석 ( Bumseok Chun ),김상우 ( Sangwoo Kim ),이태화 ( Taehwa Lee ),신용철 ( Yongchul Shin ) 한국농공학회 2019 한국농공학회 학술대회초록집 Vol.2019 No.-

토양수분은 지하수와 지표수 산정, 자연재해 예측 및 농작물의 생장과 같은 다양한 분야에서 중요한 역할을 한다. 최근 토양수분을 이용한 가뭄평가지수 제시와 토양수분이 다양한 작물에 주는 영향 등 다양한 분야에서 토양수분에 대한 관심이 증가하고 있다. 토양수분의 측정 방법 중 TDR(Time Domain Reflectometry)을 이용한 토양수분 직접 측정방법은 지점단위의 토양수분을 빠른 시간 내 정확한 측정이 가능한 장점이 있다. 하지만 직접 측정 방법은 토양수분의 공간분포를 나타내지 못하는 한계가 있다. 이러한 한계를 극복하기 위하여 원격탐사자료를 이용한 토양수분 간접측정방법이 제시되어 최근 이를 이용한 남한 지역의 토양수분 산정에 관한 연구가 진행되고 있다. 그러나 현재 북한 지역의 토양수분 산정에 관련된 연구는 미비한 상황이다. 따라서 본 연구에서는 북한 지역을 대상으로 고해상도의 Sentinel-1 SAR(Synthetic Aperture Radar) 센서 기반 후방산란계수를 이용하여 토양수분 공간분포를 산정하였다. Sentinel-1 SAR 센서 기반 토양수분과 GPM(Global Precipitation Measurement) 위성강우 이미지자료를 비교하였으며, 전체적으로 강우 변화에 따른 모의 토양수분 공간분포의 변화가 잘 반영하는 것으로 나타났다. 본 연구는 향후 북한 지역 홍수, 가뭄, 산불, 농업 등 다양한 분야에서 기초자료로 활용 될 수 있을 것으로 사료된다.

CMIP5 기반 하천유량 예측을 위한 딥러닝 LSTM 모형 평가

천범석 ( Beomseok Chun ),이태화 ( Taehwa Lee ),김상우 ( Sangwoo Kim ),임경재 ( Kyoung Jae Lim ),정영훈 ( Younghun Jung ),도종원 ( Jongwon Do ),신용철 ( Yongchul Shin ) 한국농공학회 2021 한국농공학회 학술대회초록집 Vol.2021 No.-

DNN 회귀모형을 이용한 산악 지형 토양수분 산정

천범석 ( Chun Beomseok ),이태화 ( Lee Taehwa ),김상우 ( Kim Sangwoo ),김종건 ( Kim Jonggun ),장근창 ( Jang Keunchang ),천정화 ( Chun Junghwa ),장원석 ( Jang Won Seok ),신용철 ( Shin Yongchul ) 한국농공학회 2020 한국농공학회논문집 Vol.62 No.5

In this study, we estimated soil moisture values using the Deep Neural Network(DNN) scheme at the mountainous regions. In order to test the sensitive analysis of DNN scheme, we collected the measured(at the soil depths of 10 cm and 30 cm) soil moisture and DNN input(weather and land surface) data at the Pyeongchang-gun(relatively flat) and Geochang-gun(steep slope) sites. Our findings indicated that the soil moisture estimates were sensitive to the weather variables(5 days-averaged rainfall, 5 days precedent rainfall, accumlated rainfall) and DEM. These findings showed that the DEM and weather variables play the key role in the processes of soil water flow at the mountainous regions. We estimated the soil moisture values at the soil depths of 10 cm and 30 cm using DNN at two study sites under different climate-landsurface conditions. The estimated soil moisture(R: 0.890 and RMSE: 0.041) values at the soil depth of 10 cm were comparable with the measured data in Pyeongchang-gun site while the soil moisture estimates(R: 0.843 and RMSE: 0.048) at the soil depth of 30 cm were relatively biased. The DNN-based soil moisture values(R: 0.997/0.995 and RMSE: 0.014/0.006) at the soil depth of 10 cm/30 cm matched well with the measured data in Geochang-gun site. Although uncertainties exist in the results, our findings indicated that the DNN-based soil moisture estimation scheme demonstrated the good performance in estimating soil moisture values using weather and land surface information at the monitoring sites. Our proposed scheme can be useful for efficient land surface management in various areas such as agriculture, forest hydrology, etc.

토양수분 산정을 위한 DNN 모형 평가

천범석 ( Beomseok Chun ),이태화 ( Taehwa Lee ),김상우 ( Sangwoo Kim ),김종건 ( Junggun Kim ),신용철 ( Yongchul Shin ) 한국농공학회 2020 한국농공학회 학술대회초록집 Vol.2020 No.-

본 연구에서는 DNN 기법을 이용하여 국내의 시계열 토양수분을 산정하였다. DNN 회귀모형의 민감도 분석을 시행하기 위하여 평창군 및 거창군 지점의 TDR 기반 실측토양수분(10cm 및 30cm)과 DNN 입력자료(기상 및 지표 특성)을 수집하였다. 민감도 분석 결과 산정된 토양수분이 기상 변수(5일 강우평균, 5일 선행강우 및 누적강우)와 DEM에 민감한 것으로 나타났다. 이러한 분석 결과는 DEM과 기상 변수가 토양수분 유출 과정에서 중요하다는 것을 나타낸다. 민감도분석 결과를 바탕으로 DNN 회귀모형을 사용하여 연구 지점에서 10cm 및 30cm 깊이에서의 모의토양수분을 산정하였다. 평창군 지점에서는 10cm 깊이에서의 모의토양수분(R: 0.890, RMSE: 0.041)은 실측값과 비교하여 비슷한 것으로 나타났으며, 30cm 깊이에서의 모의토양수분(R: 0.843, RMSE: 0.048)은 10cm 깊이에서의 모의토양수분과 비교하여 상대적으로 불확실성이 높게 나타났다. 거창군 지점의 경우 10cm 및 30cm 깊이에서의 DNN 기반 모의토양수분(R: 0.997/0.995, RMSE: 0.014/0.006)이 모두 실측값과 유사한 것으로 나타났다. 비록 산정된 모의토양수분에서 불확실성이 발생하였으나, DNN 회귀모형으로 산정한 모의토양수분이 TDR 기반 실측토양수분을 잘 반영하는 것으로 나타났다. 본 연구는 농업, 산림 및 수문 등의 다양한 분야에서 활용될 것으로 사료된다.

Surface Plasmon Resonance Immunosensor for the Detection of yersinia enterocolitica

Bum Suk Chun(천범석),Woochang Lee(이우창),Doo-Bong Lee(이두봉),Jeong-Woo Choi(최정우) 한국생물공학회 2004 한국생물공학회 학술대회 Vol.2004 No.4

Production of E6 oncoprotein in recombinant Escherichia coli and its porous protein chip

이우창,김용완,천범석,이두봉,김희주,최정우 한국화학공학회 2007 화학공학의이론과응용 Vol.10 No.1

In most high-risk human papillomaviruses (HPVs) infection, E6 oncogenic protein plays a critical role in inducing cervical cancers by interacting with p53 for inactivation of the cellular regulatory proteins [1]. E6 protein acts by stimulating degradation of P53 through the ubiquitin-dependent proteolysis pathway. The viral E6 protein is also required for the continuous growth of HPV-immortalized cells.In this study, we constructed a recombinant E. coli and produced E6 oncoprotein to investigate the molecular pathway of tumor suppression effect with a porous protein chip [2]. [This work was supported in part by the Korea Science and Engineering Foundation (KOSEF)through the ADvanced Environment Monitoring Research Center at Kwangju Institute of Science and Technology.] REFERENCES [1] Kaur P, et al. J Gen Virol 1989;70:1261-1266. [2] Oh BK, et al. Biosens Bioelectr 2003;18:605-611

Control of Antibody Immobilization Based on Flow Dynamic Self-assembly

이두봉,이우창,천범석,이원홍,최정우 한국화학공학회 2007 화학공학의이론과응용 Vol.10 No.1

Proper choice of antibody immobilization method is one of the important points in the fabrication of bioactive thin film for immunosensor and protein array. Molecular thin film based on self-assembly (SA) technique is a versatile and in vitro biosurface which mimics the naturally recognition process due to reliable control of density and environment of an immobilized recognition centre. In this study, flow dynamic self-assembly (FDSA) is used for antibody immobilization. Spray device designed in our laboratory spouts antibody fragments solution, resulting in the immobilization of antibody fragments onto the gold surface via their native thiol group (-SH). The fragment of monoclonal antibody (Mab) against human serum albumin (HSA) was prepared and the deposition on the surface was investigated using surface plasmon resonance spectroscopy (MultiskopTM, Optrel GBR, German). Topographies of antibody fragments film was analyzed using atomic force microscopy (CP, PSI, USA). From the experimental result, the amount of antibody-antigen binding was changed by sprayed immobilization method.

Sentinel-1A/B SAR와 토양수분자료동화기법을 이용한 고해상도 토양수분 산정

김상우,이태화,천범석,정영훈,장원석,서찬양,신용철 한국농공학회 2020 한국농공학회논문집 Vol.62 No.6

We estimated the spatio-temporally distributed soil moisture using Sentinel-1A/B SAR (Synthetic Aperture Radar) sensor images and soil moisture dataassimilation technique in South Korea. Soil moisture data assimilation technique can extract the hydraulic parameters of soils using observed soilmoisture and GA (Genetic Algorithm). The SWAP (Soil Water Atmosphere Plant) model associated with a soil moisture assimilation technique simulatesthe soil moisture using the soil hydraulic parameters and meteorological data as input data. The soil moisture based on Sentinel-1A/B was validatedand evaluated using the pearson correlation and RMSE (Root Mean Square Error) analysis between estimated soil moisture and TDR soil moisture. The soil moisture data assimilation technique derived the soil hydraulic parameters using Sentinel-1A/B based soil moisture images, ASOS (AutomatedSynoptic Observing System) weather data and TRMM (Tropical Rainfall Measuring Mission)/GPM (Global Precipitation Measurement) rainfall data. Thederived soil hydrological parameters as the input data to SWAP were used to simulate the daily soil moisture values at the spatial domain from 2001to 2018 using the TRMM/GPM satellite rainfall data. Overall, the simulated soil moisture estimates matched well with the TDR measurements andSentinel-1A/B based soil moisture under various land surface conditions (bare soil, crop, forest, and urban).