• Korean Journal of Agricultural and Forest Meteorology
• /
• v.24 no.2
• /
• pp.124-132
• /
• 2022

This study was aimed to visualize the spatial distribution of PM₁₀ data measured at non-uniformly distributed observation sites in South Korea. Different spatial interpolation methods were applied to irregularly distributed PM₁₀ observation data from January, 2019, when the concentration was the highest and in July, 2019, when the concentration was the lowest. Four interpolation methods with different parameters were used: Inverse Distance Weighted (IDW), Ordinary Kriging (OK), radial base function, and scattered interpolation. Six cases were cross-validated and the normalized root-mean-square error for each case was compared. The results showed that IDW using smoothing-related factors was the most appropriate method, while the OK method was least appropriate. Our results are expected to help users select the proper spatial interpolation method for PM₁₀ data analysis with comparative reliability and effectiveness.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.34 no.1
• /
• pp.11-18
• /
• 2022

In order to improve the accuracy of particle tracking prediction techniques near the Korean Strait, this study compared and analyzed a particle tracking model based on a seawater flow numerical model and a machine learning based on a particle tracking model using field observation data. The data used in the study were the surface drifter buoy movement trajectory data observed in the Korea Strait, prediction data by machine learning (linear regression, decision tree) using the tide and wind data from three observation stations (Gageo Island, Geoje Island, Gyoboncho), and prediciton data by numerical models (ROMS, MOHID). The above three data were compared through three error evaluation methods (Correlation Coefficient (CC), Root Mean Square Errors (RMSE), and Normalized Cumulative Lagrangian Separation (NCLS)). As a final result, the decision tree model had the best prediction accuracy in CC and RMSE, and the MOHID model had the best prediction results in NCLS.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.119-119
• /
• 2023

본 연구에서는 Terra MODIS(MODerate resolution Imaging Spectroradiometer) 위성영상과 토양수분 부족지수(Soil Water Deficit Index, SWDI)를 이용하여 2012년부터 2022년까지 한반도 전국의 1km 공간 증발산량을 산정하였다. 공간 증발산량을 산정하기 위한 과정은 크게 두 가지로 구분된다. 첫 번째로 MODIS의 LST(Land Surface Temperature), NDVI(Normalized Difference Vegetation Index), 선행강우 및 무강우 누적일수를 이용해 1 km 공간 토양수분을 산정하였다. 농촌진흥청 토양수분관측망 자료 중 토지피복, 토양 속성을 고려하여 선정된 70개소 토양수분 실측데이터와 비교한 결과 지점별 평균 R² 0.63~0.90으로 유의미한 상관성을 나타내었다. 산정된 공간 토양수분은 생장저해수분점과 초기위조점의 관계를 이용한 SWDI로 변환하였다. 두 번째로 순 복사량, 기온 및 NDVI의 적은 수문인자를 통해 증발산량 산정이 가능한 MS-PT(Modified Satellite-based Priestley-Taylor) 모형을 기반으로 계절별 식생과 토양수분 상태를 고려하여 1 km 공간 증발산량을 산정하였다. MS-PT 모형에서 가정한 대기 증발 변수 Diurnal temperature (DT)와 지표 수분의 상관성 문제를 해결하기 위해 DT를 SWDI로 적용하였다. 모형 결과의 검증을 위해 국내 플럭스 타워 (설마천, 청미천, 덕유산) 증발산량 관측자료와의 결정계수(Coefficient of determination, R²), RMSE(Root Mean Square Error) 및 IOA(Index of Agreement)를 산정하였다. 본 연구의 결과로 생산되는 국내 증발산량의 시, 공간적 변동성은 증발산량을 통한 수문학적 가뭄지수 및 급성 가뭄을 파악하는데 활용될 수 있을 것으로 판단된다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.161-161
• /
• 2021

본 연구에서는 금강 상류의 용담댐 유역(930.0 km²)을 대상으로 Sentinel-1 SAR(Synthetic Aperture Radar) 및 Sentinel-2 MultiSpectral Instrument(MSI) 위성영상을 활용한 토양수분 산출연구를 수행하였다. 연구에 사용된 자료는 10 m 해상도의 Sentinel-1 IW(Interferometric Wide swath) mode GRD(Ground Range Detected) product의 VV(Vertical transmit-Vertical receive) 및 VH(Vertical transmit-Horizontal receive) 편파자료와 Sentinel-2 Level-2A Bottom of Atmosphere(BOA) reflectance 자료를 2019년에 대해 각 6일 및 5일 간격으로 구축하였다. 위성영상의 Image processing은 SNAP(SentiNel Application Platform)을 활용하여 Sentinel-1 영상의 편파 별(VV, VH) 후방산란계수와 Sentinel-2의 적색(Band-4) 및 근적외(Band-8) 영상을 생성하였다. 토양수분 산출 모형은 다중선형회귀모형(Multiple Linear Regression Model)을 활용하였으며, 각 지점에 해당하는 토양 속성별로 모형을 생성하였다. 모형의 입력자료는 Sentinel-1 위성의 편파별 후방산란계수, Sentinel-1 위성에서 산출된 식생지수 RVI(Radar Vegetation Index)와 Sentinel-2 위성에서 산출된 NDVI(Normalized Difference Vegetation Index)를 활용하여 식생의 영향을 반영하고자 하였다. 모의 된 토양수분을 검증하기 위해 6개 지점의 TDR(Time Domain Reflectometry) 기반 실측 토양수분 자료를 수집하고, 상관계수(Correlation Coefficient, R), 평균제곱근오차(Root Mean Square Error, RMSE) 및 IOA(Index of Agreement)를 활용하여 전체 기간 및 계절별로 나누어 검증할 예정이다.

• Korean Journal of Remote Sensing
• /
• v.21 no.6
• /
• pp.445-456
• /
• 2005

The land surface parameters should be determined with sufficient accuracy, because these play an important role in climate change near the ground. As the surface reflectance presents strong anisotropy, off-nadir viewing results a strong dependency of observations on the Sun - target - sensor geometry. They contribute to the random noise which is produced by surface angular effects. The principal objective of the study is to provide a database of accurate surface reflectance eliminated the angular effects from MODIS 250m reflective channel data over Korea. The MODIS (Moderate Resolution Imaging Spectroradiometer) sensor has provided visible and near infrared channel reflectance at 250m resolution on a daily basis. The successive analytic processing steps were firstly performed on a per-pixel basis to remove cloudy pixels. And for the geometric distortion, the correction process were performed by the nearest neighbor resampling using 2nd-order polynomial obtained from the geolocation information of MODIS Data set. In order to correct the surface anisotropy effects, this paper attempted the semiempirical kernel-driven Bi- directional Reflectance Distribution Function(BRDF) model. The algorithm yields an inversion of the kernel-driven model to the angular components, such as viewing zenith angle, solar zenith angle, viewing azimuth angle, solar azimuth angle from reflectance observed by satellite. First we consider sets of the model observations comprised with a 31-day period to perform the BRDF model. In the next step, Nadir view reflectance normalization is carried out through the modification of the angular components, separated by BRDF model for each spectral band and each pixel. Modeled reflectance values show a good agreement with measured reflectance values and their RMSE(Root Mean Square Error) was totally about 0.01(maximum=0.03). Finally, we provide a normalized surface reflectance database consisted of 36 images for 2001 over Korea.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.24 no.4
• /
• pp.218-233
• /
• 2022

The long-term (1986~2020) predictability of the number of days of heat and cold damages for each growth stage of soybean is evaluated using the daily maximum and minimum temperature (Tmax and Tmin) data produced by Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF). The Predictability evaluation methods for the number of days of damages are Normalized Standard Deviations (NSD), Root Mean Square Error (RMSE), Hit Rate (HR), and Heidke Skill Score (HSS). First, we verified the simulation performance of the Tmax and Tmin, which are the variables that define the heat and cold damages of soybean. As a result, although there are some differences depending on the month starting with initial conditions from January (01RUN) to May (05RUN), the result after a systematic bias correction by the Variance Scaling method is similar to the observation compared to the bias-uncorrected one. The simulation performance for correction Tmax and Tmin from March to October is overall high in the results (ENS) averaged by applying the Simple Composite Method (SCM) from 01RUN to 05RUN. In addition, the model well simulates the regional patterns and characteristics of the number of days of heat and cold damages by according to the growth stages of soybean, compared with observations. In ENS, HR and HSS for heat damage (cold damage) of soybean have ranged from 0.45~0.75, 0.02~0.10 (0.49~0.76, -0.04~0.11) during each growth stage. In conclusion, 01RUN~05RUN and ENS of PNU CGCM-WRF Chain have the reasonable performance to predict heat and cold damages for each growth stage of soybean in South Korea.

• Korean Journal of Remote Sensing
• /
• v.40 no.4
• /
• pp.387-396
• /
• 2024

Most very high-resolution (VHR) satellite images provide rational polynomial coefficients (RPC) data to facilitate the transformation between ground coordinates and image coordinates. However, initial RPC often contains geometric errors, necessitating correction through matching with ground control points (GCPs). A GCP chip is a small image patch extracted from an orthorectified image together with height information of the center point, which can be directly used for geometric correction. Many studies have focused on area-based matching methods to accurately align GCP chips with VHR satellite images. In cases with seasonal differences or changed areas, edge-based algorithms are often used for matching due to the difficulty of relying solely on pixel values. However, traditional edge extraction algorithms,such as canny edge detectors, require appropriate threshold settings tailored to the spectral characteristics of satellite images. Therefore, this study utilizes deep learning-based edge information that is insensitive to the regional characteristics of satellite images for matching. Specifically,we use a pretrained pixel difference network (PiDiNet) to generate the edge maps for both satellite images and GCP chips. These edge maps are then used as input for normalized cross-correlation (NCC) and relative edge cross-correlation (RECC) to identify the peak points with the highest correlation between the two edge maps. To remove mismatched pairs and thus obtain the bias-compensated RPC, we iteratively apply the data snooping. Finally, we compare the results qualitatively and quantitatively with those obtained from traditional NCC and RECC methods. The PiDiNet network approach achieved high matching accuracy with root mean square error (RMSE) values ranging from 0.3 to 0.9 pixels. However, the PiDiNet-generated edges were thicker compared to those from the canny method, leading to slightly lower registration accuracy in some images. Nevertheless, PiDiNet consistently produced characteristic edge information, allowing for successful matching even in challenging regions. This study demonstrates that improving the robustness of edge-based registration methods can facilitate effective registration across diverse regions.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.22 no.4
• /
• pp.1-14
• /
• 2019

The 1st Forest Health Management survey was conducted to examine the health of the forests in Korea. However, in order to understand the health of the forests, which account for 63.7% of the total land area in South Korea, it is necessary to comprehensively spatialize the results of the survey beyond the sampling points. In this regard, out of the sample points of the 1st Forest Health Management survey in Gyeongbuk area, 78 spots were selected. For these spots, the species diversity index was selected from the survey sections, and the spatial interpolation method was applied. Inverse distance weighted (IDW), Ordinary Kriging and Ordinary Cokriging were applied as spatial interpolation methods. Ordinary Cokriging was performed by selecting vegetation indices which are highly correlated with species diversity index as a secondary variable. The vegetation indices - Normalized Differential Vegetation Index(NDVI), Leaf Area Index(LAI), Sample Ratio(SR) and Soil Adjusted Vegetation Index(SAVI) - were extracted from Landsat 8 OLI. Verification was performed by the spatial interpolation method with Mean Error(ME) and Root Mean Square Error(RMSE). As a result, Ordinary Cokriging using SR showed the most accurate result with ME value of 0.0000218 and RMSE value of 0.63983. Ordinary Cokriging using SR was proven to be more accurate than Ordinary Kriging, IDW, using one variable. This indicates that the spatial interpolation method using the vegetation indices is more suitable for spatialization of the biodiversity index sample points of 1st Forest Health Management survey.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.2111-2111
• /
• 2001

A chemoinfometrical method for evaluating the quantitative determination of crystallinity one polymorphs based on fourie-transformed near-infrared (FT-NIR) spectroscopy was established. A direct comparison of the data with the ones collected from using the and compared with the conventional powder X-ray diffraction method was performed. [Method] The pPure a and g forms of indomethacin (IMC) were prepared by reportedusing published methods. Six kinds of standard samples obtained by physically mixing of a and g forms. After the powder X-ray diffraction profiles of samples have been measured, the intensity values were normalized to against the intensity of silicon powder as the as an external standard. The calibration curves for quantification of crystal content were based upon the total relative intensity of four diffraction peaks from of the form g crystal. FT-NIR spectra of six calibration sample sets were recorded 5 times with the NIR spectrometer (BRAN+LUEBBE). Chemoinfometric analysis was performed on the NIR spectral data sets by applying the principal component regression (PCR). [Results] The relation between the actual and predicted polymorphic contents of form g IMC measured using by the X-ray diffraction method shows a good straight linen linear relation., and it has slope of 0.023, an intercept of 0.131 and a correlation coefficient of 0.986. PCR analyses wereis was performed based on normalized NIR spectra sets offer standard samples of known content of IMC g form. IMC. A calibration equation was determined to minimize the root mean square error of the predictionthe prediction. Figure 1 shows a plot of the calibration data obtained by NIR method between the actual and predicted contents of form g IMC. The predicted values were reproducible and had a smaller standard deviation. Figure 2 shows that the plot for the predicted transformation rate (%) of form a IMC to form g as measured by X-ray diffractomeoy against to those as measured by NIR method. The plot has a slope of 1.296, an intercept of 1,109, and a correlation coefficient of 0.992. The line represents a satisfactory correlation between the two predicted values of form g IMC content. Thus NIR spectroscopy is an effective method for the evaluation to the pharmaceutical products of quantitative of polymorph.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.24 no.3
• /
• pp.164-178
• /
• 2022

Uncertainties in weather forecasts would affect the reliability of yield prediction using crop models. The objective of this study was to compare uncertainty in crop yield prediction caused by the use of the weather forecast data. Daily weather data were produced at 10 km spatial resolution using W eather Research and Forecasting (W RF) model. The nearest neighbor method was used to downscale these data at the resolution of 5 km (W RF5K). Parameter-elevation Regressions on Independent Slopes Model (PRISM) was also applied to the WRF data to produce the weather data at the same resolution. W RF5K and PRISM data were used as inputs to the CROPGRO-SOYBEAN model to predict crop yield. The uncertainties of the gridded data were analyzed using cumulative growing degree days (CGDD) and cumulative solar radiation (CSRAD) during the soybean growing seasons for the crop of interest. The degree of agreement (DOA) statistics including structural similarity index were determined for the crop model outputs. Our results indicated that the DOA statistics for CGDD were correlated with that for the maturity dates predicted using WRF5K and PRISM data. Yield forecasts had small values of the DOA statistics when large spatial disagreement occured between maturity dates predicted using WRF5K and PRISM. These results suggest that the spatial uncertainties in temperature data would affect the reliability of the phenology and, as a result, yield predictions at a greater degree than those in solar radiation data. This merits further studies to assess the uncertainties of crop yield forecasts using a wide range of crop calendars.