• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1181-1189
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• 2022

Clouds are composed of tiny water droplets, ice crystals, or mixtures suspended in the atmosphere and cover about two-thirds of the Earth's surface. Cloud detection in satellite images is a very difficult task to separate clouds and non-cloud areas because of similar reflectance characteristics to some other ground objects or the ground surface. In contrast to thick clouds, which have distinct characteristics, thin transparent clouds have weak contrast between clouds and background in satellite images and appear mixed with the ground surface. In order to overcome the limitations of transparent clouds in cloud detection, this study conducted cloud detection focusing on transparent clouds using machine learning techniques (Random Forest [RF], Convolutional Neural Networks [CNN]). As reference data, Cloud Mask and Cirrus Mask were used in MOD35 data provided by MOderate Resolution Imaging Spectroradiometer (MODIS), and the pixel ratio of training data was configured to be about 1:1:1 for clouds, transparent clouds, and clear sky for model training considering transparent cloud pixels. As a result of the qualitative comparison of the study, bothRF and CNN successfully detected various types of clouds, including transparent clouds, and in the case of RF+CNN, which mixed the results of the RF model and the CNN model, the cloud detection was well performed, and was confirmed that the limitations of the model were improved. As a quantitative result of the study, the overall accuracy (OA) value of RF was 92%, CNN showed 94.11%, and RF+CNN showed 94.29% accuracy.

• Korean Journal of Remote Sensing
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• v.23 no.2
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• pp.145-152
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• 2007

The polarimetric backscattering coefficients of a wet-land rice field which is an experimental plot belong to National Institute of Agricultural Science and Technology in Suwon are measured using ground-based polarimetric scatterometers at 1.8 and 5.3 GHz throughout a growth year from transplanting period to harvest period (May to October in 2006). The polarimetric scatterometers consist of a vector network analyzer with time-gating function and polarimetric antenna set, and are well calibrated to get VV-, HV-, VH-, HH-polarized backscattering coefficients from the measurements, based on single target calibration technique using a trihedral corner reflector. The polarimetric backscattering coefficients are measured at $30^{\circ},\;40^{\circ},\;50^{\circ}\;and\;60^{\circ}$ with 30 independent samples for each incidence angle at each frequency. In the measurement periods the ground truth data including fresh and dry biomass, plant height, stem density, leaf area, specific leaf area, and moisture contents are also collected for each measurement. The temporal variations of the measured backscattering coefficients as well as the measured plant height, LAI (leaf area index) and biomass are analyzed. Then, the measured polarimetric backscattering coefficients are compared with the rice growth parameters. The measured plant height increases monotonically while the measured LAI increases only till the ripening period and decreases after the ripening period. The measured backscattering coefficientsare fitted with polynomial expressions as functions of growth age, plant LAI and plant height for each polarization, frequency, and incidence angle. As the incidence angle is bigger, correlations of L band signature to the rice growth was higher than that of C band signatures. It is found that the HH-polarized backscattering coefficients are more sensitive than the VV-polarized backscattering coefficients to growth age and other input parameters. It is necessary to divide the data according to the growth period which shows the qualitative changes of growth such as panicale initiation, flowering or heading to derive functions to estimate rice growth.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.1
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• pp.47-56
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• 2018

Providing high-quality meteorological observation data at sites that represent actual farming environments is essential for useful agrometeorological services. The Automated Agricultural Observing System (AAOS) of the Korean Meteorological Administration, however, has been deployed on lawns rather than actual farm land. In this study, we show the inaccuracies that arise in AAOS data by analyzing temporal and vertical variation and by comparing them with data recorded by the National Center for AgroMeteorology (NCAM) tower that is located at an actual farming site near the AAOS tower. The analyzed data were gathered in August and October (before and after harvest time, respectively). Observed air temperature and water vapor pressure were lower at AAOS than at NCAM tower before and after harvest time. Observed reflected shortwave radiation tended to be higher at AAOS than at NCAM tower. Soil variables showed bigger differences than meteorological observation variables. In August, observed soil temperature was lower at NCAM tower than at AAOS with smaller diurnal changes due to irrigation. The soil moisture observed at NCAM tower continuously maintained its saturation state, while the one at AAOS showed a decreasing trend, following an increase after rainfall. The trend changed in October. Observed soil temperature at NCAM showed similar daily means with higher diurnal changes than at AAOS. The soil moisture observed at NCAM was continuously higher, but both AAOS and NCAM showed similar trends. The above results indicate that the data gathered at the AAOS are inaccurate, and that ground surface cover and farming activities evoke considerable differences within the respective meteorological and soil environments. We propose to shift the equipment from lawn areas to actual farming sites such as rice paddies, farms and orchards, so that the gathered data are representative of the actual agrometeorological observations.

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.2
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• pp.95-102
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• 2002

Spatial interpolation has become a common procedure in converting temperature forecasts and observations at irregular points for use in regional scale ecosystem modeling and the model based decision support systems for resource management. Neglection of terrain effects in most spatial interpolations for short term temperatures may cause erroneous results in mountainous regions, where the observation network hardly covers full features of the complicated terrain. A spatial interpolation model for daytime hourly temperature was formulated based on error analysis of unsampled site with respect to the site topography. The model has a solar irradiance correction scheme in addition to the common backbone of the lapse rate - corrected inverse distance weighting. The solar irradiance scheme calculates the direct, diffuse and reflected components of shortwave radiation over any surfaces based on the sun-slope geometry and compares the sum with that over a reference surface. The deviation from the reference radiation is used to calculate the temperature correction term by an empirical conversion formula between the solar energy and the air temperature on any sloped surfaces at an hourly time scale, which can be prepared seasonally for each land cover type. When this model was applied to a 14 km by 22 km mountainous region at a 10 m horizontal resolution, the estimated hourly temperature surfaces showed a better agreement with the observed distribution than those by a conventional method.

• Spatial Information Research
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• v.22 no.1
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• pp.75-84
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• 2014

Power lines are one of the main obstacles causing an aircraft crash and thus their realtime detection is significantly important during flight. To avoid such flight obstacles, the use of LIDAR has been recently increasing thanks to its advantages that it is less sensitive to weather conditions and can operate in day and night. In this study, we suggest a fast method to detect power lines from LIDAR data for flight obstacle avoidance. The proposed method first extracts non-ground points by eliminating the points reflected from ground surfaces using a filtering process. Second, we calculate the eigenvalues for the covariance matrix from the coordinates of the generated non-ground points and obtain the ratio of eigenvalues. Based on the ratio of eigenvalues, we can classify the points on a linear structure. Finally, among them, we select the points forming horizontally long straight as power-line points. To verify the algorithm, we used both real and simulated data as the input data. From the experimental results, it is shown that the average detection rate and time are 80% and 0.2 second, respectively. If we would improve the method based on the experiment results from the various flight scenario, it will be effectively utilized for a flight obstacle avoidance system.

• Korean Journal of Agricultural and Forest Meteorology
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• v.2 no.1
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• pp.9-15
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• 2000

The objective of this study is to evaluate the use of RADARSAT and Landsat TM data for the monitoring of rice growth. The relationships between backscatter coefficients($\sigma$$^{0}$ ) of RADARSAT data and digital numbers (DN) of Landsat TM and rice growth parameters were investigated. Radar backscatter coefficients were calculated by calibration process and then compared with rice growth parameters; plant height, leaf area index (LAI), and fresh and dry biomass. When radar backscatter coefficient ($\sigma$$^{0}$ ) of rice was expressed as a function of time, it is shown that the increasing trend ranged from -22--20dB to -9--8dB as growth advances. The temporal variation of backscatter coefficient was significant to interpret rice growth. According to the relationship between leaf area index and backscatter coefficient, backscatter coefficient underestimated leaf area index at the beginning of life history and overestimated, at the reproductive stage. The same increasing trend between biomass and backscatter coefficient was shown. From these results, RADARSAT data appear positive to the monitoring of rice growth. Each band of time-series Landsat TM data had a significant trend as a rice crop grows during its life cycle. Spectral indices, NDVI[(TM4-TM3)/(TM4+TM3)] and RVI(TM4/TM2), derived from Landsat TM equivalent bands had the same trend as leaf area index.

• Geophysics and Geophysical Exploration
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• v.12 no.4
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• pp.361-366
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• 2009

Layered-earth Green's functions in electormagnetic (EM) surveys play a key role in modeling the response of exploration targets. They are computed through the Hankel transforms of analytic kernels. Computational precision depends upon the choice of algebraically equivalent forms by which these kemels are expressed. Since three-dimensional (3D) modeling can require a huge number of Green's function evaluations, total computational time can be influenced by computational time for the Hankel transform evaluations. Linear digital filters have proven to be a fast and accurate method of computing these Hankel transforms. In EM modeling for 3D inversion, electric fields are generally evaluated by the secondary field formulation to avoid the singularity problem. In this study, three components of electric fields for five different sources on the surface of homogeneous half-space were derived as primary field solutions. Moreover, reflection coefficients in TE and TM modes were produced to calculate EM responses accurately for a two-layered model having a sea layer. Accurate primary fields should substantially improve accuracy and decrease computation times for Green's function-based problems like MT problems and marine EM surveys.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.11
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• pp.1307-1314
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• 2012

To reduce the effects of clutters with subsurface inhomogenities in ground-penetrating radar(GPR) images, an eigenimage based signal-processing technique is presented. If the conventional eigenimage filtering technique is applied to B-scan images of a GPR survey, relatively homogeneous clutters such as antenna ringing, direct coupling between transmitting and receiving antennas, and soil-surface reflection, can be removed sufficiently. However, since random clutters of subsurface inhomogenities still remain in the images, target signals are distorted and obscured by the clutters. According to a comparison of the eigenimage filtering results, there is different coherency between subsurface clutters and target signals. To reinforce the pixels with high coherency and reduce the pixels with low coherency, the pixel-by-pixel geometric-mean process after the eigenimage filtering is proposed here. For the validity of the proposed approach, GPR survey for detection of a metal target in a randomly inhomogeneous soil is numerically simulated by using a random media generation technique and the finite-difference time-domain(FDTD) method. And the proposed signal processing is applied to the B-scan data of the GPR survey. We show that the proposed approach provides sufficient enhancement of target signals as well as remarkable reduction of subsurface inhomogeneous clutters in comparison with the conventional eigenimage filtering.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.3
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• pp.12-21
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• 2019

In order to investigate the effect of various pavement materials (artificial grass, natural grass, and clay sand) on the human thermal environment, the microclimate data in early autumn (air temperature, humidity, wind speed, and shortwave and longwave radiation) were measured and compared on each surface. The mean air temperature, humidity and wind speed of the pavement materials did not differ significantly and showed the greatest difference in the mean radiant temperature. Natural grass, which has the highest albedo, has the highest amount of shortwave radiation. The artificial turf had the highest surface temperature and the highest amount of longwave radiation. In the human thermal environment index PET, artificial grass > clay sand > natural grass. Natural grass had a maximum 2/3 level lower and a mean 1/2 level lower in PET as compared to artificial grass. The clay sand pavement had a maximum 2/3 level lower and a mean 1/3 level lower than the artificial grass. Natural grass had a maximum 1/3 level lower than the clay sand pavement. Their UTCIs showed smaller differences than the PETs. Therefore, it is necessary to carefully choose materials from the planning stage when designing outdoor spaces, including playgrounds.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.409-418
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• 2021

The land cover map is a very important data that is used as a basis for decision-making for land policy and environmental policy. The land cover map is mapped using remote sensing data, and the classification results may vary depending on the acquisition time of the data used even for the same area. In this study, to overcome the classification accuracy limit of single-period data, multi-series satellite images were used to learn the difference in the spectral reflectance characteristics of the land surface according to seasons on a U-Net model, one of the deep learning algorithms, to improve classification accuracy. In addition, the degree of improvement in classification accuracy is compared by comparing the accuracy of single-period data. Seoul, which consists of various land covers including 30% of green space and the Han River within the area, was set as the research target and quarterly Sentinel-2 satellite images for 2020 were aquired. The U-Net model was trained using the sub-class land cover map mapped by the Korean Ministry of Environment. As a result of learning and classifying the model into single-period, double-series, triple-series, and quadruple-series through the learned U-Net model, it showed an accuracy of 81%, 82% and 79%, which exceeds the standard for securing land cover classification accuracy of 75%, except for a single-period. Through this, it was confirmed that classification accuracy can be improved through multi-series classification.