• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.283-294
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• 2021

Solar radiation is an important meteorological factor in the agricultural sector. The ground exposed to sunlight is highly influenced by the surrounding terrains especially in South Korea where the topology is complex. The solar radiation on an inclined surface is estimated using a solar irradiance correction factor for the slope of the terrain along with the solar radiation on a horizontal surface. However, such an estimation method assumes that there is no barrier in surroundings, which blocks sunlight from the sky. This would result in errors in estimation of solar radiation because the effect of shading caused by the surrounding terrain has not been taken into account sufficiently. In this study, the shading effect was simulated to obtain the brightness value (BV), which was used as a correction factor. The shaded relief images, which were generated using a 30m-resolution digital elevation model (DEM), were used to derive the BVs. These images were also prepared using the position of the sun and the relief of the terrain as inputs. The gridded data where the variation of direct solar radiation was quantified as brightness were obtained. The value of cells in the gridded data ranged from 0 (the darkest value) to 255 (the brightest value). The BV analysis was performed using meteorological observation data at 22 stations installed in study area. The observed insolation was compared with the BV of each point under clear and cloudless condition. It was found that brightness values were significantly correlated with the solar radiation, which confirmed that shading due to terrain could explain the variation in direct solar radiation. Further studies are needed to accurately estimate detailed solar radiation using shaded relief images and brightness values.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.3
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• pp.36-51
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• 2011

Global climate change is disturbing the water circulation balance by changing rates of precipitation, recharge and discharge, and evapotranspiration. Groundwater, which occupies a considerable portion of the world's water resources, is related to climate change via surface water such as rivers, lakes, and marshes. In this study, the authors selected a relevant climate change scenario, A1B from the Special Report on Emission Scenario (SRES) which is distributed at Korea Meteorological Administration. By using data on temperature, rainfall, soil, and land use, the groundwater recharge rate for the research area was estimated by periodically and embodied as geographic information system (GIS). In order to calculate the groundwater recharge quantity, Visual HELP3 was used as main model, and the physical properties of weather, temperature, and soil layers were used as main input data. General changes to water circulation due to climate change have already been predicted. In order to systematically solve problems of ground circulation system, it may be urgent to recalculate the groundwater recharge quantity and consequent change under future climate change. The space-time calculation of changes of the groundwater recharge quantity in the study area may serve as a foundation to present additional measures to improve domestic groundwater resource management.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1179-1194
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• 2020

This study investigates the feasibility of three algorithms, K-Nearest Neighbors (K-NN), Random Forest (RF) and Neural Network (NN), for estimating the air temperature of an unobserved area where the weather station is not installed. The satellite image were obtained from Landsat-8 and MODIS Aqua/Terra acquired in 2019, and the meteorological ground weather data were from AWS/ASOS data of Korea Meteorological Administration and Korea Forest Service. In addition, in order to improve the estimation accuracy, a digital surface model, solar radiation, aspect and slope were used. The accuracy assessment of machine learning methods was performed by calculating the statistics of R² (determination coefficient) and Root Mean Square Error (RMSE) through 10-fold cross-validation and the estimated values were compared for each target area. As a result, the neural network algorithm showed the most stable result among the three algorithms with R² = 0.805 and RMSE = 0.508. The neural network algorithm was applied to each data set on Landsat imagery scene. It was possible to generate an mean air temperature map from June to September 2019 and confirmed that detailed air temperature information could be estimated. The result is expected to be utilized for national disaster safety management such as heat wave response policies and heat island mitigation research.

• The Korean Journal of Ecology
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• v.16 no.3
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• pp.239-259
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• 1993

The vegetation types and distributional characteristics of the forest vegetation on Mt. Ilwol were investigated by the Z-M school method. Geographical distribution of the vegetation was a central/montane(CEMO) type of the Korean Peninsula subtype. The forest was classified into five vegetation units: Artemisio-Quercetum mongolicae (Artemisio-Quercetum typicum subass. nov., Artemsio-Quercetum pinetosum dens~florae subass. nov. ). Lychno-Quercetum mongolicae (Lychno-Quercetum Calamagrostetosum arundinaceae subass. nov.. Lychno-Quercetum ligularetosum f~scherii subass. nov.), Fraxinus rhynchophylla community, Quercus variabilis community and Larix leptolepis afforestation. Species with percentage constance degree more than 61% are Quercus mongolica (89.5%, V), Lespedeza maximowiczii (73.7%, IV), Isodon japonicus(73.7%, IV), Fraxinus rhynchophylla (68.%, IV), Arlemisia stolonifera (65, 8%, IV), etc. Artemisio-Quercetum mongolicae was chiefly distributed on the ridge lines of low altitudes (570~700m) and on the ground with exposed rocks in sloping surface (800~1, 000m). Both Fraxinus sievoldiana and Rhododendrou schlippenbachii of the Artemisio-Quercetum mongolicae showed a predominant condition all over the subtree and shrub layers. Of Artemisio-Quercetum pinetosum densifloraes, Pinus densiflora becomes powerful over the tree layer and, on the other hand, Quercus mongolica over the subtree layer. Lychno-Quercetum mongolicae was distributed somewhat on the humid region of the high altitude area (840~1.200m). Fraxinus rhynchophylla community was chiefly distributed in the vicinity of the ravines. Quereus rariabilis community was distributed at subxeric slope on southeast side of the mountain. Larix leptolepis has been reforested at the ecotone between Artemisio-Quercetum mongolicae and Lychno-Quercetum mongolicae. Percentage cover degrees of the subtree layer In Lychno-Quercetum mongolicae, Quereus rariabilis community and Larix leptolepis afforestation were relatively low, 15.3%, 5% and 2.5%, respectively.The contents of moisutre and organic matter in the soil were much higher in Luchno-Quercetum monogolicae and Larix leptolepis afforestaion than in the other communities. Contents o Ca, Mg, K and the available phosphorus were relaively high compared with forests of the other area. This result seems to be influenced by a forest fire. As the altitude increases, the moisure content in the forest soil also increases.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.5
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• pp.443-451
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• 2015

In photogrammetry, GCPs (Ground Control Points) have traditionally been used to determine EOPs (Exterior Orientation Parameters) and to produce DEM (Digital Elevation Model). The existing DEM can be used as GCPs, where the observer’s approach is a difficult area, because it is very restrictive to survey in the field. For this, DEM matching should be performed. This study proposed the fusion method using ICP (Iterative Closest Point) and RT (proposed method by Rosenholm and Torlegard, 1988) in order to improve accuracy of the DEM matching. The proposed method was compared to the ICP method to evaluate its usefulness. Pseudo reference DEM with resolution 10m, and modified DEM (random-numbers are added from 0 to 2 at height; scale is 0.9; translation is 100 meters in 3-D axes; rotation is from 10° to 50° from the reference DEM) were used in the experiment. The results proposed accuracy was highest in the matching and absolute orientation. In the case of ICP, according to rotation of the modified DEM being increased, absolute orientation error is increased, while the proposed method generally showed consistent results without increasing the error. The proposed method would be applied to matching when the DEM is modified up to 30° rotation, compared to the reference DEM, based on the results of experiments. In addition when we use Drone, this method can be utilized to identify EOPs or detect 3-D surface deformation from the existing DEM of the inaccessible area.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.6
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• pp.619-627
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• 2016

Cells of a PV (photovoltaic) module can suffer defects due to various causes resulting in a loss of power output. As a malfunctioning cell has a higher temperature than adjacent normal cells, it can be easily detected with a thermal infrared sensor. A conventional method of PV cell inspection is to use a hand-held infrared sensor for visual inspection. The main disadvantages of this method, when applied to a large-scale PV power plant, are that it is time-consuming and costly. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule was applied to automatically detect defective panels using the mean intensity and standard deviation range of each panel by array. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97% or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule. In this study, we used a panel area extraction method that we previously developed; fault detection accuracy would be improved if panel area extraction from images was more precise. Furthermore, the proposed algorithm contributes to the development of a maintenance and repair system for large-scale PV power plants, in combination with a geo-referencing algorithm for accurate determination of panel locations using sensor-based orientation parameters and photogrammetry from ground control points.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.102-114
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• 2017

The purpose of this study was the assessment of the accuracy of Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG), a rainfall data source derived from satellite images, for evaluation of its applicability to use in ungauged or inaccessible areas. The study area was the overall area of the Korean peninsula divided into six regions. Automated Surface Observing System (ASOS) rainfall data from the Korean Meteorological Administration and IMERG satellite rainfall were used. Their average correlation coefficient was 0.46 for a 1-h temporal resolution, and it increased to 0.69 for a 24-h temporal resolution. The IMERG data quantitatively estimated less than the rainfall totals from ground gauges, and the bias decreased as the temporal resolution was decreased. The correlation coefficients of the two rainfall events, which had relatively greater rainfall amounts, were 0.68 and 0.69 for a 1-h temporal resolution. Additionally, the spatial distributions of the ASOS and IMERG data were similar to each other. The study results showed that the IMERG data were very useful in the assessment of the hydro-meteorological characteristics of ungauged or inaccessible areas. In a future study, verification of the accuracy of satellite-derived rainfall data will be performed by expanding the analysis periods and applying various statistical techniques.

• Journal of the Korean Geophysical Society
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• v.2 no.1
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• pp.45-56
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• 1999

Seismic refracrion and reflection surveys were conducted along an E-W trending track of 482 m long in Ilwall-dong, Pohang. End-on spread was employed as source-receiver configuration with 2 m for both geophone interval and offset. Seismic data were acquired using 24 channels at every shot fired every 2 m along the track. Refraction data were interpreted using equations for multi-horizontal layers. Reflection data were processed in the sequence of trace edit, gain control, CMP sorting, NMO correction, mute, common offset gathering, and filtering to produce a single fold seismic section. There are two layers in shallow subsurface of the study area. Upper layer has the P-wave velocities ranging from 267 to 566 m/s and is interpreted as a layer of unconsolidated sediments. Lower layer has P-wave velocities of 1096-3108 m/s and is interpreted as weathered rock to hard rock. Most of the lower layer classified as soft rock. Upper layer has lateral variations in both P-wave velocity and thickness. The upper layer in the eastern part of the seismic line is 3-5 m thick and has P-wave velocity of 400 m/s in average. The upper layer in the western part is 8-10 m thick and has P-wave velocity of 340 m/s in average. The eastern part is interpreted as unconsolidated beach sand, while the western part is interpreted as infilled soil to develop a construction site. Three fault systems of high angle are imaged in seismic reflection section. It is interpreted that the area between these fault systems are relatively safe. Large buildings should be located in the safe ground condition of no fault and footings should be designed to be in the basement rock of 3-10 m deep below the surface.

• The Journal of Engineering Geology
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• v.29 no.1
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• pp.13-21
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• 2019

The rainfall intensity-duration curve (I-D curve) was used for selecting the dredging time of sediments behind a debris barrier which is located at the study area in Inje-gun, Kangwon Province. The I-D curve was newly suggested by using the data of rainfall-induced landslides for about 30 years from June to September in Kangwon Province. According to the monitoring results, the landslides have been not occurred during the monitoring period of the dredged sediments management system at the study area, and also all of the rainfall events were located below the I-D curve. The weight of the dredged sediments measured at the management system in the field was increased but the weight increment was small. It means that the increase of the dredged sediments was not the effect of landslide but the effect of soil erosion at the ground surface due to heavy rainfall. The weight of the dredged sediments behind a debris barrier could be known in real time using the rainfall data measured at the management system. Also, when the I-D curve is used with the management system, it is possible to select the optimum dredging time for sediments behind debris barrier.

• Korean Journal of Radiology
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• v.23 no.7
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• pp.720-731
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• 2022

Objective: We aimed to develop and test a deep learning algorithm (DLA) for fully automated measurement of the volume and signal intensity (SI) of the liver and spleen using gadoxetic acid-enhanced hepatobiliary phase (HBP)-magnetic resonance imaging (MRI) and to evaluate the clinical utility of DLA-assisted assessment of functional liver capacity. Materials and Methods: The DLA was developed using HBP-MRI data from 1014 patients. Using an independent test dataset (110 internal and 90 external MRI data), the segmentation performance of the DLA was measured using the Dice similarity score (DSS), and the agreement between the DLA and the ground truth for the volume and SI measurements was assessed with a Bland-Altman 95% limit of agreement (LOA). In 276 separate patients (male:female, 191:85; mean age ± standard deviation, 40 ± 15 years) who underwent hepatic resection, we evaluated the correlations between various DLA-based MRI indices, including liver volume normalized by body surface area (LV_BSA), liver-to-spleen SI ratio (LSSR), MRI parameter-adjusted LSSR (aLSSR), LSSR × LV_BSA, and aLSSR × LV_BSA, and the indocyanine green retention rate at 15 minutes (ICG-R15), and determined the diagnostic performance of the DLA-based MRI indices to detect ICG-R15 ≥ 20%. Results: In the test dataset, the mean DSS was 0.977 for liver segmentation and 0.946 for spleen segmentation. The Bland-Altman 95% LOAs were 0.08% ± 3.70% for the liver volume, 0.20% ± 7.89% for the spleen volume, -0.02% ± 1.28% for the liver SI, and -0.01% ± 1.70% for the spleen SI. Among DLA-based MRI indices, aLSSR × LV_BSA showed the strongest correlation with ICG-R15 (r = -0.54, p < 0.001), with area under receiver operating characteristic curve of 0.932 (95% confidence interval, 0.895-0.959) to diagnose ICG-R15 ≥ 20%. Conclusion: Our DLA can accurately measure the volume and SI of the liver and spleen and may be useful for assessing functional liver capacity using gadoxetic acid-enhanced HBP-MRI.