• Korean Journal of Environment and Ecology
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• v.29 no.1
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• pp.14-20
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• 2015

We created species richness maps of mammals, birds and plants using "Nnational Ecosystem Survey" data and identified correlations between species richness maps of each taxa. We examine the distribution of species richness of each taxa and calculated conservation priority rank through plotting species-area curves using an additive benefit function in Zonation. The conclusions of this study are as follows. First, plant showed high species richness in Gangwon province and Baekdudaegan, and mammals showed high species richness at eastern slope of Baekdudaegan in Gangwon province unusually and the species richness of mammals distributed equally except Gyeonggi and Chungnam province. However, birds showed high species richness in the west costal because the area is the major route of winter migratory birds. Second, correlation of each taxa's distribution is not significant. Correlation between mammals and birds is positive but correlations between birds and others are negative. Because mammals inhabit in forest but birds mostly live in coastal wetlands and rivers. Therefore, bird's habitats are not shared with other habitats. Third, the probability of mammals occurrence is very low under 25% in species-area curve, others increase proportionally to area. Birds increase dramatically richness at 10% because bird's habitat is concentrated in coastal wetlands and rivers. Plants increased gently species richness due to large forest in Gangwon province. We can calculate the predicted number of species in curves and plan various conservation strategies using the marginal number of species. Finally, high priority ranks for conservation distributed mainly in Gangwon province and Baekdudaegan. When we compared with priority map and terrestrial national parks, the parks were evaluated as high priority ranks. However, the rank of parks away from Baekdudaegan was low. This study has the meaning of selecting conservation priority area using National Ecosystem Survey. In spite of the omission of survey data in national parks and Baekdudaegan, the results were good. Therefore, the priority rank method using species distribution models is useful to selecting protected areas and improving conservation plans. However, it is needed to select protected areas considering various evaluation factors, such as rarity, connectivity, representativeness, focal species and so on because there is a limit to select protected area only using species richness.

• Geophysics and Geophysical Exploration
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• v.10 no.4
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• pp.252-258
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• 2007

We have studied the feasibility of geostatistics approach to enhancing analysis of sparsely obtained seismic data by combining with satellite gravity data. The shallow depth and numerous fishing nets in The Yellow Sea, west of Korea, makes it difficult to do seismic surveys in this area. Therefore, we have attempted to use geostatistics to integrate the seismic data along with gravity data. To evaluate the feasibility of this approach, we have extracted only a few seismic profile data from previous surveys in the Yellow Sea and performed integrated analysis combining with the results from gravity data under the assumption that seismic velocity and density have a high physical correlation. First, we analyzed the correlation between extracted seismic profiles and depths obtained from gravity inversion. Next, we transferred the gravity depth to travel time using non-linear indicator transform and analyze residual values by kriging with varying local means. Finally, the reconstructed time structure map was compared with the original seismic section given in the previous study. Our geostatistical approach demonstrates relatively satisfactory results and especially, in the boundary area where seismic lines are sparse, gives us more in-depth information than previously available.

• Journal of Korea Water Resources Association
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• v.43 no.8
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• pp.695-707
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• 2010

The frequency and size of typhoon and local severe rainfall are increasing due to the climate change and the damage also increasing from typhoon and severe rainfall. The flood forecasting and warning system to reduce the damage from typhoon and severe rainfall needs forecasted rainfall using radar data and short-term rainfall forecasting model. For this reason, this study examined the applicability of short-term rainfall forecast using translation model with weather radar data to point out that the utilization of flood forecasting in Korea. This study estimated the radar rainfall using Least-square fitting method and estimated rainfall was used as initial field of translation model. The translation model have verified accuracy of forecasted radar rainfall through the comparison of forecasted radar rainfall and observed rainfall quantitatively and qualitatively. Almost case studies showed that accuracy is over 0.6 within 4 hours leading time and mean of correlation coefficient is over 0.5 within 1 hours leading time in Kwanak and Jindo radar site. And, as the increasing the leading time, the forecast accuracy of precipitation decreased. The results of the calculated Mean Area Precipitation (MAP) showed forecast rainfall tend to be underestimated than observed rainfall but the correlation coefficient more than 0.5. Therefore it showed that translation model could be accurately predicted the rainfall relatively. The present results indicate that possibility of translation model application of Korea just within 2 hours leading forecasted rainfall.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.4
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• pp.162-167
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• 2015

This paper describes the reason and the problem solving for the double-layer pattern of a Daejeon correlator operated in Korea-Japan Correlation Center. When the electric power of an input signal in the correlator is charged small enough to be buried in the noise, it is hard to see a signal with a specific pattern in the input signal, but when the electric power is large, a specific one is reported to be seen. By comparing data from observation with one from software correlator, it was confirmed from the analysis using the AIPS software that the amplitude gain of a source signal was affected about 3%. Therefore, in order to solve the problem of double-layer patterns, we found that a problem in the memory management module responsible for both the data input and the data serialization of the correlator is a cause for the double-layer pattern detected periodically. In other words, while data is serialized and read repeatedly in the memory area assigned to serialize the data from the serialization module, redundant last data is generated and an overlap for the memory allocation is occurred. Therefore, by modifying the program of the FPGA memory sections on serialization module to correct the problem, we confirmed that double-layer pattern is disappeared and correlation results are normally acquired.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.2
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• pp.65-81
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• 2019

This study analyzed the relationship of land cover type, urban temperature in Gimhae city, Gyeongsangnam-do, South Korea. Date were used for land cover map, MODIS LST, and detailed temperature data on the Korean Peninsula based on RCP between 2000 and 2010. The correlation between urban area and surface temperature was 0.417, 0.512 for agricultural area and -0.607 for forest area. The correlation between surface temperature and air temperature was 0.301. The relationship with air temperature was analyzed as 0.275 for urban area, agriculture area 0.226, forest area 0.350. Urban and agricultural areas showed increased surface and air temperature as the area increased, while forest areas showed opposite improvements. In structural equation models, urban and agricultural areas had direct effects on the rise of surface temperature, whle forest areas had direct effects on the reduction of air temperature. In the future, it is necessary to use measured temperature data near the surface to understand the relationship between surface temperature and temperature according to the changes in spatial characteristics, which will prepare measures for urban heat island mitigation at the level of urban and environmental planning.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.581-588
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• 2018

The aim of this study is to propose a fine co-registration approach for multi-temporal satellite images acquired from RapidEye, which has an advantage of availability for time-series analysis. To this end, we generate multitemporal ortho-rectified images using RPCs (Rational Polynomial Coefficients) provided with RapidEye images and then perform fine co-registration between the ortho-rectified images. A DEM (Digital Elevation Model) extracted from the digital map was used to generate the ortho-rectified images, and the RNCC (Registration Noise Cross Correlation) was applied to conduct the fine co-registration. Experiments were carried out using 4 RapidEye 1B images obtained from May 2015 to November 2016 over the Yeonggwang area. All 5 bands (blue, green, red, red edge, and near-infrared) that RapidEye provided were used to carry out the fine co-registration to show their possibility of being applicable for the co-registration. Experimental results showed that all the bands of RapidEye images could be co-registered with each other and the geometric alignment between images was qualitatively/quantitatively improved. Especially, it was confirmed that stable registration results were obtained by using the red and red edge bands, irrespective of the seasonal differences in the image acquisition.

• Journal of Environmental Health Sciences
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• v.48 no.2
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• pp.86-95
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• 2022

Background: As high concentrations of uranium and radon have been detected in some areas in Korea, it is considered necessary to investigate natural radioactive materials in the Gwangju area. Objectives: This study aimed to identify the hydrochemical characteristics of groundwater in Gwangju and investigate the distribution characteristics of uranium and radon, which are naturally radioactive substances. Methods: To determine the uranium and radon concentrations in groundwater according to the geology of the Gwangju area, we measured 62 groundwater wells. A geological distribution map of uranium and radon content was prepared for this study. Results: The groundwater type, defined using a Piper diagram, was mainly Ca-HCO₃. The concentration of uranium in the groundwater ranged from 0 to 29.3 ㎍/L, with a mean of 3.3 ㎍/L and a median of 0.9 ㎍/L. The median concentration of uranium in groundwater was highest in alluvium, granitic gneiss, and biotite granite (classified by geological unit), in that order. The concentration of radon in the groundwater ranged from 4.8 to 313.2 Bq/L, with a mean of 75.6 Bq/L and a median of 59.6 Bq/L. The median concentration of radon in groundwater was highest in biotite granite, alluvium, and granitic gneiss, in that order. As a result of the correlation analysis of groundwater in the study area, there was no significant correlation between uranium and radon. Conclusions: In this study area, uranium was shown to be far below the concentrations allowed by drinking water quality standards, but radon concentrations exceeded drinking water quality monitoring standards in 11% of the samples. It was judged that appropriate measures, such as the installation of radon reduction facilities, will be required after a thorough review of high-concentration radon detection sites of in the research area.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.597-608
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• 2022

Remotely sensed data have been used in various fields, such as disasters, agriculture, urban planning, and the military. Recently, the demand for the multitemporal dataset with the high-spatial-resolution has increased. This manuscript proposed an automatic image matching algorithm using a deep learning technique to utilize a multitemporal remotely sensed dataset. The proposed deep learning model was based on High Resolution Net (HRNet), widely used in image segmentation. In this manuscript, denseblock was added to calculate the correlation map between images effectively and to increase learning efficiency. The training of the proposed model was performed using the multitemporal orthophotos of the National Geographic Information Institute (NGII). In order to evaluate the performance of image matching using a deep learning model, a comparative evaluation was performed. As a result of the experiment, the average horizontal error of the proposed algorithm based on 80% of the image matching rate was 3 pixels. At the same time, that of the Zero Normalized Cross-Correlation (ZNCC) was 25 pixels. In particular, it was confirmed that the proposed method is effective even in mountainous and farmland areas where the image changes according to vegetation growth. Therefore, it is expected that the proposed deep learning algorithm can perform relative image registration and image matching of a multitemporal remote sensed dataset.

• Journal of Korean Society of Forest Science
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• v.111 no.3
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• pp.428-434
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• 2022

This study was conducted to derive the site index of forest productivity of Robinia pseudoacacia (honey plant) to characterize suitable planting sites and to investigate the effect of the site environmental factors on the site index using the quantification theory I method. The data used in the analysis were growth factors (stand age, dominant height, etc.) of the 6th national forest resources survey and various site environmental factors of a forest soil map (1:5,000). The average site index value of the R. pseudoacacia stand in Korea was 14 (range, 8 to 18). The environmental factors affecting the site index were parent rock, climatic zone, soil texture, local topography, and altitude. The accuracy of the estimation model using quantification theory I was only 33%. However, the correlation between the site index and the site environmental factors was statistically significant at the 1% level. Results of quantification analysis between site index and site environmental factors revealed that metamorphic and igneous rocks received high grades as parent rocks, climate zones received higher grades than central temperate zone, clay loam and silt loam received high grades in soil texture, and hillside received a high grade in local topography. Analysis of the partial correlation between site topographical factors and forest productivity (site index) found that soil class and altitude were partially correlated to x by 0.4129 and 0.4023, respectively, indicating that these factors are the most influential variables.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.855-863
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• 2022

In this study, the Artificial Neural Network (ANN) was used to mapping air temperature in Seoul. MODerate resolution Imaging Spectroradiomter (MODIS) data was used as auxiliary data for mapping. For the ANN network topology optimizing, scatterplots and statistical analysis were conducted, and input-data was classified and combined that highly correlated data which surface temperature, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), time (satellite observation time, Day of year), location (latitude, hardness), and data quality (cloudness). When machine learning was conducted only with data with a high correlation with air temperature, the average values of correlation coefficient (r) and Root Mean Squared Error (RMSE) were 0.967 and 2.708℃. In addition, the performance improved as other data were added, and when all data were utilized the average values of r and RMSE were 0.9840 and 1.883℃, which showed the best performance. In the Seoul air temperature map by the ANN model, the air temperature was appropriately calculated for each pixels topographic characteristics, and it will be possible to analyze the air temperature distribution in city-level and national-level by expanding research areas and diversifying satellite data.