• Journal of Environmental Science International
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• v.33 no.6
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• pp.435-442
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• 2024

We investigated the accuracy of surface air temperature prediction according to the selection of land-use data and initial meteorological data using the Weather Research and Forecasting model-v4.2.1. A numerical experiment was conducted at the Daegu Dyeing Industrial Complex. We initially used meteorological input data from GFS (Global forecast system)and GDAPS (Global data assimilation and prediction system). High-resolution input data were generated and used as input data for the weather model using the land cover data of the Ministry of Environment and the digital elevation model of the Ministry of Land, Infrastructure, and Transport. The experiment was conducted by classifying the terrestrial and topographic data (land cover data) and meteorological data applied to the model. For simulations using high-resolution terrestrial data(10 m), global data assimilation, and prediction system data(CASE 3), the calculated surface temperature was much closer to the automatic weather station observations than for simulations using low-resolution terrestrial data(900 m) and GFS(CASE 1).

• Journal of Environmental Science International
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• v.17 no.5
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• pp.485-491
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• 2008

The present study examined the relation between land cover condition and temperature in various types of urban green spaces. The diagram of temperature distribution showed that high-temperature zones are formed around paved areas, and low temperature zone around planted areas and grassy areas. Even in planted areas where low-temperature zones were formed, temperature was different according to hierarchical structure. That is, temperature was relatively low in areas covered with arbor + sub-arbor. With regard to land cover ratio, the increase of planted areas and grassy areas had an effect on the fall of temperature and the effect was higher in order of planted areas and grassy areas. On the contrary, paved areas and bare areas had an effect on the rise of temperature. According to the results of factor analysis, in case of the highest temperature, planted area and grassy area were put together into a factor lowering temperature, paved area and temperature into a factor raising temperature, and bare area alone into a factor of low significance. In case of the lowest temperature, grassy area and bare area were put together into a factor, and the validity of the factor analysis was proved by the analysis of urban heat islands. An increase in the number of trees by height was effective in lowering temperature, and the effect was high in order to arbor and sub-arbor, and the source of coldness in planted area was tall trees.

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.537-544
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• 2013

Baton Peninsula, where Sejong station is located, mainly covered with snow and vegetation. Because this area is sensitive to climate change, monitoring of surface variation is important to understand climate change on the polar region. Due to the inaccessibility, the remote sensing is useful to continuously monitor the area. The objectives of this research are 1) map classification of land-cover types in the Barton Peninsular around King Sejong station and 2) grasp distribution of vegetation species in classified area. A KOMPSAT-2 multispectral satellite image was used to classify land-cover types and vegetation species. We performed classification with hierarchical procedure using KOMPSAT-2 satellite image and ground reference data, and the result is evaluated for accuracy as well. As the results, vegetation and non-vegetation were clearly classified although species shown lower accuracies within vegetation class.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.859-872
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• 2016

Land cover classification is an important tool for preventing natural disasters, collecting environmental information, and monitoring natural resources. Hyperspectral imaging is widely used for this task thanks to sufficient spectral information. However, the curse of dimensionality, spatiotemporal variability, and lack of labeled data make it difficult to classify the land cover correctly. We propose a novel classification framework for land cover classification of hyperspectral data based on convolutional neural networks. The proposed framework naturally incorporates full spectral features with the information from neighboring pixels and has advantages over existing methods that require additional feature extraction or pre-processing steps. Empirical evaluation results show that the proposed framework provides good generalization power with classification accuracies better than (or comparable to) the most advanced existing classifiers.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.1
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• pp.15-22
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• 2015

To create a wind resource map, we need a contour map, a roughness map and wind data. We need a land cover map for the roughness map of these data. A land cover map represents the area showing similar characteristics after color indexing based on the scientific method. The features of land cover is classified by Remote sensing technique. In this study, we verified the application of the NDVI technique is reasonable after we created the wind resource map using roughness maps by unsupervised classification and NDVI technique. As a result, the wind resource map using the NDVI technique showed a 60% accordance rate and difference in class less than one. From the results, The NDVI technique is found alternative to create roughness maps by the unsupervised classification.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.3
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• pp.19-27
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• 2012

Wind is an important variable for various scientific communities such as meteorology, climatology, and renewable energy. In this study, numerical simulations using WRF mesoscale model were performed to produce temporal and spatial wind information over the Republic of Korea during 2006. Although the spatial features and monthly variations of the near-surface wind speed were well simulated in the model, the simulated results overestimated the observed values as a whole. To correct these simulated wind speeds, a regression-based statistical algorithm with different constants and coefficients by land cover type was developed using the satellite-derived LST and NDWI. The corrected wind speeds for the algorithm validation showed strong correlation and close agreement with the observed values for each land cover type, with nearly zero mean bias and less than 0.4 m/s RMSE. Therefore, the proposed algorithm using remotely sensed surface observations may be useful for correcting simulated near-surface wind speeds and producing more accurate wind information over the Republic of Korea.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.3
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• pp.65-71
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• 2012

Recently the high-resolution satellite images are helpfully using the land cover, status data for the natural resources or environment management. The effective satellite analysis process for these satellite images that require high investment can be increase the effectiveness has become increasingly important. In this Study, the statistical value of the training data is calculated and analyzed during the preprocessing. Also, that is explained about the maximum likelihood classification of traditional classification method, artificial neural network (ANN) classification method and Support Vector Machines(SVM) classification method and then the IKONOS high-resolution satellite imagery was produced the land cover map using each classification method. Each result data had to analyze the accuracy through the error matrix. The results of this study prove that SVM classification method can be good alternative of the total accuracy of about 86% than other classification method.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.1-12
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• 2012

The agricultural area was classified into dry and paddy fields in this study using the near-infrared band of Landsat TM to extract land cover classes that need to the application of Expected Mean Concentration (EMC) in nonpoint source works. The accuracy of image classification of the land cover map from Landsat TM image showed 83.61% and 78.41% respectively by comparing with the large and middle scale land cover map of Ministry of Environment. As the result of Soil Conservation Service (SCS) Curve Number (CN) using the land cover map from image classification, Dongbok dam and Dongbok stream basin were analyzed high. Also Geymbaek water-gage and Bosunggang upstream basin showed high in the analysis of EMC of BOD, TN, TP by basin. And also Geymbaek water-gage and Bosunggang upstream basin showed high in the analysis of non-point source through coupling with direct runoff. Therefore these basins were selected with the main area for the management of nonpoint source.

• Korean Journal of Remote Sensing
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• v.25 no.2
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• pp.175-184
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• 2009

This study attempts to analyze the difference among the variations of ecological distribution in Gobi desert and Manchuria through satellite based land cover classification. This was motivated by two well-known facts: 1) Gobi desert, which is an old source region, had been gradually expanded eastward; 2) Manchuria, which is located in east of Gobi desert, was observed as a new source region of yellow dust. An unsupervised classification called ISODATA clustering method was employed to detect the land cover change and to characterize the status of desertification and its expanding trends using NDVI (Normalized Difference Vegetation Index) derived from VEGETATION sensor onboard the SPOT satellite for 1999 and 2007. We analyzed NDVI annual variation pattern for every classes and divide into 5 level according to their vegetation's density level based on NDVI. As results, Gobi desert is showed positive variation: a decrease $78,066km^2$ in central Gobi desert and out skirts of Gobi desert (level-0) but Manchuria area is worse than previous time: an increase $25,744km^2$.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.425-440
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• 2023

Monitoring water surface has become one of the most prominent areas of research in addressing environmental challenges.Accurate and automated detection of watersurface in remote sensing imagesis crucial for disaster prevention, urban planning, and water resource management, particularly for a country where water plays a vital role in human life. However, achieving precise detection poses challenges. Previous studies have explored different approaches,such as analyzing water indexes, like normalized difference water index (NDWI) derived from satellite imagery's visible or infrared bands and using k-means clustering analysis to identify land cover patterns and segment regions based on similar attributes. Nonetheless, challenges persist, notably distinguishing between waterspectralsignatures and cloud shadow or terrain shadow. In thisstudy, our objective is to enhance the precision of water surface detection by constructing a comprehensive water database (DB) using existing digital and land cover maps. This database serves as an initial assumption for automated water index analysis. We utilized 1:5,000 and 1:25,000 digital maps of Korea to extract water surface, specifically rivers, lakes, and reservoirs. Additionally, the 1:50,000 and 1:5,000 land cover maps of Korea aided in the extraction process. Our research demonstrates the effectiveness of utilizing a water DB product as our first approach for efficient water surface extraction from satellite images, complemented by our second and third approachesinvolving NDWI analysis and k-means analysis. The image segmentation and binary mask methods were employed for image analysis during the water extraction process. To evaluate the accuracy of our approach, we conducted two assessments using reference and ground truth data that we made during this research. Visual interpretation involved comparing our results with the global surface water (GSW) mask 60 m resolution, revealing significant improvements in quality and resolution. Additionally, accuracy assessment measures, including an overall accuracy of 90% and kappa values exceeding 0.8, further support the efficacy of our methodology. In conclusion, thisstudy'sresults demonstrate enhanced extraction quality and resolution. Through comprehensive assessment, our approach proves effective in achieving high accuracy in delineating watersurfaces from satellite images.