• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.751-762
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• 2017

A crop information system can provide information regarding crop distribution, crop growth conditions, crop yield in various forms such as monitoring, forecasting, estimation or analysis. This paper presents the design and construction of a crop information system based on data collected in Korea, USA, and China. Therein, climate data including temperature, precipitation,solar radiation are used to evaluate the impact on crop growth, NDVI (Normalized Difference Vegetation Index) data is used in crop monitoring, and crop map data is utilized for the management of crop distribution. The system has achieved three prominent results: 1) Providing information with high frequency, 2) Automatically creating the report through the analysis of the data, 3) The users to easily approach the system and retrieve the information.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.677-687
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• 2017

In this paper, we propose the use of hierarchical classification for winter crop mapping based on satellite imagery. A hierarchical classification is a classifier that maps input data into defined subsumptive output categories. This classification method can reduce mixed pixel effects and improve classification performance. The methodology are illustrated focus on winter cropsin Gimje city, Jeonbuk with Landsat-8 imagery. First, agriculture fields were extracted from Landsat-8 imagery using Smart Farm Map. And then winter crop fields were extracted from agriculture fields using temporal Normalized Difference Vegetation Index (NDVI). Finally, winter crop fields were then classified into wheat, barley, IRG, whole crop barley and mixed crop fields using signature from Unmanned Aerial Vehicle (UAV). The results indicate that hierarchical classifier could effectively identify winter crop fields with an overall classification accuracy of 98.99%. Thus, it is expected that the proposed classification method would be effectively used for crop mapping.

• Journal of Urban Science
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• v.6 no.2
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• pp.17-27
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• 2017

Recently, civil complaints have increased due to water pollution and bad smell in rivers. Therefore, attention is focused on improving the river environment. The purpose of this study is to acquire RGB and thermal infrared images using UAV for sewage outlet and to monitor the status of stream pollution and the applicability UAV based images for river embankment maintenance plan was examined. The accuracy of the 3D model was examination by SfM(Structure from Motion) based images analysis on river embankment maintenance area. Especially, The wastewater discharged from the factory near the river was detected as an thermal infrared images and the flow of wastewater was monitored. As a result of the study, we could monitor the cause and flows of wastewater pollution by detecting temperature change caused by wastewater inflow using UAV images. In addition, UAV based a high precision 3D model (DTM, Digital Topographic Map, Orthophoto Mosaic) was produced to obtain precise DSM(Digital Surface Model) and vegetation cover information for river embankment maintenance.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.16 no.6 s.97
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• pp.615-621
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• 2005

In this paper, a model for simulating synthetic aperture radar(SAR) images of earth surfaces. The earth surfaces include forest area, rice crop field, other agricultural fields, grass field, road, and water surface. At first, the backscattering models are developed for bare soil surfaces, water surfaces, short vegetation fields such as rice fields and grass field, other agriculture areas, and forest areas. Then, the SAR images are generated from the digital elevation model(DEM) and digital terrain map. The DTM includes ten parameters, such as soil moisture, surface roughness, canopy height, leaf width, leaf length, leaf density, branch length, branch density, trunk length, and trunk density, if applicable. The scattering models are verified with measurements, and applied to generate an SAR image for an area.

• The Transactions of the Korea Information Processing Society
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• v.3 no.7
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• pp.1763-1772
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• 1996

The conventional training set selection methods for the satellite image classification usually depend on the manual selection using data from the direct measurements of the ground or the ground map. However this task takes much time and cost, and some feature values vary in wide ranges even if they are in the same class. Such feature values can increase the robustness of the neural net but learning time becomes longer. In this paper,we propose anew training set selection algorithm using a rule-based method. By the technique proposed, the SPOT multispectral Imagery is classified in 3 bands, and the pixels which satisfy the rule are employed as the training sets for the neutralist classifier. The experimental results show faster initial convergence and almost the same or better classification accuracy. We also showed an improvement of the classification accuracy by using texture features and NDV1.

• Korean Journal of Remote Sensing
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• v.29 no.6
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• pp.611-619
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• 2013

Although the need of crop production has increased in Mongolia, crop cultivation is very limited because of the harsh climatic and topographic conditions. Crop lands are sparsely distributed with relatively small sizes and, therefore, it is difficult to survey the exact area of crop lands. The study aimed to find an easy and effective way of accurate classification to map crop lands in Mongolia using satellite images. To classify the crop lands over the study area in northern Mongolia, four classifications were carried out by using 1) Thematic Mapper (TM) image August 23, 2) TM image of July 6, 3) combined 12 bands of TM images of July and August, and 4) both TM images of July and August by layered classification. Wheat and potato are the major crop types and they show relatively high variation in crop conditions between July and August. On the other hands, other land cover types (forest, riparian vegetation, grassland, water and bare soil) do not show such difference between July and August. The results of four classifications clearly show that the use of multi-temporal images is essential to accurately classify the crop lands. The layered classification method, in which each class is separated by a subset of TM images, shows the highest classification accuracy (93.7%) of the crop lands. The classification accuracies are lower when we use only a single TM image of either July or August. Because of the different planting practice of potato and the growth condition of wheat, the spectral characteristics of potato and wheat cannot be fully separated from other cover types with TM image of either July or August. Further refinements on the spatial characteristics of existing crop lands may enhance the crop mapping method in Mongolia.

• Journal of Conservation Science
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• v.31 no.4
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• pp.395-402
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• 2015

This study was considered utilization of hyperspectral image analysis for monitoring. Accordingly we applied to stone cultural properties to data correction methods, image classification techniques, NDVI computation techniques using hyperspectral image. As the results, hyperspectral image analysis was possible making detailed deterioration map, accurate calculation of deterioration rate, mapping of normalized difference vegetation index on the basis of reflectance of each materials. Therefore, hyperspectral image analysis will be used for effective monitoring techniques of stone cultural heritages.

• Journal of the Korean earth science society
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• v.22 no.2
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• pp.138-146
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• 2001

The land cover over Korean peninsula was classified using a multi-temporal NOAA/AVHRR (Advanced Very High Resolution Radiometer) data. Four types of phenological data derived from the 10-day composited NDVI (Normalized Differences Vegetation Index), maximum and annual mean land surface temperature, and topographical data were used not only reducing the data volume but also increasing the accuracy of classification. Self organizing feature map (SOFM), a kind of neural network technique, was used for the clustering of satellite data. We used a decision tree for the classification of the clusters. When we compared the classification results with the time series of NDVI and some other available ground truth data, the urban, agricultural area, deciduous tree and evergreen tree were clearly classified.

• Journal of the Korean Geographical Society
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• v.32 no.1
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• pp.15-30
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• 1997

This paper concerns the Holocene environmental change with vegetational history and sea-level fluctuation at Ilsan area by the analytical data of pollen, sedimentary facies and $C^14$-dating. The hypothetic palaeogeographic maps of the vegetation cover have been reconstructed with the reference to the periods of pollen zone. The environmental characteristics from the pollen zonation have been summerized as follows. 1)Pollenzone I(3.75~5.75m) showed the period of Alnus-and EMW-dominance. The study area was very humid under the influence of the transgression spreading widely from the rapid sea-level rise during the period(8,000~4,200y.BP). 2)Pollen zone II(5.75~6.35m) has been influenced by the fall of the sea-level and ground water surface. This zone(4,200~2,300y.BP) represented the period of spore~ and NAP-dominance with the increase of Pinus. 3) Pollen zone III(6.35~6.55m) has reflected the influence of the transgression and human interferences together. This zone(2,300~1,800y.BP) represented the period of NAP-dominance. The boundary between Subzone Ilb and Pollen zone III represents the same characteristics as what Weber says Grenzhorizont.

• The Korean Journal of Ecology
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• v.23 no.5
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• pp.359-368
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• 2000

Landscape change is the modification and replacement of landscape elements in accordance with human management and natural disturbance on land mosaics. During landscape change, changes in patterns such as heterogeneity, diversity and shape, and juxtaposition of spatial elements are also accompanied. For the sustainable landscape system, therefore, spatial characteristics of the landscape should be considered in implementation of landscape conservation and restoration planning. Short-term changes of land-use and landscape pattern during the 10 years of 1980s and 1990s were investigated in the agriculture-forestry dominated landscape system through the statistics and the analysis of landscape-vegetation map. Study area is Yangdong-myon, Yangpyung-gun (37°27′30"N, 127°46′50"E), Kyonggi-do, in central Korea. Landscape change of this region was significantly related to the recent industrialization according to socio-economic development. Analyses of landscape pattern show that the area of secondary forest sustained by human activity decreased and it was replaced with large exotic plantations during this period. Area of paddy field was also extended. Fractal dimension of the total landscape increased, but that of paddy field area decreased due to rearrangement for mechanized farming. Moreover, the area of landscape management regimes such as plantation and cultivation increased in land mosaics during this period.