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Volume 3(2); July 2021
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Articles
The Funga of Higher Fungi of the Mongolian Oak Forest in Mt. Jeombong, Korea
Ju-Kyeong Eo, Eunsu Park, Ho-Yeon Won, Young Sang Lee, Dongsu Yu, Areum Han, Hwa-Yong Lee, Hee-Su Lee
GEO DATA. 2021;3(2):1-11.   Published online July 21, 2021
DOI: https://doi.org/10.22761/DJ2021.3.2.001
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AbstractAbstract PDF
This study was conducted to find the biodiversity of higher fungi at the supersite of Mt. Jeombong from July to October 2016 during the Second Long Term Ecology Research project. A total of 31 families, 52 genera, and 120 species of higher fungi were found within the permanent 1 ha qaudrat and in a 10 m radius of the ecological flux tower. From a taxonomical and ecological perspcetive, Russulaceae (22 species, 18.0%), Boletaceae (17 species, 13.9%), and Amanitaceae (10 species, 8.2%) were the top 3 taxa with the most species found in mycorrhizal mushrooms. Marasmiaceae (10 species, 9.8%), Mycenaceae (8 species, 6.6%), and Polyporaceae (6 species, 4.9%) were also the top 3 taxa with the most species found in saprophytic mushrooms.
Dataset for Water Body Detection Using Satellite SAR Images
SeungJae Lee, Han Oh
GEO DATA. 2021;3(2):12-19.   Published online July 21, 2021
DOI: https://doi.org/10.22761/DJ2021.3.2.002
  • 473 View
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AbstractAbstract PDF
Satellite synthetic aperture radar (SAR) generates valid image information in all-weather. Thus, it can be effectively used for near real-time monitoring and damage analysis of flood areas which always involve overcast skies. Water body detection (WBD) using SAR images can be implemented by various techniques which discriminate electromagnetic characteristics between water and non-water areas. Especially, semantic segmentation exploiting artificial intelligence techniques can be used to develop a high-performance WBD model. To this end, Korea Aerospace Research Institute has built an WBD dataset using KOMPSAT-5 images. The dataset is currently available through the website, aihub.or.kr.
The Spatial Maps of Paddy Rice Yield over Northeast Asia Using COMS Geostationary Satellite and Reanalysis Meteorological Data
Seungtaek Jeong, Jonghan Ko, Jong-Min Yeom
GEO DATA. 2021;3(2):20-24.   Published online July 21, 2021
DOI: https://doi.org/10.22761/DJ2021.3.2.003
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AbstractAbstract PDF
This study estimated rice yield maps for Northeast Asia by using the Communication, Ocean and Meteorological satellite (COMS), Terra satellite, and Regional Data Assimilation and Prediction System (RDAPS) of the numerical model. The rice yield is highly useful in the study for crop information monitoring according to climate change as well as agriculture information, industry, and economy. This study produced rice yield maps for Northeast Asia including Korea, North Korea, Japan, and three northeastern provinces of China (Heilongjiang, Jilin, and Liaoning) from 2011 to 2017. The estimated spatial resolution of the rice yield maps in Northeast Asia is 500 m. The spatial observation range is 25 ° N ~ 47 ° N and 115 ° E ~ 145 ° E. In order to estimate rice yield, Remote Sensing-integrated Crop Model was employed in this study. The inputs of the RSCM are vegetation indices from Geostationary Ocean Color Imager (GOCI) of the COMS, solar radiation from Meteorological Imager of the COMS, Land Surface Water Index from the MODerate Resolution Imaging Spectroradiometer, and the temperature from the RDAPS were considered as input data. In particular, this study applied the Bidirectional Reflectance Distribution Function to the GOCI time-series images to calculate more improved vegetation indices by minimizing the directional error generated in the satellite observation location. These indices were very effective in the simulation of the rice yield.
Mapping of Groundwater Productivity in Entire South Korea using Probabilistic Model
Saro Lee, Panahi Mahdi
GEO DATA. 2021;3(2):25-31.   Published online July 21, 2021
DOI: https://doi.org/10.22761/DJ2021.3.2.004
  • 522 View
  • 61 Download
AbstractAbstract PDF
In this study, the correlation between specific capacity (SPC) and transmissivity (T) values of groundwater and various geological, topographical, soil, clinical, and forest-related factors was calculated using a probability technique-frequency ratio. Then, the groundwater potential maps were created using the frequency ratio model with a resolution of 30 m for the entire South Korea. The maps were validated using the quantitative ROC (Receiver Operating Characteristic)-AUC (Area Under the Curve) method and the results showed the accuracy of 83.52% for specific capacity and 81.92% for Transmissivity. The groundwater potential maps can be used as basic data of groundwater development and downloaded free of charge from the environmental big data platform (www.bigdata-environment.kr).
Mapping of Groundwater Pollution Vulnerability in Entire South Korea Using DRATIC Model
Won-Kyung Baek, Sung-Hwan Park, Jin-Woo Yu, Young-Woong Yoon, Hyung-Sup Jeong
GEO DATA. 2021;3(2):32-38.   Published online July 21, 2021
DOI: https://doi.org/10.22761/DJ2021.3.2.005
  • 641 View
  • 35 Download
AbstractAbstract PDF
Groundwater pollution vulnerability was mapped for entire South Korea using groundwater, topography, geology, and soil data. For this, the DRASTIC model developed by the US Environmental Protection Agency was used and the geographic information system (GIS) was used as the basic tool. This groundwater pollution vulnerability map can be usefully used as basic data for groundwater development and conservation management. The constructed data is provided as entire South Korean and regional data, respectively. In addition, in order to expand the accessibility of the data, it is converted and provided in three data formats: ASCII, ArcGIS Grid, and GeoTIFF. All these satellite image analysis data can be downloaded free of charge from the Environment Big Data Platform website (www.bigdata-environment.kr).

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