• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.4
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• pp.307-316
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• 2010

Complex terrain refers to irregular surface properties of the earth that influence gradients in climate, lateral transfer of materials, landscape distribution in soils properties, habitat selection of organisms, and via human preferences, the patterning in development of land use. Complex terrain of mountainous areas represents ca. 20% of the Earth's terrestrial surface; and such regions provide fresh water to at least half of humankind. Most major river systems originate in such terrain, and their resources are often associated with socio-economic competition and political disputes. The goals of the TERRECO-IRTG focus on building a bridge between ecosystem understanding in complex terrain and spatial assessments of ecosystem performance with respect to derived ecosystem services. More specifically, a coordinated assessment framework will be developed from landscape to regional scale applications to quantify trade-offs and will be applied to determine how shifts in climate and land use in complex terrain influence naturally derived ecosystem services. Within the scope of TERRECO, the abiotic and biotic studies of water yield and quality, production and biodiversity, soil processing of materials and trace gas emissions in complex terrain are merged. There is a need to quantitatively understand 1) the ecosystem services derived in regions of complex terrain, 2) the process regulation occurred to maintain those services, and 3) the sensitivities defining thresholds critical in stability of these systems. The TERRECO-IRTG is dedicated to joint study of ecosystems in complex terrain from landscape to regional scales. Our objectives are to reveal the spatial patterns in driving variables of essential ecosystem processes involved in ecosystem services of complex terrain region and hence, to evaluate the resulting ecosystem services, and further to provide new tools for understanding and managing such areas.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.95-118
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• 2017

Recent centrally the downtown area, the transaction between the row housing and multiplex housing is activated and platform services such as Zigbang and Dabang are growing. The row housing and multiplex housing is a blind spot for real estate information. Because there is a social problem, due to the change in market size and information asymmetry due to changes in demand. Also, the 5 or 25 districts used by the Seoul Metropolitan Government or the Korean Appraisal Board(hereafter, KAB) were established within the administrative boundaries and used in existing real estate studies. This is not a district classification for real estate researches because it is zoned urban planning. Based on the existing study, this study found that the city needs to reset the Seoul Metropolitan Government's spatial structure in estimating future housing prices. So, This study attempted to classify the area without spatial heterogeneity by the reflected the property price characteristics of row housing and Multiplex housing. In other words, There has been a problem that an inefficient side has arisen due to the simple division by the existing administrative district. Therefore, this study aims to cluster Seoul as a new area for more efficient real estate analysis. This study was applied to the hedonic model based on the real transactions price data of row housing and multiplex housing. And the K-Means Clustering algorithm was used to cluster the spatial structure of Seoul. In this study, data onto real transactions price of the Seoul Row housing and Multiplex Housing from January 2014 to December 2016, and the official land value of 2016 was used and it provided by Ministry of Land, Infrastructure and Transport(hereafter, MOLIT). Data preprocessing was followed by the following processing procedures: Removal of underground transaction, Price standardization per area, Removal of Real transaction case(above 5 and below -5). In this study, we analyzed data from 132,707 cases to 126,759 data through data preprocessing. The data analysis tool used the R program. After data preprocessing, data model was constructed. Priority, the K-means Clustering was performed. In addition, a regression analysis was conducted using Hedonic model and it was conducted a cosine similarity analysis. Based on the constructed data model, we clustered on the basis of the longitude and latitude of Seoul and conducted comparative analysis of existing area. The results of this study indicated that the goodness of fit of the model was above 75 % and the variables used for the Hedonic model were significant. In other words, 5 or 25 districts that is the area of the existing administrative area are divided into 16 districts. So, this study derived a clustering method of row housing and multiplex housing in Seoul using K-Means Clustering algorithm and hedonic model by the reflected the property price characteristics. Moreover, they presented academic and practical implications and presented the limitations of this study and the direction of future research. Academic implication has clustered by reflecting the property price characteristics in order to improve the problems of the areas used in the Seoul Metropolitan Government, KAB, and Existing Real Estate Research. Another academic implications are that apartments were the main study of existing real estate research, and has proposed a method of classifying area in Seoul using public information(i.e., real-data of MOLIT) of government 3.0. Practical implication is that it can be used as a basic data for real estate related research on row housing and multiplex housing. Another practical implications are that is expected the activation of row housing and multiplex housing research and, that is expected to increase the accuracy of the model of the actual transaction. The future research direction of this study involves conducting various analyses to overcome the limitations of the threshold and indicates the need for deeper research.

• Korean Journal of Ecology and Environment
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• v.34 no.1 s.93
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• pp.1-8
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• 2001

During April 1993 to November 1994, cations, anions, and conductivity were analyzed to examine how summer monsoon influences the ionic content of Taechung Reservoir, Korea. Interannual variability of ionic content reflected hydrological characteristics between the two years(high-flood year in 1993 vs. draught year in 1994). Cations, anions and conductivity were lowest during peak inflow in 1993 and highest during a drought in 1994. Floods in 1993 markedly decreased total salinity as a result of reduced Ca$^{2+}$ and HCO$_{3}\;^{-}$ and produced extreme spatial heterogeneity (i.e., longitudinal, vertical, and horizontal variation) in ionic concentrations. The dominant process modifying the longitudinal (the headwaters-to-downlake) and vertical (top-to-bottom) patterns in salinity was an interflow current during the 1993 monsoon. The interflow water plunged near a 27${\sim}$37 km-location (from the dam) of the mid-lake and passed through the 10${\sim}$30m stratum of the reservoir, resulting in an isolation of epilimnetic high conductivity water (>100 ${\mu}$S/cm) from advected river water with low conductivity (65${\sim}$75 ${\mu}$S/cm), During postmonsoon 1993, the factors regulating salinity differed spatially; salinity of downlake markedly declined as a result of dilution through the mixing of lake water with river water, whereas in the headwaters it increased due to enhanced CaCO$_{3}$ (originated from limestone/metamorphic rock) of groundwaters entering the reservoir. This result suggests an importance of the basin geology on ion compositions with hydrological characteristics. In 1994, salinity was markedly greater (p<0.001) relative to 1993 and ionic dilution did not occur during the monsoon due to reduced inflow. Overall data suggest that the primary factor influencing seasonal ionic concentrations and compositions in this system is the dilution process depending on the intensity of monsoon rainfall.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.721-732
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• 2017

With the increasing socio-economic importance of rice as a global staple food, several models have been developed for rice yield estimation by combining remote sensing data with carbon cycle modelling. In this study, we aimed to estimate rice yield in Korea using such an integrative model using satellite remote sensing data in combination with a biophysical crop growth model. Specifically, daily meteorological inputs derived from MODIS (Moderate Resolution imaging Spectroradiometer) and radar satellite products were used to run a light use efficiency based crop growth model, which is based on the MODIS gross primary production (GPP) algorithm. The modelled biomass was converted to rice yield using a harvest index model. We estimated rice yield from 2003 to 2014 at the county level and evaluated the modelled yield using the official rice yield and rice straw biomass statistics of Statistics Korea (KOSTAT). The estimated rice biomass, yield, and harvest index and their spatial distributions were investigated. Annual mean rice yield at the national level showed a good agreement with the yield statistics with the yield statistics, a mean error (ME) of +0.56% and a mean absolute error (MAE) of 5.73%. The estimated county level yield resulted in small ME (+0.10~+2.00%) and MAE (2.10~11.62%),respectively. Compared to the county-level yield statistics, the rice yield was over estimated in the counties in Gangwon province and under estimated in the urban and coastal counties in the south of Chungcheong province. Compared to the rice straw statistics, the estimated rice biomass showed similar error patterns with the yield estimates. The subpixel heterogeneity of the 1 km MODIS FPAR(Fraction of absorbed Photosynthetically Active Radiation) may have attributed to these errors. In addition, the growth and harvest index models can be further developed to take account of annually varying growth conditions and growth timings.

• Journal of the Korean Geographical Society
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• v.50 no.3
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• pp.255-276
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• 2015

This paper presents a multi-agent system model of land-use and cover changes, which is developed and applied to the Gariwang-san and its vicinity, located in Pyeongchang and Jeongseon-gun, Gangwon province, Korea. The Land Use Dynamics Simulator (LUDAS) framework of this study is well suited for representing the spatial heterogeneity and dynamic interactions between human and natural environment, and capturing the impacts of forest-opening policy interventions to future socio-economic and natural environment changes. The model consists of four components: (1) a system of human population, (2) a system of landscape environment, (3) decision-making procedures integrating human(or household), environmental and policy information into forest land-use decisions, and (4) a set of policy scenarios that are related to the forest-opening. The results of model simulation by different combination of various forest management scenarios are assessed by the levels of household income, ecosystem service value and income inequality in the study region. As a result, the optimal scenario of forest-opening policies in the study region is to open the forest to local residential community for the purpose of recreation, considering the distinctive topographical feature. The model developed in this research is expected to contribute to a decision support system for sustainable forest management and various land-use policies in Korea.