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        검색결과 2

        1.
        2018.11 KCI 등재 서비스 종료(열람 제한)
        The real-time monitoring of surface vegetation is essential for the management of droughts, vegetation growth, and water resources. The availability of land cover maps based on remotely collected data makes the monitoring of surface vegetation easier. The vegetation index in an area is likely to be proportional to meteorological elements there such as air temperature and precipitation. This study investigated relationship between vegetation index based on Moderate Resolution Image Spectroradiometer (MODIS) and ground-measured meteorological elements at the Yongdam catchment station. To do this, 16-day averaged data were used. It was found that the vegetation index is well correlated to air temperature but poorly correlated to precipitation. The study provides some intuition and guidelines for the study of the droughts and ecologies in the future.
        2.
        1996.12 KCI 등재 서비스 종료(열람 제한)
        This study was performed to analyze the variation characteristics of water qulity, correlation analysis of water quality data at each site and among the items of water quality data. Water quality for analysis was monthly values of water temperature, pH, dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solid, T-N and T-P checked in Daecheong Lake from January to December, 1995. It was analyzed variation of monthly water qulity was well from February to April, water temperature and COD seemed to have high correlationships at all sites. Regression equation is COD = 0.07 Water temperature + 1.23 (R^2 = 0.7616) . Results of the correlation analysis of water quality data showed that DO had higt correlationships between site 1 and site 2, BOD did site 1 and 3, COD did site 1 and 2, SS did site 5 and 6, T-N did 2 and 3, T-P did site 4 and 6. Regression equations for estimate of water quality data are as follows. DO_1 = 4.46 + 0.59 DO_2 (R^2 = 0.8868), BOD_1 = 0.52 + 0.63 BOD_3 (R^2 = 0.6390) COD_2 = 0.44 + 0.71 COD_1 (R^2 = 0.9183), SS_6 = 0.89 + 0.70 SS_5 (R^2 = 0.9155) TN_3 = 0.151 + 0.886 TN_2 (R^2 = 0.9415), TP_4 = 0.004 + 0.758 TP_6 (R^2 = 0.9669)