• Journal of the Korean Geographical Society
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• v.47 no.6
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• pp.825-835
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• 2012

The aim of this research is to develope technique and mapping for detecting distribution of vegetation dieback areas in the subalpine zone of Mt. Baekdu. A detection technique developed the rule-based model using MODIS images. Dieback areas could be classified as 4 categories of initial dieback, middle dieback, and end dieback by pruning stages of leaves. Dieback area was $28km^2$ from year 2001 to year 2006, intial dieback was $16km^2$, middle dieback was $10km^2$, and end dieback was $2km^2$ by the each stage. Dieback area was $35km^2$ from year 2006 to year 2011. Total area was $35km^2$ from year 2001 to year 2011, areas of middle dieback and end dieback were increased. The research method for this study may help to support in application with preliminary detection of dieback areas in the mountains by the global warming.

• Spatial Information Research
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• v.22 no.1
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• pp.55-63
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• 2014

Near surface air temperature data which are one of the essential factors in hydrology, meteorology and climatology, have drawn a substantial amount of attention from various academic domains and societies. Meteorological observations, however, have high spatio-temporal constraints with the limits in the number and distribution over the earth surface. To overcome such limits, many studies have sought to estimate the near surface air temperature from satellite image data at a regional or continental scale with simple regression methods. Alternatively, we applied various Kriging methods such as ordinary Kriging, universal Kriging, Cokriging, Regression Kriging in search of an optimal estimation method based on near surface air temperature data observed from automatic weather stations (AWS) in South Korea throughout 2010 (365 days) and MODIS land surface temperature (LST) data (MOD11A1, 365 images). Due to high spatial heterogeneity, auxiliary data have been also analyzed such as land cover, DEM (digital elevation model) to consider factors that can affect near surface air temperature. Prior to the main estimation, we calculated root mean square error (RMSE) of temperature differences from the 365-days LST and AWS data by season and landcover. The results show that the coefficient of variation (CV) of RMSE by season is 0.86, but the equivalent value of CV by landcover is 0.00746. Seasonal differences between LST and AWS data were greater than that those by landcover. Seasonal RMSE was the lowest in winter (3.72). The results from a linear regression analysis for examining the relationship among AWS, LST, and auxiliary data show that the coefficient of determination was the highest in winter (0.818) but the lowest in summer (0.078), thereby indicating a significant level of seasonal variation. Based on these results, we utilized a variety of Kriging techniques to estimate the surface temperature. The results of cross-validation in each Kriging model show that the measure of model accuracy was 1.71, 1.71, 1.848, and 1.630 for universal Kriging, ordinary Kriging, cokriging, and regression Kriging, respectively. The estimates from regression Kriging thus proved to be the most accurate among the Kriging methods compared.