Estimating Spatio-Temporal Distribution of Climate Factors in Andong Dam Basin

Geomatics, Natural Hazards and Risk

Won

et al. 2019

Self Cite

To assess which data type is more effective for spatial modeling in the Republic of Korea, we conducted geostatistical analysis based on frequency, intensity, and spatial autocorrelation using two types of forest fire occurrence data: that collected through field survey of the Korea Forest Service (KFS) and satellite active fire data of Moderate Resolution Imaging Spectroradiometer (MODIS). The maximum entropy (MaxEnt) model was used with environmental factors in the spatial modeling of fire probability to compare the accuracy of the two data types based on 10 years of historical data. The results showed a clear difference in fire frequency and similar fire intensity patterns. The spatial autocorrelation between the fire frequency and intensity of the two data types was analyzed using a semi-variogram. Fire intensity was significantly correlated, with the MODIS data having a higher correlation than the KFS data. Examination of the spatial autocorrelation and related factors by fire source also indicated that MODIS data had higher spatial autocorrelation, with remarkable distinction found in climate factors. In spatial the modeling, MODIS data showed a similar outcome to that of hotspot analysis, with higher accuracy and better model performance attributable to high spatial autocorrelation. Even though the KFS data were collected from post-fire surveys, they resulted in low spatial autocorrelation and reduced model accuracy owing to the wide distribution of data. MODIS had many detection errors. With spatial filtering, however, the model accuracy can be improved with relatively high spatial autocorrelation.

Section: Spatial Autocorrelation Analysis Using Semi-variogrammentioning

confidence: 99%

Can satellite-based data substitute for surveyed data to predict the spatial probability of forest fire? A geostatistical approach to forest fire in the Republic of Korea

Lim

Geomatics, Natural Hazards and Risk

Won

et al. 2019

Self Cite

“…Where the semi-variogram model attains range, the y-axis value is called the sill and it shows the variance of the data [54]. The partial sill is the sill subtracted from the nugget, and a high partial sill means high changes in spatial distribution [54,55].…”

Section: Materials: Chemical Factory and Chemical Accident Datamentioning

confidence: 99%

Chemical accident hazard assessment by spatial analysis of chemical factories and accident records in South Korea

Heo

International Journal of Disaster Risk Reduction

et al. 2018

This study identified the potential chemical accident occurrence in Korea by analyzing the spatial distribution of chemical factories and accidents. The number of chemical factories and accidents in 25-km 2 grids were used as the attribute value for spatial analysis. First, semivariograms were conducted to examine spatial distribution patterns and to identify spatial autocorrelation of chemical factories and accidents. Semi-variograms explained that the spatial distribution of chemical factories and accidents were spatially autocorrelated. Second, the results of the semi-variograms were used in Ordinary Kriging to estimate chemical hazard levels. The level values were extracted from the Ordinary Kriging result and their spatial similarity was examined by juxtaposing the two values with respect to their location. Six peaks were identified in both the factory hazard and accident hazard estimation result, and the peaks correlated with major cities in Korea. Third, the estimated two hazard levels were classified with geometrical interval and could be classified into four quadrants: Low Factory 2 and Low Accident (LFLA), High Factory and Low Accident (HFLA), Low Factory and High Accident(LFHA), and High Factory and High Accident (HFHA). The 4 groups identified different chemical safety management issues in Korea; safe LFLA group, many chemical reseller factories were found in HFLA group, chemical transportation accidents were in the LFHA group, and an abundance of factories and accidents were in the HFHA group. Each quadrant represented different safety management obstacles in Korea, and studying spatial differences can support the establishment of an efficient risk management plan.

“…If differences in residuals exist at the regional level because of other factors such as climate and topographic factors, residuals will show spatial autocorrelation. The semi-variogram has been used to analyze spatial variability [38]. Therefore, semi-variogram analysis was performed in this study to identify spatial autocorrelation.…”

Section: Semi-variogram Analysis For Residuals Of the Dbh Estimation mentioning

confidence: 99%

Development of an Integrated DBH Estimation Model Based on Stand and Climatic Conditions

Piao

Choi

et al. 2018

Forests

Using Korean National Forest Inventory (NFI) data, our study developed a model to estimate stand mean diameter at breast height (DBH) reflecting the influence of site and climate factors on forest growth for the major tree species in South Korea. A DBH estimation model was developed using stand-level variables (stand age, site index and number of trees per hectare) as independent factors. The spatial autocorrelation of residuals of the model was identified using semi-variogram analysis for each tree species. Further, a residual model, in which residuals were estimated by climatic factors (mean temperature, sum temperature in the growing season and precipitation), was developed assuming that the spatial autocorrelation of residuals reflects the differences in regional climatic conditions. Linear regression analysis showed that residuals of all tree species were significantly correlated with temperature and precipitation. The DBH and residual models were integrated to estimate the current DBH under different climatic factors (temperature and precipitation) and stand-level variables. This model had high reliability (R 2 = 0.74-0.79), and no obvious dependencies or patterns in residuals were noted. Our results indicated that temperature increases caused by climate change would negatively affect the DBH estimate of coniferous trees, but not of oak species.