For effective control of Frankliniella occidentalis, one of polyphagous pests with resistance to insecticides, necessitates the implementation of an integrated pest management strategy. Therefore, estimation of pest density is essential and this is achieved through the application of spatial statistical analysis methods. Because traditional methods often overlook the correlation between sampling locations and data, geostatistical analysis using variogram and kriging is introduced. Variogram provides information on the independent distance between data points. Kriging is a spatial interpolation technique for estimating the values at unsampled locations. For assessing model fitness, cross-validation is used by comparing predicted values with actual observations. This study focuses on the application of geostatistical techniques to estimate F. occidentalis density in hot pepper greenhouse, thereby contributing to making decision.

콘크리트 도로 하부의 이상대를 찾기 위해 전기비저항 탐사를 수행하였다. 콘크리트의 접지저항효과를 줄이기 위해 전기전도성이 좋은 매질과 평판 전극을 이용하였다. 전기비저항 탐사 결과를 분석하고 같은 장소에서 수행한 지하투과레이더 탐사, 충격응답기법, 다중채널 표면파 탐사 결과와 비교하였다. 전기비저항 탐사 결과는 함몰과 포장 구간에서 높은 비저항 분포를 보였으며, 지하투과레이더 탐사 결과는 보강으로 인한 형태를 보였다. 또한 충격응답기법과 전기비저항 탐사 결과의 비교를 통하여 보강 구간에서의 높은 동적강성도가 높은 비저항 분포의 원인임을 확인하였다. 동일한 장소에서 수행한 전기비저항 탐사와 다중채널 표면파 탐사 결과를 공동 크리깅한 결과, 지구통계학적 복합 해석이 각 지구물리 탐사결과에 대한 개별적인 분석보다 더 명확하게 이상대를 확인 할 수 있었다. 이 연구는 지구물리 탐사에 기초한 의사결정 과정에서 지구통계학을 이용한 복합 해석 결과의 활용 가능성을 제시한다.

This paper generated time-series temperature maps and analyzed the characteristics of temperature distributions from monthly average temperature observations between 2010 and 2011 in Jirisan areas using topographic data and geostatistics. From variogram modeling, all months except May to August showed that the spatial variability of temperature was the greatest along the direction perpendicular to coasts. Monthly temperature has negative correlations with elevation and distances from coasts and especially the correlation between temperature and distances from coasts was very weak in summer like the variogram modeling result. For temperature distribution mapping, kriging with a trend and ordinary kriging were separately applied as a univariate kriging algorithm by considering the spatial variability structures of temperature. Simple kriging with varying local means was applied as a multivariate kriging algorithm for integrating topographic data sets. From the cross validation results, the use of topographic data in spatial prediction of temperature showed the improved predictive performance, compared with univariate kriging. This improvement in predictive performance was dependent mainly on mean and variation values of monthly temperature and the spatial auto-correlation strength of residuals, as well as the correlation between topographic data and temperature. Based on these analysis results, spatial variability analysis using variogram is effectively used to account for spatial characteristics of monthly temperature and the correlation with topographic data. Topographic data can also be a useful information source for reliable temperature mapping.