• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.69-74
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• 2003

The estimation of pest density is a prime concern of Integrated Pest Management (IPM) because the success of artificial intervention such as spraying pestcides or natural enemies depends on pest density. Also, the spatial pattern of pest population within plants or plots has been studies in various ways. In this study, we applied generalized linear mixed model to Tetranychus urticae Koch , two-spotted spider mite count in glasshouse grown roses. For this analysis, the subject-specific as well as pupulation-averaged approaches are used.

• Journal of Korean Society of Rural Planning
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• v.29 no.3
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• pp.79-89
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• 2023

As the demand for environmental-friendly agricultural products continues to rise due to increased concerns regarding food safety and ecosystem conservation, it is becoming important to identify regions and spatial locations where environmental-friendly should be intensively established for production integration. This study aims to analyze the spatial distribution of environmental-friendly certified farms in Jeollanam-do, South Korea. Spatial statistical analysis based on Local Moran's I and Getis-Ord Gi^* were used to identify spatial cluster characteristics and landscape indices were utilized to analyze spatial patterns of environmental-friendly certified farms. The results indicated that Haenam-gun, Gangjin-gun, Muan-gun, and Jindo-gun were identified as hotspots, while Muan-gun, Goheung-gun, and Jindo-gun exhibited high connectivity. This suggests that environmental-friendly certified farms in Muan-gun and Jindo-gun were clustered and closely connected to one another. Based on the results of the spatial distribution of environmental-friendly certified farms, areas belonging to the hotspot and with high connectivity should be managed as clustered districts to secure the foundation and system of environmental-friendly certified farms. Areas that belong to cold spots and have low connectivity should be preceded by measures to promote conversion to environmental-friendly agriculture. In addition, it is necessary to make it possible to create a large-scale cluster district through a long-term spatial planning strategy to expand the environmental-friendly certified farms. The findings of this study can provide quantitative data on policies and discussions for developing a model for rural spatial planning.

• Journal of Biosystems Engineering
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• v.29 no.3
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• pp.267-274
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• 2004

Using geo-statistical method, yield data of different fields were analyzed to examine their field variability according to examining year, analysis method. Semivariogram and Kriged maps of geo-statistical analysis were used to examine their spatial dependence within a filed. The results obtained were as follows. 1) Descriptive statistical results of the yield showed that the yield and the difference of yield ranged from 100 to 946kg/10a and from 272 to 653kg/10a, respectively within a field. The coefficient of variation also ranged from 5.9 to 22.4 %. 2) More than 90% of yield data were placed between 350 to 850kg/10a. e results indicated that the gram mass flow sensor should have the measuring range from 0.34 to 0.82kg/s considering the yields when 4 rows head-feeding combine with 0.8 m/s of working speed was utilized. 3) A high spatial dependence was found within paddy field. The Q values ranged from 0.20 to 0.97, and the range of spatial dependence was from 6.9 to 53.3m. From this result, the rational sampling interval for yield investigation was estimated 6.9m. 4) Yields within a field between observation years showed considerable variability even if the field was evenly cultivated and managed. To apply precision agriculture in a paddy field, the field test should be continued to build a solid data-base including meteorological data, blight damage and insect damage.

• Proceedings of the KSRS Conference
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• v.2
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• pp.574-577
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• 2006

In this paper, we developed a forest fire detection algorithm which uses a regression function between NDVI and land surface temperature. Previous detection algorithms use the land surface temperature as a main factor to discriminate fire pixels from non-fire pixels. These algorithms assume that the surface temperatures of non-fire pixels are intrinsically analogous and obey Gaussian normal distribution, regardless of land surface types and conditions. And the temperature thresholds for detecting fire pixels are derived from the statistical distribution of non-fire pixels’ temperature using heuristic methods. This assumption makes the temperature distribution of non-fire pixels very diverse and sometimes slightly overlapped with that of fire pixel. So, sometimes there occur omission errors in the cases of small fires. To ease such problem somewhat, we separated non-fire pixels into each land cover type by clustering algorithm and calculated the residuals between the temperature of a pixel under examination whether fire pixel or not and estimated temperature of the pixel using the linear regression between surface temperature and NDVI. As a result, this algorithm could modify the temperature threshold considering land types and conditions and showed improved detection accuracy.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.835-847
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• 2016

To analyze lattice or areal data, a conditionally autoregressive (CAR) model has been widely used in the eld of spatial analysis. The spatial neighborhoods within CAR model are generally formed using only inter-distance or boundaries between regions. Kyung and Ghosh (2010) proposed a new class of models to accommodate spatial variations that may depend on directions. The proposed model, a directional conditionally autoregressive (DCAR) model, generalized the usual CAR model by accounting for spatial anisotropy. Properties of maximum likelihood estimators of a Gaussian DCAR are discussed. The method is illustrated using a data set of median property prices across Greater Glasgow, Scotland, in 2008.

• Journal of the Korean Geotechnical Society
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• v.23 no.3
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• pp.51-59
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• 2007

A reliability-based approach that can systematically model various sources of uncertainty is presented in the context of slope stability. Expressions for characterization of soil properties are developed in order to incorporate sampling errors, spatial variability and its effect of spatial averaging. Reliability analyses of slope stability with different statistical representations of soil properties show that the incorporation of sampling error, spatial correlation, and conditional simulation leads to significantly lower probability of failure than that obtained by using simple random variable approach. The results strongly suggest that the spatial variability and sampling error have to be properly incorporated in slope stability analysis.

• Environmental Engineering Research
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• v.19 no.3
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• pp.197-203
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• 2014

This study estimated spatial and seasonal variation of water quality to understand characteristics of Nakdong river basin, Korea. All together 11 parameters (discharge, water temperature, dissolved oxygen, 5-day biochemical oxygen demand, chemical oxygen demand, pH, suspended solids, electrical conductivity, total nitrogen, total phosphorus, and total organic carbon) at 22 different sites for the period of 2003-2011 were analyzed using multivariate statistical techniques (cluster analysis, principal component analysis and factor analysis). Hierarchical cluster analysis grouped whole river basin into three zones, i.e., relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) based on similarity of water quality characteristics. The results of factor analysis/principal component analysis explained up to 83.0%, 81.7% and 82.7% of total variance in water quality data of LP, MP, and HP zones, respectively. The rotated components of PCA obtained from factor analysis indicate that the parameters responsible for water quality variations were mainly related to discharge and total pollution loads (non-point pollution source) in LP, MP and HP areas; organic and nutrient pollution in LP and HP zones; and temperature, DO and TN in LP zone. This study demonstrates the usefulness of multivariate statistical techniques for analysis and interpretation of multi-parameter, multi-location and multi-year data sets.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.2
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• pp.29-37
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• 2011

Forest fire in Korea has been controlled by local government, so that it is required to understand the characteristics of regional forest fire occurrences for the effective management. In this study, to analyze the patterns of regional forest fire occurrences, we divided South Korea into nine zones based on administrative boundaries and performed spatial statistical analysis using the location data of forest fire occurrences for 1991-2008. The spatial distributions of forest fire were analyzed by the variogram, and the risk of forest fire was predicted by kriging analysis. As a result, forest fires in metropolitan areas showed strong spatial correlations, while it was hard to find spatial correlations of forest fires in local areas without big city as Gangwon-do, Chungcheongbuk-do and Jeju island.

• Economic and Environmental Geology
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• v.33 no.4
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• pp.321-332
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• 2000

The purpose of this study is to analyze landslide that occurred in Yongin area in 1991 using spatial database. For this, landslide locations are detected from aerial photographs interpretation and field survey. The locations of landslide, topography, soil, forest and geology were constructed to spatial database using Geographic Information System (GIS). To establish occurrence factors of landslide, slope, aspect and curvature of topography were calculated from the topographic database. Texture, material, drainage and effective thickness of soil were extracted from the soil database, and type, age, diameter and density of wood were extracted from the forest database. Lithology was extracted from the geological database, and land use was classified from the TM satellite image. Landslide was analyzed using spatial correlation between the landslide and the landslide occurrence factors by bivariate probability methods. GIS was used to analyze vast data efficiently and statistical programs were used to maintain specialty and accuracy. The result can be used to prevention of hazard, land use planning and construction planning as basic data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.457-461
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• 2007

In many applications, received signal strength indicator is used for location tracking and sensor nodes localization. For location finding, the distances between sensor nodes can be estimated by converting received signal's power into distance using path loss prediction model. Many researches have done the analysis of power-distance relationship for radio channel characterization. In indoor environment, the general conclusion is the non-linear variation of RSSI values as distance varied linearly. This has been one of the difficulties for indoor localization. This paper presents works on indoor RSSI characterization based on statistical methods to find the overall trend of RSSI variation at different places and times within the same room From experiments, it has been shown that the variation of RSSI values can be determined by both spatial and temporal factors. This two factors are directly indicated by the two main parameters of path loss prediction model. The results show that all sensor nodes which are located at different places share the same characterization value for the temporal parameter whereas different values for the spatial parameters. Using this relationship, the characterization for location estimation can be more efficient and accurate.