• 한국환경복원기술학회지
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• 제25권3호
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• pp.29-45
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• 2022

For biodiversity conservation, the importance of beta-diversity which is changes in the composition of species according to environmental changes has become emphasized. However, given the systematic investigation of species distribution and the accumulation of large amounts of data in the Republic of Korea(ROK), research on the spatialization of beta-diversity using them is insufficient. Accordingly, this research investigated the applicability of the Generalized Dissimilarity Modeling(GDM) to ROK, which can predict and map the similarity of compositional turnover (beta-diversity) based on environmental variables. A brief overview of the statistical description on using GDM was presented, and a model was fitted using the flora distribution data(410,621points) from the National Ecosystem Survey and various environmental spatial data including climate, soil, topography, and land cover. Procedures and appropriated spatial units required to improve the explanatory power of the model were presented. As a result, it was found that geographical distance, temperature annual range, summer temperature, winter precipitation, and soil factors affect the dissimilarity of the vegetation community composition. In addition, as a result of predicting the similarity of vegetation composition across the nation, and classifying them into 20 and 100 zones, the similarity was high mainly in the central inland area, and tends to decrease toward the mountainous areas, southern coastal regions, and island including Jeju island, which means the composition of the vegetation community is unique and beta diversity is high. In addition, it was identified that the number of common species between zones decreased as the geographic distance between zones increased. It classified the spatial distribution of plant community composition in a quantitative and objective way, but additional research and verification are needed for practical application. It is expected that research on community-level biodiversity modeling in the ROK will be conducted more actively based on this study.

• 한국환경복원기술학회지
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• 제22권4호
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• pp.1-14
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• 2019

The 1st Forest Health Management survey was conducted to examine the health of the forests in Korea. However, in order to understand the health of the forests, which account for 63.7% of the total land area in South Korea, it is necessary to comprehensively spatialize the results of the survey beyond the sampling points. In this regard, out of the sample points of the 1st Forest Health Management survey in Gyeongbuk area, 78 spots were selected. For these spots, the species diversity index was selected from the survey sections, and the spatial interpolation method was applied. Inverse distance weighted (IDW), Ordinary Kriging and Ordinary Cokriging were applied as spatial interpolation methods. Ordinary Cokriging was performed by selecting vegetation indices which are highly correlated with species diversity index as a secondary variable. The vegetation indices - Normalized Differential Vegetation Index(NDVI), Leaf Area Index(LAI), Sample Ratio(SR) and Soil Adjusted Vegetation Index(SAVI) - were extracted from Landsat 8 OLI. Verification was performed by the spatial interpolation method with Mean Error(ME) and Root Mean Square Error(RMSE). As a result, Ordinary Cokriging using SR showed the most accurate result with ME value of 0.0000218 and RMSE value of 0.63983. Ordinary Cokriging using SR was proven to be more accurate than Ordinary Kriging, IDW, using one variable. This indicates that the spatial interpolation method using the vegetation indices is more suitable for spatialization of the biodiversity index sample points of 1st Forest Health Management survey.