Excessive development and urbanization have destroyed animal, plant, habitats and reduced biodiversity. In order to preserve species diversity, habitat prediction studies are have been conducted at home and overseas using various modeling techniques. This study was conducted to suggest optimal habitat modeling research by comparing HSI and MaxEnt, which are widely used among habitat modeling techniques. The study was targeted on the endangered species of Prionailurus bengalensis in nearby areas (5460.35km2) including Cheonan City, and the same data were used for analysis to compare those models. According to the HSI analysis, Prionailurus bengalensis's habitat probability was 74.65% for less than 0.5 and 25.34% for more than 0.5 and the top 30% were forest (99.07%). MaxEnt's analysis showed that 56.22% of those below 0.5 and 43.79% of those above 0.5 were found to have a high explanatory power of 78.3% of AUC. The Paired Wilcoxn test, which evaluated the significance of thoes models, confirmed that the mean difference between the two models was statistically significant (p<0.05). Analysis of the differences in the results of those models using the matrix table shows that score 24.43% HSI and MaxEnt was accordance,12.44% of the 0.0 to 0.2 section, 7.22% of the 0.2 to 0.4 section, 2.73% of the 0.4 to 0.6 section, 1.96% of the 0.6 to 0.8, and 0.08% of the 0.9 to 1.0. To verify where the score difference appears, the result values of those models were reset to values from 1 to 5 and overlaid. Overlapping analysis resulted in 30.26% of the Strongly agree values, 56.77% of the agree values, and 11.92% of the Disagree values. The places where the difference in scores occurs were analyzed in the order of forest (45.23%), agricultural land (34.57%), and urbanization area (7.65%). This confirmed that the analysis of the same target species within the same target site also has differences in forecasts depending on the modelling method. Therefore, a novel analysis method combining the advantages of each modeling in habitat prediction studies should be developed, and future study may be used to select Prionailurus bengalensis and species-protected areas and species protection areas in the future. Further research is judged to require higher accuracy studies through the use of various modeling techniques and on-site verification.

This study was carried out to suggest an appropriate plant combination by evaluating the growth of flowering ground-cover plants planted with Festuca glauca 'Eljiah Blue' on the roof-top environment. As for the plant materials, Allium senescens and Chrysanthemum coreanum which are shorter than Festuca glauca 'Eljiah Blue' and Sedum takesimense and Agastache rugosa which are taller than Festuca glauca 'Eljiah Blue' were selected. Festuca glauca 'Eljiah Blue' was planted on Conrol, and Festuca glauca 'Eljiah Blue' with Allium senescens (T1), Festuca glauca 'Eljiah Blue' with Sedum takesimense (T2), Festuca glauca 'Eljiah Blue' with Agastache rugosa(T3), and Festuca glauca 'Eljiah Blue' with Chrysanthemum coreanum (T4) were planted in each experimental plot. Plant height and covering rate were measured to evaluate the growth of Festuca glauca 'Eljiah Blue'. Also, relative growth rate (RGR) of plant height, RGR of plant width, and mortality rate of the flowering ground-cover plants were estimated. Plant height and cover rate of Festuca glauca 'Eljiah Blue' was greatest in T3. RGR of plant height was greater in the order of Agastache rugosa, Allium senescens, Chrysanthemum coreanum, and Sedum takesimense. In particular, RGR of plant width was also greatest for Agastache rugosa. Mortality rates of Agastache rugosa and Allium senescens were lowest at 11%. Therefore, based on good growth of Festuca glauca 'Eljiah Blue' planted with Agastache rugosa, these results were suggested as a desirable combination of plant species for rooftop gardening.

Increase in avian species richness is one of the important issues of urban biodiversity policies, since it can promote diverse ecosystem services such as seed dispersal, education, and pollination. However, though human disturbance can significantly affect avian species richness, there are limited studies on the way to reflect the dynamics of floating population. Therefore, this study analyzed the spatial relationship between avian species richness, floating population, and vegetation cover using telecommunications information to identify the areas that requiring targeted monitoring and restoration action. Bivariate Local Moran's I was applied to identify LISA cluster map that showing representative biotopes, which reflect significant spatial relationship between species richness and population distribution. Edge density and distribution of ndvi were identified for evaluating relative adequacy of selected biotopes to strengthen the robust biodiversity network. This study offers insight to consider human disturbance in spatial context using innovative big data to increase the effectiveness of urban biodiversity measures.

A field survey was carried out targeting 59 pine wilt disease control areas distributed in Andong, and the ecological characteristics of the areas affected by pine wilt disease were analyzed using vegetation information, ecological information, and land use information. Vegetation characteristics of Pine wilt disease affected forest area showed a 3-layer vegetation structure, high percentage of accidental occurrence species (37%) and secondary vegetation species (59.6%), appearing 12 taxa naturalized plants and 3 taxa ecosystem disturbance organisms designated by the Ministry of Environment. Ecological information of Pine wilt affected area showed frequent occurrence of water stress in south and west slopes, low lying grounds in mountains, and in well-drained soils. Also, surrounding the area has been used as roads, tombs, and cultivation where intensive human activities were the cause of disturbance and stress to the pine forest. It was analyzed that the pine forest in Andong city suffered extensive damage due to the onset of pine wilt disease while the pine trees were weakened due to continuous human activities. Conclusively, the spread and onset of pine wilt disease are worsened by artificial factors than natural environmental conditions.