Understanding the Impact of the COVID-19 Pandemic on the Perception and Use of Urban Green Spaces in Korea

Faced with the prospect that the impact of the COVID-19 pandemic and climate change will be far-reaching and long-term, the international community is showing interest in urban green space (UGS) and urban green infrastructure utilization as a solution. In this study, we investigated how citizens’ perceptions and use of UGS have changed during COVID-19. We also collected their ideas on how UGS can raise its usability. As a result, more people became to realize the importance of UGS. In particular, the urban environmental purification function from UGS was recognized as giving great benefits to respondents. On the other hand, the patterns of UGS use were mixed with decreasing UGS use to maintain social distancing or increasing UGS use to maintain health or substitute other restricted facilities. More than half of respondents had their UGS visit patterns impacted by COVID-19. In particular, the increase rate of UGS use was rather high in the group that seldom used UGS before COVID-19. In addition, they increased the use of UGS to replace other limited facilities, and thus tended to demand an increase in rest facilities. Based on these results, this paper suggested securing social support and sustainability for the policy by reflecting users’ demand in landscape planning related to the increase of UGS in the city. This study can contribute to improving the resilience of UGS and the sustainability of urban space planning

Understanding the Impact of the COVID-19 Pandemic on the Perception and Use of Urban Green Spaces in Korea

Faced with the prospect that the impact of the COVID-19 pandemic and climate change will be far-reaching and long-term, the international community is showing interest in urban green space (UGS) and urban green infrastructure utilization as a solution. In this study, we investigated how citizens’ perceptions and use of UGS have changed during COVID-19. We also collected their ideas on how UGS can raise its usability. As a result, more people became to realize the importance of UGS. In particular, the urban environmental purification function from UGS was recognized as giving great benefits to respondents. On the other hand, the patterns of UGS use were mixed with decreasing UGS use to maintain social distancing or increasing UGS use to maintain health or substitute other restricted facilities. More than half of respondents had their UGS visit patterns impacted by COVID-19. In particular, the increase rate of UGS use was rather high in the group that seldom used UGS before COVID-19. In addition, they increased the use of UGS to replace other limited facilities, and thus tended to demand an increase in rest facilities. Based on these results, this paper suggested securing social support and sustainability for the policy by reflecting users’ demand in landscape planning related to the increase of UGS in the city. This study can contribute to improving the resilience of UGS and the sustainability of urban space planning

Development of earth observational diagnostic drought prediction model for regional error calibration: A case study on agricultural drought in Kyrgyzstan

Drought is a natural disaster that occurs globally and is a main trigger of secondary environmental and socio-economic damages, such as food insecurity, land degradation, and sand-dust storms. As climate change is being accelerated by human activities and environmental changes, both the severity and uncertainties of drought are increasing. In this study, a diagnostic drought prediction model (DDPM) was developed to reduce the uncertainties caused by environmental diversity at the regional level in Kyrgyzstan, by predicting drought with meteorological forecasts and satellite image diagnosis. The DDPM starts with applying a prognostic drought prediction model (PDPM) to 1) estimate future agricultural drought by explaining its relationship with the standardized precipitation index (SPI), an accumulated precipitation anomaly, and 2) compensate for regional variances, which were not reflected sufficiently in the PDPM, by taking advantage of preciseness in the time-series vegetation condition index (VCI), a satellite-based index representing land surface conditions. Comparing the prediction results with the monitored VCI from June to August, it was found that the DDPM outperformed the PDPM, which exploits only meteorological data, in both spatiotemporal and spatial accuracy. In particular, for June to August, respectively, the results of the DDPM (coefficient of determination [R2] = 0.27, 0.36, and 0.4; root mean squared error [RMSE] = 0.16, 0.13, and 0.13) were more effective in explaining the spatial details of drought severity on a regional scale than those of the PDPM (R2 = 0.09, 0.10, and 0.11; RMSE = 0.17, 0.15, and 0.16). The DDPM revealed the possibility of advanced drought assessment by integrating the earth observation big data comprising meteorological and satellite data. In particular, the advantage of data fusion is expected to be maximized in areas with high land surface heterogeneity or sparse weather stations by providing observational feedback to the PDPM. This research is anticipated to support policymakers and technical officials in establishing effective policies, action plans, and disaster early warning systems to reduce disaster risk and prevent environmental and socio-economic damage