Environmental crisis challenges the human race harder than ever before. Ecologists have produced a massive amount of data to cope with the crisis. Accordingly, many national scale ecological database systems have been developed worldwide to manage and analyse these datasets. However, in Korea, ecological datasets produced by different research institutes for different purposes have not been integrated or serviced due to poorly designed information infrastructure. To address this obstacle, we present EcoBank (www.nie-ecobank.kr), an open, web-based ecological database platform designed to play an important role in ecosystem analysis, not only in Korea, but also worldwide. The architecture of EcoBank comprises core technologies of WebGIS, Application Programming Interface (API), responsive web and open-source software (OSS). Comprehensive ecological datasets from three different sources, including the National Institute of Ecology (NIE) in Korea, related national and international platforms and repositories, enter the three conceptual modules in EcoBank: data management, analysis and service. Diverse potential stakeholders of EcoBank can be classified into three groups: researchers, policy-makers and public users. EcoBank aims to expand its horizons through mutual communication amongst these stakeholders. We opened and launched the EcoBank service in December 2019 and have now begun to broaden its network by linking it to other data platforms and repositories over the globe to find possible solutions to ecological issues in Korea.

Vegetation constitutes the primary component of terrestrial ecosystems and plays a crucial role in examining global climate change and its impacts. Assessing vegetation dynamics over significant periods of time can provide critical information on changes in vegetation cover and regional climate. Satellite-based remote sensing products offer a unique opportunity to analyze these changes at a country scale. South Korea has a unique history of forest regeneration, and studying this relationship provides an opportunity to examine its impact. To achieve this, we employ the vegetation–precipitation relationship using time series segmentation on residual trends (TSS-RESTREND), a statistical method that calculates the correlation between the annual maximum vegetation index and cumulative rainfall. This method helps identify time-step changes related to structural changes in the ecosystem. Using the enhanced vegetation index (EVI) and rainfall, this study aims to uncover the underlying dynamics and driving forces behind observed ecosystem changes, facilitating a deeper comprehension of the intricate interactions between vegetation and precipitation through the application of TSS-RESTREND. The findings increase knowledge of the complex relationship between vegetation growth and rainfall, given the country’s commendable forest regeneration program, even in the face of rapid urbanization. Moreover, the integration of finer resolution land cover images derived from in situ data not only provides a nuanced understanding of vegetation dynamics across diverse regions, but also underscores the critical need for continued collection of in situ data to provide more accurate and comprehensive analyses. The findings underline the pivotal role played by land resource management practices and underscores the significance of implementing adaptive conservation strategies, thereby contributing to the preservation of a stable and sustainable vegetation cover. The findings serve as a call for the ecosystem monitoring and strategic adaptation required to examine the environmental and ecosystem structural changes in the region. This study sheds light on the critical relationship between land management practices, environmental sustainability, and the resilience of the region’s vegetation amidst an ever-changing landscape.

Urban plans for pedestrian-friendly environments by reducing exposure to air pollutants and enhancing movement are crucial for public health and accessibility of social infrastructure. Here, we develop a novel network analysis-based approach, which identifies pivotal local walkways that lower exposure risk to fine particulate matter (PM_2.5) while improving the urban landscape connectivity. We employ an exponential distance-decay model and partial correlation analysis to estimate traffic-induced PM_2.5 and to test the relationship between the proxies and actual PM_2.5 concentrations, respectively. We use a proxy for pedestrians’ PM_2.5 exposure as a movement cost when conducting network analyses to compute pedestrian network centrality, reflecting both low PM_2.5 exposure risk and landscape connectivity. As a result, we found a significant contribution of traffic to the estimated PM_2.5 exposure and PM_2.5 concentrations. We also found that walkways make a large contribution to regional connectivity regardless of the estimated PM_2.5 exposure risk owing to the composition and configuration of urban landscape elements. Regarding the spatial features and planning context, this study suggests four types of pedestrian networks to provide urban authorities with useful practical information in city-wide urban plans for enhancing walkability: PM_2.5 reduction required; PM_2.5 reduction recommended; optimal areas; and alternatives of optimal areas.

The Korean traditional farm pond called dumbeong is an important rural landscape element that supports local biodiversity and is useful in irrigating agricultural fields during dry periods. This study assesses how plant communities in dumbeongs respond to adjacent land use, water depth, open-water surface, and nutrient levels and irrigation usage. Plant functional and species groups, based on trait and species data respectively from 20 dumbeongs in Seocheon-gun, South Korea, were classified by hierarchical analysis and non-metric multidimensional scaling. Relationships between the plant community composition and explanatory variables at both the species and functional group levels were tested through redundancy analysis. The results showed that irrigation usage prevented nutrient accumulation and water depth reduction of the ponds, and we found water depth was the only significant factor that determined plant composition at both species and functional group levels. The plant functional groups were more useful than plant species in predicting plant composition in dumbeongs, owing to their collective response to water depth and open-water surface. Our results demonstrate that management practices of dumbeong, such as periodic drainage, sediment removal and control of dominant plant species, alter its plant communities and thus need to be considered for biodiversity conservation in agricultural landscapes.