A Framework for Quantitative Assessment of Urban Green Space Biodiversity

Thesis Abstract

Urban green spaces are increasingly recognized as vital components of sustainable city planning due to their ecological, aesthetic, and socio-economic benefits. However, the lack of standardized and quantitative frameworks for assessing biodiversity within these spaces hampers effective management, conservation, and policy development. This study aims to develop a comprehensive, evidence-based framework for the quantitative assessment of biodiversity in urban green spaces, thereby facilitating informed decision-making and sustainable urban environmental management. The specific objectives are (i) to identify relevant biodiversity indicators and metrics suitable for urban contexts; (ii) to establish reliable data collection methodologies; (iii) to model relationships between urban green space attributes and biodiversity levels; and (iv) to validate the framework through empirical application in selected urban parks. The research adopts a mixed-methods approach, combining quantitative biodiversity surveys with spatial analysis and statistical modeling. The population comprises twenty urban green spaces within the metropolitan area of Green City, with a total area range of 2 to 15 hectares. A stratified random sampling technique is employed to select thirty sample plots within each green space, ensuring representation across different habitat types. Biodiversity data are collected through standardized point-count methods for avian species and Quadrat sampling for plant and invertebrate species, utilizing field equipment such as binoculars, quadrats, and identification guides. Complementary remote sensing data and GIS layers are integrated to assess habitat heterogeneity, connectivity, and anthropogenic disturbances. The study also employs structured interviews with urban park managers to gather contextual information on management practices. Data analysis involves the application of multivariate statistical techniques, including Principal Component Analysis (PCA) to identify key biodiversity indicators, and multiple regression analysis to model relationships between urban green space attributes—such as size, habitat diversity, and buffer zones—and biodiversity indices. The framework's validity is tested through cross-validation procedures and sensitivity analysis. The study hypothesizes that increased habitat heterogeneity and larger green space sizes are positively correlated with higher biodiversity indices, consistent with the Theory of Island Biogeography and the Habitat Heterogeneity Hypothesis. Expected findings include the identification of a set of core biodiversity indicators that reliably reflect ecological health in urban green spaces, and a validated modeling framework that quantifies the influence of specific urban landscape features on biodiversity levels. The results are anticipated to demonstrate significant positive relationships between habitat diversity, connectivity, and species richness, providing empirical evidence for urban ecological theories. The proposed framework integrates ecological data with spatial analysis tools, offering a replicable model adaptable to different urban settings. This research contributes to existing knowledge by providing a standardized, quantitative tool for biodiversity assessment that bridges ecological theory and urban planning practice. It advances the understanding of how urban landscape configuration influences biodiversity and offers practical guidance for urban green space management aimed at enhancing ecological resilience. The conclusion underscores the importance of integrating biodiversity metrics into urban planning processes and recommends policy measures that prioritize habitat connectivity and diversity. It also suggests avenues for future research, particularly in longitudinal studies to monitor biodiversity dynamics over time. Overall, the study aims to support the development of sustainable, biodiversity-friendly urban environments through rigorous, evidence-based assessment frameworks.