He Zhu's scientific contributions

Urban park green spaces exhibit significant cool island effects, which can effectively mitigate the urban heat environment. Clarifying the characteristics and differences in the cool island effects of urban parks across different spatial gradients within cities is instrumental in identifying potential issues and optimizing the structure and resource allocation of park green spaces in a scientifically rational manner. This study focuses on parks within the central urban area of Beijing, utilizing remote sensing image interpretation and land surface temperature (LST) inversion to extract relevant characteristics of park green spaces and the park cool island intensity (PCI) index. Various mathematical and statistical methods including correlation analysis, regression analysis, and cluster analysis are employed to conduct comparative studies across three gradients: within the 3rd ring road, between the 3rd and 5th ring roads, and outside the 5th ring road. The analysis reveals that both park green space characteristics and urban heat island effects exhibit spatial gradient differences, collectively influencing the cool island effects of urban park green spaces. PCI gradually decreases across the three spatial gradients. Cluster analysis identifies four distinct types of parks with different cool island effect characteristics, highlighting the need for optimization and improvement in over half of the parks. Various indicators of park green space characteristics show different correlations with PCI, with variations in correlation strength and thresholds across gradients. The fitting effects of regression equations for each characteristic indicator and PCI gradually worsen from within the 3rd ring road to outside the 5th ring road, with different factors playing important roles across gradients. This study enhances our understanding of the cool island effects of urban park green spaces and facilitates the proposition of differentiated optimization management strategies for urban park green space planning and system construction in different regions.

The contribution of biodiversity to the global economy, human survival, and welfare has been significantly increasing. However, nature reserves have long been subject to a sequence of ecological environmental issues caused by human activities. Therefore, quantitatively assessing the spatiotemporal evolution characteristics of habitat quality due to land use changes and exploring the mechanisms of potential influencing factors can provide a scientific basis for the stable and sustainable development of natural ecosystems. This study aims to analyze 18 nature reserves in Hubei Province to identify the spatiotemporal evolution of habitat quality within these reserves and to explore the influence of multifactorial dynamics from nature, humanity, and policy on this evolution. Initially, the study utilizes land use transition matrices and land use dynamic degree methods to understand the spatiotemporal characteristics of land conversion within the study area. Subsequently, it analyzes the spatiotemporal changes in habitat quality from 2000–2020 based on the InVEST model and tools like spatial autocorrelation (Moran’s I) in ArcGIS. Finally, 14 potential influencing factors are selected from natural environment, socio-human, and policy regulation aspects and analyzed in the Geodetector software to understand the factors affecting the spatiotemporal evolution of habitat quality. The results show that, during the study period, the land area of 18 nature reserves in Hubei Province increased from 2000 to 2020, while the water area decreased. There were slight increases in farmland, construction land, and forest land, with significant decreases in grassland and water areas. This reveals the erosion of water bodies due to artificial lake filling during rapid urbanization, leading to a decline in overall habitat quality within the reserves and a gradual increase in spatial heterogeneity. Among the influencing factors, single-factor influences such as land use intensity and distance to county roads and slopes have a strong negative linear relationship with habitat quality, with land use intensity being the most significant human activity factor. The interaction strength among different types of influencing factors in the bivariate interaction detection results is ranked as follows: the interaction between natural geographical and socio-human factors > the interaction within socio-human factors > the interaction within natural geographical factors. This study has diverged from the past focus on the selection of a single continuous natural reserve as the empirical subject. Consequently, it allows for an integrated analysis of physical geographical dimensions such as locational topography with socio-cultural and policy elements including land use and transportation facilities, thereby facilitating a multifactorial assessment of the interactive impacts on habitat quality.