Spatio-temporal Characteristics and Driving Forces of Landscape Ecological Risks in Highly Urbanized Areas: A Case Study of Suzhou City

Objective Increasing ecological risks have emerged during urbanization in the process of urbanization. Scientific assessment and management of these ecological risks are prerequisites for establishing harmonious human-land relations and advancing ecosystem health. Landscape Ecological Risk (LER), a critical subset of ecological risk assessment, focuses on the coupling of ecological processes and spatial patterns, emphasizing the changes in regional ecological risk under the combined influence of human behavior and natural activities. Numerous studies have concentrated on risk evolution, the identification of influencing factors or drivers, and the simulation and prediction of risks in natural areas such as watersheds, coastal zones, wetlands, and ecological reserves. However, relatively little attention has been given to urbanized areas characterized by high rates of population growth and land development. The characteristics of landscape patterns in highly urbanized areas at the optimal scale, the patterns of landscape ecological risk evolution, and the spatiotemporal heterogeneity of driving forces remain underexplored. The formation mechanism, spatiotemporal heterogeneous driving effect and scale response law of landscape ecological risk in highly urbanized areas still need to be systematically explored. Methods Utilizing multi-source data from 1995 to 2020, including Landsat TM/ETM imagery, DTM and NASA datasets, this study focuses on exploring the characteristics of spatial and temporal variability of Landscape Ecological Risk (LER) and its drivers in Suzhou at the optimal scale, utilizing the Landscape Ecological Risk Evaluation Model (LEREM) and the Geographically and Temporally Weighted Regression (GTWR) framework. Results (1) The landscape pattern of Suzhou City is scale-dependent, with significant variations in the landscape pattern index for each land type at the optimal scale. In this study, the optimal granularity and magnitude are identified as 550 meters and 1.25 kilometers; (2) The overall LER demonstrates an improving trend. Low-risk and lower-risk zones are predominant, and the spatial heterogeneity of risk zones across all levels appears to be increasing. The LERI exhibits a pattern of increase followed by a decrease, characterized by phases of and recovery, and has gradually improved since 2010. The proportion of low-risk areas remains consistently high, widely distributed, and stable. In contrast, the proportions of medium- and high-risk areas show continuous growth, with small fluctuations in their respective increases, indicating significant aggregation; (3) The LER is primarily influenced by socio-economic factors, exhibiting notable differences in the degree of spatial variability and the characteristics of the coefficients associated with these driving factors. The analysis revealed a positive correlation between natural and socio-economic factors and changes in the LER, while proximity factors demonstrated a negative correlation. The spatial intensity of each driver varies significantly. The natural environment factor displays the most stable pattern, forming a concentric distribution centered around high-value areas such as the southern region of Taihu Lake, southwestern Kunshan, and northern Zhangjiagang. In contrast, socio-economic factors, particularly population and GDP, contribute to an outward expansion of influence from the city center. Additionally, the neighborhood factor has undergone significant changes in its pattern since 2010, with these changes being more pronounced than those observed prior to that year. The alterations in the proximity factor are also more evident after 2010, with its influence increasingly concentrated in the city centers and sub-centers, rather than being dispersed over a broader area. Conclusion This study advances the cale-integrated assessment of landscape ecological risk (LER) in urban environments by determining optimal granularity (550 m) and magnitude (1.25 km). It reveals that the precision of landscape analysis is enhanced when the scale effect is integrated with the assessment of ecological risk in urban landscapes. The optimal granularity and magnitude identified in this study are 550 m and 1.25 km, respectively. In general, the LER of Suzhou City is favorable and is characterized by a progression through the stages of The LER in Suzhou City is showing an overall improvement, marked by the stage of "slowing down-exacerbating-recovery ". The LER in Suzhou City is generally improving, characterized by the "slowing down-exacerbating-recovery "phase. According to the distribution characteristics of landscape ecological risk evolution in Suzhou, low-risk and lower-risk areas consistently dominate the landscape. However, in recent years, there has been a notable shift from low-risk areas to medium-risk areas, and subsequently from medium-risk areas to higher-risk areas. It is imperative that high-risk zones be subjected to stringent control measures to prevent the indiscriminate expansion of construction land and to reinforce the efficient and intensive utilization of existing land resources. A primary focus should be placed on regulating medium- and high-risk zones, which are predominantly located within the interconnected areas of forest land, grassland, arable land, water bodies, and construction land. Furthermore, it is essential to strengthen the mechanisms that balance the occupation of ecological land with the compensation of ecological land. Reason: The revised text improves clarity, coherence, and technical accuracy while correcting grammatical and punctuation errors. It also enhances vocabulary and readability.