Constructing ecological networks in urban areas improves ecosystem stability and biodiversity protection. However, most studies focus on optimizing ecological environments through objective assessments, often neglecting species diversity. This study developed a biodiversity grading framework for the Jianghan Plain using species observation and ecosystem diversity data. Supported by ArcGIS, ecological sources were identified via MSPA and graded using the Guidelines and MaxEnt model. The MCR model was used to simulate connectivity barriers between ecological sources and calculate the minimum cumulative resistance distance, thereby generating corridors and ultimately constructing a hierarchical biodiversity conservation network for the Jianghan Plain. Our findings indicated the following: (1) The Jianghan Plain hosts 21 major ecological sources, primarily natural water bodies at the plain’s edge, which can be classified into five primary and 16 secondary sources based on biodiversity grades. (2) The recessive corridors, comprising 10 primary and 95 secondary ones, are mainly concentrated in the central Jianghan Plain, with primary corridors located centrally and westward, characterized by a large overall span. (3) Changhu Lake and Honghu Lake, two critical water bodies with high-quality habitats and significant biodiversity, were identified as key ecological nodes from the ecological sources, bridging and guiding the central and southern corridors. (4) Based on the ecological network distribution and key nodes and corridors, a “three zones, three belts, and two points” strategy was proposed for optimizing the Jianghan Plain’s ecological network. This study provides a novel framework and theoretical support for regional habitat, biodiversity conservation, and sustainable development.

The rapid increase in urban population density driven by urban development has intensified inequity in urban green space distribution. Identifying the causes of changes in green equity and developing strategies to improve urban greening are crucial for optimizing resource allocation and alleviating social inequalities. However, the long-term spatio-temporal evolution of green visibility and equity remains underexplored. This study utilized the “Time Machine” feature to capture street view images from 2014, 2017, and 2021, analyzing changes in green visibility and its equity across residential communities in Wuhan. Deep learning techniques and statistical methods, including the Gini coefficient and location quotient (LQ), were employed to assess the distribution and spatial equity of street-level greenery. The results showed that overall green visibility in Wuhan increased by 4.18% between 2014 and 2021. However, this improvement did not translate into better spatial equity, as the Gini coefficient consistently ranged between 0.4 and 0.5. Among the seven municipal districts, only the Jiang’an District demonstrated relatively equitable green visibility in 2017 and 2021. Despite a gradual reduction in disparities in green visibility, a spatial mismatch persisted between UGS growth and population distribution, leading to uneven patterns in UGS equity. This study explores the factors driving inequities in green visibility and proposes strategies to enhance urban greening. Key recommendations include integrating the green visibility equity evaluation framework into urban planning to guide fair green space allocation, prioritizing greenery in low-income neighborhoods, and reducing hardscapes to support the planting and maintenance of tall canopy trees. These measures aim to enhance accessible and visible green resources and promote equitable access across communities.