###### Abstract
This method has good timeliness and consistency at the global scale, basically conforming to the urbanization process assumption that impervious surfaces grow from none to existence, continuously increase, and are irreversible, and is therefore widely adopted in urban expansion identification and land use change analysis. However, the problem is that existing binary approaches fail to capture urbanization intensity gradients, particularly in areas with sparse development or discontinuous expansion patterns common in contemporary urban growth [13] and cannot reveal the actual proportion of impervious coverage within pixels. To address this limitation, we constructed Impervious Surface Coverage (ISC) data generated from multi-temporal remote sensing imagery. This data spatially aggregates the 30 m resolution binary impervious surface products provided by GAIA, calculating the proportion of impervious pixels within each 1 km grid to obtain continuous impervious surface proportion data with sub-pixel scale continuity. Compared to traditional binary classification results, this method can more accurately reflect the degree of imperviousness within pixels, demonstrating higher expressive capability and application value in urban impervious intensity assessment, expansion process characterization, and spatial heterogeneity analysis. In contrast, ISC provides a more continuous and refined urban expansion monitoring method, particularly suitable for revealing dynamic processes such as early sparse development and building land density gradient changes [14]. It has good comparability and scalability, suitable for urbanization dynamic monitoring at multiple temporal and spatial scales [15]. Urban expansion impacts on ecosystems are complex and spatially heterogeneous [16], requiring integrated assessment frameworks that capture both urbanization intensity and ecological response patterns [17]. Current ecological vulnerability assessments often rely on subjective weighting schemes [18; 19] and fail to establish quantitative relationships between urbanization intensity gradients and ecosystem responses. We address this by developing an Ecological Vulnerability Index (EVI) [20] that integrates natural factors with anthropogenic pressures. Using Genetic Projection Pursuit modeling, we eliminate subjective weighting bias while establishing quantitative ISC-EVI relationships across different urbanization intensity zones. This study aims to construct a long-term ISC dataset for the Beijing-Tianjin-Hebei region through spatial aggregation methods and by analyzing urban expansion processes and ecological vulnerability patterns, as well as establishing quantitative relationships between urbanization intensity and ecosystem responses to provide scientific support for sustainable regional development strategies. The technical path and analytical framework proposed in this study have multi-dimensional scientific support value for urban ecological security monitoring and spatial regulation in the Beijing-Tianjin-Hebei region. For the ecological vulnerability early warning of high-density urban agglomerations, its core lies in establishing an associated analysis paradigm of \"inpervious surface dynamics--multi-factor ecological response\": first, converting 30 m resolution impervious surface binary data into 1 km resolution Impervious Surface Coverage (ISC) data through spatial aggregation methods, which solves the scale matching problem between micro data and macro analysis; second, introducing the projection pursuit model to calculate the weights of ecological sensitive factors, avoiding the deviation caused by subjective weighting and improving the objectivity of evaluation results; finally, based on the coupling characteristic analysis of key time nodes, it can capture the evolution law of ecological vulnerability in different development stages.