A High-Resolution Remote-Sensing-Based Method for Urban Ecological Quality Evaluation

Urban ecological quality evaluation attracts more and more attention in urban land use and ecosystem planning optimization due to continuity problems from rapid urbanization and population growth. Remote sensing was always considering contribute to the evaluation. However, accurate and efficient evaluation of urban ecological quality is being challenged, as traditional remote-sensing-based methods were mainly based on low spatial resolution data, pixel-based land cover classification, and vegetation condition factors, and ignored object-oriented high spatial resolution classification and urban landscape pattern. Thus, method for urban ecological quality evaluation based on high-resolution remote sensing is greatly needed to support spatially explicit decision-making in urban planning. In this study, a novel high-resolution remote-sensing-based method based on six ecological indicators from vegetation conditions and landscape patterns was proposed to evaluate urban ecological quality. The six ecological indicators were derived from high-resolution remote sensing data using an object-oriented land cover classification. Factor analysis indicated that the sensitivity of landscape patterns to ecological quality is relatively weaken. Therefore, vegetation conditions and landscape patterns were used as two respective variables to generate a linear evaluation model, with their weights calculated from the loadings of factor analysis, to evaluation urban ecological quality. The results showed that the proposed linear model, considering both vegetation conditions and landscape patterns, is effective and trustworthy, and can provide more suitable support to urban land use and ecological planning.

Automated summary

Urban ecological quality evaluation is gaining increasing attention in urban land use and ecosystem planning. This study proposes a novel method based on high-resolution remote sensing to evaluate urban ecological quality. By considering vegetation conditions and landscape patterns, the method provides effective and trustworthy results to support urban planning.