Evaluating eco-environment in urban agglomeration from a vegetation-impervious surface-soil-air framework: an example in Ningxia, China

Urban agglomerations (UA) are the fastest growing regional types during recent years, especially in developing countries. Monitoring and evaluating the eco-environment quality of UA is significant for sustainability. The previous remote sensing models of urban eco-environment are generally based on the vegetation-impervious surface-soil framework, which neglects the air quality in urban areas. We constructed a vegetation-impervious surface-soil-air (VISA) framework and derived a remote sensing model of UA eco-environment (RSUAE) based on the VISA. The RSUAE can integrate the greenness, dryness and imperviousness, moisture, heat, and air turbidity. The new model was evaluated by comparing with land cover types and the existing remote sensing-based ecological index and eco-environmental quality index models. Results demonstrated that RSUAE is valid to depict the difference of eco-environment quality among varied land cover types. The RSUAE model has general consistency with the existing models, while RSUAE takes into account well the air quality. The RSUAE was utilized to evaluate the eco-environment of UA along the Yellow River in Ningxia, China (NXUA) with Mann-Kendall trend test. Results identified 78.69% of the area has no significant change trend, 18.73% of the area has a significant increasing trend, and 2.58% of the area suffers significant decreasing trend in eco-environment quality from 2001 to 2019. The Yanchi county has the best improvement in eco-environment quality, whereas sensitivity analysis indicates that it is more vulnerable than other counties. The analysis tool and method presented in this study provide a reference for other UAs. The evaluation results in NXUA are noteworthy for local management. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

Automated summary

Urban agglomerations (UA), the fastest growing regional types in recent years, are significant for sustainability. A new remote sensing model of UA eco-environment (RSUAE) based on a vegetation-impervious surface-soil-air (VISA) framework was developed to integrate various factors such as greenness, dryness, imperviousness, moisture, heat, and air turbidity. The RSUAE model proved effective in depicting differences in eco-environment quality among varied land cover types in UA, with implications for comprehensive evaluation of UA eco-environment quality.