Identification and extraction of a current urban ecological network in Minhang District of Shanghai based on an optimization method

Abstract

The urban ecological network has gained substantial interest in the context of urban sustainable development research in recent years. It guides the optimization of the urban ecological spatial layout, which is an important means for inventory development and ecological construction. However, traditional construction methods may not be suitable for urban environments with complex matrices. This can result in the inaccurate extraction of urban ecological networks. This study aimed to improve understanding of the current distribution and composition of key elements in urban ecological networks. In the present study, an optimized evaluation method involving ecological source patches and resistance surfaces was used to identify and extract an urban ecological network. The Minhang District (MhD) of Shanghai formed the study area. The results showed that: (1) There was spatial heterogeneity in habitat quality and the location importance of cores in the MhD. These two factors were inconsistent with the area size. (2) The habitat quality for the same land use types were significantly different. This was because of both vegetation quality and the external environment. (3) The urban ecological network for the MhD had 82 source patches. These were unevenly distributed and mainly concentrated in Pudong. (4) A total of 133 least-cost paths were extracted from between the source patches. However, the cost of least-cost paths were generally high in Puxi. This was not conducive to forming functional connections between patches. This study provided an optimization method for identifying and extracting ecological networks in urban areas by combining Geographical Information System software (GIS), Guidos, Graphab, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Ecological indicators such as NDVI, the percentage variation of the probability of connectivity (dPC), and habitat quality were applied to improve accuracy. The study findings lay the foundation for further layout optimization and supporting ecological planning.