Ecological risk assessment based on road network development analysis of Xiamen city, China. Issue 5 (4th July 2018)

• Record Type: Journal Article
• Authors: Yang, Xinyi; Wang, Lin; Wu, Tong; Qiu, Quanyi
• Is Part Of: International journal of sustainable development and world ecology. Volume 25:Issue 5(2018)
• Page Start: 458
• Page End: 467
• Publication Date: 2018-07-04
• Subjects: Road network -- habitat quality -- urban ecological risk -- landscape fragmentation -- risk assessment; Sustainable development -- Periodicals; Ecology -- Periodicals; Economic development -- Environmental aspects -- Periodicals; Ecologie; Duurzame ontwikkeling; Ecology; Economic development -- Environmental aspects; Sustainable development; Periodicals; Electronic journals; 338.72
• DOI: 10.1080/13504509.2017.1412369
• Languages: English
• ISSNs: 1745-2627

Abstract

ABSTRACT: Urban road network development (RND) plays an important role in social and economic evolution. However, the unlimited expansion of roads leads to great changes in urban landscape patterns, which further affect ecosystems. To better characterize the urban ecological risk (UER) of RND, in this study, RND was considered the stressor and habitat provision the assessment endpoint in a UER assessment. According to the theory of landscape ecology, habitat quality disturbance intensity (HQDI) is used to quantify the negative effect of RND on an urban ecosystem. In particular, we aimed to explore the exposure-response function between road density and HQDI under RND stress. A case study was carried out in Xiamen City to examine this association. In terms of spatial distribution, this study showed that developed areas had the highest HQDI values, whereas low HQDI values were mostly associated with suburban areas. In addition, the probability distribution of HQDIs was uneven and the urban ecosystem showed unequal sensitivities to different types of roads. Based on a multilevel characterization of UER, results of this provide a framework to predict UER under RND stress and may enhance the ability of risk managers to develop scientifically based control measures.