Dynamic changes of green-space connectivity based on remote sensing and graph theory: A case study in Zhengzhou, China
Abstract
© 2017, Ecological Society of China. All rights reserved. Green space is the main part of human and natural ecological environmental systems. The connectivity of green space is fundamental for the survival of flora and fauna, human outdoor recreation, and urban sustainability. The objective of this study was to quantify the dynamic changes of green-space connectivity of Zhengzhou, including the rapid urbanization processes, determination of external and internal causes, and analysis of the graph theory applied to the study of greenspace connectivity diagnosis and optimization bu using remote sensing data and graph metrics. The results indicated that green-space connectivity in Zhengzhou increased by 4.7-fold with the growth of the green-space area and patch size from 2000 to 2013, with the largest increment in 2009-2013. The green patch size was positively correlated with the integral index of connectivity, size of the largest component, and node degree. The characteristics of green-space connectivity in different urban districts were consistent with the distribution of green space. Graph metrics were calculated using Graphab software and to reflect the green-space connectivity in different scales. Node degree and betweenness centrality can be used to reflect the location and changes of the keystone green patches, which need more consideration in the next step of greenspace planning. Therefore, the application of graph theory combined with remote sensing is important for the future greensystem planning and optimization.