Multi-order Landscape Expansion Index: Characterizing urban expansion dynamics

• An improved spatial metric is proposed to characterize new urban patches.
• The metric measures the expansion degree of patches using time series data.
• The metric can be used to detect expansive areas and outlying urban clusters.
• The metric helps to characterize the spatial structure of urban expansion dynamics.

Most of the landscape metrics used in urban expansion studies are capable of reflecting the spatial characteristics for individual time points, but are not efficient to capture the integrated information from time series data. A few spatial metrics, for example, Landscape Expansion Index (LEI), are calculated based on two-time-point data. These metrics are insufficient for the analysis of urban expansion dynamics based on multi-temporal data. In this study, we propose an improved spatial metric, Multi-order Landscape Expansion Index (MLEI), to measure the expansion degree of newly grown urban patches by considering their relationships with old patches and their spatial context in the process of urban expansion. A case study is conducted in Wuhan, a fast-growing metropolis in central China, based on remote sensing images from three time points (2000, 2005 and 2010). The MLEI map in 2010 clearly shows the areas that have experienced expansive growth. The greatest difference between MLEI and LEI occurs where outlying clusters are formed gradually through time. Some spatial analysis methods are applied on the MLEI map in 2010 to delineate outlying urban clusters in urban expansion. The map of urban clusters clearly shows the spatial heterogenicity of urban expansion and the most expansive areas in Wuhan. This study suggests that MLEI is capable to capture multi-temporal information, and can be used to characterize the spatial structure of urban expansion dynamics.