Abstract
Vegetation plays an irreplaceable role for urban ecosystem services. Urban greenness represents all vegetation cover in and around cities. Understanding spatiotemporal patterns of the changes in urban greenness (CUG) provides fundamental clues for urban planning. The impact on CUG can be roughly categorized as being climate-induced and human-induced. Methods for mapping human-induced CUG (H-CUG) are rare. In this paper, a new framework, known as Localized Spatial Association Analysis under Temporal Context (LSAA-TC), was proposed to explore H-CUG. Localized spatial association analysis (LSAA) was performed first to extract local spatial outliers (LSOs), or locations that differ significantly in urban greenness from those located in the neighborhood. LSOs were then analyzed under the temporal context to map their intertemporal variations known as spatiotemporal outliers. We applied LSAA-TC to mapping H-CUG in the Wuhan Metropolitan Area, China during 2000–2015 using the vegetation index from Moderate-resolution Imaging Spectroradiometer (MODIS) 13Q1 as the proxy for urban greenness. The computed H-CUG demonstrated apparent spatiotemporal patterns. The result is consistent with the fact that the traditional downtown area presents the lowest H-CUG, while it is found that the peripheral area in the circular belt within 14–20 km from the urban center demonstrates the most significant H-CUG. We conclude that LSAA-TC can be a widely applicable framework to understand H-CUG patterns and is a promising tool for informative urban planning.
Highlights
Urbanization is undergoing rapid increases across the world, especially in some developing countries like China [1,2]
The spatiotemporal patterns of P-A-C distribution examined from the perspective of the distance to the urban center at an annual step (2000–2015) are illustrated in Figure 4, where the x-axis indicates the distance from the urban center and y-axis denotes the division of the temporal span
Our study demonstrated that the proposed Localized Spatial Association Analysis under Temporal Context (LSAA-TC) could be a promising tool for mapping the spatiotemporal patterns of H-changes in urban greenness (CUG)
Summary
Urbanization is undergoing rapid increases across the world, especially in some developing countries like China [1,2]. Urbanization is typically associated with an improved living infrastructure, such as convenient transportation networks and more commercial centers, as well as a synchronized process of spatial growth and scattering of built-up areas that used to be for agricultural purposes [5,6]. The continuously shrinking green vegetation space in urban and suburban areas has been a critical issue for sustainable urban development and public health in many cities [7]. The changes in urban vegetation space, typically associated with urban expansion, critically need to be investigated to provide their inhabitants with more sustainable urban ecosystem services [8,11,12]
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