ABSTRACT
Furthermore, recent studies have suggested that land patches in the real world often display a hierarchical landscape structure owing to the self-organized behaviors of complex systems (Wu & David, 2002). In other words, spatial patterns of land patches of varying sizes can be depicted as a nested landscape structure at different scales. This new notion of landscape composition has been considered as a paradigm shift in ecology (Gómez, White, & Wulder, 2011; Kotliar & Wiens, 1990; Wu, 2007). As a result, simulations of urban growth and ecological system evolution by using hierarchical patch dynamics have been increasingly reported in urban landscape planning and ecological modeling literature (Sozio et al., 2013; Wu & Loucks, 1995; Zhang, Wu, Grimm, McHale, & Buyantuyev, 2013). However, an easy-to-apply approach to generate hierarchical-structured networks from censored or observed data sets is still desirable for studying patchy landscape. The goal of this article is to develop a new approach to examine spatial patterns of urban landscape in a hierarchical structure by applying GIS and remote sensing techniques. GIS provides a unique toolbox that depicts geographic characteristics of urban landscape so as to reveal complex spatial relationships between features in study and also links quantitative attributes with the geographic features so that, for the first time, physical space can be measured and analyzed in relation to the socio-economic forces that shape it (Moudon, 1997; Wheeler, 1997). In addition, remotely sensed images have proven very valuable mapping both historical and contemporary land use and land cover changes (Xie, Yu, Tian, & Xing, 2005; Xie et al., 2010).
