A global comparative analysis of urban form: Applying spatial metrics and remote sensing

Quantifying different types of urban growth and the change dynamic in Guangzhou using multi-temporal remote sensing data

“…Several spatial metrics are highly correlated, although no single metric can capture the complex patterns of urban change (Luck and Wu, 2002;Herold et al, 2003;Huang et al, 2007;Hung et al, 2010). We combined four landscape-level spatial metrics selected to characterize the urban landscape pattern, i.e.…”

“…Developing a quantitative method to identify different types of urban growth is useful and meaningful to help local, regional and state land use planners better understand and address the issues attributed to sprawl. In many of the previous researches, urban growth analyses were conducted either at the metropolitan scale as a whole (Weng, 2002;Yang and Lo, 2002;Serra et al, 2003;Xian and Crane, 2005;Huang et al, 2007;Jat et al, 2008a,b), or within natural landscape boundaries such as those of a watershed (Clapham, 2003). Technology about quantifying different urban growth types from remote sensing data has not been adequately investigated.…”

“…Remote sensing provides a spatially consistent coverage of large areas with a high spatial detail and a high temporal frequency. It has advantages in characterizing the spatial temporal trends of urban growth using multi-stage images (Weng, 2002;Yang and Lo, 2002;Serra et al, 2003;Xian and Crane, 2005;Huang et al, 2007;Jat et al, 2008a,b). Combined with spatial metrics, remote sensing data offer the potential to characterize the change of urban growth dynamics (Herold et al, 2003).…”

Sustainable city – flexible or durable? Socio-economics aspects of urban patterns

“…Generally higher density is correlated with bigger size of the city. Analysis presented in the paper is based on the urban typology proposed by Huang, Lu and Sellers (2007). They proposed following characteristics of the urban patterns, based on the metric analysis of patches in the satellite images of metropolitan areas: 1) complexity (determined by the irregularity of patch shapes; the more different and the smaller the patches -the higher the complexity); 2) centrality (average distance of the dispersed parts to the city centre, which was defined as the centroid of the largest patch); 3) compactness (defined as the fragmentation of the overall urban landscape); 4) porosity (defined as the ratio of open space compared to the total urban area).…”

Spatiotemporal Analysis of Urban Land Cover Changes in Kigali, Rwanda Using Multitemporal Landsat Data and Landscape Metrics

“…Therefore, monitoring spatio-temporal urban growth patterns is paramount for supporting controlled urban development and satellite based data are potential for such kind of monitoring. A growing body of literature found the combination of spatio-temporal satellite data and landscape metrics (LM) worthwhile in urban land cover change analyses and environmental impact assessment (Aguilera et al, 2011;Furberg & Ban, 2010Haas & Ban, 2014;Herold et al, 2005;Herold et al, 2003;Huang et al, 2007;Kuffer & Barrosb, 2011;Liu & Yang, 2015;Seto & Fragkias, 2005). However, few case studies deriving landscape indices based on satellite data exist for Sub-Saharan African cities, giving impetus for the proposed study.…”