A Comparison of SatelliteBased Estimates of Urban Agglomeration Size for the Accra Area Data on the extension of urban areas are important for analyzing growth dynamics and to support the planning of transport and service provision. Satellitebased remote sensing has proven extremely useful, especially in cities that experience fast spatial growth. Different approaches to satellitebased mapping may, however, produce different results concerning urban categorization and delineation, often making direct comparison misleading. This study analyses four different satellitebased studies of urban land cover in Accra, Ghana and presents a new land cover map based on visual interpretation of segmented Sentinel2 imagery. The methods and results, as well as the underlying definition of xe2x80x9curbanxe2x80x9d, are compared and discussed. One method identifies exclusively areas with manmade, impervious surfaces, such as roads and buildings, as proxies for urban extent. Other methods aim to identify a broader set of land cover types, including green spaces, which are treated as part of the mixed urban fabric. Further differences are found in the way urban fringe areas under development are classified depending on their degree of urbanization, and in the distance threshold values used for defining the urban agglomeration. For the most recent maps we identify a difference in the measured size of the Accra agglomeration of almost 100%.

A quantitative framework for analysing long term spatial clustering and vegetation fragmentation in an urban landscape using multitemporal landsat data Abstract   Rapid urbanization threatens urban green spaces and vegetation, demonstrated by a decrease in connectivity and higher levels of fragmentation. Understanding historic spatial and temporal patterns of such fragmentation is important for habitat and biological conservation, ecosystem management and urban planning. Despite their potential value, Local Indicators of Spatial Autocorrelation (LISA) measures have not been sufficiently exploited in monitoring the spatial and temporal variability in clustering and fragmentation of vegetation patterns in urban areas. LISA statistics are an important structural measure that indicates the presence of outliers, zones of similarity (hot spots) and of dissimilarity (cold spots) at proximate locations, hence they could be used to explicitly capture spatial patterns that are clustered, dispersed or random. In this study, we applied landscape metrics, LISA indices to analyse the temporal variability in clustering and fragmentation patterns of vegetation patches in Harare metropolitan city, Zimbabwe using Landsat series data for 1994, 2001 and 2017. Analysis of landscape metrics showed an increase in the fragmentation of vegetation patches between 1994xe2x80x932017xe2x80xafas shown by the decrease in mean patch size, an increase in number of patches, edge density and shape complexity of vegetation patches. The study further demonstrates the utility of LISA indices in identifying key hot spot and cold spots. Comparatively, the highly vegetated northern parts of the city were characterised by significantly high positive spatial autocorrelation (p