Does urban climate follow urban form? Analysing intraurban LST trajectories versus urban form trends in 3 cities with different background climates.

The current climate change trend urges the application of efficient spatial planning to mitigate the effects of urbanization on local urban warming. Nevertheless, how urban temperatures respond to urban form changes inside cities is still insufficiently understood. In this paper, we explored the relationship between urban form and diurnal space-time land surface temperature (LST) trends (2003-2019) in Beijing (continental climate), Cairo (arid) and Santiago (temperate). We analysed changes in land cover, white sky albedo (WSA), night-time lights (NL) and the enhanced vegetation index (EVI) inside areas representing clustered thermal performance (steady cold and hot spots and warming cold and hot spots). The structure of local climate zones (LCZs) was assessed for each LST trend. To test the relevance of other urban form dimensions, we analysed the hierarchical influence of the employed 2D metrics (i.e., built-up cover, WSA, NL and EVI) and additional 3D indicators (i.e., height and volume) on LST, applying machine learning classification and regression trees (CARTs) to Beijing's data. Despite diverse patterns of urban form change, cities in our sample present common LST trends, with thermal differences as a consequence of local climate. LCZs are composed of highly heterogeneous built-up areas inside LST trend categories. In the case of Beijing, LST is hierarchically driven by footprint, WSA and EVI. Moreover, by adding height and volume, urban form differences between LST trend classes that are not evident with 2D data were found. Our findings suggest that a compact green urban tissue is necessary to cope with the current trends of urban warming, taking into account city-specific measures based on the local background climate.

Intraurban heterogeneity of space-time land surface temperature trends in six climate-diverse cities.

Surface urban heat islands (SUHIs) are present in all cities, derived from their thermal properties. While looking at the spatiotemporal variability of land surface temperature (LST), there is still a gap in understanding patterns of change. In this paper, we analysed diurnal and nocturnal annual mean LST trends in continental (Beijing), temperate (Mexico City and Santiago), and arid (Cairo, Hyderabad, and Riyadh) cities employing 1 km MODIS data (2003-2019). Each time-series was assessed with the structure of a space-time cube. Hot and cold spots were detected for each year and the LST trends were analysed. Each pixel was classified into different space-time LST trends and their SUHIs were estimated. Cities exhibit trends of increasing temperatures in cold and hot spots for diurnal and nocturnal data. Temperatures are increasing faster in hot spots for diurnal and in cold spots for nocturnal scenes. Steady hot spots and warming hot spots exhibit the highest SUHIs for day and night. Our approach provides a framework to empirically delineate the spatial intraurban heterogeneity of LST patterns over time. This spatially explicit information provides insights into urban areas requiring heat mitigation strategies and can be used to monitor the performance of measures already implemented for climate adaptation.

Urban form datasets of 194 cities delineated based on the contiguous urban fabric for 1990 and 2015.

The present research datasets were processed for the article "The global homogenization of urban form. An assessment of 194 cities across time" [1]. They consist of land cover spatial layers, longitude and latitude point data and tabulated data with computed landscape metrics and the characterization of urban form of 194 cities for 1990 and 2015. Contiguous urban fabric at 30 m spatial resolution was derived from the Atlas of Urban Expansion database for 1990 and 2015 [2]. Landscape metrics were computed as quantitative measures of composition and spatial arrangement of each city and dimensions of the database were reduced employing correlation and principal components analysis. Hierarchical clustering was employed to group cities according to the similarity of their urban form and analysis of variance was applied to test for significant differences between them. The spatial layers contained in this article can be complemented with past and future land cover data to model urban form change at broader temporal scales. The landscape metrics are useful for cross-city comparisons at regional, national and global levels in combination with other complementary indicators. The datasets are valuable for urban planners, urban ecologists, NGO's, decision makers and other with interest on local and global landscape change in urban areas, particularly urban expansion and its impacts.