Does multidimensional (2D & 3D) urban study inspire urban ‎sustainability?‎

Authors and Affiliations: 

Saddrodin Alavipanah, Johannes Schreyer, Salman Qureshi, Dagmar Haase, Tobia Lakes

Landscape ecology Lab and Applied Geoinformation Science Lab, Department of Geography, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany.

Corresponding author:

The increasing urban population is expanding the impervious surface and this leads to a ‎more ‎intense urban heat island (UHI) effect. Therefore, most of the world’s cities experience ‎higher ‎temperatures in their urban core than in the surrounding sub-urban and rural areas. However, ‎there are still shortcomings. First, the ‎effect of UHI is not well recognized in arid and semi-arid ‎regions. Second, the association of multi-‎dimensional information with surface temperature in urban ‎areas must be examined. This study ‎focuses on the height-related aspects of urban geometry in an ‎arid region. A range of multispectral ‎and spatial vector data were used to derive the surface ‎temperature and two-dimensional (2D) and ‎three-dimensional (3D) information of the study area. ‎All 2D and 3D information was aggregated into a grid ‎with common spatial resolution to create a ‎homogeneous dataset. The machine learning statistical ‎model of a boosted regression tree (BRT) ‎was used to reflect the relative influence of 2D and ‎‎3D ‎indicators with land surface temperature. ‎Our results showed a cooler surface temperature in the ‎city than in the surrounding area. This leads ‎to the question of whether the established UHI definition ‎encompasses all types of cities or not? ‎The thermal band was able to distinguish different ‎spatial structures in the study area in one hand. ‎On the other hand, the BRT analysis demonstrated that both multi-dimensional 2D ‎and 3D ‎indicators affect the surface temperature. In particular, the 3D indicators play a more ‎important role ‎than 2D indicators in shaping the surface temperature at different urban geometries ‎of the study ‎area. This new method can help urban planners identify the most influential 2D and 3D ‎indicators ‎that affect the surface temperature in different districts of a city.‎


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