Abstract
Artificial lighting is strongly associated with urbanisation and is increasing in its extent, brightness and spectral range. Changes in urban lighting have both positive and negative effects on city performance, yet little is known about how its character and magnitude vary across the urban landscape. A major barrier to related research, planning and governance has been the lack of lighting data at the city extent, particularly at a fine spatial resolution. Our aims were therefore to capture such data using aerial night photography and to undertake a case study of urban lighting. We present the finest scale multi-spectral lighting dataset available for an entire city and explore how lighting metrics vary with built density and land-use. We found positive relationships between artificial lighting indicators and built density at coarse spatial scales, whilst at a local level lighting varied with land-use. Manufacturing and housing are the primary land-use zones responsible for the city’s brightly lit areas, yet manufacturing sites are relatively rare within the city. Our data suggests that efforts to address light pollution should broaden their focus from residential street lighting to include security lighting within manufacturing areas.
Highlights
As the global population grows and becomes increasingly urban [1,2], cities are increasing in their spatial extent [3], intensity of use [4] and physical heterogeneity [5]
Whilst the RGB bands in our image did not correspond exactly to the band widths proposed by Elvidge et al [47], we considered it feasible that they would be sufficient to differentiate between the major classes of street lamps present in the city: mercury vapour (MV), metal halide (MH), low pressure sodium (LPS) and high pressure sodium (HPS)
The composition of lamp types changes along the 1 km2 urban gradient (Fig. 3D), with LPS lamps dominating provision at low built densities, shifting to HPS and MH lamps in densely built areas
Summary
As the global population grows and becomes increasingly urban [1,2], cities are increasing in their spatial extent [3], intensity of use [4] and physical heterogeneity [5]. The lack of high resolution mapping of artificial lighting is increasingly recognised as an important barrier to related research and management [36]. Datasets exist globally at a coarse spatial (,3 km) and spectral resolution, allowing broad variations in urban lighting to be detected [27]; but sub-city patterning cannot be explored effectively [36,37]. Finer spatial resolution data do exist, but typically have a limited spatial extent [36,39] (but see [40]) This hinders the development of a strong evidence base to support urban lighting strategies, as cities can be highly heterogeneous even at fine spatial scales [5,9]. There is a need to secure lighting datasets at the city scale; and at a spatial and spectral resolution sufficient to advance lighting research and the planning and governance of urban lighting
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