Pervasiveness of biological impacts of artificial light at night

This is the author accepted manuscript. The final version is available on open access from Oxford University Press via the DOI in this recordArtificial light at night (ALAN) and its associated biological impacts have regularly been characterised as predominantly urban issues. Although far from trivial, this would imply that these impacts only affect ecosystems that are already heavily modified by humans and are relatively limited in their spatial extent, at least as compared with some key anthropogenic pressures on the environment that attract much more scientific and public attention, such as climate change or plastic pollution. However, there are a number of reasons to believe that ALAN and its impacts are more pervasive, and therefore need to be viewed from a broader geographic perspective rather than an essentially urban one. Here we address, in turn, 11 key issues when considering the degree of spatial pervasiveness of the biological impacts of ALAN. First, the global extent of ALAN is likely itself commonly underestimated, as a consequence of limitations of available remote sensing data sources and how these are processed. Second and third, more isolated (rural) and mobile (e.g.,vehicle headlight) sources of ALAN may have both very widespread and important biological influences. Fourth and fifth, the occurrence and impacts of ALAN in marine systems and other remote settings, need much greater consideration. Sixth, seventh and eighth, there is growing evidence for important biological impacts of ALAN at low light levels, from skyglow, and over long distances (because of the altitudes from which it may be viewed by some organisms), all of which would increase the areas over which impacts are occurring. Ninth and tenth, ALAN may exert indirect biological effects that may further expand these areas, because it has a landscape ecology (modifying movement and dispersal and so hence with effects beyond the direct extent of ALAN), and because ALAN interacts with other anthropogenic pressures on the environment. Finally, ALAN is not stable, but increasing rapidly in global extent, and shifting towards wavelengths of light that often have greater biological impacts.Natural Environment Research Council (NERC

Assessing the Ability of Luojia 1-01 Imagery to Detect Feeble Nighttime Lights

The Luojia1-01 (LJ1-01) satellite launched on 2 June 2018 provides a new option for nighttime light (NTL) application research. In this paper, four types of human settlements, such as cities, counties, towns and villages, are sampled to evaluate the potential of LJ1-01 to detect feeble NTL by comparing with the NTL images from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-Orbiting Partnership Satellite. First, the landscape indices and cutoff threshold method are applied to enhance signal-noise ratio (SNR). Then, the detection accuracy of samples is evaluated to determine the optimal cutoff threshold for each NTL data source. After that, the spatial correspondence of different NTL images and the area consistency between the samples and NTL footprints are compared. Finally, after the discussion of feeble NTL detection and the influence of clouds, moonlight and image composites, it can be concluded that LJ1-01 is more suitable for detection feeble NTL objects, while great importance should be attached to the measures to eliminate the noise in LJ1-01 image and make LJ1-01 more widely used: (1) In the study area, a suitable cutoff threshold of LJ1-01 image can be set to 0.1 nano-Wcm−2sr−1, which is lower than that of VIIRS image (0.3 nano-Wcm−2sr−1), and this enables LJ1-01 to reserve more information of NTL, especially the feeble NTL. Moreover, the minimum area that can be identified by NTL footprints from LJ1-01 is 0.02 km2, while that of VIIRS and DMSP are 0.3 km2 and 4.5 km2, respectively. (2) The cutoff threshold method can identify the range of NTL with more noise, but cannot eliminate the noise separately. The filtering method and the image composition method may play more important role in the applications of LJ1-01 data