Illuminating the capabilities of Landsat 8 for mapping night lights
Introduction
Artificial night lights allow us to monitor human society from space (Croft, T. A., 1973, Croft, T., 1978, Elvidge, C., et al., 1997). Light pollution relates to unwanted and negative impacts that artificial light has on our view of the sky at night (astronomic light pollution; Cinzano et al., 2001) as well as to the negative impacts that light pollution has on flora, fauna and human health due to changes in circadian exposure to light and the wavelengths organisms are exposed to (Falchi, F., et al., 2011, Gaston, K.J., et al., 2013, Longcore, T. and Rich, C., 2004). Studies from the DMSP/OLS sensor have shown population size and economic activity to be among the most important factors explaining spatial patterns of night lights globally (Elvidge, C., et al., 1997, Sutton, P., et al., 2001). While both DMSP and VIIRS sensor onboard the Suomi NPP offer global mapping of night lights (Doll, C.N., 2008, Miller, S.D., et al., 2012, Miller, S. D., et al., 2013), both sensors have coarse spatial resolution ( > 0.5 km), limiting their usefulness to study the nightscape of urban areas in details (Small et al., 2005). Astronaut photography taken from the International Space Station (ISS) offers medium spatial resolution imagery (30–100 m pixels) of cities in color (Elvidge et al., 2007a), however these images are not acquired on a routine basis, and are not calibrated (de Miguel, A.S., 2015, de Miguel, A. S., et al., 2014). The interest in and applications for fine spatial resolution images (pixels < 10 m) of cities at night is increasing, as shown in studies examining spatial patterns within cities using night-time aerial photos (Hale, J.D., et al., 2013, Kim, M. and Hong, S. H., 2015, Kuechly, H.U., et al., 2012), or using the EROS-B satellite (Katz, Y. and Levin, N., 2016, Levin, N., et al., 2014) and summaries of numerous other applications developing in this area (Zhang et al., 2015). Elvidge, C. D., et al., 2007a, Elvidge, C. D., et al., 2007b aimed to define the required spatial and spectral resolutions of a space-borne sensor optimized for imaging cities at night, and proposed the Nightsat mission, however such a satellite is yet to be planned and funded.
Landsat 8, recently launched in February 11th, 2013 has enhanced optical imaging capabilities, including two new spectral bands (blue 0.43–0.45 and a cirrus band 1.36–1.38 μm), two thermal bands, improved signal to noise ratio and enhanced radiometric capability (12 bits instead of the 8 bits of previous Landsat missions) on all bands. In addition, Landsat 8 acquires images globally more frequently, according to the Landsat 8 Long Term Acquisition Plan (LTAP-8) (Irons, J. R., et al., 2012, Roy, D. P., et al., 2014, Wulder, M. A., et al., 2015), which currently reaches up to 725 scenes per day (http://landsat.usgs.gov/LTAP8.php, accessed August 18th, 2015), compared with about 200 scenes per day for Landsat 7 in the past (Arvidson et al., 2006) and 470 images per day currently acquired by Landsat 7 (Wulder et al., 2015). While Landsat was designed for mostly acquiring daytime images for studying regional to global scale land cover, land surface processes and land use patterns and changes, it is capable of acquiring night-time images as well in its ascending mode. Croft (1978) has demonstrated that gas flares could be detected from a night-time Landsat-MSS image over Algeria. However, previous studies which employed night-time images of Landsat were mostly based on Landsat's thermal and SWIR bands, for mapping coal fires (e.g., Prakash et al., 1999), peat fires (Elvidge et al., 2015), volcanic activity (Oppenheimer, C., et al., 1993, Reddy, C. S. S., et al., 1993) and urban heat island characteristics (e.g., Pelta et al., 2016). Whereas previous studies have demonstrated the capability of shortwave infrared channels of older Landsat instruments to detect gas flares and other combustion sources at night (due to their heat), we are not aware of studies which have examined the ability to Landsat TM, ETM + or OLI optical sensors to observe artificial night lights in the visible bands.
Our aim was therefore to assess the capability of the Operational Land Imager (OLI) sensor of Landsat 8, for detecting brightly lit areas from night-time images. More specifically, we aimed to examine what light levels were necessary for each Landsat 8 OLI spectral band to detect bright features at night. Landsat 8 nighttime radiance data were compared to the VIIRS Day/Night Band, astronaut photos from the ISS and additional sources of night-time light imagery for several large urban areas around the world and gas-flare sites, which are two of the main sources for night-time lights (additional sources include wildfires and fishing fleets). In addition we examined the spectral characteristics of night light sources which are detected by Landsat 8.
Section snippets
Landsat 8
We examined existing night-time Landsat 8 scenes covering cities and gas flares areas, where such imagery was available on the USGS EarthExplorer website (http://earthexplorer.usgs.gov) and where we expected to find bright night lights based on VIIRS imagery. After surveying various images, we chose to focus on seven Landsat 8 night-time scenes to assess its capabilities for detecting and measuring night-time lights over urban areas and gas flares. Five of these images covered major cities with
Visual examination of Landsat 8 OLI night-time scenes
Brightly lit urban areas and gas flares exhibited significant differences from their background. Berlin had a large lit area with evident differences in lighting types used in West Berlin and in East Berlin as seen on the ISS image (Fig. 1). Lit pixels within Landsat 8 scenes covering Berlin were concentrated in some of the most brightly lit areas, such as Alexanderplatz, a large public square and transport hub in the central Mitte district of Berlin (Fig. 1). Las Vegas was brighter than
Discussion
In recent years there has been a gradual increase in the scientific community's recognition of the importance of understanding and mapping of light pollution using remote sensing (Cinzano, P., et al., 2001, Huang, Q., et al., 2014). While the recently launched VIIRS sensor offers night light images whose radiometric and geometric resolution is much better than that of the DMSP (Cao, C. and Bai, Y., 2014, Miller, S. D., et al., 2013), and although there is now a commercial satellite offering
Conclusions
Our results have demonstrated that the OLI sensor onboard the recently launched Landsat 8, has the capability to detect artificial light in the visible bands only when emitted from very bright features in urban areas and from gas flares in at–sensor level measurements. As Landsat 8′s OLI was not designed for acquiring night-time images with low light levels, it is less sensitive than the VIIRS Day/Night band, and its detection limits are not low enough to provide meaningful mapping and
References (47)
- et al.
Limiting the impact of light pollution on human health, environment and stellar visibility
Journal of Environmental Management
(2011) - et al.
The next Landsat satellite: The Landsat data continuity mission
Remote Sensing of Environment
(2012) - et al.
Quantifying urban light pollution — A comparison between field measurements and EROS-B imagery
Remote Sensing of Environment
(2016) - et al.
Aerial survey and spatial analysis of sources of light pollution in Berlin, Germany
Remote Sensing of Environment
(2012) - et al.
A new source for high spatial resolution night time images — The EROS-B commercial satellite
Remote Sensing of Environment
(2014) - et al.
Spatio-temporal behavior of brightness temperature in Tel-Aviv and its application to air temperature monitoring
Environmental Pollution
(2016) - et al.
Landsat-8: Science and product vision for terrestrial global change research
Remote Sensing of Environment
(2014) - et al.
Spatial analysis of global urban extent from DMSP-OLS night lights
Remote Sensing of Environment
(2005) - et al.
Satellite survey of gas flares: Development and application of a Landsat-based technique in the Niger Delta
International Journal of Remote Sensing
(2014) - et al.
Building a global, consistent, and meaningful Landsat 7 data archive
Landsat-7 long-term acquisition plan
Photogrammetric Engineering & Remote Sensing
Quantitative analysis of VIIRS DNB nightlight point source for light power estimation and stability monitoring
Remote Sensing
Daytime gas flare detection using Landsat-8 multispectral data
The first world atlas of the artificial night sky brightness
Monthly Notices of the Royal Astronomical Society
Nighttime images of the earth from space
Scientific American
Burning waste gas in oil fields
Nature
CIESIN thematic guide to night-time light remote sensing and its applications
Why VIIRS data are superior to DMSP for mapping nighttime lights
Proceedings of the Asia-Pacific Advanced Network
The Nightsat mission concept
International Journal of Remote Sensing
Spectral identification of lighting type and character
Sensors
Potential for global mapping of development via a nightsat mission
GeoJournal
Automatic boat identification system for VIIRS low light imaging data
Remote Sensing
Methods for global survey of natural gas flaring from visible infrared imaging radiometer suite data
Energies
Cited by (21)
Quantitative Evaluation of Urban Expansion using NPP-VIIRS Nighttime Light and Landsat Spectral Data
2022, Sustainable Cities and SocietyCitation Excerpt :Firstly, in the future, the integration of NPP-VIIRS NTL data with daytime optical remote sensing data, and the fusion processing between NPP-VIIRS and DMSP-OLS data, or with higher-resolution Luojia-01 NTL data, should be further studied. Meanwhile, the novel model can be constructed by combining NTL data with other remote sensing data, and statistical analysis can be carried out to explore the interaction between variables (Levin and Duke, 2012; Levin and Phinn, 2016) to obtain high-resolution information on urban expansion with longer time series, which is vital to address the environmental problems caused by rapid and long-term urban expansion. Secondly, although NTL remote sensing data are of benefit in many applications, further calibration of ground and space measurements is needed to make these data more quantifiable.
High-resolution mapping of mainland China's urban floor area
2021, Landscape and Urban PlanningCitation Excerpt :The spatial resolutions of DMSP/OLS and VIIRS/DNB images are 3000 and 740 m, which are not enough to reflect the urban inside information. The Landsat 8 could be treated as TNL images with a high spatial resolution of 30 m, but only a very few bright objects can be detected in the images (Levin and Phinn, 2016). The NTL images or pictures from the International Space Station are also available at a high spatial resolution (5–200 m); however, the irregularly archine data causes the difficulty of application (Sánchez de Miguel et al., 2019).
A novel low-cost method for assessing intra-urban variation in night time light and applications to public health
2020, Social Science and MedicineRemote sensing of night lights: A review and an outlook for the future
2020, Remote Sensing of EnvironmentCitation Excerpt :While Landsat satellites do acquire night-time images, these are mostly useful for their thermal information, as the optical sensors onboard the TM and ETM + sensors were not designed for low light levels prevalent at night-time. However, the OLI sensor onboard Landsat 8, with its improved radiometric sensitivity, has been shown to be able to detect night-time lights from very bright areas such as gas flares and city centers (Levin and Phinn, 2016). Unfortunately, no sensor has been launched yet which offers operational multispectral monitoring of the Earth's night lights at medium spatial resolution.
New advances in benthic monitoring technology and methodology
2018, World Seas: An Environmental Evaluation Volume III: Ecological Issues and Environmental ImpactsAdvances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics
2017, Remote Sensing of EnvironmentCitation Excerpt :First, research should consider integrating other remotely sensed datasets into NTL-based models rather than just using them for validation. Several studies, for instance, validate OLS and/or VIIRS imagery with photographs taken from the International Space Station (Elvidge et al., 2007; Levin and Duke, 2012) or Landsat data (Levin and Phinn, 2016; Castrence et al., 2014; Shi et al., 2014; Small et al., 2005; Zhang and Seto, 2011). But rather than just using non-NTL imagery for examining the “accuracy” of NTL, more insight into the factors driving observed lights may come from combining both datasets into the same model.