4. Results
4.1. Visual assessment
We applied the VZA-COLD algorithm to all the selected tiles (Fig. 1) to
examine its change detection performances for various kinds of
human-related NTL changes over different regions. The annual and
day-of-year NTL change maps from 2013 to 2021 were created for every
pixel. To demonstrate the algorithm’s abilities in monitoring NTL
changes, we investigated a range of urban and peri-urban regions with
human activity changes corresponding to different land use, demographic,
and socioeconomic typologies (Stokes and Seto, 2019). The results, shown
in Figs. 9-16, illustrated how the algorithm can accurately capture NTL
changes caused by the major types of both short-term and long-term
transitions. These factors include, but are not limited to, urbanization
processes in sub-urban areas, non-residential constructions of public
facilities, land cultivation of a new modern agriculture field,
redevelopment of a pre-existing urban area triggered by the economic
growth, de-electrification derived by the renovation of lighting
technologies and environmental policies, armed conflicts, and power grid
loss caused by natural hazards. Meanwhile, the identified changed areas
covered a wide range of human footprints with different land cover and
land use types, including the highly populated urban areas, urban green
space, suburban and rural areas, agricultural fields, roads, and barren
land regions with human activities.
Urbanization with constructions of residential and non-residential
developments is one of the most prevalent human-driven land cover and
land use changes. Fig. 9 showed the urban expansion process of a new
residential community in the suburban area of Melbourne, Australia that
converted the agriculture fields to impervious surfaces. A gradual NTL
change was captured by the algorithm at this site, which is consistent
with the built-up period of this new settlement from 2017 to 2021 (Fig.
9c). Figs. 10-11 showed the construction actions of a newly built
international airport in the suburban areas of Beijing, China (Fig. 10)
and the Olympic Parks in Rio de Janeiro, Brazil (Fig. 11). Multiple NTL
changes were identified for the new international airport, which aligned
well with the different construction stages (Fig. 10c). The timing of
these three identified changes agreed with the start of land clearance
in February 2016, the time when the major construction of the airport
was finished in 2018, and its opening date in September 2019. At the
Olympic Parks in Rio, an increase in artificial light emissions was
caused by the new facilities and the large gathering event of the
Olympic Games in summer 2016, and this abrupt NTL change was also
successfully captured (Fig. 11). In addition to urban developments, the
land cultivation engendered by the food consumption in agricultural land
can also be captured by the DNB time series. Fig. 12 showed a new modern
organic vegetable greenhouse built with LED plant light at night in the
low light area of Canada. According to the time series result of the
selected pixel (Fig. 12c), a dramatic increase of the NTL was captured
in 2017 after the greenhouse was put into use. Redevelopment driven by
the population and economic growth, and de-electrification caused by new
technologies and environmental policies can shift intensities of the
artificial NTL over pre-existing developments without land conversions.
Fig. 13 showed the redevelopment of urban areas in Abidjan, Ivory Coast.
Foreign investments promoted both GDP and population density of the
urban environment of Abidjan (Ramiaramanana et al., 2021), which was
detected in 2014. The large-scale renovation of LED streetlights in the
suburban areas of Milan that were planned by environmental policies
encouraged by the International Registered Exhibition in 2015 (World
EXPO) can also be detected from the annual NTL change map (Fig. 14). A
significant drop in the NTL radiances was captured in 2014 after the new
energy-saving LED streetlights with less upward emission were installed.
Changes in human behavior at night, armed conflict, and power grid loss
can lead to short-term NTL shifts. VZA-COLD successfully detected these
short-term changes in a timely manner.
In
urban areas with high dynamics of NTL caused by armed conflicts such as
the Syrian Civil War in Raqqa, the algorithm identified multiple NTL
changes between the stable periods with relatively short durations (Fig.
15). In September 2017, Puerto Rico was hit by two powerful hurricanes,
and the abrupt power outage and gradual restoration were successfully
identified by the VZA-COLD algorithm (Fig. 16).