5. Discussion and Conclusions
Understanding heterogeneity in human settlements is important for
linking development patterns to ecological, economic, and social health
(Stokes and Seto, 2019). While daytime optical remote sensing has
tracked urban land cover change for decades (Zhu et al., 2019), there is
a need for new data and analytical tools to be able to monitor whether
changes in urban infrastructure are keeping pace with key factors tied
to global sustainability trends and opportunities. NTL data derived from
the Suomi-NPP and NOAA-20 VIIRS DNB, have the potential to add our
understanding of urban infrastructural transitions. But to date, the
high temporal variation of the VIIRS DNB observations (Elvidge et al.,
2022; Li et al., 2019; Wang et al., 2021) has made it challenging to
monitor and detect these factors, at the characteristic temporal scales
necessary to capture urban development processes. With the major
environmental effects of atmosphere and moonlight removed, the
atmospheric- and Lunar-BRDF-corrected NASA’s Black Marble products
provide new opportunities to use extremely dense (e.g., daily) NTL time
series. In this study, we developed a VZA stratified algorithm for
continuous monitoring of NTL changes based on the daily Black Marble
products. With the observations stratified into four VZA intervals, this
approach estimated an individual time series model for each VZA
interval. Based on the predicted and the observed values, consistent
breakpoints were identified and applied to all VZA stratified models as
potential NTL changes when any of the VZA interval models detected
consecutive anomaly observations (can tolerate one exception).
Quantitative evaluation of the NTL change detection result showed that
the algorithm can detect NTL changes with omission and commission error
rates < 30%. In addition to the detected breakpoints of the
NTL patterns, slopes of the estimated models can also provide extra
information on the trend of gradual NTL radiances changes.
We developed new methods to filter, fit, and mitigate the high temporal
uncertainties of the daily DNB time series observations. The applied 5x5
pixel buffer filtered potential outliers surrounding cloud and snow
pixels to mitigate the cloud and snow contamination. The single term
harmonic model can estimate the intra-annual patterns of the DNB time
series as a result of joint factors including vegetation phenology,
winter snow coverage, and cyclical human activity changes. We mitigated
and simplified the complex and combined effects of the wide ranges of
sensor viewing angle and different surface geometry conditions by
stratifying the DNB time series based on four sets of VZA intervals.
Compared with correcting the viewing angle effect (Tan et al., 2022),
the stratification strategy avoided the need for collecting local
3-dimensional structure data and would not be influenced by the
potential bias introduced in the correction approaches. The combined
knowledge derived from VZA interval subsets, and all data models reduced
the variance across VZAs without decreasing the temporal frequency of
the data. This new VZA stratification approach would be particularly
useful for pixels with strong angular effects and changes that can only
be observed under specific viewing angles. For pixels with homogeneous
emission among different viewing angles and short-term NTL changes, all
data models with 0-60 VZAs could provide accurate and timely results.
The VZA-COLD algorithm can accurately detect different types of
human-related NTL changes, either with or without land cover conversion
(e.g., urban expansion). For NTL changes with land cover conversion, the
concurrent changes of urbanization processes, construction actions, and
land cultivation can be well detected with the potential in providing
additional information about the land cover conversion stages. For the
NTL changes without land cover conversion, the algorithm can identify
the long-term and short-term land cover condition change, such as
de-electrification of the electric infrastructure, electric grid
variations, and human behavior related disturbances. Compared with the
daytime optical remote sensing imagery, the NTL change metrics can
provide complementary information of the human activity patterns that do
not necessarily cause changes in land cover and provide a better
understanding of coupling human-environment systems with a unique
perspective.
Although VZA-COLD can model and detect the abrupt, cyclical, and gradual
NTL changes for both the view angle affected and unaffected regions, it
also has some limitations. For example, high latitude areas with large
coverage of winter snow/ice could still lead to many commission errors
if the cloud/snow QA and their buffers did not exclude them well. This
error could be reduced in the upcoming Collection 2 Black Marble
products after reprocessing all the data with an improved snow flag
derived from the VIIRS snow product with higher spatial resolution.
Moreover, the change probability threshold for each time series model is
currently calculated based on the same RMSE for all periods, and the
temporally varying fluctuations caused by the winter weather are more
likely to be identified as NTL changes. To overcome this issue, further
adjustments could be performed to consider the changing temporal
variation of NTL data within a year, such as applying the temporally
adjusted RMSE values for change detection (Zhu et al., 2015). Moreover,
further post-processing steps such as filtering or including NTL changes
based on their spatial patterns could also be explored.
In conclusion, we developed a VZA stratified COLD algorithm to
continuously monitor NTL changes based on the daily atmospheric- and
Lunar-BRDF-corrected Black Marble product. This method enabled the dense
daily time series analysis of the DNB data by reducing the viewing angle
introduced variations without decreasing the temporal frequency of the
data. The results indicate that this method could be applied for
operational mapping of global NTL changes at a spatial resolution of
15-arc-second with a daily updating frequency.