especially those dealing with high dimensional time-series
data (e.g., Cho and Fryzlewicz 2015), could be explored in the
future. Another limitation for our analytical approach is that
annual mapping of urban areas could not be accomplished
without the support from
NLCD
. For this study,
NLCD
2001 and
2011 served as the input data to derive urban change masks.
In our future studies, we plan to update our annual urban
maps to include all study years from 1992 to 2016. The
NLCD
Retrofit data (1992–2001 change) and
NLCD
2016 data will be
used as references for the longer term time-series analysis.
Conclusion
This study was designed for characterizing annual urban
changes using time series Landsat and
NLCD
data. We ex-
amined both 17-year and 30-year Landsat-derived annual
MVC NDVI
time series using three different change detection
algorithms to determine optimal combinations for annual
urban change mapping. Using Google Earth’s high resolu-
tion imagery as reference, detailed accuracy assessment was
implemented for urbanization and urban-intensification pixel
groups, defined by the initial land cover types of change
pixels. The result showed that the combination of break-point
algorithm with a time series of 1998–2014 reached the high-
est overall accuracy for estimating both urbanization years
(overall accuracy of 88%) and urban-intensification years
(overall accuracies of 76% and 61% for subgroup 21 and sub-
group 22, respectively). The relatively low overall accuracy
for subgroup 22 suggests that it is particularly challenging in
determining urban intensification years for pixels labeled as
low-intensity urban in the
NLCD
data. Pixels with the same
or close urban change years identified by the break-point
algorithm tend to be clustered together on the map, further
validating the overall good performance. In addition, the
break-point algorithm is easy to implement and can be fully
automated without user adjustment. It could be generalized
to other fast-growing urban regions for annual urban change
mapping across the U.S.
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