Mapping Annual Urban Change Using Time Series Landsat and NLCD

Abstract

Annual urban change information is important for an improved understanding of urban dynamics and continuous modeling of urban ecosystem processes. This study examined Landsat-derived Normalized Difference Vegetation Index (NDVI) time series for characterizing annual urban change. To reduce impacts from cloud contamination and missing data, United States Geological Survey (USGS) Landsat Analysis Ready Data were processed to derive annual NDVI layers using a maximum value composite algorithm. National Land Cover Database land cover products from 2001 and 2011 were used as references for generating a decadal urban change mask. Within the decadal urban change mask and using annual NDVI as input, we examined three time-series change detection methods to pinpoint specific year of urban change: (a) minimum-value method, (b) break-point detection, and (c) simple-threshold identification. For accuracy assessment, we divided change pixels into urbanization and urban-intensification pixel groups, defined by initial land cover types. We used Google Earth's High-Resolution Imagery Archive as primary reference data for detailed accuracy assessment. Overall, the urbanization pixel group has good change detection accuracies of above 82% for all three change detection algorithms. The break-point detection method resulted in the highest overall accuracy of 88%. Overall accuracies for urban intensification pixel group were in the range of 35%–76%, depending on choice of change detection algorithm, length of input time-series, and further division of pixel subgroups.