Mapping urban growth of Dallas-Fort Worth metropolitan area from 1984 to 2019 using Landsat data




Journal Title

Journal ISSN

Volume Title



The Dallas-Fort Worth (DFW) Metropolitan Area is one of the fastest growing metropolitan areas in the U.S. Its rapid growth requires research investigation. Various studies have been conducted to understand the urbanization patterns and the impacts of urban expansion in this region using satellite data. Although the emphasis of previous studies was different, each of them discussed urban impervious surface change at different spatial and temporal scales. All previous studies agreed that the DFW metroplex experienced fast urban development and rapid increase in impervious surface cover over the recent decades, with the expansion of fringe areas around the DFW city cores. In this study, I applied an empirical, machine learning method to retrieve the long-term impervious surface cover for the DFW Metropolitan Area. I used the high-resolution planimetric maps obtained from the municipalities and the National Agriculture Imagery Program (NAIP) images as reference data for training and evaluation. I used Landsat data to generate annual continuous maps of impervious surface cover. The Landsat images were composited to summer and winter predictor variables according to vegetation seasonality. Composited seasonal images were able to reduce the variation and noise caused by vegetation phenology, atmospheric effect and cloud contamination. I trained a classification and regression tree (CART) model to predict impervious surface cover. The resultant maps were per-pixel continuous representation of impervious surface cover at a spatial resolution of 30-m annually from 1984 to 2019. I found that the area of impervious surface of DFW metroplex grew from 1,194 square kilometers to 2,880 square kilometers over the 35-year span. The counties of Dallas, Tarrant, Denton and Collin had the largest urban growth during the study time period. I also found that the quantified urban impervious surface increase at the county scale had high correlations with population growth over the same time period, with an r2 ranged from 0.83 to 0.96. The empirical method I applied can reliably map and monitor annual impervious surface cover change over a long period. The method can be potentially applied to other land cover types such as forest and cropland in other regions.



Impervious surface cover, Landsat, Urbanization, Google Earth engine