Annual Impervious Surface Data from 2001–2020 for West African Countries: Ghana, Togo, Benin and Nigeria

Abstract

Impervious surface data are increasingly important for research and planning. Despite the availability of global and local urban land cover maps, regional data are lacking in Africa. We generated annual 30 m impervious cover data from 2001–2020 for Ghana, Togo, Benin, and Nigeria using the Landsat archive. We used random forest to predict impervious cover using 11 spectral indices and applied pixel-level temporal segmentation with the LandTrendr algorithm. Processing with LandTrendr improved the accuracy of the random forest predictions, with higher predicted-observed r2 (0.81), and lower mean error (−0.03), mean absolute error (5.73%), and root mean squared error (9.93%). We classified pixels >20% impervious as developed and < = 20% impervious as undeveloped. This classification had 93% overall accuracy and similar producer’s (79%) and user’s (80%) accuracies for developed area. Our maps had higher accuracy and captured more developed areas than comparable global datasets. This is the first regionally calibrated 30 m resolution impervious dataset in West Africa, which can support research on drivers and impacts of urban expansion and planning for future growth.