Annual tree cover data from 2001-2020 across the Eastern Guinean Forest in Ghana and Nigerian Lowlands Forest in Nigeria

We combined machine learning and LandTrendr temporal segmentation to generate annual tree cover from 2001-2020 from the Landsat archive for Eastern Guinean Forest in Ghana and Nigerian Lowlands Forest in Nigeria. The continuous tree canopy cover generated using only the machine‑learning model exhibited larger errors than the temporally segmented tree canopy cover estimates derived from LandTrendr. The LandTrendr‑based approach produced higher predicted–observed, lower mean absolute error, and lower root mean squared error, indicating that the processed time‑series segmentation more effectively estimates areas with high versus low tree cover across Eastern Guinean Forest and Nigerian Lowlands Forest. Using a 30% threshold, the classified tree cover achieved a producer accuracy of 82% and a user accuracy of 83%. The data is available as continuous tree canopy cover (CanopyCover_Continuous.zip) and binary tree cover (TreeCover_Classified.zip) in GeoTIFF format.