Forest disturbance detection by using remote sensing and artificial intelligence in Africa

The dataset arises from the "Forest Disturbance Detection Using Remote Sensing and Artificial Intelligence in Africa" (EO4Forest) project, a collaboration funded by the European Space Agency and conducted by Wrocław University of Environmental and Life Sciences (Poland) and Lagos State University (Nigeria). Designed for forest monitoring in the Ogun and Lagos States, the dataset includes detailed land cover classification maps for the years 2015, 2019, 2022, and 2023, all at a 20-meter spatial resolution to ensure accurate representation of land cover. A legend file accompanies the maps, clarifying six defined land cover classes: water, urbanized areas, soil, cropland, grasslands, and forest.

The dataset includes over 112,000 training and 11,900 validation samples, which are essential for the accurate development and evaluation of the Random Forest classification models used. Special emphasis was placed on the forest class, ensuring a diverse representation of forest types, including tropical humid forests, mangroves, and dry woodlands.

In addition to land cover maps, the dataset also contains forest gain and loss maps, with a particular focus on recent updates for selected subareas in 2022 and 2023, available in the NTR_ForestUpdate folder.

Based on optical satellite imagery from Sentinel-2 and Landsat-8, the dataset leverages spectral indices and the Random Forest algorithm to classify land cover types. It provides valuable insights for environmental research related to deforestation, reforestation, and afforestation. Forest change maps are included to highlight areas of forest loss and gain, capturing the dynamic shifts in Nigeria's forest cover and offering detailed geographic context.

The methodology, including data acquisition, preprocessing, feature extraction (spectral index calculation), classification, and accuracy assessment, is fully documented in Python scripts available in the associated GitHub repository. This ensures transparency and reproducibility, offering users both the processed outputs and the tools necessary for custom analyses and advanced forest monitoring.