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.本数据集源自欧洲空间局（European Space Agency）资助的“非洲遥感与人工智能森林扰动检测”（EO4Forest）项目，该项目由波兰弗罗茨瓦夫环境与生命科学大学与尼日利亚拉各斯州立大学联合实施。本数据集专为奥贡州与拉各斯州的森林监测工作打造，包含2015、2019、2022及2023年的高精度土地覆盖分类图，空间分辨率统一为20米，以精准还原地表覆盖实况。配套图例文件对六大预设土地覆盖类别进行了明确说明：水体、建成区、裸土、农田、草地与森林。 本数据集包含逾11.2万份训练样本与1.19万份验证样本，可为所用随机森林（Random Forest）分类模型的精准开发与性能评估提供核心支撑。研究团队重点聚焦森林类别，确保样本涵盖热带湿润林、红树林与干旱林地等多样森林类型。 除土地覆盖图外，本数据集还收录森林增益与损失图，其中针对2022年及2023年部分子区域的最新更新数据已收纳于NTR_ForestUpdate文件夹中。 本数据集基于Sentinel-2与Landsat-8光学卫星影像，通过光谱指数与随机森林算法完成土地覆盖类型分类，可为森林砍伐、再造林与人工造林相关的环境研究提供重要参考。森林变化图用于标注森林损失与增益区域，捕捉尼日利亚森林覆盖的动态变迁，并提供详尽的地理背景信息。 相关GitHub仓库中提供了完整方法学的Python脚本，涵盖数据获取、预处理、特征提取（光谱指数计算）、分类与精度评估等环节，确保研究的透明度与可复现性，可为用户提供处理后输出结果与自定义分析、进阶森林监测所需的全部工具。