Tree Density Dataset of Mountainous Regions in Northeast China

This dataset represents tree densities in mountainous regions of Northeast China, derived from field surveys of trees with a diameter at breast height (DBH) of ≥10 cm across 1926 plots. Using recursive feature elimination (RFE), six key variables influencing tree density were identified: soil silt content, soil clay content, elevation, NDVI, precipitation in the wettest month, and precipitation in the coldest quarter. A stacking ensemble learning algorithm, combining extreme random trees (ERT), support vector regression (SVR), CatBoost, and a ridge regression metamodel, was used for tree density estimation. The algorithm significantly improved model performance, with an average R² increase of 43.69% and reductions in RMSE and MAE by 11.16% and 10.14%, respectively. The dataset includes a 30 m spatial resolution map of tree densities, estimating approximately 27.497 billion trees in the region. This dataset provides valuable insights for forest carbon sequestration modeling, targeted forest conservation strategies, and carbon management practices.