Spatially-aware machine learning predictions of tropical forest structure for Morobe Province, Papua New Guinea: derived from spaceborne GEDI LiDAR and multi-source earth observation data

The raster layers provided here were gererated from wall-to-wall predictions of GEDI-derived forest structure characteristics (canopy height and cover) of a diverse tropical landscape in Papua New Guinea (PNG) using spatially-aware machine learning modelling, synergising multi-source earth observation (EO) data particularly, Sentinel-1 SAR, Sentinel-2 optical, AlphaEarth Satellite Embedding, environmental variables and synthetic spatial features. In addition, the CSV files contain the out-of-fold (OOF) prediction data, overall prediction metrics, and the feature importance for each model prediction. The paper is currently under review by the GIScience & Remote Sensing Journal, Taylor & Francis. As part of the Journal's Data Sharing Policy (Taylor & Francis open + FAIR data policy), this dataset is being openly shared under CCBY & CC0 Licenses. Paper Title: Tropical forest structure mapping using spatially-aware machine learning with GEDI metrics and multi-source earth observation data Paper Highlights Introduction of a mean GEDI canopy height metric (RHm) Development of a spatially-aware LightGBM optimization framework (Nested Bayesian Optimizer) Local Outlier Factor threshold Eigenvector Spatial Filtering 5-fold Spatial Block Cross-validation Creation of a comprehensive forest structure index (FSI) from GEDI multi-metric model predictions First spatially-explicit GEDI-derived forest structure wall-to-wall mapping effort specific to PNG