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本数据集提供的栅格图层（raster layers），基于空间感知机器学习建模方法，融合多源地球观测（Earth Observation, EO）数据——包括Sentinel-1合成孔径雷达（SAR）、Sentinel-2光学影像、AlphaEarth卫星嵌入特征、环境变量与合成空间特征——对巴布亚新几内亚（PNG）多样热带景观的GEDI反演森林结构特征（冠层高度与盖度）开展全覆盖预测并生成。此外，配套CSV文件包含折外（out-of-fold, OOF）预测数据、整体预测指标，以及各模型预测的特征重要性。 本论文目前正由泰勒弗朗西斯（Taylor & Francis）旗下的《GIScience & Remote Sensing Journal》（地理信息科学与遥感学报）审稿。依据该期刊的数据共享政策（泰勒弗朗西斯开放+FAIR数据政策），本数据集采用CC BY与CC0协议进行开源共享。 论文标题：基于空间感知机器学习结合GEDI指标与多源地球观测数据的热带森林结构制图 论文亮点： - 提出平均GEDI冠层高度指标（RHm） - 研发空间感知LightGBM优化框架（嵌套贝叶斯优化器） - 局部离群因子阈值（Local Outlier Factor threshold） - 特征向量空间滤波（Eigenvector Spatial Filtering） - 5折空间块交叉验证（5-fold Spatial Block Cross-validation） - 基于GEDI多指标模型预测结果构建综合森林结构指数（FSI） - 首次针对巴布亚新几内亚开展空间显式的GEDI反演森林结构全覆盖制图工作