Local Prediction of Temperate Forest Structure in Eastern North America Using LiDAR, Radar, and Optical Data
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Forest structure underpins the emergence of ecological patterns and processes yet remains costly and labor-intensive to measure at broad scales. NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission provides three-dimensional Light Detection and Ranging (LiDAR) measurements at discrete footprints, leaving spatial gaps that complicate wall-to-wall mapping. Few studies have produced high-resolution, broad-extent predictions of multiple GEDI-derived metrics while explicitly accounting for spatial nonstationarity in predictor–response relationships. We addressed this gap with a local modeling framework that predicted 11 GEDI-based structural metrics at 30-m resolution across temperate broadleaf and mixed forests of eastern North America (1.17 million km2) for 2019–2022. Using Google Earth Engine, we integrated Landsat and Sentinel-2 multispectral imagery, Sentinel-1 synthetic aperture radar, and auxiliary variables (topography, land cover, leaf traits, and soil properties) to derive 93 environmental covariates. We partitioned the study area into 1,693 overlapping tiles, trained tile-specific random forest (RF) models with 80% of GEDI observations, and aggregated overlaps using weights based on model performance and pixel location. Across all metrics, local model predictions correlated strongly with GEDI measurements (Pearson’s r > 0.65). On the 20% held-out test set, median R2 of local models exceeded 0.4 for seven metrics, with canopy height and canopy cover both reaching 0.63. Sentinel-2, topography, and Landsat ranked among the most important predictor groups in at least 69.6% of local models for each metric. Across 30 randomly sampled tiles, local models outperformed a single global RF model in 56.7% of cases, with the largest gains where the global model performed worst. Our results show that integrating spaceborne LiDAR with multisource environmental covariates in a local modeling framework delivers robust predictions of forest structure and offers a transferable approach across broad geographic regions.