Harmonizing GEDI and LVIS Data for Accurate and Large-Scale Mapping of Foliage Height Diversity
Bibliographic record
Abstract
Remote sensing is key for large-scale forest mapping, yet the limited integration of LiDAR sensors restricts the spatial coverage of forest attribute estimation. Our study aimed to accurately map Foliage Height Diversity (FHD) in five key North American regions, vital for ongoing research in ecosystem dynamics in western North America. We used a combination of GEDI data and LVIS data, incorporating measures of forest complexity and relative forest heights (RH25, RH50, RH75, and RH98), to predict FHD with a random forest regression in the Kluane region, southwest Yukon—a northernmost site where GEDI data are not available. This method was designed to overcome spatial coverage limitations of different sensors, enabling the production of consistent, precise, multi-temporal FHD maps across all sites. We found strong agreement between predicted and observed FHD values estimated from Airborne Laser Scanning in the Yukon (R2 = 0.72; RMSE = 0.46). Additionally, we upscaled GEDI FHD predictions in all sites by integrating Landsat imagery, ALOS PALSAR, and topographical data, resulting in high accuracy (R2 = 0.85; RMSE = 0.26). Our findings demonstrate that by harmonizing full-wave form LiDAR sensors, we can significantly expand the coverage of LiDAR data, allowing for consistent broad-scale analyses of forest attributes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".