Improving UAV-Based LAI Estimation for Forests Over Complex Terrain by Reducing Topographic Effects on Multispectral Reflectance
Bibliographic record
Abstract
Leaf area index (LAI) is a key parameter for characterizing the dynamics of terrestrial ecosystems and is also one of the important structural parameters that can be retrieved from remote sensing (RS) data. LAI over mountainous areas, however, is still difficult to retrieve reliably due to the topographical variation that introduces significant uncertainties into observed reflectance. In this article, we proposed a new scheme to estimate topographic influence on ratio-based vegetation indices (VIs) from diffuse radiation, which is not yet adequately considered in existing topographic correction schemes. In our scheme, unmanned aerial vehicle (UAV) light detecting and ranging (LiDAR) data were first used to model the sky view factor (SVF) and terrain view factor of target pixels in a slope coordinate system. Based on these view factors, the total incident solar radiation on slope (ISRS) was corrected, specifically for the diffuse sky irradiance and adjacent terrain-reflected irradiance over complex terrains. we then recalculated the multispectral reflectance of UAV images and evaluated the topographic effects on the normalized difference vegetation index (NDVI) because the magnitudes of correction on red and near-infrared (NIR) reflectances are quite different. Finally, large-scale LAI distribution was retrieved by empirical models developed from the relationships between terrain-corrected NDVI and field-measured LAI. Our results show that the proposed topographic correction scheme can significantly improve the LAI retrievals over a growing season. Given that forests are widely distributed in complex terrains around the globe, this study would have significance in improving the mapping of global LAI that is essential for terrestrial carbon cycle studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".