Study of The Transferability of Rfr-Based and Cnn-Based Algorithms for Canopy Height Prediction from Sentinel-2 Images
Bibliographic record
Abstract
Recently, studies have focused on integrating LiDAR data and satellite images to improve forest canopy height monitoring of large areas. Notably, algorithms based on Random Forest Regression (RFR) and Convolutional Neural Networks (CNN) have shown enhanced accuracy in predicting canopy heights. This study explores the transferability of RFR-based and CNN-based prediction algorithms using airborne LiDAR data and Sentinel-2 images from 2018 and 2021. The 2018 LiDAR and Sentinel-2 were used to train the RFR and CNN prediction algorithms. The trained RFR and CNN algorithms were then used to predict 2018 canopy height from 2018 Sentinel-2 and 2021 canopy height from 2021 Sentinal-2, respectively. Validation results reveal that the RFR-based algorithm achieved a mean absolute error (MAE) of 2.93m for 2018 canopy height and 3.35m for 2021 canopy height. The CNN-based algorithm yielded a MAE of 1.71m for 2018 and 3.78m for 2021. These findings demonstrate the feasibility of predicting forest canopy heights in the same and different years once the RFR and CNN prediction algorithms are properly trained.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".