Comparative Analysis of Forest Canopy Height Estimation using Random Forest and Support Vector Machine Models with Synthetic Aperture Radar and Optical Imagery
Bibliographic record
Abstract
In this study, the accuracy of forest canopy height estimation was assessed using Synthetic Aperture Radar (SAR), including backscatter and Polarimetric SAR (PolSAR), as well as optical indices derived from optical imagery, and Random Forest (RF) and Support Vector Machine (SVM) models were applied by using canopy heights derived from Light Detection and Ranging (LiDAR) as a reference for validation. Accurate measurement of canopy height is critical for effective forest management, biodiversity conservation, and climate change analysis, so this study attempted to address the challenges posed by traditional measurement methods, which are time-consuming and limited in scope. SAR with its all-weather, day and night imaging capability, has the distinct advantage of being able to continuously monitor forest canopy dynamics over a wide area, thus overcoming the spatial time and cost constraints of ground-based observations. Approaches in this study involved pre-processing of SAR and LiDAR data to reduce inherent data inaccuracies, as well as calculating optical indices to facilitate indirect estimation of canopy height. This study provided a comparative assessment of the performance of RF and SVM models using various data integrations, highlighted the higher accuracy was achieved through the synergistic combination of PolSAR and optical indices. The results showed that the data-integrated approach improved the accuracy of canopy height estimation, with the RF model performing slightly better than the SVM model in terms of prediction under the optimal data configurations of the two models in this study. These findings support the advanced application of incorporating remote sensing techniques, validated against LiDAR benchmarks, as a viable strategy for refining forest canopy height estimation, thereby providing insights for forest management and ecological modelling programs.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".