Mapping the Big Trees of Vancouver Island with LiDAR, Sentinel-1, Sentinel-2 and Deep Learning
Bibliographic record
Abstract
Decades of overharvesting have transformed much of Vancouver Island’s productive temperate rainforests into young, homogeneous forests, making the few remaining large-tree forests (height >55m) both rare and valuable. To map and conserve these forests, we produce a high-resolution canopy height model using a UNET deep-learning approach trained on LiDAR-derived 99th percentile height returns (~29% of the study area) and Sentinel-1, -2, ALOS PALSAR, and geographical predictors. The best-performing model (R2=0.76, MAE=4.73 m, and BIAS= -0.86 m), trained with Sentinel-1(VVVH) and Sentinel-2 (RGBNIR), outperformed all global canopy height products. We found a total of 135,482 location with canopy height above 55 m. Large tree forests were disproportionally found on upper slopes (~55%, 1.2x higher than expected) and valleys (28%, > 4x higher), and underrepresented in mid-slopes (7.4% in mid-slope, ~4x lower) and plateaus (1.4% in plateaus, 7x lower). Over half (63%) of forests sustaining big trees remain unprotected. These rare forests hold immense value yet constitute only a small fraction of the forested landscape, underscoring the urgency of a targeted conservation strategy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".