Monitoring lianas from space: Using Sentinel-2 imagery to observe liana removal in logged tropical forests
Bibliographic record
Abstract
Liana removal – the cutting of over-abundant woody climbing plants (lianas) – has the potential to substantially increase tree growth and biomass accumulation across millions of hectares of degraded tropical forest. Satellite imagery could provide data capable of observing the effect of liana removal on the forest canopy, enabling the large-scale monitoring and validation of liana removal, which remains a key hurdle to its widespread implementation. Using a 320-ha liana removal experiment in Sabah, Malaysian Borneo, we tested whether a time series of Sentinel-2 images could observe the canopy signature of liana removal. Calculating a range of metrics derived from the Normalized Burn Ratio – a vegetation index based on spectral reflectance that differentiates leaf from non-leaf – we quantified satellite-derived canopy disturbance and fragmentation across a range of liana removal intensities and examined how canopy disturbance changed in the 12-months following removal treatments. We find that liana removal significantly increases canopy disturbance and fragmentation metrics one month after removal, with partial removal having a smaller effect than complete removal. The impact of liana removal on the canopy metrics declined over time, with measures of canopy disturbance and fragmentation largely indistinguishable from control forest within 12-months of treatment. Our findings evidence that freely available satellite imagery can be used to efficiently monitor large-scale liana removal applied at a range of intensities and suggest that partial liana removal could significantly reduce canopy disturbance of this restoration method.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".