Predicting Forest Age up to 250 Years in Old-Growth Boreal Mixedwoods of Western Quebec, Canada, Using Airborne Laser Scanning Data and Modelled Tree Species Composition
Bibliographic record
Abstract
Old-growth boreal forests play a key role in global conservation policies. Forest age maps, however, tend to lose accuracy as stands age, making it impossible to consider the full successional diversity of old-growth forests. Our aim was therefore to develop remote sensing models that can predict old-growth boreal forest ages up to 250 years. As ground-truth data, we used a network of 432 field plots located in the boreal mixedwood forest of eastern Quebec, Canada. Forest age (time since last fire) was known for all plots, ranging from 74 to 258 years. We extracted indices from airborne laser scanning (ALS) and tree species prediction data for these plots at 4 different resolutions (400, 1600, 4900 and 10,000 m<sup>2</sup>) to capture variations in structure and composition at different grain sizes. The models obtained showed high predictive accuracy, at all resolutions (R<sup>2</sup>: 0.574–0.759, RMSE: 28.686–40.026, Bias: −1.093 − 3.933). Predicted tree species composition data contributed the most to model accuracy. These models provided an unprecedented mapping of the successional diversity of old-growth forests, compared with available data. Our methodology, therefore, would be an essential resource for improving our forest management strategies, allowing better conservation of old-growth boreal forests. Combination of airborne laser scanning (ALS) and predicted species composition data allows accurate forest age prediction in mixedwood boreal old-growth forestsModels developed predicted the age of old-growth forests up to 250 years, covering a wider successional gradient than currently available dataWithout tree species prediction data, ALS data need low resolution to properly characterize the structural heterogeneity resulting from forest succession.Our methodology provides a detailed characterization of old-growth forest internal diversity, improving conservation strategiesAge prediction models must be adapted to ecosystem-specific characteristics, but would be preferable to large-scale models (e.g., national scale) Combination of airborne laser scanning (ALS) and predicted species composition data allows accurate forest age prediction in mixedwood boreal old-growth forests Models developed predicted the age of old-growth forests up to 250 years, covering a wider successional gradient than currently available data Without tree species prediction data, ALS data need low resolution to properly characterize the structural heterogeneity resulting from forest succession. Our methodology provides a detailed characterization of old-growth forest internal diversity, improving conservation strategies Age prediction models must be adapted to ecosystem-specific characteristics, but would be preferable to large-scale models (e.g., national scale)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".