Predicting Carbon Accumulation in Temperate Forests of Ontario Using a LiDAR-Initialized Growth-and-Yield Model
Bibliographic record
Abstract
Climate warming has led to a need for improved estimates of aboveground carbon accumulation (CA); in Ontario, this is particularly true for uneven-aged, mixedwood temperate forests, where there remains high uncertainty. This study investigated the feasibility of using LiDAR-derived tree lists to initialize a Growth and Yield (G&Y) model at the Petawawa Research Forest (PRF) in eastern Ontario, Canada. We aimed to see if models based on LiDAR-derived estimates of tree attributes provide comparable predictions of aboveground CA in complex temperate forests to those from inventory measurements. \nApplying a local G&Y model (i.e., FVSOntario), we predicted aboveground carbon stock (CS; tons/ha) and CA (tons/ha/yr) using recurring plot measurements from 2012-2016, FVS1. We then used a suite of statistical predictors derived from LiDAR to predict stem density (SD), stem diameter distribution (SDD), and basal area distribution (BA_dist) using an algorithm. These data, along with measured species abundance, were used to construct tree lists and initialize a second G&Y model (i.e., FVS2). Another G&Y model was tested using LiDAR-initialized tree lists and Forest Resource Inventory (FRI) estimates of species abundance (i.e., FVS3). Models were validated using inventory data at years zero (2012) and four (2016). \nThe inventory-based model predicted equivalent CS at year four compared to validation data at all size-class levels, while LiDAR-based models did not; however, models were statistically dissimilar to validation data for CA. There was no significant difference between LiDAR-based models using inventory and FRI species through a 9-year period at the plot level (p<0.05), although forest type groupings were not always equivalent. We found equivalency of inventory and photo-interpreted models for size classes ≥17 cm. \nOur results demonstrate the importance of precise tree size information when initializing G&Y models using LiDAR-derived estimates, the possibility of circumventing precise species abundance information when using G&Y models for CA, and that more work is needed on the use of LiDAR to quantify individual tree measurements in G&Y models for accurate predictions of CA. These results provide insight into techniques that can be applied regionally to quantify and forecast CA changes in Ontario, and for more informed carbon mitigation strategies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".