Predicting forest growth based on airborne light detection and ranging data, climate data, and a simplified process-based model
Bibliographic record
Abstract
Increasing use of airborne light detection and ranging (LiDAR) in forest inventories offers new possibilities to apply process-based forest models (PBM) in practice. We present a new approach, where a simplified PBM is run using inputs derived from the LiDAR data. The PBM was built by combining several existing models together, and it was tested with 52 Scots pine (Pinus sylvestris L.) dominated sample plots in Finland with the LiDAR (PBM_LIDAR) and field (PMB_FIELD) inputs. The results were compared with empirical growth predictions (EM_FIELD) and field reference growth. LiDAR-based stand variables (mean height of tree and crown base and leaf area index) were, on average, well in line with the field measurements. Basal area growth was slightly underestimated with the PBM_LIDAR (bias 4.1%, root mean square prediction error (RMSPE, 26.7%) and overestimated with the PBM_FIELD (bias –10.2%, RMSPE 33.3%), the EM_FIELD being the least biased (bias –1.9%, RMSPE of 24.6%). The bias varied with stand age and fertility. The dependence on field reference growth was highest with EM_FIELD and PBM_LIDAR (R 2 = 0.47 and 0.34, respectively), and lowest with PBM_FIELD (R 2 = 0.18). Despite several development needs, the approach is promising for easy incorporation of canopy and weather data into forest growth predictions.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".