Mapping mortality rates in boreal mixedwood forest using airborne laser scanning and permanent plot data
Bibliographic record
Abstract
Abstract Accurate and spatially explicit predictions of tree mortality are critical for understanding forest dynamics and guiding management practices. Airborne Laser Scanning (ALS) can cover large spatial areas, allowing the estimation of forest attributes and characterization of forest canopy vertical structure and canopy gaps over various forest environments. This study integrated field measurements from permanent growth and yield plots with ALS-derived attributes to develop zero-inflated beta regression models for estimating basal area mortality rates. Specifically, we combined a set of attributes related to canopy complexity and canopy gaps derived from ALS data to predict and map (20 m pixel resolution) mortality rates over a large boreal mixedwood forest in northern Ontario, Canada. We evaluated how the mortality rates vary depending on stand-level factors, such as stand age and forest type defined by species composition proportions. Our findings demonstrate that canopy gaps and structural attributes significantly predict basal area mortality rates. In particular, we found that higher mortality rates are associated with more complex canopy structures and larger canopy gaps. However, the magnitude varied by species composition. The resulting spatially explicit mortality probability and mortality rate maps showed highly variable predictions across forest types and structural attributes, offering the possibility of analyzing the spatial correlation of mortality occurrence with other variables like soil and climate attributes. The results support using ALS data in Enhanced Forest Inventory systems for more precise and timely interventions in operational silvicultural planning.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".