Mapping Dominant Boreal Tree Species Groups by Combining Area-Based and Individual Tree Crown LiDAR Metrics with Sentinel-2 Data
Bibliographic record
Abstract
Airborne light detection and ranging (LiDAR) data are increasingly used to inform sustainable forest management practices. Information about species composition is needed for a range of applications; however, commonly used area-based summaries of LiDAR data are limited to accurately differentiate tree species. The objective of this study was to map dominant species groups across a large (>580,000 ha) boreal forest by combining area-based and individual tree metrics derived from single photon LiDAR data with multispectral information derived from Sentinel-2 imagery. Classification of the forest into jack pine (Pinus banksiana), black spruce (Picea mariana), mixed conifer, mixedwood, and hardwood species groups resulted in an overall accuracy of 67.8% compared with field data. A more generalized classification into softwood, hardwood, and mixedwood had an overall accuracy of 83.2%. The reflectance in the red edge region of the electromagnetic spectrum (λ = 740 nm), the area and volume of tree crowns, and the cumulative distribution of LiDAR returns in the canopy were particularly important variables to discriminate between species groups. Wall-to-wall predictions of species groups based on remotely sensed data—as derived herein—could provide a spatially-detailed alternative to stand-level species composition information traditionally derived from photo-interpreted aerial imagery.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".