Towards a Generalized Method for Tree Species Classification Using Multispectral Airborne Laser Scanning in Ontario, Canada
Bibliographic record
Abstract
The aim of this paper was to develop a generalized classification model using multispectral airborne laser scanning (ALS) data to provide species or genus level tree identification. We tested the robustness, transferability, and generalizability of the developed method across two sites in Ontario, Canada. We focused on the generalization of the approaches to consider the various common species as well as their variable characteristics along the latitudinal gradient (e.g., the shape variations of pine), and variations of multispectral ALS features. The crown sample for training and validation was composed of 984 and 762 crowns for each site respectively. The generalized model for nine individual tree species was developed using a random forest machine learning algorithm with k-fold-cross validation for each study site accuracy assessment. We have extracted both 3D and intensity features from multispectral ALS data to identify trees. Our preliminary analysis revealed that intensity feature varied across two study sites and among the species, while 3D features were comparatively less variable. In addition, both 3D and intensity features are influenced by tree height. We also found that the three most useful features in tree species classification were multispectral vegetation indices (based on CI-1550 nm and C3-532 nm channels) and intensity features derived from CI-1550 nm. Our preliminary analysis shows that a generalized method can identify nine species with a 73% overall accuracy, whereas the site-specific overall accuracies were 75% and 66% respectively. Our work demonstrated the potential of a multispectral ALS sensor to develop a generalized classification model for the identification of diverse tree species.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".