Simultaneously acquired airborne laser scanning and multispectral imagery for individual tree species identification
Bibliographic record
Abstract
The objective of this study was to investigate the use of multispectral imagery in addition to measurements from airborne laser scanning (ALS) for tree species identification. Multispectral imagery from a medium-format digital frame camera acquired simultaneously with ALS data were utilized and compared with imagery from a large-format digital frame camera acquired on a separate flight mission from a higher altitude. The two acquisitions represent cost efficient methods for data collection of both three-dimensional and spectral information. The classification accuracy was assessed using 1520 segmented spruce, pine, and deciduous trees. Furthermore, ALS intensity was normalized using the range from sensor to the target (range normalization). In addition, a source of variation in intensity known as banding, is described together with a normalization procedure for diminishing this effect. The normalized intensity was better than using the raw intensity, but it did not improve the classification compared with using only ALS structural information, which provided overall classification accuracies of 74%–77%. The combined use of ALS and multispectral imagery from the medium-format imagery acquired simultaneously and the separate acquisition of large-format imagery provided overall accuracies of 87%–89% and 83%–85%, respectively. Simultaneous acquisition of ALS and medium-format digital imagery provides an efficient data acquisition strategy for tree species identification in forest inventory and will likely reduce data acquisition costs by 10%–20%.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".