Northern Conifer Forest Species Classification Using Multispectral Data Acquired from an Unmanned Aerial Vehicle
Why this work is in the frame
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Bibliographic record
Abstract
Object-based image analysis and machine learning classification procedures, after field calibration and photogrammetric processing of consumer-grade unmanned aerial vehicle (<small>UAV</small>) digital camera data, were implemented to classify tree species in a conifer forest in the Great Lakes/St Lawrence Lowlands Ecoregion, Ontario, Canada. A red-green-blue (<small>RGB</small>) digital camera yielded approximately 72 percent classification accuracy for three commercial tree species and one conifer shrub. Accuracy improved approximately 15 percent, to 87 percent overall, with higher radiometric quality data acquired separately using a digital camera that included near infrared observations (at a lower spatial resolution). Interpretation of the point cloud, spectral, texture and object (tree crown) classification Variable Importance (<small>VI</small>) selected by a machine learning algorithm suggested a good correspondence with the traditional aerial photointerpretation cues used in the development of well-established large-scale photography northern conifer elimination keys, which use three-dimensional crown shape, spectral response (tone), texture derivatives to quantify branching characteristics, and crown size, development and outline features. These results suggest that commonly available consumer-grade <small>UAV</small>-based digital cameras can be used with object-based image analysis to obtain acceptable conifer species classification accuracy to support operational forest inventory applications.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| 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 it