Seasonal <i>Phragmites australis</i> classification in Long Point National Wildlife Area wetlands using a remotely piloted aircraft system and random forest machine learning
Bibliographic record
Abstract
This study produced a high-accuracy remotely piloted aircraft system (RPAS) imagery classification method for identifying the invasive reed Phragmites australis ( Cav.) Trin. Ex Steud subsp. australis using random forest (RF) machine learning. RPAS imagery was collected in the spring and fall of 2019 using a fixed-wing RPAS equipped with a visible spectrum camera (eBee X, S.O.D.A. 3D; senseFly) in Long Point, Ontario, Canada. Imagery was used to produce separate early and late season classifications and a bi-temporal classification which used imagery from both dates. The overall accuracy achieved for each was 97%, 96%, and 91%, respectively. Digital surface models (DSMs) were the most important variable for identifying Phragmites in all classifications due to their greater height when compared to surrounding herbaceous vegetation. The bi-temporal classification, which utilized change in DSM value during the growing season, resulted in an estimated 47.8% new growth of Phragmites and appeared to capture sparse growth better than traditional classification differencing alone. This study highlights the promising use of high-resolution DSMs produced from RPAS imagery to classify invasive Phragmites and monitor within-year patch expansions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".