Mapping common and glossy buckthorns (<i>Frangula alnus</i>and<i>Rhamnus cathartica</i>) using multi-date satellite imagery WorldView-3, GeoEye-1 and SPOT-7
Bibliographic record
Abstract
Buckthorns (Glossy buckthorn, Frangula alnus and common buckthorn, Rhamnus cathartica) represent a threat to biodiversity. Their high competitivity lead to the replacement of native species and the inhibition of forest regeneration. Early detection strategies are therefore necessary to limit invasive alien plant species’ impacts, and remote sensing is one of the techniques for early invasion detection. Few studies have used phenological remote sensing approaches to map buckthorn distribution from medium spatial resolution images. Those studies highlighted the difficulty of detecting buckthorns in low densities and in understory using this category of images. The main objective of this study was to develop an approach using multi-date very high spatial resolution satellite imagery to map buckthorns in low densities and in the understory in the Québec city area. Three machine learning classifiers (Support Vector Machines, Random Forest and Extreme Gradient Boosting) were applied to WorldView-3, GeoEye-1 and SPOT-7 satellite imagery. The Random Forest classifier performed well (Kappa = 0.72). The SVM and XGBoost's coefficient Kappa were 0.69 and 0.66, respectively. However, buckthorn distribution in understory was identified as the main limit to this approach, and LiDAR data could be used to improve buckthorn mapping in similar environments.
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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) | 0.001 | 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".