No place to hide: Rare plant detection through remote sensing
Bibliographic record
Abstract
Abstract Aim Detection of rare species is limited by their intrinsic nature and by the constraints associated with traditional field surveys. Remote sensing (RS) provides a powerful alternative to traditional detection methods through the increasing availability of RS products. Here, we assess the capacity of RS at high and medium resolution to detect rare plants with direct and indirect approaches, and how the performance of RS can be influenced by the characteristics of species. Methods An extensive literature review was conducted to synthesize the use of RS to detect or predict rare plant occurrence at high and medium resolution (<30 m and 30–300 m, respectively). The concept of “rarity” was based on Rabinowitz's rare species classification. The literature review was performed in Scopus for the period 1990–2020. Results While direct detection is often limited, it is possible with high and very high spatial resolution data for rare plants with distinctive traits. RS is also able to capture biophysical conditions driving rare plant distributions, which can indirectly provide accurate predictions for them. Both approaches have the potential to discover new populations of rare plants. RS can also feed SAMs of rare plants, which combined with SDMs can provide a valuable approach for rare plant detection. While direct detection is limited by the space occupied by a species within its habitat and its morphological, phenological and physiological characteristics, the predictive performance of RS‐based SDMs (indirect detection) can be influenced by habitat size, habitat specificity and phenological features of rare plants. Similarly, model predictive performance can be influenced by the rarity form of the target species according to the rarity classification criteria. Main conclusions. With this synthesis, the strong potential of RS for the purposes of detection and prediction of rare plant has been highlighted, with practical applications for conservation and management.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 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".