Characterizing the effect of small-scale topographic variability on co-existing native and invasive species in a heterogeneous grassland using airborne hyperspectral remote sensing
Bibliographic record
Abstract
Abstract Characterizing the distribution, mechanism, and behaviour of invasive species is crucial to implementing an effective plan for the protection and management of native grassland ecosystems. Hyperspectral remote sensing has been used for mapping and monitoring invasive species at various spatial and temporal scales. However, most studies focus either on invasive tree species mapping or on the landscape-level using low-spatial resolution remote sensing imagery. These low-resolution images are not fine enough to distinguish individual invasive grasses, especially in a heterogeneous environment where invasive species are small, fragmented, and co-existing with native plants with similar color and texture. To capture the small yet highly dynamic invasive plants at different stages of the growing season and under various topography and hydrological conditions, we use airborne high-resolution narrow-band hyperspectral imagery (HrHSI) to map invasive species in a heterogeneous grassland ecosystem in southern Ontario, Canada. The results show that there is high spectral and textural separability between invasive species and between invasive and native plants, leading to an overall species classification accuracy of up to 89.6%. The combination of resultant species-level maps and the digital elevation model (DEM) showed that seasonality is the dominant factor that drives the distribution of invasive species at the landscape level, while small-scale topographic variations partially explain local patches of invasive species. This study provides insights into the feasibility of using HrHSI in mapping invasive species in a heterogeneous ecosystem and offers the means to understand the mechanism and behaviour of invasive species for a more effective grassland management strategy.
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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 | 0.001 |
| 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 | 0.000 |
| 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".