Assessing Remote Sensing Approaches to Map Invasive Phragmites australis at Multiple Spatial Scales
Bibliographic record
Abstract
Phragmites australis (Cav.) Trin. ex Steud., the invasive common reed, is a perennial grass with a cosmopolitan distribution. Unlike the native subspecies (Phragmites australis subsp. americanus) in North America, this invasive haplotype is an aggressive competitor and has firmly established itself throughout the Great Lakes basin by dominating wetlands and wet habitat, forcing out native plants and creating monocultures of little use to native fauna. Growing clonally and from seed, invasive Phragmites can quickly dominate wet areas throughout North America. It has also become a prominent feature in roadside habitats, where native plants are subject to increased disturbance under which invasive Phragmites will thrive competitively. In order to effectively manage this aggressive invader, we must be able to accurately map its distribution at multiple spatial scales, understand its invasion ecology, and determine efficacy of current removal efforts. In this thesis, I evaluated multiple remote sensing methods to determine the extent of invasive Phragmites. The basin-wide wetland mapping project based on satellite image data was a collaborative effort between U.S. and Canadian scientists to document the current and potential distribution of invasive Phragmites throughout 10-km of the shoreline of the Great Lakes, including all coastal marshes. To elucidate its distribution through road networks, I used provincial orthophotography databases to map changes in the distribution of Phragmites in road corridors between 2006 and 2010. Based on these data, I created a conceptual model to show the relationships among the main factors that govern the establishment of invasive Phragmites in roadsides within Ontario. These factors included habitat quality, habitat availability, and propagule dispersal. I also showed how unmanned aerial vehicles can be used with very high accuracy to map the distribution of very small stands of Phragmites at the beginning of an invasion, and to determine short-term changes in habitat availability in smaller wetlands. Using various remote sensing approaches, I was able to determine the efficacy of treatment programs implemented by provincial agencies on roadway corridors at the scale of the entire southwestern, southcentral and central regions of Ontario. This is the first quantitative evidence of invasive Phragmites removal along roads and one of the largest spatial and temporal time scales used to evaluate these processes. Finally, I synthesized the capabilities and limitations of these remote sensing methods to create an evaluative framework that outlines how to best map invasive Phragmites across varying landscapes. This research integrates geography and biology to create novel mapping techniques for invasive Phragmites and has furthered our understanding of this aggressive plant and how its invasion can be controlled.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".