Mapping the distributions of two invasive plant species in urban areas with advanced remote sensing data
Bibliographic record
Abstract
Invasive plants are increasingly present in ecosystems, producing both positive and negative effects. Proactive management of plant invasions is critical to curbing their spreads, especially in urban areas which often act as centres of invasions. Therefore, municipalities require new tools to map invasions for both management and information. Remote sensing technologies provide opportunities to detect plant invasions over large areas at fine spatial resolutions. In Surrey, British Columbia, Canada, Himalayan blackberry (HB; Rubus armeniacus) and English ivy (EI; Hedera helix) are two understory invasive plants that can negatively influence native ecosystems and harm users of urban natural areas. Two remote sensing technologies, hyperspectral imagery and light detection and ranging (LiDAR) data, were utilized to map these two species across the entire area of Surrey. Analysis of spectral characteristics of HB and EI were used with hyperspectral imagery to examine the feasibility of spectrally detecting these species. Spectra were obtained from a ground-based handheld spectrometer from the two species and other common species in Surrey and processed through a spectral channel selection algorithm to identify key wavelengths for distinguishing these species. Once identified, a spectral classification routine used these wavelengths and training plots to detect HB and EI across open areas in Surrey. Results showed accuracies of 76.4% for HB and 80.0% for EI. Mapping HB and EI across all land covers of Surrey required detecting the two species in forested areas. Field plots, LiDAR-derived topographic and forest structure variables, hyperspectral data, a land cover classification, and a LiDAR-derived irradiance model were all used as inputs into random forest models to detect the species across the entire land base. Model accuracies ranged from 77.8% to 87.8%. Open areas were classified better than forested areas. EI was found more across the city than HB. The research in the thesis has advanced detection of invasive plants by demonstrating the feasibility of mapping understory invasions of EI and HB in urban areas at fine spatial resolutions and can form the basis for a future monitoring system using data acquired at regular intervals. Future work is recommended to enhance data collection and increase map specificity.
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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 | 0.001 |
| 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".