Application of multispectral remote sensing to monitoring water quality of urban stormwater retention ponds
Bibliographic record
Abstract
Stormwater retention ponds are used to reduce the burden on storm sewer systems during heavy rain events and filter pollutants, although they are prone to eutrophication and cyanobacterial blooms. Two nearby ponds with contrasting designs (traditional vs. naturalized) in Winnipeg, Manitoba, were regularly sampled from late spring to early fall for two years using a boat and tested for a suite of water quality parameters in order to compare them. Near-surface measurements of water reflectance were also collected via remote sensing using a handheld spectroradiometer. A drone equipped with a multispectral sensor was deployed prior to sampling to collect imagery of the ponds in order to evaluate the utility of airborne remote sensing for monitoring water quality. Compared to established remote sensing methods such as satellites and handheld spectroradiometers, drones are a relatively new technology that have potential to fill a niche for monitoring small water bodies or areas of interest at high spatial and temporal resolutions. Cloud-free skies with low wind were considered ideal flight conditions, however, data were also collected under suboptimal conditions. Results showed that the naturalized retention pond typically had lower concentrations of chlorophyll-a and total suspended solids at its surface layer and was dominated by green algae and diatoms as opposed to cyanobacteria at the traditional pond. Water quality tended to decline throughout the sampling season at both ponds, but the traditional pond experienced an intense cyanobacterial bloom in 2021. At the naturalized pond, the timing of water quality decline coincided with the senescence of green algae and pondweed along its surface layer. Results of linear regressions between select waveband ratios and chlorophyll-a or total suspended solids demonstrated that while the near-surface method was typically stronger, airborne remote sensing was viable and even outperformed it under certain conditions. The influence of suboptimal conditions on regression strength varied, as did the performance of wavebands among ponds, however, at least one option performed moderately well in all cases, and under ideal circumstances R2 values were exceptional. Three key waveband ratios performed well under numerous circumstances: red-edge to red, red-edge to green, and near-infrared to red.
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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".