TRAIL ROAD LANDFILL SITE MONITORING USING MULTI-TEMPORAL LANDSAT SATELLTE DATA
Bibliographic record
Abstract
The disposal of the solid wastes in landfill sites should be properly monitored by analyzing samples from soil, water, and landfill gases within the landfill sites. Nevertheless, ground monitoring scheme requires intensive efforts and cost, and sometime it is difficult to be achieved in large geographic extent. Remote sensing technology has been introduced for waste disposal management and monitoring effects of the landfill sites on the environment. This paper presents a case study to evaluate the use of multi-temporal remote sensing data to monitor and assess the effects of landfill sites on the environment. The study area covers the Nepean and Trail Road landfill sites (the main municipal waste disposal site for the city of Ottawa). The Nepean landfill site was opened in 1960s, accepted waste until 1980s and finally capped in 1993. With the increasing amount of waste disposal, the Trail Road landfill was then constructed and was in operation in early 1980. The Trail Road landfill, which is still in operation, is comprised of four phases developed sequentially. Thirteen bi-yearly multi-temporal Landsat satellite images acquired during July and August from the year 1985 to 2009 are used to calculate the Soil Adjusted Vegetation Index (SAVI) and the Land Surface Temperature (LST). The differences of the LST between the landfill sites (due to the release of the landfill gases within the landfill site) and the surrounding areas are analyzed. Furthermore, the LST of the landfill sites are monitored to assess the decomposition activities of the waste disposal. Preliminary data analysis reveals that the LST of the landfill site is higher than the immediate surrounding areas and the air temperature during the decomposition process by up to 9 °C and 14 °C, respectively. In the Trail Road landfill site, the LST of the active phases of the landfill site is higher than the closed phases of the landfill site by around 3 to 5 °C. The SAVI is used to
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".