REMOTE SENSING TECHNIQUES AS A TOOL FOR ENVIRONMENTAL MONITORING
Bibliographic record
Abstract
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 site. Nevertheless, ground monitoring systems require intensive efforts and cost. Furthermore, ground monitoring may be 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. In this paper, two case studies are presented in the Trail Road landfill, Ottawa, Canada and the Al-Jleeb landfill, Al-Farwanyah, Kuwait to evaluate the use of multi-temporal remote sensing images to monitor the landfill sites. The work objectives are: 1) to study the usability of multi-temporal Landsat images for landfill site monitoring by studying the land surface temperature (LST) in the Trail Road landfill, 2) to investigate the relationship between the LST and the amount of the landfill gas emitted in the Trail Road landfill, and 3) to use the multi-temporal LST images to detect the suspicious dumping areas within the Al-Jleeb landfill site. Free archive of multi-temporal Landsat images are obtained from the USGS EarthExplorer. The Landsat images are then atmospherically corrected and the LST images are derived from the thermal band of the corrected Landsat images. In the Trail Road landfill, the results reveal that the LST of the landfill site is always higher than the air temperature by 10°C in average as well as the surroundings. A correlation is also observed between the recorded emitted methane (CH4) from the ground monitoring stations and the LST derived from the Landsat images. Based on the findings in the Al-Jleeb landfill, five locations are identified as suspicious dumping areas by overlaying the highest LST contours generated from the multi-temporal LST images. The study demonstrates that the use of multi-temporal remote sensing images can provide supplementary information for landfill site monitoring.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".