Sensitivity of Landsat MSS and TM to land cover change in the Golden Horseshoe, Ontario, Canada
Bibliographic record
Abstract
An ideal situation for conducting change detection is to use multi-temporal images acquired from the same sensor. However, many conditions (such as the discontinuity of sensors, weather conditions) would bring an end to the ideal temporal change detection. Imagery availability issues will force change detection studies in the future to increasingly incorporate multiple sensors. This study conducted change detection between Landsat TM (TM) and Landsat MSS (MSS) images from July 30, 1995 to June 2, 2003. The study area was centered on the Greater Toronto Area (GTA) in south-central Ontario, Canada. Post-classification change detection was used to determine the type of change between the images. Results demonstrated that despite the different spatial resolution of the MSS and TM data, the change detection using both MSS and TM was similar in results to that of TM alone. A change detection where MSS is resampled to 30 meters was most effective in capturing the amount and type of change in the TM change study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".