Comparison of seasonal change detection from multi-temporal MODIS and TM images in Southern Ontario
Bibliographic record
Abstract
In this paper a change detection study was conducted using multi-temporal images from two commonly used sensors, MODIS and TM, between June and October, 2003 over Southern Ontario, Canada to evaluate the sensitivity of MODIS images for seasonal land cover changes. Post-classification change detection was used to determine the type of change that had occurred and allow for from-to types of changes to be evaluated. NDVI image differencing was also performed on the MODIS and TM images to compare the vegetation index changes at different spatial resolutions. It was found that MODIS classifications approximated those produced with TM data only when incorporating the thermal band in the classification procedure which takes advantage of the urban heat island effect. Results demonstrate that MODIS post-classification change detection can approximate the levels of change/no-change compared to TM post-classification however the type of change was not accurate due to the spectral mixing that occurs at the coarser 250 meter spatial resolution of MODIS data. The more change at TM level for a MODIS pixel, the higher the likelihood of this corresponding to change at the MODIS level. This study demonstrates that MODIS data would be best suited for detecting changes in large agricultural areas with large field size of homogeneous crop type and growth stage or large areas of forest stands with similar characteristics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".