Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 2. Classification
Bibliographic record
Abstract
Mapping landscape dynamics is necessary to assess cumulative impacts due to climate change and development in Arctic regions. Landscape changes produce a range of temporal reflectance trajectories that can be obtained from remote sensing image time-series. Mapping these changes assumes that their trajectories are unique and can be characterized by magnitude and shape. A companion paper in this issue describes a trajectory visualization method for assessing a range of landscape disturbances. This paper focusses on generating a change map using a time-series of calibrated Landsat Tasseled Cap indices from 1985 to 2011. A reference change database covering the Mackenzie Delta region was created using a number of ancillary datasets to delineate polygons describing 21 natural and human-induced disturbances. Two approaches were tested to classify the Landsat time-series and generate change maps. The first involved profile matching based on trajectory shape and distance, while the second quantified profile shape with regression coefficients that were input to a decision tree classifier. Results indicate that classification of robust linear trend coefficients performed best. A final change map was assessed using bootstrapping and cross-validation, producing an overall accuracy of 82.8% at the level of 21 change classes and 87.3% when collapsed to eight underlying change processes.
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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".