Interpretation of land cover changes using aerial photography and satellite imagery in the Foothills Model Forest of Alberta
Bibliographic record
Abstract
Aerial photographs acquired in 1948-1952, Corona "spy-satellite" imagery acquired in 1963, and Landsat multispectral scanner (MSS), thematic mapper (TM), and enhanced thematic mapper plus (ETM+) imagery acquired since 1974 were used to interpret land cover changes in the Foothills Model Forest of Alberta. The different image characteristics of aerial photographs and satellite sensor data require different analysis methods, but increasingly these data are being used together to determine changes in land cover and structure over a longer time period than is possible using satellite image data alone. The interpretations from this study suggest that in an active forest management area, conifer forest cover was reduced, broadleaf and mixed-forest cover was increased, and the forests were structured in smaller patches with greater edge density over the years circa 1950–1999. Another part of the study area, recovering from a major fire in the early part of the century, was interpreted to have experienced an increase in conifer forest area and mean patch size over this same time period. Overall, changes in the Foothills Model Forest landscape were relatively easily identified and deemed significant by resource managers; this study suggests additional work is warranted on understanding the effect of these changes in applications such as wildlife management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".