Forest cover classification using Landsat Thematic Mapper data for areal expansion of line LAI estimate generated through airborne laser profiler
Bibliographic record
Abstract
A simple cover classification of Canadian boreal forest was conducted using Landsat Thematic Mapper (TM) imagery to expand a line estimate of leaf area index (LAI) into a two-dimensional regional one. The line estimate had been made through a 600km long continuous vegetation profile obtained by airborne laser altimetry. The present study area of 170×30km straddles the central portion of the laser profiling transect, from Wandering River north to Fort McMurray, Alberta, Canada. A total of eight land cover types were identified first in the field, and then some 83 training points and another 74 reference points were chosen and recorded for a supervised classification and its accuracy assessment respectively. By applying a supervised procedure to Landsat TM data in two different seasons, these eight cover types, consisting of six vegetated covers, i. e. closed and open conifer forests, conifer woodland, closed and open broad-leaved forests and marsh thicket, and two nonvegetated covers, i. e. bare ground and water surface, were classified. The classification was basically successful with an overall accuracy of 76%. Finally, using an overlay of this land cover map and the airborne laser profiling flight track, the mean LAI for each type of vegetation cover was obtained, and subsequently fed back to the land cover map to form a false-color map showing the two-dimensional distribution of LAI over the entire study area.
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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 | 0.005 | 0.004 |
| 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) | 0.003 | 0.001 |
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".