A remote sensing based multilevel rangeland classification for the Lac-Du-Bois rangelands, Kamloops, British Columbia
Bibliographic record
Abstract
With 1.2 million hectares of open grassland and 6 million hectares of forested rangeland in British Columbia, a classification and Inventory system had to be developed for rangeland which could be compiled quickly, accurately, and on an annual basis with minimal time spent collecting ground data. Remote sensing techniques provided the tool whereby large tracts of land were classified, inventoried and monitored. The major objective of this study was to use multiscale and multidate remote sensor data to produce a rangeland classification and legend for the open grassland of the Lac-du-Bois range located north of Kamloops, British Columbia. Aerial photography, obtained at various scales and dates throughout the summers of 1975 and 1976, was used to produce a range inventory map of the Lac-du-Bois area. Landsat maps were prepared from September 1972, July 1973 and July 1975 imagery. The developed classification legend was based on an ecologically-based, remote-sensing legend and was expanded for the central interior of British Columbia. The developed rangeland legend is hierarchical in format, flexible, expandable and adaptable to other rangeland areas. Six boundary type-lines were designed to separate (1) sharp distinct changes in vegetation types; (2) to indicate a known transition or ecotone between verified types; (3) to denote interpreted boundaries between known types; (4) to map interpreted vegetation areas; (5) to indicate fencelines boundaries, and (6) to indicate obscured land. Landsat maps produced from 1972, 1973 and 1975 imagery provided a broad overview of the major range types found on the Lac-du-Bois range. Landsat imagery combined with small scale, 1:63,000 colour and colour-infrared photography provided the first step in the inventory phase. 1:20,000 colour and colour-infrared photography is not recommended for detailed range inventory mapping since the entire study area requires ground work. 1:10,000 colour and colour-infrared original diapositives provide the required detail to identify range types and the species present without spending unnecessary time in the field. Original 1:10,000, colour and colour-infrared diapositives must be employed for interpretation purposes. Paper prints are required for field work. Species identification was possible on large scale 1:4,000 original colour-infrared diapositives. The range classification was developed and a inventory map of the Lac-du-Bois range was produced which illustrates the classification legend and boundary types.
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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 | 0.002 | 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) | 0.003 | 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".