Development of a circa 2000 land cover map of northern Canada at 30 m resolution from Landsat
Bibliographic record
Abstract
Previous land cover maps covering northern Canada have been of insufficient spatial or thematic detail to address emerging northern issues such as wildlife habitat, land use planning, and fine-scale land cover dynamics. Mapping northern land cover requires medium-resolution (~30 m) remote sensing data to effectively characterize cover types that are spatially heterogeneous and cannot be consistently represented at coarser (250 m – 1 km) scales. In this paper, we present a land cover map of northern Canada at 30 m spatial resolution suitable for application in northern land use planning, wildlife habitat assessment, and climate change impact assessment and adaption. Orthorectified circa 2000 Landsat data were acquired from the Centre for Topographic Information, with coverage from the treeline to the northern tip of Ellesmere Island, and combined into 16 radiometrically balanced large-area mosaics. A stratified unsupervised cluster labelling approach was used for map generation. Literature on northern land cover and vegetation mapping and numerous northern vegetation surveys were examined to define a land cover legend containing 15 classes. Field data gathered during several campaigns were used in conjunction with other available medium-resolution land cover maps to develop a dataset for training and validation. Standardized accuracy assessment is limited due to the cost of field data acquisition and the small archive of reference data in northern regions. Comparison with field data used only to aid cluster labelling suggests 81.5% accuracy for 76 plots, and examination of subpixel land cover distribution within each 1 km Circumpolar Arctic Vegetation Map (CAVM) class shows good agreement. The map is publicly available through the Natural Resources Canada Geogratis portal in 1 : 250 000 scale National Topographic System (NTS) map sheets.
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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 | 0.003 | 0.003 |
| 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) | 0.004 | 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".