Impacts of Spatial Resolution on Remote Sensing Land Cover Classification and NDVI Estimates for Southern Baffin Island, Nunavut, Canada
Bibliographic record
Abstract
Accurate land cover classification of tundra is critical to modelling of future climate impacts in the Arctic. Existing classification systems (CAVM, NALCMS, GLC2000) are based on low- to medium-resolution remote sensing imagery which may result in low accuracy where land cover is variable. In this study we focus on southern Baffin Island to create a new classification based on 10-m resolution NDVI from 2018 Sentinel-2 satellite imagery, compare this new classification with existing systems, and characterize dependence of NDVI on spatial resolution based on three ~25-km2 0.5-m resolution WorldView-2 images. We recognized six different land cover types including Polar Semi-Desert (40%), Mesic Tundra (21%), and Wet Sedge Meadow (18%), based on previous studies of Baffin Island. Percent area of these six types within CAVM, NALCM, and GLC2000 cover classes was highly variable. As measured by the relative coefficient of variation, variations in NDVI were greatest at higher spatial resolutions, increasing by 800% with a shift from 0.5-10m2, versus 200% from 10-100 m2. Our results suggest that existing pan-Arctic classifications may not accurately capture land cover patterns on Baffin Island and likely other high-latitude regions, and highlight the need to use these classifications with caution when used to generalize Arctic ecosystem responses to climate change.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".