An approach to derive regional snow lines and glacier mass change from MODIS imagery, western North America
Bibliographic record
Abstract
Abstract. We describe a method to calculate regional snow line elevations and annual equilibrium line altitudes (ELAs) from daily MODIS imagery (MOD02QKM) on large glaciers and icefields in western North America. An automated cluster analysis of the cloud-masked visible and near-infrared bands at 250 m resolution is used to delineate glacier facies (snow and ice) for ten glacierized regions between 2000–2011. For each region and season, the maximum observed value of the 20th percentile of snow-covered pixels (ZS(20)) is used to define a regional ELA proxy (ELAest). Our results indicate significant increases in the regional ELA proxy at two continental sites (Peyto Glacier and Gulkana Glacier) over the period of observation, though no statistically significant trends are identified at other sites. To evaluate the utility of regional ELA proxies derived from MOD02QKM imagery, we compare standard geodetic estimates of glacier mass change with estimates derived from historical mass balance gradients and observations of ZS(20) at three large icefields. Our approach yields estimates of mass change that more negative than traditional geodetic approaches, though MODIS-derived estimates are within the margins of error at all three sites. Both estimates of glacier mass change corroborate the continued mass loss of glaciers in western North America. Between 2000 and 2009, the geodetic change approach yields mean annual rates of surface elevation change for the Columbia, Lillooet, and Sittakanay icefields of −0.29 ± 0.05, −0.26 ± 0.05, and −0.63 ± 0.17 m a−1, respectively. This study provides a new technique for glacier facies detection at daily timescales, and contributes to the development of regional estimates of glacier mass change, both of which are critical for studies of glacier contributions to streamflow and global sea level rise.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".