DEM Control in Arctic Alaska With ICESat Laser Altimetry
Bibliographic record
Abstract
Use of Ice, Cloud, and land Elevation Satellite (ICESat) laser altimetry is demonstrated for control of a digital elevation model (DEM) that is synthesized from repeat-pass ERS-1 and 2 synthetic aperture radar (SAR) imagery using interferomet-ric SAR (InSAR). Our study area is 15 650 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of the Barrow, AK coastal plain adjacent to the Arctic Ocean; a vast expanse of tundra, lakes, and arctic wetlands of such low relief as to be nearly devoid of terrain features. The accuracy of the ICESat-derived elevation measurements is assessed by comparison with differential global positioning system (DGPS) data acquired along ICESat ground tracks. The ICESat-derived elevations have a mean accuracy, relative to the DGPS elevations, of -0.01 plusmn 0.18 m. ICESat-derived elevations on the Arctic coastal plain provide an excellent source for DEM control. We employ the ICESat-derived ground control points (GCPs) in two distinct InSAR processing steps: 1) selected points are used to perform baseline refinements, which improves the ERS-1 and 2 interfero-grams and 2) the ICESat-derived GCP position data (latitude, longitude, elevation) are then used as control in mosaicking multiple InSAR-derived DEMs. The resulting ICESat-controlled DEM has a mean accuracy of -1.11plusmn 6.3 m relative to an independent standard, which is a commercial airborne InSAR-derived DEM having 0.5 m rms accuracy. This easily meets DTED-2 standards and suggests that DEMs derived using only ICESat altimetry for ground control would meet similar standards in other regions of low relief.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".