Vegetation height products between 60° S and 60° N from ICESat GLAS data
Bibliographic record
Abstract
Abstract. We present a new method to obtain coarse resolution (0.5° × 0.5°) vegetation height and vegetation-cover fraction data sets between 60° S and 60° N for use in climate models and ecological models. The data sets are derived from the Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud and land Elevation Satellite (ICESat), which is the only LiDAR instrument that provides close to global coverage when all data collected for 2003–2009 are combined. Filters are applied to the GLAS data to identify and eliminate spurious observations, e.g. data that are affected by clouds, atmosphere and terrain and as such result in erroneous estimates of vegetation height or vegetation cover. GLAS vegetation height estimates are aggregated in histograms from 0 to 70 m in 0.5 m intervals. The GLAS vegetation height product is evaluated in four ways. First, unfiltered and filtered individual GLAS vegetation height measurements are compared with aircraft LiDAR measurements of the same from seven sites in the Americas, Europe, and Australia. Application of filters increases the correlation with aircraft data from r = 0.36 to r = 0.67 and decreases the root-mean-square error by a factor 3. Second, the global aggregated GLAS vegetation height product is tested for sensitivity towards the choice of data quality filters; areas with frequent cloud cover and areas with steep terrain are the most sensitive to the choice of thresholds for the filters. Thirdly, the GLAS global vegetation height product is compared with two other global vegetation height products and is believed to produce more realistic characteristics: dominant vegetation height for tropical forests between 30 and 60 m versus 20 and 40 m in existing products. Finally, the GLAS bare soil cover fraction is compared globally with the MODIS bare soil fraction (r = 0.55) and with the FASIR bare soil cover fraction estimates (r = 0.58); the correlation between GLAS and MODIS tree-cover fraction was (r = 0.76). The evaluation indicates that filters applied to the GLAS data are conservative and eliminate a large proportion of spurious data, while only in a minority of cases at the cost of removing reliable data as well. The present GLAS vegetation height product appears more realistic than previous data sets used for input to climate models and ecological models and hence should significantly improve simulations that involve the land surface.
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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 | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".