A systematic evaluation of multi-resolution ICESat-2 ATL08 terrain and canopy heights in boreal forests
Bibliographic record
Abstract
The launch of NASA's Ice, Cloud, And Elevation Satellite-2 (ICESat-2) in September 2018 provides the scientific community an opportunity to observe high-resolution and three-dimensional surface elevations with global coverage. ICESat-2's Land and Vegetation Height (ATL08) data product focuses on the along-track terrain and canopy heights observations at a 100 m × 11 m spatial resolution. This work expands on past ATL08 validation studies to assess a higher spatial resolution (30 m × 11 m) version of ATL08's height product. This new dataset enables higher resolution mapping and fusion with Landsat data, but has not previously been validated across large geographic extents. In this paper, we examine the accuracy of multi-resolution ICESat-2 ATL08 across North America boreal forests using Land, Vegetation, and Ice Sensor (LVIS), an airborne laser ranging system as reference datasets. Overall, strong agreements of terrain elevation and canopy height were found between ATL08 and LVIS at both 100 m × 11 m (RMSEterrain = 2.35 m; biasterrain = −0.17 m; RMSEcanopy = 4.17 m; biascanopy = 0.08 m) and 30 m × 11 m (RMSEterrain = 3.19 m; biasterrain = 0.49; RMSEcanopy = 4.75 m; biascanopy = 0.88 m) spatial resolutions. We found the accuracy of high-resolution terrain and canopy height measurements were constrained by sensor and external conditions during the time of data acquisition with lower uncertainties observed from samples along high-intensity ground tracks and with low topography/slope variabilities. Through this work, we provide insight into the use of multi-resolution ICESat-2 ATL08 for terrain and canopy heights characterization in northern forests. The results found in our study serve as a benchmark for end users to select high-quality ATL08 for a variety of scientific applications.
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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 | 0.001 | 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".