Evaluating ICESat-2 for monitoring, modeling, and update of large area forest canopy height products
Bibliographic record
Abstract
Forests represent the world's largest terrestrial ecosystem and their monitoring is therefore critical from scientific, ecological, and management perspectives. Present day sustainable forest management practices go beyond forest inventory and increasingly include aspects such as carbon accounting and regeneration assessments. Such monitoring requires often unavailable, spatially exhaustive and up-to-date information on forest attributes over broad areas. Recent developments in the acquisition of broad-scale forest attribute information from remotely sensed data has included the use of multiple technologies that take advantage of globally available data products to derive forest attribute layers. However, less is known about the applicability and performance of such products when used to produce broad-scale, accurate, and up-to-date forest information products. This study aimed to evaluate the agreement between two broad-scale forest canopy height products – Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) and the National Terrestrial Ecosystem Monitoring System (NTEMS) imputed canopy height layers for Canada – across a variety of ecological gradients. Overall, the two datasets showed high correspondence, with a root-mean-square difference of 4.87 m, and 85% of ICESat-2 canopy heights falling within the 95% confidence interval of the NTEMS height estimate. Across ecozones, canopy heights in the Taiga Shield West and Boreal Shield West had stronger agreement (91% of ICESat-2 segments within the 95% confidence interval of NTEMS), while the Taiga Cordillera and Taiga Shield East had lower agreement (< 75% of ICESat-2 segments within the 95% confidence interval of NTEMS). Interestingly, we found that the modeled heights based upon optical satellite data had a less generalized distribution than heights from ICESat-2 as well as achieving a greater representation for the taller (overall and by ecozone) height classes. An increase in absolute difference between data products was also found as a function of increasing slope. Finally, the correspondence between products was evaluated across disturbed areas (35 to 10 years since disturbance) to assess the agreement of the two products in areas of regenerating forest. In general, the analysis found that burned areas, which tend to be more structurally heterogeneous, had lower agreement between products then harvested areas. The high overall correspondence between the data products demonstrate the potential for integration of ICESat-2 to inform (via calibration / validation) or update height products based upon optical satellite data.
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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".