Forest canopy height mapping using ICESat-2 data to aid forest management in a Canadian Arctic community: A case study of Kluane First Nation, Yukon, Canada
Bibliographic record
Abstract
As an essential indicator of a forest's growth capacity and rate, the increased accuracy, ease of access, processing and visualization of canopy height information can facilitate a targeted range of strategies for sustainable forest management, especially among citizen scientists and community members. Here, forest canopy height is estimated for several land parcel segments in a subarctic locale using ground-based measurements as well as photon and elevation data obtained from NASA's Ice, Cloud, and land Elevation satellite (ICESat-2). ICESat-2 offers a comprehensive view of vegetation structure and provides a unique opportunity to quantify forest canopy height changes, productivity and distribution in remote locations where it is often arduous and cost prohibitive to acquire ground data. Average canopy heights returned from ICESat-2 data compared with field measurements of above-ground biomass yielded a R 2 of 0.53, and root mean square error of 1.45 m, amplifying the use and potential value of this dataset and novel platform for multiple user groups interested in forestry mapping and ongoing monitoring of forest canopy height with increasing frequency to facilitate community-led decision making. This study demonstrates the utility and benefit of integrating remotely sensed data and field-based survey measurements to generate complementary information related to forest structure and diversity in this region.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".