Characterizing tree species in the Northwest Territories using spectral mixture analysis and multi-temporal satellite imagery
Bibliographic record
Abstract
Natural resource management in northern boreal forests requires tree species identification for improved decision making. Satellite remote sensing provides a more cost-effective and time-efficient way to obtain this information in these large, remote, inaccessible areas. However, satellite signals are highly mixed due to increased tree shadows and visible understory vegetation in higher latitude, lower density open forests. Thus, methods used in southern forests are largely unsuitable. Therefore, spectral mixture analysis (SMA) was tested as it separates these signal components (trees, understory, shadow) at sub-pixel scales, allowing improved forest information. In this study, SMA was used to identify the dominant species near Fort Providence NWT using Landsat-5 Thematic Mapper imagery. An accuracy of 79 % was achieved for four species validated against 48 ground plots using multiple-date imagery acquired at different stages of the growing season. These positive results indicate SMA’s capability to retrieve species information of highly mixed open stands.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".