Employing advanced airborne remotely sensed data to improve terrestrial ecosystem mapping
Bibliographic record
Abstract
Information representing the species composition and structural configuration of forested ecosystems is critical for effective, sustainable management. In Canada, the methods employed to map forest species and structure vary, however, they conventionally include photogrammetric techniques. Despite common use, aerial photograph delineation and interpretation is time consuming and laborious, often yielding subjective results which cannot be easily updated, and is thus not well suited for quantitative mapping over extensive areas. In contrast, advanced methods for remotely quantifying forest characteristics show promise for improving conventional approaches. Two data sources of particular interest are hyperspectral and light detection and ranging (LiDAR). Hyperspectral sensors acquire data simultaneously in upwards of hundreds of narrow spectral channels, providing an unprecedented tool for differentiating between vegetation species. LiDAR systems directly measure the vertical distribution of foliage, providing detailed information on height, cover, and structure. This thesis integrated new generation remote sensing technologies with field data to improve forest species and structural information in the British Columbian southern Gulf Islands (SGI). Results indicate the objective was met, providing a state-of-the-art, step-by-step protocol for forest managers and ecologists to undertake detailed and accurate species and structural mapping of protected areas, while decreasing associated labor, time and subjectivity, and increasing repeatability, at a cost comparable, if not less, than conventional aerial photography. The unique outcomes of this thesis include the first spectral library of dominant tree species in Canada’s coastal Pacific Northwest, the first SGI inventory of LiDAR-metrics able to characterize and differentiate forest structure, significantly improved data for rare Garry oak habitat, markedly more detailed and accurate distribution information for 11 dominant tree species derived using an innovative classification approach and newly developed LiDAR metrics, and the first assessment in any environ of hyperspectral metrics for describing and differentiating avifaunal guilds based on diversity. In addition, results provide the first tree species heterogeneity predictions for the SGI, yielded through an object-based classification incorporating airborne hyperspectral data and space-borne multispectral data. The innovative methods described are not limited to the SGI, and can be replicated where targeted species/structural characteristics can be defined and differentiated based on hyperspectral-derived and/or LiDAR-derived metrics.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".