LiDAR as an Advanced Remote Sensing Technology to Augment Ecosystem Classification and Mapping
Bibliographic record
Abstract
Observing landscape patterns at various temporal and spatial scales is central to classifying and mapping ecosystems. Traditionally, ecosystem mapping is undertaken through a combination of fieldwork and aerial photography interpretation. These methods, however, are time-consuming, prone to subjectivity, and difficult to update. Light Detection and Ranging (LiDAR) is an advanced remote sensing technology that has rapidly increased in application in the past decade and has the potential to significantly increase and refine information content of ecosystem mapping, especially in the vertical dimension. LiDAR technology is therefore well-suited to providing detailed information on topography and vegetation structure and has considerable potential to be used for ecosystem classification and mapping. In this article, the potential to use LiDAR data to advance ecosystem mapping is examined. The current state of the science for using LiDAR data to classify and map key ecosystem attributes within an existing ecosystem mapping scheme is discussed by focusing on British Columbia Terrestrial Ecosystem Mapping and its associated Predictive Ecosystem Mapping. The article concludes by summarizing which components of ecosystem mapping and classification are best suited to the application of LiDAR data, followed by a discussion of the feasibility and future directions for mapping ecosystems with LiDAR technology.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".