LiDAR plots - A new large-area data collection option: context, concepts, and case study, Canadian Remote Sensing Journal
Bibliographic record
Abstract
Abstract. Forests are an important global resource, playing key roles in both the environment and the economy. The implementation of quality national monitoring programs is required for the generation of robust national statistics, which in turn support global reporting. Conventional monitoring initiatives based on samples of field plots have proven robust but are difficult and costly to implement and maintain, especially for large jurisdictions or where access is difficult. To address this problem, air photo- and satellite-based large area mapping and monitoring programs have been developed; however, these programs also require ground measurements for calibration and validation. To mitigate this need for ground plot data we propose the collection and integration of light detection and ranging (lidar) based plot data. Lidar enables accurate measures of vertical forest structure, including canopy height, volume, and biomass. Rather than acquiring wall-to-wall lidar coverage, we propose the acquisition of a sample of scanned lidar transects to estimate conditions over large areas. Given an appropriate sampling framework, statistics can be generated from the lidar plots extracted from the transects. In other instances, the lidar plots may be treated similar to ground plots, providing locally relevant information that can be used independently or integrated with other data sources, including optical remotely sensed data. In this study we introduce the concept of ‘‘lidar plots’ ’ to support forest inventory and scientific applications, particularly for large areas. Many elements must be considered when planning a transect-based lidar survey, including survey design, flight and sensor parameters, acquisition considerations, mass data processing, and database development.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.016 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".