<i>sgsR</i>: a structurally guided sampling toolbox for LiDAR-based forest inventories
Bibliographic record
Abstract
Abstract Establishing field inventories can be labor intensive, logistically challenging and expensive. Optimizing a sample to derive accurate forest attribute predictions is a key management-level inventory objective. Traditional sampling designs involving pre-defined, interpreted strata could result in poor selection of within-strata sampling intensities, leading to inaccurate estimates of forest structural variables. The use of airborne laser scanning (ALS) data as an applied forest inventory tool continues to improve understanding of the composition and spatial distribution of vegetation structure across forested landscapes. The increased availability of wall-to-wall ALS data is promoting the concept of structurally guided sampling (SGS), where ALS metrics are used as an auxiliary data source driving stratification and sampling within management-level forest inventories. In this manuscript, we present an open-source R package named sgsR that provides a robust toolbox for implementing various SGS approaches. The goal of this package is to provide a toolkit to facilitate better optimized allocation of sample units and sample size, as well as to assess and augment existing plot networks by accounting for current forest structural conditions. Here, we first provide justification for SGS approaches and the creation of the sgsR toolbox. We then briefly describe key functions and workflows the package offers and provide two reproducible examples. Avenues to implement SGS protocols according to auxiliary data needs are presented.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.064 | 0.062 |
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".