Demystifying field application of Critical Height Sampling in estimating stand volume
Bibliographic record
Abstract
Critical Height Sampling (CHS) estimates stand volume free from any model and tree form assumptions. Despite its introduction more than four decades ago, CHS has not been widely applied in the field due to perceived challenges in measurement. The objectives of this study were to compare estimated stand volume between CHS and sampling methods that used volume or taper models, the equivalence of the sampling methods, and their relative efficiency. We established 65 field plots in planted forests of two coniferous tree species. We estimated stand volume for a range of Basal Area Factors (BAFs). Results showed that CHS produced the most similar mean stand volume across BAFs and tree species with maximum differences between BAFs of 5–18 m 3 ·ha −1 . Horizontal Point Sampling (HPS) using volume models produced very large variability in mean stand volume across BAFs with the differences up to 126 m 3 ·ha −1 . However, CHS was less precise and less efficient than HPS. Furthermore, none of the sampling methods were statistically interchangeable with CHS at an allowable tolerance of ≤55 m 3 ·ha −1 . About 72% of critical height measurements were below crown base indicating that critical height was more accessible to measurement than expected. Our study suggests that the consistency in the mean estimates of CHS is a major advantage when planning a forest inventory . When checking against CHS, results hint that HPS estimates might contain potential model bias. These strengths of CHS could outweigh its lower precision. Our study also implies serious implications in financial terms when choosing a sampling method. Lastly, CHS could potentially benefit forest management as an alternate option of estimating stand volume when volume or taper models are lacking or are not reliable.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".