Improving tree diameter measurements above irregularities in Central African forests: a Close-Range Photogrammetric approach
Bibliographic record
Abstract
Accurate measurement of tree diameter in forests is essential for sustainable management of forest resources, ecological assessment, and scientific research. However, most trees in tropical forests have irregularities at the base of the trunk, making it challenging to measure the trunk diameter above them with a tape measure. To meet the increasing demand for data accuracy and reliability, approaches using three-dimensional (3D) point clouds offer a valuable new source of data for tree measurements. This study examines the accuracy of diameter measurements above irregularities using the Close-Range Photogrammetric approach, with diameter tape serving as the reference. A total of 212 trees measured in the north of the Republic of Congo were reconstructed in three dimensions (3D), including 128 trees in semi-deciduous forest and 84 trees in evergreen forest. Comparisons were made in terms of dependence (simple linear regression), correlation (Pearson, Kendall, and Spearman tests), agreement (Bland and Altman method), and difference (Mean Absolute Error - MAE, Root Mean Square Error - RMSE, bias - BIAS, and coefficient of variation - CV). In addition to a near perfect match, a strong association of diameter measurements and a good degree of agreement, the results indicated the presence of differences between diameter measurement approaches in semi-deciduous forest (MAE = 9.25 cm, RMSE = 16.95 cm, BIAS = 7.45 cm) and evergreen forest (MAE = 3.88 cm, RMSE = 8.47 cm, BIAS = 2.37 cm). These differences are minor in the evergreen forest. The magnitude of the differences found is mostly due to the size of the large-diameter classes. In addition, the coefficients of variation (CV) of diameter obtained from the Close-Range Photogrammetric approach were lower than those obtained from the classic conventional approach in both forests, indicating the higher accuracy of the former approach. Further studies could use larger data samples to provide more accurate estimates and verify the limits of these applications’ measurement capabilities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".