Using three-dimensional point clouds to improve characterizations of forest structure across spatial and temporal scales in mixedwood forest stands
Bibliographic record
Abstract
Sustainably managing the world’s forests requires detailed inventories of the resource at varying spatial and temporal scales. The structural and compositional diversity of the boreal mixedwood forest, one of Canada’s largest forest types, provides valuable timber resources and ecological services. However, the extent and complexity of this forest type poses challenges for inventories. The objective of this dissertation was to develop and assess the utility of three-dimensional remote sensing techniques for enhancing forest inventories by characterizing forest structure in boreal mixedwood forests. These technologies are scalable and adaptable for use in forest inventory as they provide consistent spatial and temporal detail. Digital terrestrial photogrammetry from spherical cameras at known locations was used to model individual tree stems and sample plots. For individual trees, stem diameters at different heights were estimated very accurately (RMSE < 1 cm for stem heights below 10 m), which matched or exceeded the accuracy of conventional ground-based inventories. Plot-level point clouds based on a relatively small set of images were used to locate and model trees on sample plots to an accuracy that was comparable to other studies on homogeneous plots (mean 72% detection and 19% RMSE of diameter at breast height). At broader scales, airborne laser scanning (ALS) was used to characterize forest structure by estimating stem size distributions (SSD) across a large forest management unit. First, ALS was used to differentiate unimodal and bimodal stands. Next, parameters of functions describing the SSDs were estimated with ALS metrics (r2 = 0.5) and the resulting functions were more accurate in characterizing field-measured SSDs than without differentiating stands by modality. For assessing temporal patterns of forest structure, photo-interpreted polygons of fire and harvesting were used with the derived SSDs to characterize structural development following stand-replacing disturbance. It was determined that stands that had burned had significantly more trees in larger diameter classes than harvested stands (at a = 0.05). This dissertation outlined the methods required when applying three-dimensional remote sensing technologies to enhancing forest inventories in mixedwood stands and demonstrated the utility of these technologies for deriving information to inform responsible decision-making for management of these forests.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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".