FROM PIXELS TO PREDICTIONS: UAV LiDAR-BASED METHODOLOGICAL APPROACHES TO UNDERSTAND ASPEN COPSES IN SOUTHERN SASKATCHEWAN
Bibliographic record
Abstract
The Canadian Prairies of Southern Saskatchewan are characterized by native, woody vegetation that occurs naturally. These regions are home to a wide variety of tree, shrub, grass, and forb species, and they typically develop in and around stands of trembling aspen (Populus tremuloides Mich. X). Since native tree species in Southern Saskatchewan play role in carbon sequestration, shelterbelt formation, wildlife habitat, agricultural pollination, and biodiversity maintenance, it is critical that we find better ways to study them with precise estimation which minimizes effort and cost. In this study, five native aspen copses within the Black, Brown and Dark Brown soil zones of Southern Saskatchewan were identified. At each copse, tree height and diameter at breast height (DBH) were recoded as ground truthed data and unmanned arial vehicle (UAV) based light detection and lidar (LiDAR) data were collected and processed for different pixel sizes for estimating tree height and DBH. This study initiated the first approach to study aspen copses with UAV based LiDAR. The first paper in this thesis explored the optimal pixel size for accurate aspen tree height estimation and evaluates the impact of various filtering strategies (e.g., raw, median, spike-free) for its precision. This study highlighted significant differences among all pixel sizes (5 cm to 100 cm) and underlined an optimal pixel size of 40 cm for accurate tree height estimation and the choice of canopy height models (CHM) filtering techniques should be based on contextual needs (e.g., processing time, data noise, software availability). The second paper of this thesis developed a robust and flexible method for DBH estimation from LiDAR point cloud data that doesn’t rely on site specific parameters and then evaluates a LiDAR based model by leveraging multiple structural metrics from LiDAR data. The results showed that the proposed methodology can correctly detect up to 68% of tree stems. The developed models showed moderate levels of accuracy (RMSE 1.77 for GAM and 2.05 for RF, MAE 1.02 for generalized additive model [GAM] and 1.04 for random forest [RF]) and precision (R2 0.61. for GAM and 0.46 for RF) for DBH estimation from fully remotely sensed data which was comparatively better than existing research. By optimizing pixel resolution and DBH estimation methods, this study minimized the budget and time of aspen forest inventory with accurate estimation. It showed that a non-site-specific DBH estimation method can successfully detect tree stems and link LiDAR-derived structural metrics to forest inventory demands. These findings improved the methodological foundation for UAV-LiDAR applications in Prairie aspen copses and will enable more accurate biomass and carbon measurements.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".