Vegetation Phenology Driving Error Variation in Digital Aerial Photogrammetrically Derived Terrain Models
Bibliographic record
Abstract
Digital aerial photogrammetry (DAP) and unmanned aerial systems (UAS) have emerged as synergistic technologies capable of enhancing forest inventory information. A known limitation of DAP technology is its ability to derive terrain surfaces in areas with moderate to high vegetation coverage. In this study, we sought to investigate the influence of flight acquisition timing on the accuracy and coverage of digital terrain models (DTM) in a low cover forest area in New Brunswick, Canada. To do so, a multi-temporal UAS-acquired DAP data set was used. Acquired imagery was photogrammetrically processed to produce high quality DAP point clouds, from which DTMs were derived. Individual DTMs were evaluated for error using an airborne laser scanning (ALS)-derived DTM as a reference. Unobstructed road areas were used to validate DAP DTM error. Generalized additive mixed models (GAMM) were generated to assess the significance of acquisition timing on mean vegetation cover, DTM error, and proportional DAP coverage. GAMM models for mean vegetation cover and DTM error were found to be significantly influenced by acquisition date. A best available terrain pixel (BATP) compositing exercise was conducted to generate a best possible UAS DAP-derived DTM and outline the importance of flight acquisition timing. The BATP DTM yielded a mean error of −0.01 m. This study helps to show that the timing of DAP acquisitions can influence the accuracy and coverage of DTMs in low cover vegetation areas. These findings provide insight to improve future data set quality and provide a means for managers to cost-effectively derive high accuracy terrain models post-management activity.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".