Time Series of a Forest Canopy: Detecting Changes using the Visible Atmospherically Resistant Index and a low-cost Drone
Bibliographic record
Abstract
The drone industry has expanded as the technology has become more affordable in the last few years. The use of drone images in remote-sensing research became an increasingly attractive alternative to other methods such as satellite imagery as it allows for a faster, more efficient method to capture spatial phenomenon. Unfortunately, drones are often costly and require additional sensors and lenses to capture multispectral data, making the technology difficult to access without financial support. However, modern drones designed for more casual flights are now affordable and equipped with high-quality cameras. This major research paper aims to find out whether the combination of a low-cost (< $1000 CAD), lightweight (< 249 grams) drone such as the DJI Mavic Mini is an adequate tool to monitor and detect subtle changes in forest canopy. The Visible Atmospherically Resistant Index is utilized to assess vegetation changes and monitor vegetation growth while minimizing research costs. After capturing a forest canopy for three months, the results show that the DJI Mavic Mini is an adequate tool for research purposes, given that the study area is relatively small and has temperate weather. In addition, the Visible Atmospherically Resistant Index showed mixed results when detecting fine changes in the canopy of the study area. It showed inconsistencies and significant variances in terms of the acquired images. For the index to detect subtle changes in the canopy accurately, the study area needs to be under the same weather conditions and similar sunlight at the time of capture, which is not a realistic expectation.
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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 | 0.000 | 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".