Using unmanned aerial vehicles for the study of eelgrass habitat
Bibliographic record
Abstract
Unmanned Aerial Vehicles (UAVs), also referred to as “drones,” are cheap, powerful platforms that are capable of capturing high-quality imagery rapidly and with little operator training. In this report, we describe our use of drones to measure the size of eelgrass (Zostera marina) beds in the nearshore waters of Newfoundland. <br>Our primary research questions were: 1) Is it possible to detect eelgrass beds using drones? 2) To what extent are measurements of drone bed size reproducible with drones (i.e. how much measurement uncertainty do drones introduce?) 3) How does the ability of drones to detect eelgrass change with the altitude of drone flight, and what is the trade-off between resolution (increased by flying at low altitude) and possible area assessed given a unit of sampling time (increased by flying at high altitude)? In addition, we qualitatively assessed how wind, waves, and turbidity affected the clarity of our imagery. <br>We found that eelgrass beds were indeed detectable with drones, and that image clarity was very good. Qualitatively speaking, the best imagery was collected when there was no fog, when the water was still (any choppiness at all made measurement of bed size much more difficult), and when the eelgrass beds were shallow. Wind speed was less detrimental than expected because drones were capable of compensating effectively. <br>In addition to this primary research question, we conducted removals of invasive green crab using Fukui traps. Our reasons for doing so were twofold: First, to reduce their population density and thereby protect eelgrass beds in invaded areas. Second, to determine a relative density, so that if the eelgrass beds do decline over time, we can test to what extent the density of green crabs in a given area is correlated with decline rate. We also conducted an opportunistic study to determine whether a modified Fukui trap could increase catch per unit effort (CPUE) of green crabs relative to regular traps. We found that the modified traps were indeed more effective, confirming other work we have done on this topic. <br>This paper represents a project report that we have not submitted for peer-review. While future studies will likely derive from this, we present this guide as-is for any researcher interested in using drones to study eelgrass or other sub-tidal habitat.
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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 | 0.000 |
| 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) | 0.002 | 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".