Assessing intertidal sediment photopigment content from spectral reflectance with an UAV-mounted 10-band multispectral sensor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multispectral sensors mounted to unoccupied aerial vehicles (UAVs) can be leveraged to quantify microphytobenthos (MPB) biomass in intertidal mudflats, providing data products with cm-scale pixels. However, no standard protocol currently exists for calibrating UAV-acquired spectral information to sediment MPB content. Here, we present a new protocol for calibrating data from a UAV-mounted multispectral sensor to sediment MPB biomass as measured by photopigment content. To do so, we developed a methodology for acquiring and analyzing UAV imagery and sediment photopigment field data. We then implemented the protocol in the Fraser River Estuary, Canada to build a statistically valid calibration equation, testing the effectiveness of several spectral indices and photopigment measurements. Calibrated spectral index values can provide a very accurate measurement of MPB biomass, able to achieve 90 % correlation between the normalized difference vegetation index (NDVI) and sediment chlorophyl-a (chl-a) concentration. This high performance was achieved by closely pairing georeferenced sediment samples to corresponding multispectral imagery and minimizing the lag between sediment sample collection and UAV imagery acquisition. This protocol can facilitate the use of calibrated UAV-acquired multispectral imagery for investigating ecologically-important fine-scale spatial heterogeneity of MPB biomass.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 it