Vegetation monitoring using multispectral sensors — best practices and lessons learned from high latitudes
Bibliographic record
Abstract
Rapid technological advances have dramatically increased affordability and accessibility of unmanned aerial vehicles (UAVs) and associated sensors. Compact multispectral drone sensors capture high-resolution imagery in visible and near-infrared parts of the electromagnetic spectrum, allowing for the calculation of vegetation indices, such as the normalised difference vegetation index (NDVI) for productivity estimates and vegetation classification. Despite the technological advances, challenges remain in capturing high-quality data, highlighting the need for standardized workflows. Here, we discuss challenges, technical aspects, and practical considerations of vegetation monitoring using multispectral drone sensors and propose a workflow based on remote sensing principles and our field experience in high-latitude environments, using the Parrot Sequoia (Pairs, France) sensor as an example. We focus on the key error sources associated with solar angle, weather conditions, geolocation, and radiometric calibration and estimate their relative contributions that can lead to uncertainty of more than ±10% in peak season NDVI estimates of our tundra field site. Our findings show that these errors can be accounted for by improved flight planning, metadata collection, ground control point deployment, use of reflectance targets, and quality control. With standardized best practice, multispectral sensors can provide meaningful spatial data that is reproducible and comparable across space and time.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".