Optimal spatial resolution of Unmanned Aerial Vehicle (UAV)-acquired imagery for species classification in a heterogeneous grassland ecosystem
Bibliographic record
Abstract
Species composition is an essential biophysical attribute of vegetative ecosystems. Unmanned aerial vehicle (UAV)-acquired imagery with ultrahigh spatial resolution is a valuable data source for investigating species composition at a fine scale, which is extremely important for species-mixed ecosystems (e.g., grasslands and wetlands). However, the ultrahigh spatial resolution of UAV imagery also poses challenges in species classification since the imagery captures very detailed information of ground features (e.g., gaps, shadow) which would add substantial noise to image classification. In this study, we obtained multi-temporal UAV imagery with 5 cm resolution and resampled them to acquire imagery with 10, 15, and 20 cm resolution. The images were then utilized for species classification using Geographic Object-Based Image Analysis (GEOBIA) aiming to assess the influence of different imagery spatial resolution on the classification accuracy. Results show that the overall classification accuracy of imagery with 5, 10, and 15 cm resolution are close, while the classification accuracy on 20-cm imagery is much lower. These results are expected because the object features (e.g., vegetation index values and standard deviation) of same species vary slightly between 5 and 15 cm resolution, but not at the 20-cm resolution. We also found that the same species show different producer’s and user’s accuracy when using imagery with different spatial resolutions. These results suggest that it is essential to select the optimal spatial resolution of imagery for investigating a vegetative ecosystem of interest.
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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 | 0.001 |
| 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 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".