Detection of Prairie Grassland Mowing Using Polarimetric Microwave Radar
Bibliographic record
Abstract
Remote sensing methods have historically been used to monitor grassland vegetation height as changes may be accompanied by negative environmental impacts. Previous studies analyzed the suitability of satellites for monitoring grassland mowing and grazing events, but there is limited knowledge of the usability of ground-based systems for this purpose. The goal of this study was to determine if C- and L-band ground based scatterometers could be used to detect and quantify changes in grass height. We compared the normalized radar cross sections (NRCS) of C- and L-band scatterometers before and after a grassland mowing event. Co-polarized (VV, HH) and cross-polarized signals were analyzed at the elevation angles of 30° and 55° for both scatterometer systems. Results showed as much as 5.1 dB decrease in backscatter for C-band occurred after the mowing event. The decrease at L-band was less significant (2.4 dB at most). The results of this study confirmed that ground-based scatterometer systems operating in C- and L-band can be used to detect mowing events.
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.
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 | 0.000 |
| 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) | 0.000 | 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".