Studying mixed grassland ecosystems II: optimum pixel size
Bibliographic record
Abstract
It was determined in a companion paper that the litter-corrected adjusted transformed soil-adjusted vegetation index (L-ATSAVI) was the best leaf area index (LAI) indicator in a mixed grassland ecosystem. To optimize the sampling procedures and address the scaling issues for the mixed grass ecosystem, this study examined the dominant scale of spatial variation in both LAI and L-ATSAVI using two methods, namely Mexican hat wavelet analysis and semivariogram analysis. The results showed that both methods can identify grassland spatial variation, and the cyclicity (the nature repetition in a dataset) of grassland LAI was about 140 m along the central transect of five parallel transects within the study area. The advantage of wavelet analysis over semivariogram analysis for spatial pattern interpretation was that it could identify the exact location of the transition. The wavelet analysis demonstrated that the cyclicity of L-ATSAVI also corresponded well with features of grassland LAI along the transect. Therefore, following the sampling theorems, a pixel size of less than 35 m will retain most of the spatial variation of grassland LAI in our study area. In terms of this optimum pixel size, the scale of ground-based hyperspectral data and LAI along the transect was simulated using a low-pass filtering procedure with a 30 m moving window. Statistical analysis indicated that scale-simulated L-ATSAVI could significantly explain more grassland LAI (r2 up to 89%) than the original 3 m resolution. This conclusion can be further applied to select the optimal pixel size of remote sensing images and detect the hierarchical characteristics in a grassland landscape.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".