Detecting grassland spatial variation by a wavelet approach
Bibliographic record
Abstract
Insight into the spatial variation of an ecosystem can provide better understanding of ecological processes and patterns in different scales. Detecting these multiple scales of spatial variation in grassland landscapes is valuable for determining management options, designing proper sampling regimes, and selecting suitable resolutions of remote sensing products. The objective of this study is to examine how environmental factors affect spatial variation of biophysical properties in mixed grassland ecosystems. Field leaf area index (LAI), soil moisture, and topographical parameters (relative elevation, upslope length, and a wetness index) were obtained in three parallel transects of a grassland ecosystem in Saskatchewan, Canada in 2004. One 20‐m resolution SPOT 4 (HRVIR) image was acquired at the same period of the growing season but in the following year. Normalized difference vegetation index (NDVI) was calculated from the satellite image of the centre 381‐m transect and two extensive 2560‐m perpendicular transects. A wavelet approach was used to identify the scales of variations. Statistical results showed that LAI is significantly correlated to the wetness index (r2 = 0.37) and soil moisture (r2 = 0.43). The wetness index is better than relative elevation and upslope length in demonstrating the effect of topography on grassland vegetation. The variation of soil moisture is significant at two small scales of about 20 m and 40 m, and that of the wetness index is at the large scale of 120 m. The variation of grassland LAI is significant at three scales (20 m, 40 m, and 120 m), which indicates that the spatial variation of LAI might be controlled by both topography and soil moisture, though the 120 m is the dominant scale of variation in LAI. NDVI significantly correlated with grassland LAI along the centre transect. The effect of topography on grassland LAI is also proven by the significant relationships between NDVI and the wetness index. The wavelet analysis identifies the variation of two extensive transects at the scale of about 120 m, which is similar to the dominant variation scale of grassland LAI. These results confirmed that the effect of topography on spatial variation can be identified from the appropriate satellite image. This study suggests that the spatial scales of soil and topographic data aid in the selection of appropriate satellite image resolution for monitoring and managing ecosystems.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".