Quantifying biomass production on rangeland in southern Alberta using SPOT imagery
Bibliographic record
Abstract
Vegetation biomass was estimated for ungrazed pastures in two grassland ecoregions of Alberta, Canada, using multispectral 20 m SPOT satellite imagery and vegetation indices (VIs) for multitemporal imagery acquired throughout the growing season with associated field validation data at four study areas. Eight VIs were tested as well as four different types of transformations (linear, log, exponential, power) to ascertain the best predictive model. The Renormalized Difference Vegetation Index and Transformed Vegetation Index provided the best overall prediction (r2 = 0.68) of the amount of above-ground green biomass production, but only marginally better than the Normalized Difference Vegetation Index, Modified Simple Ratio, and other indices tested. When assessed by subregion, the Foothills Fescue study areas had higher discrimination (r2 = 0.72) and from more VIs than for Dry Mixedgrass (r2 = 0.61). In almost all cases a power function best described the form of the relationship between biomass and imagery variables. Compared with green biomass (current-year growth), the predictive power was lower when nonphotosynthetic vegetation (NPV, or carryover: dry, dead matter primarily from the previous year) was included in the analysis (total biomass = green biomass + NPV). The six VIs that used red and near infrared bands consistently outperformed the two VIs that used the green band. There was no clear preference for a specific VI from this battery of tests, likely owing to the functional equivalence of many VIs. ANOVA and Tukey tests showed significant variation between region and by sampling date for six imaging dates and field sampling periods throughout the growing season, with a possible mid-season change in the rate of biomass production evident for both green and total biomass. It was concluded that for regional studies elsewhere, a variety of VIs should be considered and that transformations are recommended to improve statistical predictive capabilities. Other methods such as spectral mixture analysis may be required to achieve improved results, particularly when including the important NPV component of biomass. The ability of SPOT satellites to acquire imagery every 2–3 days enabled a more comprehensive multitemporal study using high-spatial resolution data throughout the growing season, with important implications in terms of operational monitoring programs.
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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 | 0.000 |
| 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 | 0.000 |
| 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".