Updating the Grassland Vegetation Inventory Using Change Vector Analysis and Functionally-Based Vegetation Indices
Bibliographic record
Abstract
The Grassland Vegetation Inventory (GVI), which represents a comprehensive biophysical, anthropogenic, and land-use inventory of grasslands in Alberta, is widely used as a baseline for grassland conditions. An up-to-date GVI is essential for understanding grassland changes and for planning management or conservation actions on grasslands. In this study, a hybrid change detection method is proposed that incorporates change vector analysis and a set of vegetation indices (VIs) measuring different vegetation attributes for mapping the conversion of native grassland to cultivated agriculture, and ultimately to update the GVI based on multiseasonal and multiyear Landsat images. Vegetation indices that contribute significantly to differentiation between existing native grassland and land recently converted from native grassland to cultivated cropland were identified by using stepwise regression analyses and were used as inputs for mapping the conversion between 2006 and 2011 or 2015. The results showed that land conversion can be detected using a single image acquired during the growing season, but that the accuracy of identification is affected by the date of image collection and the nature of the VIs used. The greatest accuracy in detecting land conversion between 2006 and 2011 was achieved using the difference in VI between years (dVI) for the Shortwave Infrared Reflectance 3/2 Ratio (SWIR32) and the Enhanced Vegetation Difference Index (EVI) derived from July imagery (accuracy = 95.2 %; Kappa = 0.86). The same combination of SWIR32 and EVI was also effective, although with lower accuracy (accuracy = 86.0 %; Kappa = 0.64) when tested on a larger geographical area and for detecting land use change between 2006 and 2015. The method proposed here could be applied to detect the land cover conversion in other grassland regions, although the optimal VIs and image acquisition date may need to be modified depending on the type of land use activities implemented in each region.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".