Analysis of Changes in Vegetation Condition in Grasslands National Park Using Remote Sensing
Bibliographic record
Abstract
This study uses a variety of change detection techniques in order to study changes in vegetation in Grasslands National Park, Saskatchewan, Canada. Techniques such as image differencing and principal component analysis were used to determine what types of changes in vegetation have occurred over the last thirty years. A normalized difference vegetation index (NDVI) was also used to highlight changes in vegetation productivity. Grasslands National Park is located in Southern Saskatchewan along the Canadian-US border. The Frenchman River runs through the study area. Three Landsat images were acquired. The spatial extent of the images is from about 47deg 50'N to 49deg50'N and 106deg30'W to 108deg20'W. The three images were first georeferenced to one another, and then clipped to include the area from 49degN to 49deg15'N and from 106deg30'45"Wto 106deg45'56"W. Change analysis techniques were then performed on the data. These included a Visual Analysis, a Post-Classification Analysis, NDVI Image Differencing and a Principal Component Analysis. Analyses were performed comparing the 1978 and 1987 images, the 1987 and 1999 images and the 1978 and 1999 images in order to get a better understanding of the nature of changes. The analyses showed a decrease in vegetation productivity around the Frenchman River and other small bodies of water from 1978 to 1987 and from 1987 to 1999. There is a lot of change in the agricultural areas. These areas saw both increases and decreases in productivity. It is possible that changes in vegetation productivity around water bodies is related to climate variations and the changes in agricultural areas are related to changes in land use. It will be necessary in a future study to acquire climatological data for the area, as well as information about the conversion from agricultural land to native prairie. It is also necessary to acquire images from other years to better understand the changes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.000 | 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".