Methodologies for mapping the spatial extent and fragmentation of grassland using optical remote sensing
Bibliographic record
Abstract
Grassland is an important part of the ecosystem in the Canadian prairies and its loss and fragmentation affect biodiversity, as well as water and carbon fluxes at local and regional levels.Over the years, native grasslands have been lost to agricultural activities, urban development and oil and gas exploration.This research reports on new methodologies developed for mapping the spatial extent of native grasslands to an unprecedented level of detail and assessing how the grasslands are fragmented.The test site is in the Newell County region of Alberta (NCRA).72 Landsat and 34 SPOT images from 1985 to 2008 were considered for the analysis.With an airport runway used as a pseudo-invariant feature (PIF), relative radiometric correction was applied to 17 Landsat and 8 SPOT images that included the same airport runway.All the images were classified using the Support Vector Machine (SVM) classification algorithm into grass land, crop, water and road infrastructure classes.The classification results showed an average of 98.2 % overall accuracy for Landsat images and SPOT images.Spatial extents and their temporal change were estimated for all the land cover classes after classifying the images.Fragmentation statistics were obtained using FRAGSTATS 3.3 software that calculated land cover pattern metrics (patch, class and landscape).Based on the available satellite image data, it is found that in Newell County there is almost no significant change found in the grassland and road infrastructure land cover in over two decades.Also, the fragmentation results suggest that fragmentation of grassland was not due to the result of road infrastructure.encouragement, guidance, infrastructure, funding and many very unique opportunities throughout the course of my program.Working with Dr. Phil has been a very intellectually stimulating and rewarding experience.I would also like to thank all the committee members, Dr. Karl Staenz, Dr. Anne M. Smith and Dr. Adriana Predoi-Cross for their help, encouragement and expertise.I would like to extend a special
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".