Detecting Land-Cover Change using Stochastic Simulation Models and Multivariate Analysis of Multi-Temporal Landsat Data for Cass County, North Dakota
Bibliographic record
Abstract
Understanding forest transiting at wildland-urban interfaces offers a glimpse into the effect of anthropogenic activities that may threaten biota.We examined forest conversion from 2006 to 2011 at urban-wildland fringes in Cass County, North Dakota. Grid data from the National Agricultural Statistic Service, published by USDA, was used as preliminary inputs to ascertain land-use and land-cover dynamics. Markovian transition probabilities were derived for each pair of years from 2006 to 2011. These transition probabilities were further subjected to multivariate analysis to detect forest change in one-year time steps. From this study, pairwise combinations of years yielded two distinct statistical groups. The first group comprised of seven pairs of year combinations displaying high transition probability of unchanged forest (0.54 ? Pff ? 0.68), while the second group comprised of eight pairs of year combinations and showed a low transition probability of unchanged forest (0.26 ? Pff ? 0.37). A third group displayed comparatively high transition probabilities of forest transiting to non-forest (0.26 ? Pfnf ? 0.36), such as forest to row crops, with an increasing trend over time. We also generated the forest cover in relation to soil characteristics. We can surmise that forest cover at poorly drained soils showed a higher distribution, which could be due to the unsuitability of this soil for crop cultivation. The results of this study on how land-cover has changed in Cass County for the last six years could be used by policy makers and forest managers in applying BMPs (Best Management Practices).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".