Analysis and prediction of land cover changes by applying Cellular Automata-Markov model and geo-information: An arid and semi-arid river basin, Iran
Bibliographic record
Abstract
The prediction of future land cover changes is an important step in proper planning and management of watersheds. Various methods exist for this purpose. In this study, land cover changes were investigated in the Hable-Rud River basin in Iran, an arid and semi-arid region, using remote sensing and Geographic Information Systems (GIS). First, a supervised classification technique was applied to Landsat images acquired for 1986, 2000 and 2017 using the maximum likelihood method. Then, using pixel-by-pixel change detection, the land cover changes were predicted for 2017 and 2040 using a Cellular Automata (CA)-Markov model. The descriptive variables used included slope, aspect, elevation, and calculated distances from various land features such as rivers, roads, industrial areas, residential areas, saline land, and land in agricultural production. The predictions for 2017 were validated using the derived map from a Landsat image of 2017 with a resulting standard Kappa index of 0.74. According to the prediction results for 2040, the areas of rangeland and saline land will increase by approximately 6.5% and 2%, respectively, whereas the areas of bare land and agricultural land will decrease by approximately 6% and 2%, respectively. Moreover, the analysis of historical records since 1986 showed that the annual streamflow and precipitation have reduced by almost 44% and 29%, respectively. The reductions, particularly to streamflow, can be attributed largely to agriculture expansion, rapid population growth, and industrial developments. The analysis of the results indicates a need for more effective design, planning, and development of land cover policies for ecosystem protection.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".