Analysis and prediction of land cover changes using the land change modeler (<scp>LCM</scp>) in a semiarid river basin, Iran
Bibliographic record
Abstract
Abstract Predicting future land cover (LC) changes is an important step in the proper planning and management of watersheds. As a susceptible area to salinity and desertification, receiving only about 195 mm rainfall annually, the Hable‐Rud River basin is especially sensitive to land use/cover changes. Based on corrected LANDSAT satellite images for the years 1986, 2000, and 2017, the LC were extracted using the maximum likelihood (ML) method. LC changes were predicted by applying the land change modeler (LCM) for the basin. The kappa index for classification in 1986, 2000, and 2017 was 75, 78, and 81%, respectively. Using LCM, the prediction was accomplished for the year 2017 with a kappa index of above 74%. The LC map was predicted for year 2040. The analysis indicates that over the past 32 years, bare land, saline land, agricultural, industrial, and residential areas have increased by about 8, 6.2, 2.7, 0.63, and 0.48%, respectively; while rangeland area was decreased by 18%. Results also indicate that given the predicted LC for year 2040 in comparison with the reference year (2017), saline land, agricultural, industrial, and residential areas will be likely to continue to increase by about 3, 1.5, 0.7, and 0.8%, respectively, whereas bare land and rangeland will most likely decrease by about 4.55 and 1.45%, respectively. The findings of this study assist in analyzing the future trends of LC changes in the basin. This information can be used as a guide for land planners and managers in future land use planning of the area.
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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 | 0.001 |
| 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) | 0.000 | 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".