Advancing geospatial insights in Afghanistan: Annual land cover mapping and landscape metrics analysis for rural landscape planning and restoration
Bibliographic record
Abstract
Desertification, conflict-driven degradation, and climate change increasingly threaten Afghanistan's landscapes, shaped by both natural processes and long-standing human-environment interactions. There is an urgent need to analyze land cover dynamics and methodological insights in the Hindu Kush Himalaya (HKH) region, particularly in Afghanistan, to guide landscape restoration and regeneration efforts. Addressing this gap, this study produces the first consistent, harmonized annual land cover dataset for Afghanistan from 2000 to 2018, using Google Earth Engine (GEE), the Random Forest algorithm, remote sensing techniques, and 30-meter resolution satellite images. Despite historical data constraints, the cloud-based approach enabled comprehensive national-scale mapping. In 2018, rangeland was the dominant land cover type (45.66%), followed by barren land (31.03%) and sand (7.71%). Over the 19-year period, Rangeland expanded by 1.08%, with notable expansions in built-up areas and sand-covered zones. Spatial patterns and fragmentation were assessed using five landscape metrics: greatest patch area, number of patches, overall core area, splitting index and, largest patch index. These analyses identified critical trends in urban expansion and rangeland fragmentation. The resulting annual land cover database and landscape metrics offer a robust evidence base to inform rural landscape planning, zoning, and restoration initiatives aligned with national and global sustainability goals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".