Forest expansion and glacial retreat in the Central Himalaya indicated by past observations and future projections
Bibliographic record
Abstract
• Forest cover showed strong recovery despite initial loss during 2000-2020. • CA-ANN modeling predicts forest expansion but shrinking glaciers and waterbody. • Cropland abandonment and community forestry facilitated forest recovery. Land use and land cover (LULC) changes, driven by climate variability and human activity, are increasingly threatening the ecological stability of the Himalaya, yet their long-term dynamics remain poorly understood. We address this gap by analyzing past LULC transitions and projecting future changes in the Annapurna Conservation Area from 2000 to 2050. The study area covers 7629 km 2 , considerable elevational variation (800 to >8000 m), and variable precipitation regimes (300 to 3500 mm yr -1 ). We evaluated LULC changes over two decades (2000 - 2020) based on Landsat data and simulate future patterns for 2030 and 2050 using the Cellular Automata – Artificial Neural Networks (CA–ANN) model, integrating spatial drivers and climate data. For the year 2000, a maximum likelihood supervised classification indicated that forest covered 10%, settlement 2%, barren land 65%, snow/glacier 15%, cropland 6%, and waterbody 2% of the area. In the following two decades, forest first declined (-1.2%) and then strongly increased (+3%), settlement area doubled, and cropland was lost. Snow/glacier cover (-1.7%) and waterbody (-2%) declined significantly, while barren land expanded. Under both Shared Socioeconomic Pathways (SSP245 and SSP585), projections suggest continued forest (+4.2 to +4.3%) and settlement (+1.5% to +1.7%) increase and ongoing declines in snow/glacier (-4.7 to -4.9%), waterbody (-0.4%), and cropland (-0.6 to -0.7%) by 2050. These findings highlight the strong human and climate-driven transformations in the region, underscoring the urgency of actions towards climate protection and sustainable resource stewardship for ecological stability in the Himalaya.
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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 | 0.000 |
| 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 | 0.001 | 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".