Landscape-scale analysis of shrub encroachment unveils the complexity of greening in the Carpathian Mountains
Bibliographic record
Abstract
Abstract Over recent decades, cold-climate ecosystems have exhibited a pronounced increase in vegetation greenness, and shrub encroachment is a major ecological process underlying these changes. Our knowledge of these dynamics remains limited in the temperate mountains of Eastern Europe, which have experienced significant land-use shifts, especially following the collapse of the communist regime. It is noteworthy that the contribution of shrubs has not been evaluated, partly due to the difficulty of providing high-resolution mapping of shrublands. In this study, we integrated four decades of Landsat-derived NDVI time series with a customized land cover classification based on Sentinel-2 imagery to investigate greenness dynamics above 1500 m elevation in the Carpathian Mountains. The classification targeted key shrubland types using spectral indices tailored to seasonal pigment variations. We also conducted diachronic visual analysis of aerial photographs, including Cold War-era satellite images, to evaluate long-term vegetation changes. We found significant positive greenness trends in 44% of the study area, with the highest magnitude located at mid-elevations (1800–2300 m) and on north-facing slopes. High-resolution land cover mapping revealed that Ericaceous and Juniperus -dominated shrublands were the strongest contributors to greening. Visual interpretation of historical imagery confirmed widespread woody encroachment in these areas. We suggest that the decline of traditional land-use, particularly extensive grazing practices, is a key driver of these ecological shifts, promoting the resurgence of previously more widespread subalpine shrublands. Our findings highlight the importance of integrating high-resolution remote sensing observations and diachronic analysis of aerial photographs to disentangle the complexity of vegetation greening in high-elevation ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".