Evidence of vegetation greening at alpine treeline ecotones: three decades of Landsat spectral trends informed by lidar-derived vertical structure
Bibliographic record
Abstract
Monitoring changes in vegetation at high-latitude and alpine treeline ecotones is critical for characterizing changes to carbon and energy budgets, plant species richness, and habitat suitability and is often considered a bellwether of a changing climate. Herein, we used transects of airborne laser scanning (ALS) data to identify alpine treeline ecotones in the Yukon Territory of Canada, and assessed changes in vegetation greenness using a time-series of Landsat imagery over a 30 year period from 1985 to 2015. Specifically, we calculated the enhanced vegetation index (EVI) from annual Landsat composites and assessed temporal trends within 500 m of detected forest-lines (i.e., transition point from continuous forest into treeline ecotones) using Theil–Sen’s nonparametric regression. Across 74 detected treeline ecotones, 27.5% of Landsat pixels displayed a significant positive trend in EVI and 5.6% of pixels displayed a significant negative trend ( p < 0.05). By using ALS data to determine vegetation structural class, we found that non-treed pixels had the highest percentage of significant positive trends in vegetation greenness (40.8%), followed by shrubs (30.5%), with lower percentages in sparse forests (18.9%) and open/dense forests (13.3%). These results suggest herbaceous and shrub vegetation types are undergoing the most significant changes in greenness, likely due to increases in shrub cover and herbaceous biomass in areas associated with these alpine treeline ecotones. The limited increases in EVI in forests likely indicates that vegetation cover is changing less rapidly in forests than in shrub and herbaceous vegetation types. Moreover, EVI may not be capturing increased height growth in forests near the treeline. Combining ALS data and Landsat time-series data provides a useful approach to locate and characterize alpine treeline ecotones, and enables the direct assessment of which vegetation structural classes are experiencing the greatest greening trends, thereby providing new insights to ecosystem change.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".