Landscape variability of vegetation change across the forest to tundra transition of central Canada
Bibliographic record
Abstract
Widespread vegetation productivity increases in tundra ecosystems and stagnation, or even productivity decreases, in boreal forest ecosystems have been detected from coarse-scale remote sensing observations over the last few decades. However, finer-scale Landsat studies have shown that these changes are heterogeneous and may be related to landscape and regional variability in climate, land cover, topography and moisture. In this study, a Landsat Normalized Difference Vegetation Index (NDVI) time-series (1984-2016) was examined for a study area spanning the entirety of the sub-Arctic boreal forest to Low Arctic tundra transition of central Canada (i.e., Yellowknife to the Arctic Ocean). NDVI trend analysis indicated that 27 % of un-masked pixels in the study area exhibited a significant (p < 0.05) trend and virtually all (99.3 %) of those pixels were greening. Greening pixels were most common in the northern tundra zone and the southern forest-tundra ecotone zone. NDVI trends were positive throughout the study area, but were smallest in the forest zone and largest in the northern tundra zone. These results were supported by ground validation, which found a strong relationship (R2 = 0.81) between bulk vegetation volume (BVV) and NDVI for non-tree functional groups in the North Slave region of Northwest Territories. Field observations indicate that alder (Alnus spp.) shrublands and open woodland sites with shrubby understories were most likely to exhibit greening in that area. Random Forest (RF) modelling of the relationship between NDVI trends and environmental variables found that the magnitude and direction of trends differed across the forest to tundra transition. Increased summer temperatures, shrubland and forest land cover, closer proximity to major drainage systems, longer distances from major lakes and lower elevations were generally more important and associated with larger positive NDVI trends. These findings indicate that the largest positive NDVI trends were primarily associated with the increased productivity of shrubby environments, especially at, and north of the forest-tundra ecotone in areas with more favorable growing conditions. Smaller and less significant NDVI trends in boreal forest environments south of the forest-tundra ecotone were likely associated with long-term recovery from fire disturbance rather than the variables analyzed here.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".