A Time Series of NDVI at a High Arctic Peatland
Bibliographic record
Abstract
Arctic greening has been studied as a significant and accelerating environmental change throughout the past few decades; however, most studies focus on greening across scales as large as the entire terrestrial Arctic and lack smaller-scale observations of vegetation at individual sites. Conducting such studies on peatlands is especially important, considering Arctic peatlands’ potential to act as an immense source of atmospheric carbon should they degrade as permafrost thaw accelerates. Additionally, while remote sensing studies cannot quantify any vegetation trends with complete accuracy, I aimed to prove the effectiveness of open-source, free satellite imagery in displaying the existence and strength of such trends. I produced a time series of NDVI at a well-studied catchment basin in the Canadian High Arctic, to illuminate trends of greening since the start of the 21st century. I compiled and analyzed MODIS imagery from peak growing seasons starting in 2000 until 2022. Without any in situ data to qualify the results from my analysis, I found a statistically significant trend in NDVI throughout the past 22 years; with in situ data, this data could be considered when mapping related physical attributes when trying to further quantify environmental changes at the site. Additionally, I found that, despite the inherent flaws of remote sensing’s accuracy when collecting data, remote sensing datasets with low resolution are effective in uncovering trends as long as the temporal resolution is high; with daily image products from a platform like MODIS, outliers of snow, ice, and cloud cover can be accounted for, which sensors like Landsat and Sentinel could not despite higher spatial resolution. Greening is likely to continue at this site with climate change, and future studies are warranted to observe the cascading effects of warming and permafrost thaw on vegetation cover in peatlands.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".