Understanding Arctic Greening Trends: A Multispectral Approach to Shrubification and Ecological Shifts
Bibliographic record
Abstract
The Arctic tundra vegetation is going through major changes as global warming alters atmospheric functions and weather patterns. These changes have been shown to affect for instance phenological patterns, plant community structures, herbivory patterns as well as carbon storage in biomass. Extensive remote sensing research with multispectral sensors has revealed significant greening trends and events as well as shrub expansion, also known as shrubification, across the Arctic. These trends have been hypothesized to counteract increases in carbon content in the atmosphere. However, the magnitude of this effect as well as the shrub expansion rates are still unanswered due to low data availability as well as topographic and phenological differences across the region. This research was conducted on the Yamal Peninsula in Russian Arctic, where, in addition to climate change, vegetation is strongly influenced by the reindeer grazing practiced by the indigenous Nenets reindeer herders, as well as the expanding gas and oil drilling activities, which are accompanied by extensive infrastructure development. In this study our aim is to assess the opportunities of multispectral remote sensing data with varying spatial and temporal resolutions to examine shrubification in ecologically complex Arctic landscapes. Our research questions are the following: 1) Do Landsat-derived vegetation indices from a 30-year timespan show significant amount of greening in Arctic Russia; 2) How does image availability and phenology affect the way greening trends are analyzed; 3) Has shrub height and area increased during the study period and what implications does reindeer grazing have for shrub expansion and plant community structures; 4) Are greening trends associated with increased shrub height and area.Methodologically, we first extracted several vegetation indices from Landsat-satellite collections to evaluate greening trends. After satellite sensor cross-calibration with Random Forests, we examined how phenology and imaging frequency affects these trends and the analysis. We then compared the results with high-resolution QuickBird-2 and WorldView-2/3 imagery from 2004, 2013, 2017 and 2023. Secondly, we utilized drone imagery and VHR images to upscale vegetation height field data collected in 2017, and to delineate shrub areas with GeoSAM AI algorithm. In the last part, we created a classification with machine learning to estimate shrub expansion and height as well as change in community structure. Our preliminary results suggest that Landsat maximum vegetation indices have increased slightly across the entire study area. However, we also found a connection between image availability and the amount of greening detected. In addition, we found that shrub area and height has increased during the study period which could potentially benefit herbivore grazing activity. We therefore suggest coupling plant community changes with herbivore dynamics in the future studies on shrubification in the Arctic tundra.
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 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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".