Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites
Bibliographic record
Abstract
Data across scales are required to monitor ecosystem responses to rapid warming in the Arctic and to interpret tundra greening trends.Here, we tested the correspondence among satellite-and drone-derived seasonal change in tundra greenness to identify optimal spatial scales for vegetation monitoring on Qikiqtaruk-Herschel Island in the Yukon Territory, Canada.We combined time-series of the Normalised Difference Vegetation Index (NDVI) from multispectral drone imagery and satellite data (Sentinel-2, Landsat 8 and MODIS) with ground-based observations for two growing seasons (2016 and 2017).We found high cross-season correspondence in plot mean greenness (drone-satellite Spearman's 0.67-0.87)and pixel-by-pixel greenness (drone-satellite R 2 0.58-0.69)for eight one-hectare plots, with drones capturing lower NDVI values relative to the satellites.We identified a plateau in the spatial variation of tundra greenness at distances of around half a metre in the plots, suggesting that these grain sizes are optimal for monitoring such variation in the two most common vegetation types on the island.We further observed a notable loss of seasonal variation in the spatial heterogeneity of landscape greenness (46.2%-63.9%)when aggregating from ultra-fine-grain drone pixels (approx.0.05 m) to the size of medium-grain satellite pixels (10-30 m).Finally, seasonal changes in drone-derived greenness were highly correlated with measurements of leaf-growth in the ground-validation plots (mean Spearman's 0.70).These findings indicate that multispectral drone measurements can capture temporal plant growth dynamics across tundra landscapes.Overall, our results demonstrate that novel technologies such as drone platforms and compact multispectral sensors allow us to study ecological systems at previously inaccessible scales and fill gaps in our understanding of tundra ecosystem processes.Capturing fine-scale variation across tundra landscapes will improve predictions of the ecological impacts and climate feedbacks of environmental change in the Arctic.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".