Debating the greening vs. browning of the North American boreal forest: differences between satellite datasets
Bibliographic record
Abstract
Abstract A number of remote sensing studies have evaluated the temporal trends of the normalized difference vegetation index (NDVI or vegetation greenness) in the North American boreal forest during the last two decades, often getting quite different results. To examine the effect that the use of different datasets might be having on the estimated trends, we compared the temporal trends of recently burned and unburned sites of boreal forest in central Canada calculated from two datasets: the Global Inventory, Monitoring, and Modeling Studies (GIMMS), which is the most commonly used 8 km dataset, and a new 1 km dataset developed by the Canadian Centre for Remote Sensing (CCRS). We compared the NDVI trends of both datasets along a fire severity gradient in order to evaluate the variance in regeneration rates. Temporal trends were calculated using the seasonal Mann–Kendall trend test, a rank‐based, nonparametric test, which is robust against seasonality, nonnormality, heteroscedasticity, missing values, and serial dependence. The results showed contrasting NDVI trends between the CCRS and the GIMMS datasets. The CCRS dataset showed NDVI increases in all recently burned sites and in 50% of the unburned sites. Surprisingly, the GIMMS dataset did not capture the NDVI recovery in most burned sites and even showed NDVI declines in some burned sites one decade after fire. Between 50% and 75% of GIMMS pixels showed NDVI decreases in the unburned forest compared with <1% of CCRS pixels. Being the most broadly used dataset for monitoring ecosystem and carbon balance changes, the bias towards negative trends in the GIMMS dataset in the North American boreal forest has broad implications for the evaluation of vegetation and carbon dynamics in this region and globally.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".