Missing Burns in the High Northern Latitudes: The Case for Regionally Focused Burned Area Products
Bibliographic record
Abstract
Global estimates of burned areas, enabled by the wide-open access to the standard data products from the Moderate Resolution Imaging Spectroradiometer (MODIS), are heavily relied on by scientists and managers studying issues related to wildfire occurrence and its worldwide consequences. While these datasets, particularly the MODIS MCD64A1 product, have fundamentally improved our understanding of wildfire regimes at the global scale, their performance may be less reliable in certain regions due to a series of region- or ecosystem-specific challenges. Previous studies have indicated that global burned area products tend to underestimate the extent of the burned area within some parts of the boreal domain. Despite this, global products are still being regularly used by research activities and management efforts in the northern regions, likely due to a lack of understanding of the spatial scale of their Arctic-specific limitations, as well as an absence of more reliable alternative products. In this study, we evaluated the performance of two widely used global burned area products, MCD64A1 and FireCCI51, in the circumpolar boreal forests and tundra between 2001 and 2015. Our two-step evaluation shows that MCD64A1 has high commission and omission errors in mapping burned areas in the boreal forests and tundra regions in North America. The omission error overshadows the commission error, leading to MCD64A1 considerably underestimating burned areas in these high northern latitude domains. Based on our estimation, MCD64A1 missed nearly half the total burned areas in the Alaskan and Canadian boreal forests and the tundra during the 15-year period, amounting to an area (74,768 km2) that is equivalent to the land area of the United States state of South Carolina. While the FireCCI51 product performs much better than MCD64A1 in terms of commission error, we found that it also missed about 40% of burned areas in North America north of 60° N between 2001 and 2015. Our intercomparison of MCD64A1 and FireCCI51 with a regionally adapted MODIS-based Arctic Boreal Burned Area (ABBA) shows that the latter outperforms both MCD64A1 and FireCCI51 by a large margin, particularly in terms of omission error, and thus delivers a considerably more accurate and consistent estimate of fire activity in the high northern latitudes. Considering the fact that boreal forests and tundra represent the largest carbon pool on Earth and that wildfire is the dominant disturbance agent in these ecosystems, our study presents a strong case for regional burned area products like ABBA to be included in future Earth system models as the critical input for understanding wildfires’ impacts on global carbon cycling and energy budget.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".