Satellite-based mapping of Canadian boreal forest fires: Evaluation and comparison of algorithms
Why this work is in the frame
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Bibliographic record
Abstract
Abstract. This paper evaluates annual re maps that were produced from NOAA-14/AVHRR imagery using an algorithm described in a companion paper (Li et al., International Journal of Remote Sensing, 21, 3057–3069, 2000 (this issue)). Burned area masks covering the Canadian boreal forest were created by composit-ing the daily maps of re hot spots over the summer and by examining Normalized DiŒerence Vegetation Index (NDVI) changes after burning. Both masks were compared with re polygons derived by Canadian re agencies through aerial surveillance. It was found that the majority of re events were captured by the satellite-based techniques,but burnt area was generally underestimated.The burn boundary formed by the re pixels detected by satellite were in good agreement with the polygons boundarieswithin which, however, there were some res missed by the satellite. The presence of clouds and low sampling frequency of satellite observation are the two major causes for the underestimation.While this problem is alleviated by taking advantage of NDVI changes, a simple combination of a hot spot technique with a NDVI method is not an ideal solution due to the introduction of new sources of uncertainty. In addition, the performance of the algorithm used in the International Geosphere–Biosphere Programme (IGBP) Data and Information System (IGBP-DIS) for global re detection was evaluated by comparing its results with ours and with the re agency reports. It was found that the IGBP-DIS algorithm is capable of detecting the majority of res over the boreal forest, but also includes many false res over old burned scars created by res taking place in previous years. A step-by-step comparison between the two algorithms revealed the causes of the problem and recommendations are made to rectify them. 1.
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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 | 0.000 |
| 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) | 0.000 | 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 it